JP7653584B2 - Method and kit for detecting intraductal papillary mucinous neoplasm-derived pancreatic cancer or pancreatic cancer - Google Patents

Description

The present invention relates to a method and a kit for detecting the presence of intraductal papillary mucinous neoplasm (IPMN)-derived pancreatic cancer (intraductal papillary mucinous carcinoma: IPMC) and/or pancreatic cancer in healthy individuals or patients diagnosed with intraductal papillary mucinous neoplasm (IPMN).

The number of pancreatic cancer patients is on the rise, and it is the fourth leading cause of cancer deaths, with a 5-year survival rate of less than 10%. At the time of diagnosis, approximately 80% of cases are in an unresectable advanced stage, and the establishment of a highly accurate screening method aimed at early diagnosis of cancer is an urgent issue (Non-Patent Document 1). Since cancer screening targeting all healthy individuals is not cost-effective, screening targeting high-risk groups is considered to be more efficient. For example, screening for gastric cancer involves identifying individuals already infected with Helicobacter pylori and conducting focused endoscopic examinations. Among the many high-risk groups for pancreatic cancer, IPMN has a high disease frequency of approximately 3% of the total population, and is a disease that should be noted as a target for screening.

IPMN is a general term for tumors in which mucus-producing atypical epithelium proliferates in a papillary pattern in the pancreatic duct, producing excessive mucus. IPMN progresses from low- to medium-grade lesions to high-grade lesions, some of which progress to IPMC, a pancreatic cancer derived from IPMN (Non-Patent Document 2). In this study, IPMN refers to low- to medium-grade lesions, which are benign diseases. There are two types of pancreatic cancer that arise in IPMN: IPMC, and pancreatic cancer that arises at a site distant from IPMN (hereinafter referred to as IPMN-associated pancreatic cancer). In a report from Japan, when 349 IPMN patients were followed up for 3.7 years (median), IPMC was found in 3.5% of the total, and coexisting pancreatic cancer in 2.0% (Non-Patent Document 3).

In current routine clinical practice, pancreatic cancer screening is based primarily on imaging diagnosis, as recommended in the International Guidelines for the Treatment of IPMN, but the development of effective blood biomarkers is essential for more objective and effective screening.

Strictly speaking, a pathological diagnosis based on surgical specimens is required to distinguish between the two. On the other hand, pancreatic cancer, which is a solid lesion, and IPMN, which is a cystic lesion, can be relatively easily diagnosed using imaging tests such as CT and MRI. The clinical problem is that, as mentioned above, a certain percentage of IPMN patients develop pancreatic cancer, and there are currently no effective biomarkers.

Regarding IPMC, the Japan Pancreas Society has formulated international IPMN clinical guidelines, which are widely used in daily clinical practice. In addition, the American Gastroenterological Association has also formulated clinical guidelines, which are used mainly in the United States (Non-Patent Document 4). These guidelines are primarily guidelines for diagnosing IPMN cancer (IPMC) using imaging diagnostics (CT, MRI, endoscopic ultrasound), but the ability (specificity) of definitive diagnosis is 60-80% (Non-Patent Documents 5-8). In addition, there are no biomarkers that are useful for definitively diagnosing and screening pancreatic cancer and IPMC, and the establishment of a test method with high diagnostic ability is awaited.

To date, numerous previous studies have been reported, but the purpose of most of the research is to 'distinguish IPMN from other cystic diseases' and 'distinguish IPMC/pancreatic cancer from normal control groups' (e.g., Non-Patent Document 9). On the other hand, there have been few reports of biomarker research to identify IPMC and pancreatic cancer arising from IPMN, which is a high-risk group for pancreatic cancer.

In addition, some studies have used endoscopic examinations to search for biomarkers in duodenal juice, pancreatic juice, and cyst fluid (e.g., Non-Patent Document 10), but it is thought that this method would be difficult to generalize due to its invasiveness.

Jang JY et al. “Oncological Benefits of Neoadjuvant Chemoradiation With Gemcitabine Versus Upfront Surgery in Patients With Borderline Resectable Pancreatic Cancer: A Prospective, Randomized, Open-label, Multicenter Phase 2/3 Trial.”, Ann Surg. 2018 Aug;268(2):215-222. Tanaka M et al. “Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas”, Pancreatology 17: 738-753, 2017. Maguchi H et al. “Natural history of branch duct intraductal papillary mucinous neoplasms of the pancreas: a multicenter study in Japan.” Pancreas. 2011 Apr; 40(3):364-70. Vege SS, Ziring B, Jain R, Moayyedi P, Clinical Guidelines C and American Gastroenterology A: American gastroenterological association institute guideline on the diagnosis and management of asymptomatic neoplastic pancreatic cysts. Gastroenterology 148: 819-822; quize812-813, 2015. Singhi AD, Zeh HJ, Brand RE, et al.: American Gastroenterological Association guidelines are inaccurate in detecting pancreatic cysts with advanced neoplasia: a clinicopathologic study of 225 patients with supporting molecular data. Gastrointest Endosc 83: 1107-1117 e1102, 2016. Ge PS, Muthusamy VR, Gaddam S, et al.: Evaluation of the 2015 AGA guidelines on pancreatic cystic neoplasms in a large surgically confirmed multicenter cohort. Endosc Int Open 5: E201-E208, 2017. Yamada S, Fujii T, Murotani K, et al.: Comparison of the international consensus guidelines for predicting malignancy in intraductal papillary mucinous neoplasms. Surgery 159: 878-884, 2016. Xu MM, Yin S, Siddiqui AA, et al.: Comparison of the diagnostic accuracy of three current guidelines for the evaluation of asymptomatic pancreatic cystic neoplasms. Medicine 96: e7900, 2017. Moris D, Damaskos C, Spartalis E, et al.: Updates and Critical Evaluation on Novel Biomarkers for the Malignant Progression of Intraductal Papillary Mucinous Neoplasms of the Pancreas. Anticancer research 37: 2185-2194, 2017. Tanaka M, Fernandez-Del Castillo C, Kamisawa T, et al.: Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas. Pancreatology 17: 738-753, 2017.

The objective of the present invention is to identify biomarkers for detecting the presence of IPMC and pancreatic cancer arising from IPMN in healthy individuals or patients diagnosed with IPMN, which is a high-risk group for pancreatic cancer, and to provide a method for detecting the presence of IPMC or pancreatic cancer using the biomarkers.

In order to solve the above problems, the inventors obtained gene expression profiles of 14,400 genes from whole blood of pancreatic cancer patients (12 cases), IPMC patients (4 cases), and IPMN patients (24 cases), and succeeded in selecting genes with significantly different expression levels between pancreatic cancer patient groups and IPMN patient groups, IPMC patient groups and IPMN patient groups, or between a group including pancreatic cancer patient groups and IPMC patient groups and an IPMN patient group, thereby completing the present invention.

That is, the present invention includes the following aspects:
The present invention in one aspect comprises:
[1] A method for detecting IPMC or pancreatic cancer in a test sample, comprising:
The DSC2 gene, JAK2 gene, PYGL gene, CBLN3 gene, MS4A1 gene, HIP1R gene, TMEM176B gene, MED20 gene, SCML2 gene, KITLG gene, ANXA5 gene, S100A12 gene, LRRK2 gene, ANXA3 gene, PTDSS2 gene, EMC1 gene, TRIM22 gene, NRGN gene, MX1 gene, EPSTI1 gene, IFIT1 gene, IFI6 gene, MIR100HG gene, C17orf51 gene, FA The present invention relates to a detection method comprising a measurement step of measuring the expression level of at least one gene selected from the group consisting of the M118A gene, the SPATA33 gene, the ENC1 gene, the GPR15 gene, the ZNF609 gene, the TNFAIP8L3 gene, the GRHL2 gene, the RPGRIP1 gene, the SUZ12 gene, the LIAS gene, the SMN2 gene, the RABGGTB gene, the LAMB3 gene, the ZC3HC1 gene, the USP37 gene, the MALSU1 gene, the EEF1B2 gene, the LAMC1 gene, and the R3HDM4 gene.

Here, in one embodiment of the detection method of the present invention,
[2] The detection method according to [1] above,
The test sample is characterized in that it is a sample derived from an IPMN patient.

In one embodiment, the detection method of the present invention further comprises:
[3] The detection method according to [1] or [2] above,
The measuring step is characterized in that it is a step of measuring the expression levels of the DSC2 gene, the JAK2 gene, the CBLN3 gene, the MS4A1 gene, the HIP1R gene, the TMEM176B gene, the KITLG gene, the S100A12 gene, the ANXA3 gene, the PTDSS2 gene, the EMC1 gene, the TRIM22 gene and the NRGN gene.

In one embodiment, the detection method of the present invention further comprises:
[4] The detection method according to [1] or [2] above,
The measuring step is characterized in that it is a step of measuring the expression levels of the DSC2 gene, the JAK2 gene, the PYGL gene, the CBLN3 gene, the MS4A1 gene, the HIP1R gene, the TMEM176B gene, the MED20 gene, the SCML2 gene, the KITLG gene, the ANXA5 gene, the S100A12 gene, and the LRRK2 gene.

In one embodiment, the detection method of the present invention further comprises:
[5] The detection method according to [1] above,
The method includes detecting IPMC in a test sample derived from IPMN,
The measuring step is characterized in that it is a step of measuring the expression level of at least one gene from the group consisting of the MX1 gene, EPSTI1 gene, IFIT1 gene, IFI6 gene, MIR100HG gene, C17orf51 gene, FAM118A gene, SPATA33 gene, ENC1 gene, GPR15 gene, ZNF609 gene, TNFAIP8L3 gene, GRHL2 gene, RPGRIP1 gene, SUZ12 gene, LIAS gene, SMN2 gene, RABGGTB gene, LAMB3 gene, ZC3HC1 gene, USP37 gene, MALSU1 gene, EEF1B2 gene, LAMC1 gene, and R3HDM4 gene.

In one embodiment, the detection method of the present invention further comprises:
[6] The detection method according to any one of [1] to [4] above,
The method further comprises a step of comparing the expression level of the gene obtained in the measuring step with the expression level of the corresponding gene in a sample derived from a healthy subject, an IPMN patient, an IPMC patient, or a pancreatic cancer patient.

In one embodiment, the detection method of the present invention further comprises:
[7] The detection method according to any one of [1] to [4] above,
The method further comprises a step of comparing the expression level of the gene obtained in the measurement step with the expression level of the corresponding gene in a sample derived from a healthy subject or an IPMN patient, and the expression level of the corresponding gene in a sample derived from an IPMC patient or a pancreatic cancer patient by cluster analysis.

In one embodiment, the detection method of the present invention further comprises:
[8] The detection method according to any one of [1] to [4] above,
The method further comprises a step of comparing the expression level of the gene in the test sample obtained in the measuring step with a predetermined threshold value.

In one embodiment, the detection method of the present invention further comprises:
[9] The detection method according to any one of [1] to [8] above,
The test sample is characterized in that it is blood.

In one embodiment, the detection method of the present invention further comprises:
[10] The detection method according to any one of [1] to [9] above,
In the measuring step, the expression level of the gene is measured by measuring the expression amount of mRNA of the gene.

In another aspect, the present invention provides a method for producing a pharmaceutical composition comprising the steps of:
[11] A marker gene set for detecting IPMC or pancreatic cancer, comprising: the DSC2 gene, the JAK2 gene, the PYGL gene, the CBLN3 gene, the MS4A1 gene, the HIP1R gene, the TMEM176B gene, the MED20 gene, the SCML2 gene, the KITLG gene, the ANXA5 gene, the S100A12 gene, the LRRK2 gene, the ANXA3 gene, the PTDSS2 gene, the EMC1 gene, the TRIM22 gene, the NRGN gene, the MX1 gene, the EPSTI1 gene, the IFIT1 gene, the IFI6 gene, the MIR100HG ... The present invention relates to a marker gene set comprising at least one gene selected from the group consisting of a C17orf51 gene, a FAM118A gene, a SPATA33 gene, an ENC1 gene, a GPR15 gene, a ZNF609 gene, a TNFAIP8L3 gene, a GRHL2 gene, a RPGRIP1 gene, a SUZ12 gene, a LIAS gene, a SMN2 gene, a RABGGTB gene, a LAMB3 gene, a ZC3HC1 gene, a USP37 gene, a MALSU1 gene, an EEF1B2 gene, a LAMC1 gene, and a R3HDM4 gene.

In another aspect, the present invention provides a method for producing a pharmaceutical composition comprising the steps of:
[12] A kit for detecting IPMC or pancreatic cancer, comprising:
DSC2 gene, JAK2 gene, PYGL gene, CBLN3 gene, MS4A1 gene, HIP1R gene, TMEM176B gene, MED20 gene, SCML2 gene, KITLG gene, ANXA5 gene, S100A12 gene, LRRK2 gene, ANXA3 gene, PTDSS2 gene, EMC1 gene, TRIM22 gene, NRGN gene, MX1 gene, EPSTI1 gene, IFIT1 gene, IFI6 gene, MIR100HG gene, C17orf51 gene, FAM11 The kit includes a measuring means for measuring the expression of at least one gene selected from the group consisting of the 8A gene, the SPATA33 gene, the ENC1 gene, the GPR15 gene, the ZNF609 gene, the TNFAIP8L3 gene, the GRHL2 gene, the RPGRIP1 gene, the SUZ12 gene, the LIAS gene, the SMN2 gene, the RABGGTB gene, the LAMB3 gene, the ZC3HC1 gene, the USP37 gene, the MALSU1 gene, the EEF1B2 gene, the LAMC1 gene, and the R3HDM4 gene.

Here, in one embodiment, the kit of the present invention comprises:
[13] The kit according to [12] above,
The kit is for detecting IPMC in a test sample derived from IPMN,
The present invention is characterized in that it includes a measuring means for measuring the expression of at least one gene selected from the group consisting of the MX1 gene, the EPSTI1 gene, the IFIT1 gene, the IFI6 gene, the MIR100HG gene, the C17orf51 gene, the FAM118A gene, the SPATA33 gene, the ENC1 gene, the GPR15 gene, the ZNF609 gene, the TNFAIP8L3 gene, the GRHL2 gene, the RPGRIP1 gene, the SUZ12 gene, the LIAS gene, the SMN2 gene, the RABGGTB gene, the LAMB3 gene, the ZC3HC1 gene, the USP37 gene, the MALSU1 gene, the EEF1B2 gene, the LAMC1 gene, and the R3HDM4 gene.

In one embodiment, the kit of the present invention further comprises:
[14] The kit according to [12] or [13] above,
The means for measuring the expression of the gene is characterized in that it is at least one means selected from the group consisting of a primer, a probe, or a labeled product thereof for the gene.

According to the method for detecting IPMC or pancreatic cancer of the present invention, the presence of IPMC or pancreatic cancer can be detected with high reproducibility by analyzing the expression of genes included in the marker gene set in a test sample.

The upper part shows group scatter plots showing gene expression scores of the DSC2 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression score in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the JAK2 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression scores in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the PYGL gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression scores in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the CBLN3 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curve and sensitivity and specificity curves based on the gene expression score in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the MS4A1 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression score in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the HIP1R gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression scores in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the TMEM176B gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression scores in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the MED20 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curve and sensitivity and specificity curves based on the gene expression score in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the SCML2 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression scores in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the KITLG gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression scores in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the ANXA5 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curve and sensitivity and specificity curves based on the gene expression score in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the S100A12 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression scores in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the LRRK2 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression scores in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the AMXA3 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curve and sensitivity and specificity curves based on the gene expression score in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the PTDSS2 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression score in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the EMC1 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curve and sensitivity and specificity curves based on the gene expression score in the IPMN patient group. The upper part shows group scatter plots showing gene expression scores of the TRIM22 gene in blood from IPMN, IPMC, and pancreatic cancer patients. The lower part shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression scores in the IPMN patient group. The top panel shows group scatter plots of gene expression scores of NRGN genes in blood from IPMN, IPMC, and pancreatic cancer patients. The bottom panel shows the gene expression scores in the pancreatic cancer and IPMC patient groups, and the ROC curves and sensitivity and specificity curves based on the gene expression scores in the IPMN patient group. The figure shows a dendrogram (upper part) and a heat map (lower part) of the results of cluster analysis based on the expression levels of genes included in gene set 1 in blood derived from a group of pancreatic cancer patients and a group of IPMN patients. 1 shows group scatter plots of specimen score values based on the expression levels of gene set 1 in the blood in a pancreatic cancer patient group and an IPMN patient group. 1 shows an ROC curve and sensitivity/specificity curves based on sample score values based on the expression levels of gene set 1 in the blood in a group of pancreatic cancer patients and a group of IPMN patients. The expression levels of gene set 1 in blood from a group of pancreatic cancer patients and a group of IPMN patients were measured, and a heat map (top row) showing the expression levels of gene set 1 in each sample arranged in order of sample value score, and a graph of the sample value scores (bottom row) are shown. The heat map and sample value score graphs of FIG. 22 are supplemented with the measurement results of expression levels and sample value scores of gene set 1 in four additional samples. The figure shows a dendrogram (upper part) and a heat map (lower part) of the results of cluster analysis based on the expression levels of genes included in gene set 2 in blood derived from a pancreatic cancer patient group, an IPMC patient group, and an IPMN patient group. 1 shows group scatter plots of specimen score values based on the expression levels of gene set 2 in the blood of a pancreatic cancer patient group, an IPMC patient group, and an IPMN patient group. The sample score values based on the expression levels of gene set 2 in the blood of pancreatic cancer patient groups and IPMC patient groups, and the ROC curve and sensitivity/specificity curves based on the sample score values based on the expression levels of gene set 2 in the blood of IPMN patient groups are shown. The expression levels of gene set 2 in blood from a pancreatic cancer patient group, an IPMC patient group, and an IPMN patient group were measured, and a heat map (top row) showing the expression levels of gene set 2 in each sample arranged in order of sample value score and a graph of the sample value scores (bottom row) are shown. The heat map and sample value score graphs of FIG. 27 are supplemented with the measurement results of expression levels and sample value scores of gene set 2 in four additional samples. The figure shows a dendrogram (upper part) and a heat map (lower part) of the cluster analysis performed based on the expression levels of genes included in gene set 3 in blood derived from IPMC and IPMN patient groups. 1 shows group scatter plots of specimen score values based on the expression levels of gene set 3 in blood in IPMC and IPMN patient groups. 1 shows a sample score value based on the expression level of gene set 3 in the blood of an IPMC patient group, and an ROC curve and sensitivity/specificity curves based on the sample score value based on the expression level of gene set 3 in the blood of an IPMN patient group. The expression levels of gene set 3 in blood from IPMC and IPMN patient groups were measured, and a heat map (top row) showing the expression levels of gene set 3 in each sample arranged in order of sample value score and a graph of the sample value scores (bottom row) are shown.

1. Method for detecting IPMC or pancreatic cancer using marker genes

1-1. Overview The first aspect of the present invention is a method capable of detecting the presence of IPMC and/or pancreatic cancer in healthy individuals or patients diagnosed with IPMN using the expression levels of genes included in a marker gene set as an index. The marker gene set of the present invention is composed of genes selected from a group of 43 types of genes, and by measuring the expression levels of specific genes from the group of genes in a sample from a subject, it is possible to detect the presence of IPMC and/or pancreatic cancer in healthy individuals or IPMN patients, or to distinguish healthy individuals or IPMN from IPMC or pancreatic cancer.
In addition, since the detection method of the present invention can be used in combination with a histopathological examination or the like, in one embodiment, it can also be said to be a method for assisting in the differentiation of IPMC or pancreatic cancer.

1-2. Definition Intraductal papillary mucinous neoplasm (IPMN) is a pancreatic ductal epithelial tumor characterized by pancreatic duct dilation due to mucus accumulation. IPMN has lesions that are continuous with hyperplasia, adenoma (intraductal papillary-mucinous adenoma (IPMA)), non-invasive carcinoma, microinvasive carcinoma, and invasive carcinoma derived from IPMN. In this specification, "IPMN" refers to IPMN in the low- to intermediate-grade lesions that show lesions up to non-invasive carcinoma without showing invasion.

In addition, IPMN-derived pancreatic cancer (IPMC) as used herein refers to adenocarcinoma formed as a result of progression of IPMN lesions. IPMC as used herein includes microinvasive carcinoma and invasive carcinoma. On the other hand, pancreatic cancer as used herein refers to pancreatic cancer classified as other than IPMC.

The distinction between IPMN and IPMC as defined above is well known, and a person skilled in the art can distinguish between noninvasive IPMN and IPMC using imaging diagnosis (CT, MRI, endoscopic ultrasound, etc.) based on the International Guidelines for the Treatment of IPMN.

As used herein, the term "marker gene" refers to a gene that can be used as a biomarker capable of detecting the presence of IPMC and/or pancreatic cancer in healthy individuals or patients diagnosed with IPMN. In the present invention, the expression level of each gene included in the marker gene set relates to information on the expression of the transcript (mRNA), its cDNA, or the protein encoded by each gene.

In this specification, the "gene expression score" is a score determined by the expression level of each gene or multiple genes included in the "marker gene set". There are no particular limitations on the type of score or the calculation method. In one embodiment, the gene expression score is calculated by multiplying the expression levels of 16 of the marker genes (CBLN3, HIP1R, PTDSS2, MS4A1, KITLG, EMC1, ENC1, GPR15, ZNF609, RPGRIP1, RABGGTB, ZC3HC1, USP37, MALSU1, EEF1B2, and LAMC1 genes) by -1, and the expression levels of the remaining 27 genes can be treated as they are as the "gene expression score". In another embodiment, a cutoff value for the expression level can be set for each gene, and the score can be determined by comparison with the cutoff value (for example, if the expression level is equal to or greater than the cutoff value, the gene expression score is set to "1", and if the expression level is less than the cutoff value, the gene expression score is set to "-1").

In this specification, the "specimen score" is a score calculated from the expression level of the marker genes given to each test sample. There are no particular limitations on the type of score or the calculation method, but for example, the "specimen score" can be treated as the sum of the gene expression scores of the marker genes in each test sample.

In this specification, the term "measured value" refers to a value obtained by a method for measuring gene expression levels. The measured value may be an absolute value, such as the amount of mRNA in a sample, expressed in weight units such as ng (nanograms) or μg (micrograms), or it may be a relative value, such as the absorbance or fluorescence intensity of a labeling molecule relative to a control value.

The measured value of the expression level of each gene depends on the measurement method, but can be calculated, for example, as a relative ratio (expression ratio) to a common sample (hereinafter referred to as a "common reference"). The common reference used to calculate the expression ratio can be anything as long as the measurement conditions between the samples being compared are the same. For example, it can be a specific cell line or a mixture of multiple cell lines. Alternatively, a commercially available universal reference, a known housekeeping gene, or a combination of these can be used as the common reference.

As used herein, the term "gene expression level" refers to the amount of transcription product, amount of translation product, expression intensity, or expression frequency of a marker gene. The gene expression level referred to here is not limited to the expression level of a wild-type gene of a marker gene, but may also include the expression level of a mutant gene such as a point mutation gene. Furthermore, a transcription product showing expression of a marker gene may also include atypical transcription products (variants) such as splice variants and fragments thereof. Information based on a mutant gene, a transcription product, or a fragment thereof can also be treated as the expression level of a marker gene in the present invention.
The expression level of a gene can be obtained as a measured value by measuring the amount of transcription products, i.e., mRNA, of a group of genes constituting a marker gene set.

1-3. Configuration In this specification, the term "marker gene set" refers to a set of marker genes including the DSC2 gene, the JAK2 gene, the PYGL gene, the CBLN3 gene, the MS4A1 gene, the HIP1R gene, the TMEM176B gene, the MED20 gene, the SCML2 gene, the KITLG gene, the ANXA5 gene, the S100A12 gene, the LRRK2 gene, the ANXA3 gene, the PTDSS2 gene, the EMC1 gene, the TRIM22 gene, the NRGN gene, the MX1 gene, the EPSTI1 gene, the IFIT1 gene, the IFI6 gene, the MIR1 gene, the IFIT2 gene, the IFI3 gene, the IFI4 gene, the IFI5 gene, the IFI6 gene, the IFI7 gene, the IFI8 gene, the IFI9 gene, the IFI10 gene, the IFI11 gene, the IFI12 gene, the IFI1 ... It contains 43 marker genes, including the 00HG gene, the C17orf51 gene, the FAM118A gene, the SPATA33 gene, the ENC1 gene, the GPR15 gene, the ZNF609 gene, the TNFAIP8L3 gene, the GRHL2 gene, the RPGRIP1 gene, the SUZ12 gene, the LIAS gene, the SMN2 gene, the RABGGTB gene, the LAMB3 gene, the ZC3HC1 gene, the USP37 gene, the MALSU1 gene, the EEF1B2 gene, the LAMC1 gene, and the R3HDM4 gene.

In this specification, the genes included in the "marker gene set" include genes whose base sequence includes degenerate codons that code for the same amino acid sequence, various mutants (variants) of each gene, mutant genes such as point mutation genes, and orthologous genes of other organisms such as chimpanzees. Such genes include genes whose base sequence has 70% or more (preferably 75% or more, 80% or more, 85% or more, more preferably 90% or more, 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more) base identity with the base sequence of a gene identified by the GenBank accession number in the table below, and which retain the function of the target gene.

For example, in one embodiment, the DSC2 gene used in the present invention can be specified as a gene containing the base sequence shown in SEQ ID NO: 1, and the DSC2 gene includes a gene consisting of a base sequence having a base identity of 70% or more (preferably 75% or more, 80% or more, 85% or more, more preferably 90% or more, 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more) with the base sequence shown in SEQ ID NO: 1 and retaining the function of the DSC2 gene. Note that, in this specification, "base identity" refers to the ratio (%) of the number of identical bases in the base sequences of the nucleotides being compared to the total number of bases of the genes when two base sequences are aligned and gaps are introduced as necessary to maximize the base identity between the two base sequences.

1-4. Measurement method The method for detecting IPMC or pancreatic cancer of the present invention includes, as an essential step, a step of measuring the expression level of at least one gene included in the marker gene set. More specifically, the method for detecting IPMC or pancreatic cancer of the present invention includes, as an essential step, measuring the expression level of at least one gene included in the marker gene set in a test sample, the expression level of the DSC2 gene, the JAK2 gene, the PYGL gene, the CBLN3 gene, the MS4A1 gene, the HIP1R gene, the TMEM176B gene, the MED20 gene, the SCML2 gene, the KITLG gene, the ANXA5 gene, the S100A12 gene, the LRRK2 gene, the ANXA3 gene, the PTDSS2 gene, the EMC1 gene, the TRIM22 gene, the NRGN gene, the MX1 gene, the EPSTI1 gene, the IFIT1 gene, the IFI6 gene, the MIR100 gene, the IL-1 gene, the IL-2 gene, the IL-3 gene, the IL-4 gene, the IL-5 gene, the IL-6 gene, the IL-7 gene, the IL-8 gene, the IL-9 gene, the IL-10 gene, the IL-11 gene, the IL-12 gene, the IL-13 gene, the IL-14 gene, the IL-15 gene, the IL-16 gene, the IL-17 gene, the IL-18 gene, the IL-19 gene, the IL-20 gene, the IL-21 gene, the IL-22 gene, the IL-23 gene, the IL-24 gene, the IL-25 gene, the IL-26 gene, the IL-27 gene, the IL-28 gene, the IL-29 gene, the IL-30 gene, the IL-31 gene, the IL-32 gene, the IL-33 gene The method includes measuring the expression level of at least one gene from the group consisting of the HG gene, C17orf51 gene, FAM118A gene, SPATA33 gene, ENC1 gene, GPR15 gene, ZNF609 gene, TNFAIP8L3 gene, GRHL2 gene, RPGRIP1 gene, SUZ12 gene, LIAS gene, SMN2 gene, RABGGTB gene, LAMB3 gene, ZC3HC1 gene, USP37 gene, MALSU1 gene, EEF1B2 gene, LAMC1 gene, and R3HDM4 gene.

One embodiment of the detection method of the present invention is a method for detecting IPMC in a test sample derived from IPMN, and is a method for detecting IPMC by measuring the expression level of at least one gene from the group consisting of the MX1 gene, the EPSTI1 gene, the IFIT1 gene, the IFI6 gene, the MIR100HG gene, the C17orf51 gene, the FAM118A gene, the SPATA33 gene, the ENC1 gene, the GPR15 gene, the ZNF609 gene, the TNFAIP8L3 gene, the GRHL2 gene, the RPGRIP1 gene, the SUZ12 gene, the LIAS gene, the SMN2 gene, the RABGGTB gene, the LAMB3 gene, the ZC3HC1 gene, the USP37 gene, the MALSU1 gene, the EEF1B2 gene, the LAMC1 gene, and the R3HDM4 gene. According to this embodiment, it is possible to detect IPMC caused by the progression of IPMN lesions in IPMN patients.

The measurement method is explained in detail below.

The "measurement step" refers to a step of measuring the expression level of a marker gene in a test sample to obtain a measured value. The expression level of the marker genes is preferably measured by measuring the expression level per unit amount of each marker gene.

At least one of the 43 genes included in the marker gene set can be selected as the marker gene to be measured. The number of genes selected may be any combination of 2 to 43. Increasing the number of genes to be measured is preferable in terms of improving the accuracy of detection of IPMC or pancreatic cancer. When selecting two or more genes, the combination of genes is not limited.

When two or more genes are selected and measured, in a preferred embodiment, the combination of genes can be selected so that the p-value of the expression levels between healthy subjects or IPMN groups and IPMC groups or pancreatic cancer groups is 0.05 or less, more preferably 0.01 or less. In another embodiment, the combination of genes can be selected so that when an ROC curve is drawn between healthy subjects or IPMN groups and IPMC groups or pancreatic cancer groups, cutoff values can be set that provide sensitivity and specificity of 70% or more, 75% or more, 80% or more, 85% or more, or 90% or more.

In this specification, a "test sample" refers to a sample collected from a subject. The "subject" is not particularly limited, but is preferably a healthy individual or an IPMN patient for the purpose of detecting IPMC or pancreatic cancer. The subject may also be an individual suspected of having pancreatic cancer or IPMC (hereinafter referred to as "pancreatic cancer, etc."), or an individual with a history of pancreatic cancer, etc. The term "individual with a history of pancreatic cancer, etc." as used herein includes patients currently suffering from pancreatic cancer, etc., and individuals with a history of pancreatic cancer, etc., who have suffered from pancreatic cancer, etc. in the past. In this specification, a "subject" refers to a human individual who provides a sample and is subjected to testing. A more preferred subject for the method of the present invention is a subject diagnosed with IPMN.

The "subject" used in this embodiment is not particularly limited in terms of gender, age, height, weight, or other physical conditions, or in terms of the number of people.

In this specification, the term "sample" refers to a sample collected from the subject and subjected to the present identification method, and may be, for example, a tissue, a cell, or a body fluid. The "tissue" and "cell" may be from any part of the subject, but are preferably samples collected by biopsy or surgically removed, more specifically, pancreatic tissue or pancreatic cells. Particularly preferred are pancreatic tissue or cells collected by biopsy, or pancreatic tissue or cells suspected of having pancreatic cancer. These tissues or cells may be formalin-fixed and embedded in paraffin (FFPE: Formalin-Fixed Paraffin Embedded). The term "body fluid" refers to a liquid biological sample collected from the subject. Examples include blood (including serum, plasma, and interstitial fluid), cerebrospinal fluid (cerebrospinal fluid), urine, lymph, digestive fluid, ascites, pleural effusion, periradicular fluid, and extracts of each tissue or cell. Blood is preferred.

If the sample is tissue or cells, it may be obtained by biopsy or surgical removal. If the sample is a body fluid, it may be obtained based on a known collection method in the field. For example, if the sample is blood or lymph, it may be obtained according to a known blood collection method. The amount of sample required in the detection method of this embodiment is not particularly limited. If the sample is tissue or cells, it is desirable to have at least 10 μg, preferably at least 0.1 mg. Biopsy material may also be used. If the sample is a body fluid such as blood or lymph, it is sufficient to have a volume of at least 0.1 mL, preferably at least 1 mL, more preferably at least 10 mL. The sample can be prepared and treated as necessary so that the expression level of the marker gene can be measured. For example, if the sample is tissue or cells, homogenization or cell lysis, removal of impurities by centrifugation or filtration, addition of protease inhibitors, etc. can be mentioned. Details of these treatments are described in detail in Green & Sambrook, Molecular Cloning, 2012, Fourth Ed., Cold Spring Harbor Laboratory Press, and can be used as a reference.

In this specification, the term "unit amount" refers to an arbitrarily determined amount of sample. For example, it refers to volume (expressed in μL or mL) or weight (expressed in μg, mg, or g). The unit amount is not particularly specified, but it is preferable that the unit amount measured in a series of detection methods is constant. For example, when comparing a sample derived from a subject with a sample derived from a patient with pancreatic cancer or the like, more accurate detection is possible by keeping the unit amount constant. In particular, when measuring the expression level of a marker gene as an absolute value, it is necessary to keep the unit amount constant.

Below, a specific method for measuring gene transcription products will be described. Note that methods for measuring gene transcription products are publicly known. Below, the description regarding the method for measuring gene transcription products or translation products will be described with reference or citation to the description in JP 2016-13081 A. Note that, although representative methods for measuring gene transcription products or translation products will be described below, the method is not limited to these methods, and publicly known measurement methods can be used.

Measurement of the transcription product of a marker gene can be measurement of the amount of mRNA, or may be measurement of the amount of cDNA obtained by reverse transcription from the mRNA. Generally, the transcription product of a gene is measured by using a nucleotide containing all or part of the base sequence of the above gene as a primer or probe, and measuring the expression level of the gene as an absolute value or a relative value.

The primer or probe of this embodiment is usually composed of natural nucleic acids such as DNA and RNA. DNA is particularly preferred because it is highly stable, easy to synthesize, and inexpensive. Natural nucleic acids can also be combined with chemically modified nucleic acids or pseudo nucleic acids as necessary. Examples of chemically modified nucleic acids and pseudo nucleic acids include PNA (Peptide Nucleic Acid), LNA (Locked Nucleic Acid; registered trademark), methylphosphonate DNA, phosphorothioate DNA, and 2'-O-methyl RNA. The primer and probe may be labeled or modified with a fluorescent substance and/or a quencher substance, a labeling substance such as a radioisotope (e.g., 32P, 33P, 35S), or a modifying substance such as biotin, (strept)avidin, or magnetic beads. The labeling substance is not limited, and commercially available ones can be used. For example, fluorescent substances such as FITC, Texas, Cy3, Cy5, Cy7, Cyanine3, Cyanine5, Cyanine7, FAM, HEX, VIC, fluorescamine and its derivatives, and rhodamine and its derivatives can be used. Quencher substances such as AMRA, DABCYL, BHQ-1, BHQ-2, and BHQ-3 can be used. The labeling position of the labeling substance in the primer and probe can be appropriately determined depending on the characteristics of the modifying substance and the purpose of use. In general, the 5' or 3' end is often modified. Also, one primer and probe molecule may be labeled with one or more labeling substances. The labeling of nucleotides with these substances can be performed by known methods.

The nucleotides used as primers or probes may be nucleotides consisting of either the sense strand or the antisense strand of each gene constituting the marker gene.

The base length of the primer or probe is not particularly limited. In the case of a probe, if it is used in the hybridization method described below, it is at least 10 bases or more to the full length of the gene, preferably 15 bases or more to the full length of the gene, more preferably 30 bases or more to the full length of the gene, and even more preferably 50 bases or more to the full length of the gene. If it is used in a microarray, it is 10 to 200 bases long, preferably 20 to 150 bases long, and more preferably 30 to 100 bases long. In general, the longer the probe, the higher the hybridization efficiency and the higher the sensitivity. On the other hand, the shorter the probe, the lower the sensitivity, but conversely, the higher the specificity. On the other hand, in the case of a primer, the forward primer and reverse primer each may be 10 to 50 bp, preferably 15 to 30 bp.

Preparation of the above primers or probes is known to those skilled in the art, and can be prepared, for example, according to the method described in the aforementioned Green & Sambrook, Molecular Cloning (2012). It is also possible to provide sequence information to a contract manufacturer of nucleic acid synthesis and have them manufacture on a contract basis.

The transcription products of the marker genes can be measured by any known nucleic acid detection and quantification method, and there is no particular limitation. Examples include hybridization, nucleic acid amplification, and RNA sequencing (RNA-Seq) analysis.

The "hybridization method" is a method in which a nucleic acid fragment having a base sequence complementary to all or part of the base sequence of the target nucleic acid to be detected is used as a probe, and the target nucleic acid or its fragment is detected and quantified by utilizing base pairing between the nucleic acid and the probe. In this embodiment, the target nucleic acid corresponds to the mRNA or cDNA of each gene constituting the marker gene, or a fragment thereof. In general, the hybridization method is preferably performed under stringent conditions to eliminate non-target nucleic acids that hybridize non-specifically. Highly stringent conditions at low salt concentration and high temperature are more preferable. There are several known hybridization methods with different detection means, and for example, the Northern blot method (Northern hybridization method), the microarray method, the surface plasmon resonance method, or the quartz crystal microbalance method are suitable.

The "Northern blot method" is a method for analyzing gene expression. Total RNA or mRNA prepared from a sample is separated by electrophoresis using agarose gel or polyacrylamide gel under denaturing conditions, and then transferred (blotted) to a filter. The target nucleic acid is then detected using a probe having a base sequence specific to the target RNA. By labeling the probe with an appropriate marker such as a fluorescent dye or a radioisotope, it is also possible to quantify the target nucleic acid using a measuring device such as a chemiluminescence (chemiluminescence) imaging analyzer (e.g., Light Capture; ATTO), a scintillation counter, or an imaging analyzer (e.g., FUJIFILM: BAS series). The Northern blot method is a well-known and well-known technique in the field, and see, for example, Green, M.R. and Sambrook, J. (2012) mentioned above.

The "microarray method" is a method in which nucleic acid fragments complementary to all or part of the base sequence of a target nucleic acid are arranged in small spots on a substrate at high density as probes, solid-phased microarrays or microchips, and a sample containing the target nucleic acid is reacted with the solid-phased microarrays or microchips, and the nucleic acid hybridized to the substrate spots is detected by fluorescence or the like. The target nucleic acid may be either RNA such as mRNA or DNA such as cDNA. Detection and quantification can be achieved by detecting and measuring the fluorescence due to hybridization of the target nucleic acid or the like using a microplate reader or scanner. The measured fluorescence intensity can determine the amount of mRNA or cDNA, or the abundance ratio of them relative to the reference mRNA (reference mRNA). The microarray method is also a well-known technique in the field. For example, see the DNA microarray method (DNA Microarrays and the Latest PCR Method (2000), edited by Muramatsu Masaaki and Nanami Hiroyuki, Shujunsha).

The "Surface Plasmon Resonance (SPR) method" is a method for detecting and quantifying adsorbates on the surface of a metal thin film with extremely high sensitivity by utilizing the surface plasmon resonance phenomenon, in which the reflected light intensity is significantly attenuated at a specific angle of incidence (resonance angle) when the incident angle of a laser beam irradiated onto a metal thin film is changed. In the present invention, for example, a probe having a sequence complementary to the base sequence of a target nucleic acid is immobilized on the surface of the metal thin film, and other parts of the metal thin film surface are blocked. Then, a sample collected from a subject is passed over the surface of the metal thin film to form base pairs between the target nucleic acid and the probe, and the target nucleic acid can be detected and quantified from the difference in the measured values before and after the sample is passed. Detection and quantification by the surface plasmon resonance method can be performed, for example, using an SPR sensor commercially available from Biacore. This technology is well known in the art. For example, see Kazuhiro Nagata and Hiroshi Handa, Experimental Methods for Real-Time Analysis of Biological Substance Interactions, Springer-Verlag Tokyo, Tokyo, 2000.

The "quartz crystal microbalance (QCM) method" is a mass measurement method that quantitatively captures extremely small amounts of adsorbed material by the change in resonance frequency, utilizing the phenomenon that when a substance is adsorbed on the surface of an electrode attached to a quartz crystal, the resonance frequency of the quartz crystal decreases according to the mass of the substance. Detection and quantification using this method can also be performed using a commercially available QCM sensor, as with the SPR method, and can detect and quantify target nucleic acids by base pairing between a probe having a sequence complementary to the base sequence of the target nucleic acid fixed on the electrode surface and the target nucleic acid in a sample collected from a subject. This technology is well known in the field, and reference can be made to, for example, Christopher J. et al., 2005, Self-Assembled Monolayers of a Form of Nanotechnology, Chemical Review, 105:1103-1169 and Toyoe Moriizumi, Takamichi Nakamoto, (1997) Sensor Engineering, Shokodo.

"Nucleic acid amplification method" refers to a method in which a specific region of a target nucleic acid is amplified by a nucleic acid polymerase using forward/reverse primers. Examples include PCR (including RT-PCR), NASBA, ICAN, and LAMP (registered trademark) (including RT-LAMP). PCR is preferred. A quantitative nucleic acid amplification method such as real-time RT-PCR is used to measure gene transcription products using nucleic acid amplification methods. Other known real-time RT-PCR methods include the intercalator method using SYBR (registered trademark) Green, the Taqman (registered trademark) probe method, the digital PCR method, and the cycling probe method, and any of these methods may be used. All of these are known methods and are described in appropriate protocols in the technical field, so please refer to them.

"RNA sequencing (RNA-Seq) analysis" refers to a method of converting RNA into cDNA by reverse transcription and measuring gene expression levels by counting the number of reads using a next-generation sequencer (such as, but not limited to, the HiSeq series (Illumina) and the Ion Proton system (Thermo Fisher)). All of these methods are well known and are described in appropriate protocols in the relevant technical field, so please refer to these.

A simple example of a method for quantifying gene transcription products using real-time RT-PCR is given below. Real-time RT-PCR is a nucleic acid quantification method that uses cDNA prepared by reverse transcription from mRNA in a sample as a template, and performs PCR in a reaction system in which the PCR amplification products are specifically fluorescently labeled, using a temperature cycler device equipped with a function for detecting the fluorescence intensity derived from the amplification products. The amount of the amplification products of the target nucleic acid during the reaction is monitored in real time, and the results are subjected to regression analysis by computer. Methods for labeling the amplification products include a method using a fluorescently labeled probe (e.g., TaqMan (registered trademark) PCR method) and an intercalator method using a reagent that specifically binds to double-stranded DNA. TaqMan (registered trademark) PCR uses a probe modified with a quencher substance at the 5' end and a fluorescent dye at the 3' end. Normally, the quencher substance at the 5' end suppresses the fluorescent dye at the 3' end, but when PCR is performed, the probe is degraded by the 5'→3' exonuclease activity of Taq polymerase, which releases the suppression of the quencher substance and causes the probe to emit fluorescence. The amount of fluorescence reflects the amount of amplified product. Since there is an inverse correlation between the cycle number (CT) at which the amplified product reaches the detection limit and the amount of initial template, the real-time measurement method quantifies the amount of initial template by measuring the CT. By measuring the CT using several levels of known amounts of template and creating a calibration curve, the absolute value of the amount of initial template for an unknown sample can be calculated. Reverse transcriptases used in RT-PCR include, for example, M-MLV RTase, ExScript RTase (TaKaRa), and Super Script II RT (Thermo Fisher Scientific).

Reaction conditions for real-time PCR are generally based on known PCR methods and vary depending on the base length of the nucleic acid fragment to be amplified, the amount of template nucleic acid, the base length and Tm value of the primers used, the optimal reaction temperature and optimal pH of the nucleic acid polymerase used, etc., and can be appropriately determined according to these conditions. As an example, the denaturation reaction is usually performed at 94-95°C for 5 seconds to 5 minutes, the annealing reaction is performed at 50-70°C for 10 seconds to 1 minute, and the extension reaction is performed at 68-72°C for 30 seconds to 3 minutes, and this cycle is repeated for about 15 to 40 cycles to perform the extension reaction. When using a commercially available kit from the above manufacturer, the protocol attached to the kit should be followed as a general rule.

The nucleic acid polymerase used in real-time PCR is a DNA polymerase, particularly a thermostable DNA polymerase. Various types of such nucleic acid polymerases are commercially available, and these can also be used. For example, the Taq DNA polymerase included in the Applied Biosystems TaqMan MicroRNA Assays Kit (Thermo Fisher Scientific) can be mentioned. Such commercially available kits are particularly useful because they come with buffers and other components optimized for the activity of the included DNA polymerase.

1-5. Detection Method The method for detecting IPMC or pancreatic cancer of the present invention detects the presence of IPMC or pancreatic cancer in the body of a subject based on the expression levels of the marker genes measured as described above.

A marker gene is a gene whose expression level changes when IPMN transitions to IPMC or when pancreatic cancer occurs concomitantly, and it is possible to detect whether a subject is suffering from IPMC or pancreatic cancer based on the expression profile of the marker gene or a marker gene set that combines multiple marker genes.

Here, one embodiment of the detection method of the present invention includes a step of comparing the expression level of the marker gene measured in the measurement step with the expression level of the corresponding marker gene in a sample derived from a healthy subject, an IPMN patient, an IPMC patient, or a pancreatic cancer patient, and detects the presence or risk of a healthy subject, an IPMN patient, an IPMC patient, or pancreatic cancer in the subject. Here, the comparison of the expression levels of the marker genes includes a comparison of the expression levels of a single marker gene, as well as a comparison of expression profiles obtained from the expression levels of two or more marker genes.

The expression levels of the marker genes or the expression profile of each gene included in the marker gene set of samples derived from healthy individuals, IPMN patients, IPMC patients, or pancreatic cancer patients that are used for comparison with the test sample may be previously measured, or the expression levels of each gene included in the marker gene set of samples derived from healthy individuals, IPMN patients, IPMC patients, or pancreatic cancer patients may be newly measured.

Therefore, in one embodiment, the detection method of the present invention further includes a step of measuring the expression level of at least one marker gene included in the marker gene set for detecting IPMC or pancreatic cancer in a sample derived from a healthy individual, an IPMN patient, an IPMC patient, or a pancreatic cancer patient. The expression level or expression profile (hereinafter referred to as "expression level, etc.") obtained by this step can be compared with the expression level, etc. of the test sample. The sample derived from an IPMC or pancreatic cancer patient to be detected may be a sample derived from one individual, or may include samples from two or more individuals. The more individuals derived, the more preferable it is because individual differences in the samples can be averaged out and the detection accuracy is increased.

Here, the step of comparing the expression level etc. of the marker gene obtained in the measurement step with the expression level etc. of the corresponding marker gene in a sample derived from a healthy individual, an IPMN patient, an IPMC patient or a pancreatic cancer patient can, for example, evaluate the test sample as suffering from IPMC or pancreatic cancer if it has an expression level etc. equivalent to the expression level etc. of the corresponding marker gene in a sample derived from an IPMC or pancreatic cancer patient, or evaluate the test sample as not suffering from IPMC or pancreatic cancer if it has an expression level etc. of a gene different from the expression level etc. of the corresponding marker gene in a sample derived from an IPMC or pancreatic cancer patient.

Here, "having equivalent gene expression levels, etc." means that the expression levels, etc. of each gene included in the marker gene set are the same or similar. Additionally, "having different gene expression levels, etc." means that the expression profiles of each gene included in the gene set are not similar.

A specific method for determining whether the expression levels of each gene in a gene set are equivalent or different can be a known method. Examples include, but are not limited to, (i) a method for classifying test samples into a group suffering from IPMC or pancreatic cancer or another group (such as a group of healthy subjects) based on hierarchical clustering analysis, (ii) a method for evaluation by comparing gene expression levels, and (iii) a method for evaluating whether a test sample suffers from IPMC or pancreatic cancer by setting a threshold value.

(i) Hierarchical clustering analysis In one embodiment of the detection method of the present invention, the test sample can be classified by performing cluster analysis on the expression level of each gene included in the marker gene set in the test sample. The data used for the cluster analysis is preferably an expression profile of multiple genes included in the marker gene set.

More specifically, hierarchical cluster analysis can be performed by comparing the expression profile of the marker gene set measured in the test sample with the expression profile of the corresponding marker gene set in a sample derived from a patient with pancreatic cancer or the like. When classifying whether a subject providing a test sample suffers from pancreatic cancer or the like using hierarchical clustering analysis, in order to create a hierarchical cluster, in addition to (i) the expression profile of the marker gene set in the test sample and (ii) the expression profile of the gene set in the sample derived from the patient with pancreatic cancer or the like to be classified, (iii) the expression profile of the gene set in a sample derived from a healthy individual or an IPMN patient is required.

A known method can be used for the hierarchical cluster analysis. In a particularly preferred embodiment of the present invention, the cluster analysis is performed using the group average method based on Euclidean distance. Known software can be used for the cluster analysis, and although not limited to the following, for example, ExpressionView Pro software (MicroDiagnostic, Tokyo, Japan) can be used as commercially available software.

By performing hierarchical cluster analysis, it is possible to draw a hierarchical structure (tree diagram) consisting of clusters derived from patients suffering from the target pancreatic cancer, etc., and clusters derived from healthy individuals, IPMN patients, or patients with pancreatic cancer, etc. other than the target for classification. This makes it possible to detect whether the subject providing the test sample has pancreatic cancer or IPMC, or the risk of having the disease.

(ii) Method of evaluation by comparison of gene expression levels In one embodiment, the total value of the expression levels of genes contained in a marker gene set in a test sample can be compared with the expression levels of corresponding genes in a sample derived from a patient belonging to the pancreatic cancer or other type to be classified, thereby detecting whether a subject has IPMC or pancreatic cancer, or the risk of having such cancer.

To give an example, but not limited to, of one embodiment, the expression levels of multiple marker genes are measured, and the sums of the expression levels of genes included in the marker gene set in a group of patients suffering from pancreatic cancer or the like to be detected are plotted as a group scatter plot, and it is confirmed where the sums of the expression levels of the genes in the marker gene set in the test sample are plotted. Depending on the plotted position, it is possible to evaluate whether or not the subject providing the test sample is likely to suffer from pancreatic cancer or IPMC.

When using the total value of gene expression levels, the expression level values obtained for CBLN3, HIP1R, PTDSS2, MS4A1, KITLG, and EMC1 are inverted before use. For example, when using the total value of the expression levels of each gene obtained by a method such as microarray for gene expression levels, the expression levels of CBLN3, HIP1R, PTDSS2, MS4A1, KITLG, and EMC1 (e.g., Log2 ratio to a common reference) are multiplied by -1 to obtain an inverted value, and the total value of the inverted value and the expression levels of the other genes is calculated.

(iii) A method for differentiation or classification by setting a threshold value In one embodiment, the presence or risk of IPMC or pancreatic cancer can be detected by comparing the expression level of a marker gene set in a test sample with a predetermined threshold value.

Here, the "predetermined threshold" refers to a predetermined cutoff value based on the expression level of a marker gene in a sample derived from a patient group of pancreatic cancer or the like to be detected. The cutoff value can be set, for example, as follows, but is not limited to this. That is, the expression levels of genes included in the marker gene set are measured for samples derived from the IPMC or pancreatic cancer patient group (patient group to be detected) to be detected and samples derived from healthy subjects or IPMN patients, and the gene expression level is calculated for each sample. The predetermined cutoff value can then be derived by creating an ROC curve from the obtained gene expression level values. By setting a cutoff value, it is possible to detect the presence or possibility of IPMC or pancreatic cancer depending on whether the expression level of the marker gene set obtained from the test sample exceeds the cutoff value.

An "ROC curve (Receiver Operating Characteristic curve)" is created by plotting the true positive rate (TPF: True Position Fraction), or sensitivity, on the vertical axis and the false positive rate (FPF: False Position Fraction), or (1 - specificity), on the horizontal axis while varying the threshold for which test result values are judged to indicate a finding, that is, the cutoff point, as a parameter. Specificity is the rate at which negative subjects are accurately judged to be negative.

The cutoff value can be set from the created ROC curve so as to basically increase both the sensitivity and specificity (closer to 1). To do so, the cutoff value can be set to a value that gives the point on the ROC curve closest to point (0,1). In the most preferred embodiment, the cutoff value is set to a value that clearly distinguishes between a patient group suffering from IPMC or pancreatic cancer and a patient group suffering from healthy subjects or IPMN.

1-6. Effect According to the method for detecting the risk of developing IPMC or pancreatic cancer of this embodiment, the expression level of the marker gene in a sample collected from the subject can be examined to detect whether the subject has IPMC or pancreatic cancer or the risk of developing IPMC or pancreatic cancer. The detection method of this embodiment has a high accuracy rate, making it possible to diagnose the development of IPMC or pancreatic cancer, and has the advantage of being able to consider and respond to decisions regarding treatment methods.

2. A kit for verifying the risk of IPMC or pancreatic cancer in test samples from healthy individuals or IPMN patients

Another aspect of the present invention is a reagent for distinguishing the risk of having IPMC or pancreatic cancer (a differentiation reagent). The differentiation reagent of this aspect can be applied to a sample from, for example, a healthy individual or a subject diagnosed with IPMN, to distinguish which disease the subject has.

2-2. Configuration The detection kit of this embodiment includes a measurement means for measuring the expression levels of the marker genes constituting the marker gene set. When the expression levels of the genes are measured by measuring the transcription products, i.e., mRNA or cDNA, the detection means includes a probe or primer set. Specific configurations of these are described in the measurement step section. For example, when detecting the transcription products of the four marker genes constituting the marker gene set, the identification kit can include four types of probe groups capable of detecting the transcription products of the corresponding genes.

When the measurement means of this embodiment is a probe as described above, the measurement means can be provided in the form of a DNA microarray or DNA microchip on which each probe is fixed on a substrate. The material of the substrate on which each probe is fixed is not limited, but glass plates, quartz plates, silicon wafers, etc. are usually used. The size of the substrate can be, for example, 3.5 mm x 5.5 mm, 18 mm x 18 mm, 22 mm x 75 mm, etc., and can be set in various ways depending on the number of probe spots and the size of the spots. The probe is usually 0.1 μg to 0.5 μg of nucleotide per spot. Methods for immobilizing nucleotides include a method of electrostatically binding the nucleotide to a solid phase carrier surface-treated with polycations such as polylysine, poly-L-lysine, polyethyleneimine, and polyalkylamine by utilizing the charge of the nucleotide, and a method of covalently binding a nucleotide to which a functional group such as an amino group, an aldehyde group, an SH group, or a biotin group has been introduced to a solid phase surface to which a functional group such as an amino group, an aldehyde group, or an epoxy group has been introduced.

There are no limitations on the probes or markers for the marker gene set, so long as they can be used to measure the expression levels of the marker genes, and a person skilled in the art can design them appropriately. Specific examples of probes include, but are not limited to, the probes listed in the table below.

2-3. Effect By applying the differentiation kit of this embodiment to a subject, it is possible to detect whether the subject is suffering from IPMC or pancreatic cancer, or to assess the risk of the subject suffering from IPMC or pancreatic cancer.
The detection kit of the present invention may contain other reagents necessary for detecting the marker gene, such as a buffer and a secondary antibody, and instructions for use in detection and result interpretation.
The present invention will be described in more detail below with reference to examples, but the present invention is not limited to the following embodiments.

Example 1. RNA Preparation
RNA was prepared from blood collected from living subjects. Specifically, total RNA was extracted from the collected blood using ISOGEN-LS (Nippon Gene Co., Ltd., Tokyo, Japan).
The human universal reference RNA used was Human Universal Reference RNA Type II (MicroDiagnostic).

(Example 2. Comprehensive gene expression analysis)
The DNA microarray used to obtain gene expression profiles was prepared by arraying 14,400 types of synthetic DNA (80 mers) (MicroDiagnostic) corresponding to human-derived transcripts on a glass slide using a custom arrayer.

Labeled cDNA was synthesized from 5 μg of total RNA from the samples using SuperScript II (Invitrogen Life Technologies, Carlsbad, CA, USA) and Cyanine 5-dUTP (Perkin-Elmer Inc.). Similarly, labeled cDNA was synthesized from 5 μg of total RNA for human common reference RNA using SuperScript II and Cyanine 3-dUTP (Perkin-Elmer Inc.).
Hybridization with DNA microarrays was performed using a Labeling and Hybridization kit (MicroDiagonostic).

The fluorescence intensity after hybridization with the DNA microarray was measured using a GenePix 4000B Scanner (Axon Instruments, Inc., Union city, CA, USA). The expression ratio (fluorescence intensity of Cyanine-5 labeled cDNA from the sample/fluorescence intensity of Cyanine-3 labeled cDNA from the human common reference RNA) was calculated by dividing the fluorescence intensity of Cyanine-5 labeled cDNA from the sample by the fluorescence intensity of Cyanine-3 labeled cDNA from the human common reference RNA. Furthermore, normalization was performed by multiplying the calculated expression ratio by a normalization factor using GenePix Pro 3.0 software (Axon Instruments, Inc.). The expression ratio was then converted to Log2, and the converted value was named the expression level value. The expression ratio was converted using Excel software (Microsoft, Bellevue, WA, USA) and the MDI gene expression analysis software package (MicroDiagnostic).

Example 3. Useful markers for monitoring pancreatic cancer risk
The present invention provides a set of 43 marker genes whose expression levels correlate with pancreatic cancer risk.

Gene expression profiles of 14,400 genes were obtained from blood samples taken from pancreatic cancer patients (12 cases), IPMC patients (4 cases), and IPMN patients (24 cases).

Next, genes whose expression levels were below the detection limit in two or more of the pancreatic cancer patient group (12 samples) or in three or more of the IPMN patient group (24 samples) were deleted for which no data could be obtained. In addition, the average expression level of each gene was calculated for each of the pancreatic cancer patient group and the IPMN patient group, and genes with an absolute difference between the two groups of 0.5 or more were extracted. Furthermore, a two-group comparison was performed between the pancreatic cancer patient group and the IPMN patient group using Student's t-test, and genes with a p-value of less than 0.01 (13 genes) (hereinafter referred to as "gene set 1") were extracted (Table 1).

Next, genes whose expression levels were below the detection limit in two or more of the pancreatic cancer patient group (12 samples), two or more of the IPMC patient group (4 samples), or three or more of the IPMN patient group (24 samples) were deleted for which data could not be obtained. In addition, the average expression level value of each gene was calculated for each patient group, and genes with an absolute difference of 0.5 or more between the average values of the combined pancreatic cancer patient group and IPMC patient group (hereinafter referred to as the "pancreatic cancer-IPMC patient group") and the IPMN patient group were extracted. Furthermore, a two-group comparison was performed between the pancreatic cancer-IPMC patient group and the IPMN patient group using Student's t-test, and genes with a p-value of less than 0.01 (13 genes) (hereinafter referred to as "gene set 2") were extracted.

Next, genes whose expression levels were below the detection limit in two or more of the IPMC patient group (four samples) and in three or more of the IPMN patient group (24 samples) were deleted for which no data could be obtained. In addition, the average expression level of each gene was calculated for each patient group, and genes with an absolute difference of 0.5 or more between the average value of the IPMC patient group and the average value of the IPMN patient group were extracted. Furthermore, a two-group comparison between the IPMC patient group and the IPMN patient group was performed using Student's t-test, and genes with a p-value of less than 0.001 (25 genes) (hereinafter referred to as "gene set 3") were extracted.

(Example 4. Discrimination of pancreatic cancer risk based on the score of each gene)
A total of 18 genes (DSC2, JAK2, PYGL, CBLN3, MS4A1, HIP1R, TMEM176B, MED20, SCML2, KITLG, ANXA5, S100A12, LRRK2, ANXA3, PTDSS2, EMC1, TRIM22 and NRGN), including 8 genes overlapping between gene sets 1 and 2, were identified as marker genes for IPMC or pancreatic cancer. When comparing the average expression level values of the pancreatic cancer patient group and the IPMN patient group for each gene, the average expression level value of the IPMN patient group was higher than the average expression level value of the pancreatic cancer patient group, and when comparing the average expression level values of the pancreatic cancer-IPMC patient group, which is the combined pancreatic cancer patient group and IPMC patient group, for each gene, the average expression level value of the IPMN patient group was higher than the average expression level of the pancreatic cancer-IPMC patient group (a total of 6 genes (CBLN3, HIP1R, PTDSS2, MS4A1, KITLG, and EMC1)) was calculated by multiplying the expression level value by -1 to obtain the "gene expression score," and for the remaining 12 genes, the respective expression level values were used as the "gene expression score."

For DSC2, the mean gene expression score in the IPMN patient group was 0.4561, the mean gene expression score in the IPMC patient group was 1.0294, and the mean gene expression score in the pancreatic cancer patient group was 0.9785, and the p-value between the IPMN patient group and the IPMC patient group was 0.0226, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 3.12× ^10-5 (Fig. 1). Furthermore, the area under the ROC curve (AUC) was calculated using the receiver operating characteristic curve (ROC) based on the gene expression scores in the pancreatic cancer-IPMC patient group and the gene expression scores in the IPMN patient group, and sensitivity and specificity curves were created to determine the optimal cutoff value. ROC curves and group scatter plots were created using StatFlex ver.6.0 software (Artech Co., Ltd., Osaka, Japan), and the optimal cutoff value was set to the value that maximized the sum of sensitivity and specificity (hereinafter, the creation of ROC curves, sensitivity and specificity curves, and the setting of optimal cutoff values were the same). For DSC2, the AUC was 0.8958, the cutoff value was 0.7568, and the sensitivity and specificity at these values were 83.3% (Figure 1).

For JAK2, the mean gene expression score for the IPMN patient group was 0.7309, the mean gene expression score for the IPMC patient group was 1.1818, and the mean gene expression score for the pancreatic cancer patient group was 1.2485. The p-value between the IPMN patient group and the IPMC patient group was 0.1285, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0006. Furthermore, the AUC was 0.8299, the cutoff value was 0.9428, and the sensitivity and specificity at this time were 83.3% (Figure 2).

For PYGL, the mean gene expression score for the IPMN patient group was 0.2338, the mean gene expression score for the IPMC patient group was 0.2832, and the mean gene expression score for the pancreatic cancer patient group was 0.7407. The p-value between the IPMN patient group and the IPMC patient group was 0.8030, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0085. Furthermore, the AUC was 0.8229, the cutoff value was 0.4933, and the sensitivity and specificity at this time were 75.0% (Figure 3).

For CBLN3, the mean gene expression score for the IPMN patient group was -1.4619, the mean gene expression score for the IPMC patient group was -0.9444, and the mean gene expression score for the pancreatic cancer patient group was -0.9465. The p-value between the IPMN patient group and the IPMC patient group was 0.1681, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0018. Furthermore, the AUC was 0.7708, the cutoff value was -1.1709, and the sensitivity and specificity at this time were 66.7% (Figure 4).

For MS4A1, the mean gene expression score for the IPMN patient group was -0.4420, the mean gene expression score for the IPMC patient group was 0.3660, and the mean gene expression score for the pancreatic cancer patient group was 0.3956. The p-value between the IPMN patient group and the IPMC patient group was 0.0057, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0016. Furthermore, the AUC was 0.8438, the cutoff value was -0.1149, and the sensitivity and specificity at this time were 70.8% (Figure 5).

For HIP1R, the mean gene expression score for the IPMN patient group was -0.0311, the mean gene expression score for the IPMC patient group was 0.4702, and the mean gene expression score for the pancreatic cancer patient group was 0.4833. The p-value between the IPMN patient group and the IPMC patient group was 0.0423, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0042. Furthermore, the AUC was 0.8056, the cutoff value was 0.0222, and the sensitivity and specificity at this time were 66.7% (Figure 6).

For TMEM176B, the mean gene expression score for the IPMN patient group was 1.4872, the mean gene expression score for the IPMC patient group was 2.4460, and the mean gene expression score for the pancreatic cancer patient group was 2.2641. The p-value between the IPMN patient group and the IPMC patient group was 0.0131, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0081. Furthermore, the AUC was 0.7569, the cutoff value was 1.8835, and the sensitivity and specificity at this time were 70.8% (Figure 7).

For MED20, the mean gene expression score for the IPMN patient group was -1.2305, the mean gene expression score for the IPMC patient group was -0.9317, and the mean gene expression score for the pancreatic cancer patient group was -0.6988. The p-value between the IPMN patient group and the IPMC patient group was 0.3713, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0021. Furthermore, the AUC was 0.7535, the cutoff value was -0.9721, and the sensitivity and specificity at this time were 66.7% (Figure 8).

For SCML2, the mean gene expression score for the IPMN patient group was -1.2494, the mean gene expression score for the IPMC patient group was -0.8418, and the mean gene expression score for the pancreatic cancer patient group was -0.7338. The p-value between the IPMN patient group and the IPMC patient group was 0.2221, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0046. Furthermore, the AUC was 0.8194, the cutoff value was -1.1207, and the sensitivity and specificity at this time were 66.7% (Figure 9).

For KITLG, the mean gene expression score for the IPMN patient group was 0.7887, the mean gene expression score for the IPMC patient group was 1.0510, and the mean gene expression score for the pancreatic cancer patient group was 1.2451. The p-value between the IPMN patient group and the IPMC patient group was 0.5648, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0398. Furthermore, the AUC was 0.7240, the cutoff value was 1.1233, and the sensitivity and specificity at this time were 75.0% (Figure 10).

For ANXA5, the mean gene expression score for the IPMN patient group was -1.9253, the mean gene expression score for the IPMC patient group was -1.5472, and the mean gene expression score for the pancreatic cancer patient group was -1.4123. The p-value between the IPMN patient group and the IPMC patient group was 0.1306, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0063. Furthermore, the AUC was 0.7743, the cutoff value was -1.7785, and the sensitivity and specificity at this time were 66.7% (Figure 11).

For S100A12, the mean gene expression score for the IPMN patient group was 2.7663, the mean gene expression score for the IPMC patient group was 3.2341, and the mean gene expression score for the pancreatic cancer patient group was 3.7686. The p-value between the IPMN patient group and the IPMC patient group was 0.2492, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0007. Furthermore, the AUC was 0.8438, the cutoff value was 3.3432, and the sensitivity and specificity at this time were 83.3% (Figure 12).

For LRRK2, the mean gene expression score for the IPMN patient group was 3.4415, the mean gene expression score for the IPMC patient group was 3.5821, and the mean gene expression score for the pancreatic cancer patient group was 4.0637. The p-value between the IPMN patient group and the IPMC patient group was 0.8788, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0054. Furthermore, the AUC was 0.7326, the cutoff value was 3.8564, and the sensitivity and specificity at this time were 66.7% (Figure 13).

For ANXA3, the mean gene expression score in the IPMN patient group was -0.1501, the mean gene expression score in the IPMC patient group was 0.4501, and the mean gene expression score in the pancreatic cancer patient group was 0.3424. The p-value between the IPMN patient group and the IPMC patient group was 0.0151, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0267. Furthermore, the AUC was 0.7917, the cutoff value was 0.1019, and the sensitivity and specificity at this time were 75.0% (Figure 14).

For PTDSS2, the mean gene expression score for the IPMN patient group was 0.1271, the mean gene expression score for the IPMC patient group was 0.6790, and the mean gene expression score for the pancreatic cancer patient group was 0.6117. The p-value between the IPMN patient group and the IPMC patient group was 0.0444, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0039. Furthermore, the AUC was 0.7734, the cutoff value was 0.4472, and the sensitivity and specificity at this time were 66.7% (Figure 15).

For EMC1, the mean gene expression score for the IPMN patient group was 0.6771, the mean gene expression score for the IPMC patient group was 1.5458, and the mean gene expression score for the pancreatic cancer patient group was 1.1857. The p-value between the IPMN patient group and the IPMC patient group was 0.0686, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0292. Furthermore, the AUC was 0.7474, the cutoff value was 1.030, and the sensitivity and specificity at this time were 75.0% (Figure 16).

For TRIM22, the mean gene expression score for the IPMN patient group was 2.4905, the mean gene expression score for the IPMC patient group was 3.0376, and the mean gene expression score for the pancreatic cancer patient group was 3.0150. The p-value between the IPMN patient group and the IPMC patient group was 0.0542, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0105. Furthermore, the AUC was 0.8151, the cutoff value was 2.6021, and the sensitivity and specificity at this time were 70.8% (Figure 17).

For NRGN, the mean gene expression score for the IPMN patient group was 2.6680, the mean gene expression score for the IPMC patient group was 3.3973, and the mean gene expression score for the pancreatic cancer patient group was 3.1842. The p-value between the IPMN patient group and the IPMC patient group was 0.0501, and the p-value between the IPMN patient group and the pancreatic cancer patient group was 0.0315. Furthermore, the AUC was 0.7318, the cutoff value was 2.8946, and the sensitivity and specificity at this time were 62.5% (Figure 18).

Example 5. Cluster analysis using gene set 1
Gene expression levels of gene set 1 including the 13 genes selected in Example 3 were measured (Tables 3A-D), and cluster analysis was performed. Cluster analysis was performed using ExpressionView Pro software (MicroDiagnostic) by the group average method using Euclidean distance. The results of the cluster analysis are shown in FIG. 19. As shown in FIG. 19, hierarchical cluster analysis was performed based on the expression profiles of the extracted 13 genes, and the patients were classified into a cluster mainly including IPMN patients and a cluster mainly including pancreatic cancer patients.

(Example 5. Differentiation of pancreatic cancer risk by scoring gene set 1)
First, the expression levels of four genes, CBLN3, MS4A1, HIP1R, and KITLG, which were found to have higher expression levels in the IPMN patient group than in the pancreatic cancer patient group, were multiplied by -1 to calculate gene expression scores. The expression levels of the remaining nine genes were used as gene expression scores as they were. Furthermore, the sum of the gene expression scores of the 13 genes was taken as the "specimen score (gene set 1)."

A group scatter plot of the specimen score (gene set 1) was created for the IPMN patient group and the pancreatic cancer patient group (Figure 20). The average specimen score (gene set 1) for the IPMN patient group was 3.5644, and the average specimen score (gene set 1) for the pancreatic cancer patient group was 11.4017. It was also found that there was a significant difference (p value = 6.66 x ^10-9 ) between the two groups in the specimen score (gene set 1).

Furthermore, the area under the ROC curve (AUC) was calculated using the ROC (Receiver Operating Characteristic curve) curve (Figure 21). Sensitivity and specificity curves were also created to determine the optimal cutoff value (Figure 21). The ROC curve and group scatter plots were created using StatFlex ver.6.0 software (Artech Co., Ltd., Osaka, Japan), and the optimal cutoff value was set to the value that maximized the sum of sensitivity and specificity (the optimal cutoff value was set in the same manner below). In the ROC curve, the AUC was 0.9688, and in the sensitivity and specificity curve, the optimal cutoff value was set to 8.2499, with sensitivity and specificity of 91.7% (Figure 21).

In addition, when the samples were rearranged in ascending order of sample score value (gene set 1), it was found that by setting an appropriate threshold, it was possible to distinguish between pancreatic cancer patients and IPMN patients (Figure 22).

In addition, two cases (two samples each taken from the same patient at different times) were used for verification. After two measurements, the specimen scores of the two cases were calculated and the specimens were rearranged in ascending order of specimen score value (gene set 1) (Figure 23). For the first patient, the specimen score value (gene set 1) was 4.6174 when the specimen was first taken (FMU#12875), and the specimen score value (gene set 1) was 4.8639 when the specimen was second taken (FMU#14156), indicating that the specimen score value (gene set 1) had increased. The pathological diagnosis of the first patient was IPMN when the specimen was first taken, and intraductal papillary-mucinous adenoma (IPMA) when the specimen was second taken. Next, for the second patient, the sample score value (gene set 1) was 10.399 when the sample was first taken (FMU#11788), and 15.5487 when the sample was second taken (FMU#13478), showing a higher score the second time than the first. The diagnosis for the second patient was suspected IPMC when the sample was first taken, and pancreatic cancer when the sample was second taken.

In the first verification case, the lesion had progressed from IPMN to IPMA over the two measurement periods, and in the second case, the lesion had progressed from suspected IPMC to pancreatic cancer over the two measurement periods. The sample score values for gene set 1 were consistent with the transition and progression of the disease indicated by the pathological diagnosis.

Example 6. Cluster analysis using gene set 2
Gene expression levels of gene set 2 including the 13 genes selected in Example 3 were measured (Tables 4A-D), and cluster analysis was performed. Cluster analysis was performed using ExpressionView Pro software (MicroDiagnostic) by the group average method based on Euclidean distance. The results of the cluster analysis are shown in FIG. 24. As shown in FIG. 24, hierarchical cluster analysis was performed based on the expression profiles of the extracted 13 genes, and the patients were classified into a cluster mainly including IPMN patients and a cluster mainly including pancreatic cancer patients.

(Example 7. Discrimination of pancreatic cancer risk by scoring gene set 2)
First, of the 13 genes included in gene set 2, the expression levels of six genes, CBLN3, HIP1R, PTDSS2, MS4A1, KITLG, and EMC1, which had higher expression levels in the IPMC patient group than in the pancreatic cancer patient group, were multiplied by -1 to calculate gene expression scores. For the remaining seven genes, the expression level values were used as gene expression scores as they were. Furthermore, the sum of the gene expression scores of the 13 genes was used as the "specimen score (gene set 2)."

A scatter plot of the sample score (gene set 2) was created for the IPMN, IPMC, and pancreatic cancer patient groups (Figure 25). The average sample score (gene set 2) for the IPMN patient group was 10.1070, the average sample score (gene set 2) for the IMPC patient group was 17.9437, and the average sample score (gene set 2) for the pancreatic cancer patient group was 17.7812. It was also confirmed that the sample score (gene set 2) for the IPMN patient group was significantly different from the sample score (gene set 2) for the IPMC patient group or the pancreatic cancer patient group with p values of 0.0152 and 5.9961 x ^10-7, respectively.

Furthermore, the area under the ROC curve (AUC) was calculated using the ROC (Receiver Operating Characteristic curve) curve (Figure 26). Sensitivity and specificity curves were also created to determine the optimal cutoff value (Figure 26). The optimal cutoff value was set to the value that maximized the sum of sensitivity and specificity (the optimal cutoff value was set in the same manner below). In the ROC curve, the AUC was 0.9401, and in the sensitivity and specificity curve, the optimal cutoff value was set to 14.5626, with sensitivity and specificity of 91.7% (Figure 26).

In addition, when the samples were rearranged in ascending order of sample score value, it was found that by setting an appropriate threshold, it was possible to distinguish between pancreatic cancer patients and IPMN patients (Figure 27).

In addition, two cases (two samples each obtained from the same patient) were used for verification. For the first patient, the specimen score value was 12.8807 when the specimen was first collected (FMU#12875), and 10.1967 when the specimen was second collected (FMU#14156), and the specimen score value had decreased. The pathological diagnosis of the first patient was IPMN when the specimen was first collected, and IPMA when the specimen was second collected. Next, for the second patient, the specimen score value was 17.4963 when the specimen was first collected (FMU#11788), and 19.2133 when the specimen was second collected (FMU#13478). The pathological diagnosis of the second patient was suspected IPMC when the specimen was first collected, and pancreatic cancer when the specimen was second collected (Figure 28).

In the first verification case, the lesion had progressed from IPMN to IPMA over the two measurement periods, and in the second case, the lesion had progressed from suspected IPMC to pancreatic cancer over the two measurement periods. The sample score values for gene set 2 were consistent with the transition and progression of the disease indicated by the pathological diagnosis.

(Example 8. Differentiation of pancreatic cancer risk by scoring gene set 3)
In the IPMN patient group and the IPMC patient group, the gene expression levels of gene set 3 including the 25 genes selected in Example 3 were measured (Tables 5A to 5D), and cluster analysis was performed.

Cluster analysis was performed using ExpressionView Pro software (MicroDiagnostic) by the group average method using Euclidean distance. The results of the cluster analysis are shown in Figure 29. As shown in Figure 29, hierarchical cluster analysis was performed based on the expression profiles of the extracted 25 genes, and the patients were classified into a cluster mainly including IPMN patients and a cluster mainly including IPMC patients.

Next, of the 25 genes included in gene set 3, the expression levels of 10 genes, ENC1, GPR15, ZNF609, RPGRIP1, RABGGTB, ZC3HC1, USP37, MALSU1, EEF1B2, and LAMC1, which had lower expression levels in the IPMC patient group than in the IPMN patient group, were multiplied by -1 to calculate gene expression scores. For the remaining 7 genes, the expression level values were used as gene expression scores directly. Furthermore, the sum of the gene expression scores of the 25 genes was defined as the "specimen score (gene set 3)."

A scatter plot of the sample score (gene set 3) was created for the IPMN, IPMC, and pancreatic cancer patient groups. The average sample score (gene set 3) for the IPMN, IMPC, and pancreatic cancer patients was 9.3232, 24.9817, and 11.1527, respectively. It was also confirmed that the sample score (gene set 3) for the IPMC patient group was significantly different from the sample score (gene set 3) for the IPMN patient group or the pancreatic cancer patient group with p values of 9.0662× ^10-14 and 1.1930× ^10-13 , respectively (Figure 30).

Furthermore, the area under the ROC curve (AUC) was calculated using the ROC (Receiver Operating Characteristic curve). Sensitivity and specificity curves were also created to determine the optimal cutoff value. The optimal cutoff value was set to the value that maximized the sum of sensitivity and specificity (the optimal cutoff value was set in the same manner below). In the ROC curve, the AUC was 1.0000, and in the sensitivity and specificity curve, the optimal cutoff value was set to 22.7082, at which point the sensitivity and specificity were 100% (Figure 31).

Furthermore, when the samples were rearranged in ascending order of sample score value, it was found that by setting an appropriate threshold, it was possible to distinguish between pancreatic cancer patients and IPMN patients (Figure 32).

Claims (8)

A method for assisting in the differentiation of a test sample from an IPMN patient as to whether the test sample is IPMN, IPMC, or pancreatic cancer, comprising:
The DSC2 gene, the JAK2 gene, the PYGL gene, the CBLN3 gene, the MS4A1 gene, the HIP1R gene, the TMEM176B gene, the MED20 gene, the SCML2 gene, the KITLG gene, the ANXA5 gene, the S100A12 gene, the LRRK2 gene, the ANXA3 gene, the PTDSS2 gene, the EMC1 gene, the TRIM22 gene, the NRGN gene, the MX1 gene, the EPSTI1 gene, the IFIT1 gene, the IFI6 gene, the MIR100HG gene, the C17orf51 gene, the ... C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C17orf51 gene, the C1 a measuring step of measuring expression levels of genes selected in combination from the group consisting of the IPMN gene, FAM118A gene, SPATA33 gene, ENC1 gene, GPR15 gene, ZNF609 gene, TNFAIP8L3 gene, GRHL2 gene, RPGRIP1 gene, SUZ12 gene, LIAS gene, SMN2 gene, RABGGTB gene, LAMB3 gene, ZC3HC1 gene, USP37 gene, MALSU1 gene, EEF1B2 gene, LAMC1 gene, and R3HDM4 gene; and a step of comparing the expression levels of the genes in the test sample obtained in the measuring step with the expression levels of the corresponding genes in a sample derived from an IPMN patient, an IPMC patient, or a pancreatic cancer patient,
The genes selected in combination in the measurement step are
(1) when differentiating between IPMN and pancreatic cancer, the combination of the DSC2 gene, the JAK2 gene, the PYGL gene, the CBLN3 gene, the MS4A1 gene, the HIP1R gene, the TMEM176B gene, the MED20 gene, the SCML2 gene, the KITLG gene, the ANXA5 gene, the S100A12 gene, and the LRRK2 gene is included,
(2) when distinguishing between IPMN, IPMC, or pancreatic cancer, the combination of the DSC2 gene, the JAK2 gene, the CBLN3 gene, the MS4A1 gene, the HIP1R gene, the TMEM176B gene, the KITLG gene, the S100A12 gene, the ANXA3 gene, the PTDSS2 gene, the EMC1 gene, the TRIM22 gene, and the NRGN gene is included;
(3) when distinguishing between IPMN and IPMC, a combination of the MX1 gene, the EPSTI1 gene, the IFIT1 gene, the IFI6 gene, the MIR100HG gene, the C17orf51 gene, the FAM118A gene, the SPATA33 gene, the ENC1 gene, the GPR15 gene, the ZNF609 gene, the TNFAIP8L3 gene, the GRHL2 gene, the RPGRIP1 gene, the SUZ12 gene, the LIAS gene, the SMN2 gene, the RABGGTB gene, the LAMB3 gene, the ZC3HC1 gene, the USP37 gene, the MALSU1 gene, the EEF1B2 gene, the LAMC1 gene, and the R3HDM4 gene is included;
Methods to aid in differentiation. 2. The method of claim 1 ,
The method for assisting in differentiation further includes a step of comparing the expression level of the gene obtained in the measurement step with the expression level of the corresponding gene in a sample derived from a healthy subject or an IPMN patient, and the expression level of the corresponding gene in a sample derived from an IPMC patient or a pancreatic cancer patient by cluster analysis. 2. The method of claim 1 ,
The method for assisting in differentiation further comprises a step of comparing the expression level of the gene in the test sample obtained in the measuring step with a predetermined threshold value. A method for assisting in the differentiation of a test sample from an IPMN patient as to whether the test sample is IPMN, IPMC, or pancreatic cancer, comprising:
A method for assisting in differentiation, comprising: a measuring step of measuring the expression level of the EMC1 gene in the test sample; and a step of comparing the expression level of the gene in the test sample obtained in the measuring step with the expression level of the corresponding gene in a sample derived from an IPMN patient, an IPMC patient or a pancreatic cancer patient, wherein the expression level is compared with a predetermined threshold value obtained from the expression level of the corresponding gene. The method according to any one of claims 1 to 4,
A method for assisting in differentiation, wherein the test sample is blood. The method according to any one of claims 1 to 5,
A method for assisting differentiation, wherein in the measuring step, the measurement of the expression level of the gene is the measurement of the expression amount of mRNA of the gene. A kit for assisting in the discrimination of whether a test sample derived from an IPMN patient is IPMN, IPMC, or pancreatic cancer, comprising:
DSC2 gene, JAK2 gene, PYGL gene, CBLN3 gene, MS4A1 gene, HIP1R gene, TMEM176B gene, MED20 gene, KITLG gene, ANXA5 gene, S100A12 gene, LRRK2 gene, ANXA3 gene, PTDSS2 gene, EMC1 gene, TRIM22 gene, NRGN gene, MX1 gene, IFIT1 gene, IFI6 gene, MIR100HG gene, C17orf51 gene, FAM118 A measuring means for measuring the expression of a gene selected in combination from the group consisting of the A gene, the SPATA33 gene, the ENC1 gene, the GPR15 gene, the ZNF609 gene, the TNFAIP8L3 gene, the GRHL2 gene, the RPGRIP1 gene, the SUZ12 gene, the LIAS gene, the SMN2 gene, the RABGGTB gene, the ZC3HC1 gene, the USP37 gene, the MALSU1 gene, the EEF1B2 gene, the LAMC1 gene, and the R3HDM4 gene,
The genes selected in combination are
(1) A method for distinguishing between IPMN and pancreatic cancer, comprising: a measuring means for measuring expression of the DSC2 gene, the JAK2 gene, the PYGL gene, the CBLN3 gene, the MS4A1 gene, the HIP1R gene, the TMEM176B gene, the MED20 gene, the SCML2 gene, the KITLG gene, the ANXA5 gene, the S100A12 gene, and the LRRK2 gene;
(2) A measuring means for measuring the expression of the DSC2 gene, the JAK2 gene, the CBLN3 gene, the MS4A1 gene, the HIP1R gene, the TMEM176B gene, the KITLG gene, the S100A12 gene, the ANXA3 gene, the PTDSS2 gene, the EMC1 gene, the TRIM22 gene, and the NRGN gene when distinguishing between IPMN and IPMC or pancreatic cancer,
(3) When distinguishing between IPMN and IPMC, a measuring means for measuring expression of the MX1 gene, the EPSTI1 gene, the IFIT1 gene, the IFI6 gene, the MIR100HG gene, the C17orf51 gene, the FAM118A gene, the SPATA33 gene, the ENC1 gene, the GPR15 gene, the ZNF609 gene, the TNFAIP8L3 gene, the GRHL2 gene, the RPGRIP1 gene, the SUZ12 gene, the LIAS gene, the SMN2 gene, the RABGGTB gene, the LAMB3 gene, the ZC3HC1 gene, the USP37 gene, the MALSU1 gene, the EEF1B2 gene, the LAMC1 gene, and the R3HDM4 gene is included;
A kit, wherein the means for measuring the expression of the gene is at least one means selected from the group consisting of a primer, a probe, or a labeled product thereof for the gene. A kit for assisting in the discrimination of whether a test sample derived from an IPMN patient is IPMN, IPMC, or pancreatic cancer, comprising:
A measuring means for measuring the expression level of the EMC1 gene in the test sample,
A kit, wherein the means for measuring the expression of the gene is at least one means selected from the group consisting of a primer, a probe, or a labeled product thereof for the gene.