JP2021093955A - Methods and kits for detecting intraductal papillary mucinous carcinoma or pancreatic cancer - Google Patents

Abstract

To provide methods for detecting the presence of IPMN-derived IPMC or pancreatic cancer by identifying marker genes in normal healthy individuals or patients diagnosed as IPMN, a high risk group for pancreatic cancer, and using the marker genes.SOLUTION: The method comprises determining the expression level of at least one gene in the group consisting of 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, 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.SELECTED DRAWING: None

Description

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

The number of patients with pancreatic cancer is on the rise, accounting for the fourth largest number of cancer deaths, and the 5-year survival rate is less than 10%. At the time of diagnosis, about 80% is in an unresectable advanced state, and it is an urgent task to establish a highly accurate screening method aiming at early diagnosis of cancer (Non-Patent Document 1). Since cancer screening for all healthy subjects is less cost-effective, screening for high-risk groups is considered to be efficient. For example, picking up Helicobacter pylori-infected persons in gastric cancer screening and conducting intensive endoscopy. Among the many high-risk groups for pancreatic cancer, IPMN has a high disease frequency of about 3% of the total population and is a disease that should be noted for screening.

IPMN is a general term for tumors in which mucus-producing atypical epithelium grows papillary in the pancreatic duct and produces excess mucus. It progresses to IPMC, which is pancreatic cancer (Non-Patent Document 2). IPMN in this study refers to low-medium atypia lesions, which are benign diseases. There are two types of pancreatic cancer that occur in IPMN: IPMC and pancreatic cancer that occurs in a site distant from IPMN (hereinafter referred to as IPMN coexisting pancreatic cancer). According to 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 comorbid pancreatic cancer was found in 2.0% (Non-Patent Document 3).

In the current daily practice, pancreatic cancer screening centered on diagnostic imaging recommended by the IPMN International Practice Guidelines is performed, but biomarkers in blood that are effective for more objective and effective screening Development is essential.

For strict distinction, it is necessary to use pathological diagnosis using surgical specimens. On the other hand, the distinction between pancreatic cancer, which is a solid lesion, and IPMN, which is a cystic lesion, can be relatively easily diagnosed by imaging tests such as CT and MRI. The clinical problem is that, as mentioned above, pancreatic cancer develops in a certain proportion from IPMN patients, and there is currently no effective biomarker.

Regarding IPMC, the IPMN International Practice Guidelines have been formulated by the Japanese Pancreas Society and are widely used in daily practice. In addition, clinical practice guidelines have been formulated by the American Society of Gastroenterology and are used mainly in the United States (Non-Patent Document 4). These guidelines are mainly for diagnosing IPMN canceration (IPMC) using diagnostic imaging (CT, MRI, endoscopic ultrasonography), but the ability (specificity) of definitive diagnosis is 60-. It is 80% (Non-Patent Documents 5 to 8). In addition, there are no biomarkers useful for definitive diagnosis and screening of pancreatic cancer and IPMC, and the establishment of a highly diagnostic test method is awaited.

To date, many previous studies have been reported, but most of the research objectives are'differentiation of IPMN from other cystic diseases'and'differentiation of normal control group from IPMC / pancreatic cancer'(eg, non-patent literature). 9). On the other hand, there are few reports of biomarker studies for picking up IPMC and pancreatic cancer originating from IPMN, which is a high-risk group for pancreatic cancer.

In addition, some studies have searched for biomarkers in duodenal juice, pancreatic juice, and cystic juice using endoscopy (for example, Non-Patent Document 10), and it is difficult to generalize them from the viewpoint of invasiveness. it is conceivable that.

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.

An object of the present invention is to identify a biomarker for detecting the presence of IPMC and pancreatic cancer originating from IPMN in a healthy person or a patient diagnosed with IPMN, which is a high-risk group for pancreatic cancer, and use the biomarker for IPMC. Alternatively, it is to provide a method for detecting the presence of pancreatic cancer.

In order to solve the above problems, the present inventors obtained gene expression profiles of 14,400 genes from the whole blood of pancreatic cancer patients (12 cases), IPMC patients (4 cases) and IPMN patients (24 cases), and pancreatic cancer patient group. We succeeded in picking up genes with significantly different expression levels between the IPMN patient group, the IPMC patient group and the IPMN patient group, or the pancreatic cancer patient group and the IPMC patient group and the IPMN patient group. The invention was completed.

That is, the present invention includes the following aspects:
In one aspect, the present invention
[1] A method for detecting IPMC or pancreatic cancer in a test sample.
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, 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. The present invention relates to a detection method including a step.

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

Moreover, in one embodiment, the detection method of the present invention
[3] The detection method according to the above [1] or [2].
The measurement step is a step of measuring the expression level of DSC2 gene, JAK2 gene, CBLN3 gene, MS4A1 gene, HIP1R gene, TMEM176B gene, KITLG gene, S100A12 gene, ANXA3 gene, PTDSS2 gene, EMC1 gene, TRIM22 gene and NRGN gene. It is characterized by being.

Moreover, in one embodiment, the detection method of the present invention
[4] The detection method according to the above [1] or [2].
The measurement step measures the expression levels of 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, and LRRK2 gene. It is characterized in that it is a process of performing.

Moreover, in one embodiment, the detection method of the present invention
[5] The detection method according to the above [1].
The above method is a method for detecting IPMC in a test sample derived from IPMN.
The measurement steps are 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, It is a step to measure the expression level of at least one gene in the group consisting of LIAS gene, SMN2 gene, RABGGTB gene, LAMB3 gene, ZC3HC1 gene, USP37 gene, MALSU1 gene, EEF1B2 gene, LAMC1 gene, and R3HDM4 gene. It is a feature.

Moreover, in one embodiment, the detection method of the present invention
[6] The detection method according to any one of the above [1] to [4].
It 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, an IPMN patient, an IPMC patient or a pancreatic cancer patient.

Moreover, in one embodiment, the detection method of the present invention
[7] The detection method according to any one of the above [1] to [4].
The expression level of the gene obtained in the measurement step, the expression level of the corresponding gene in the sample derived from a healthy person or IPMN patient, and the expression level of the corresponding gene in the sample derived from IPMC patient or pancreatic cancer patient are compared by cluster analysis. It is characterized by further including steps.

Moreover, in one embodiment, the detection method of the present invention
[8] The detection method according to any one of the above [1] to [4].
It is characterized by further including a step of comparing the expression level of the gene in the test sample obtained in the measurement step with a predetermined threshold value.

Moreover, in one embodiment, the detection method of the present invention
[9] The detection method according to any one of the above [1] to [8].
The test sample is blood.

Moreover, in one embodiment, the detection method of the present invention
[10] The detection method according to any one of the above [1] to [9].
In the measurement step, the measurement of the expression level of the gene is the measurement of the mRNA expression level of the gene.

Further, in another aspect, the present invention
[11] A set of marker genes for detecting IPMC or pancreatic cancer, 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, FAM118A gene, SPATA33 gene, ENC1 gene, GPR15 Consists of genes, 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. With respect to a marker gene set comprising at least one gene in the group.

Further, in another aspect, the present invention
[12] A kit for detecting IPMC or pancreatic cancer.
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, FAM118A gene, SPATA33 gene, ENC1 gene, GPR15 gene, ZNF609 gene, TNFAIP8L3 gene, GRHL2 gene, RPGRIP1 gene, SUZ12 gene, LIAS The kit comprises means for measuring the expression of at least one gene in the group consisting of the gene, SMN2 gene, RABGGTB gene, LAMB3 gene, ZC3HC1 gene, USP37 gene, MALSU1 gene, EEF1B2 gene, LAMC1 gene, and R3HDM4 gene. ..

Here, the kit of the present invention is in one embodiment.
[13] The kit according to the above [12].
The kit is a kit for detecting IPMC in a test sample derived from IPMN.
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.

Moreover, in one embodiment, the kit of the present invention
[14] The kit according to the above [12] or [13].
The measuring means for measuring the expression of the gene is at least one means selected from the group consisting of primers, probes, or labels 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 expression analysis of genes contained in a marker gene set in a test sample.

The upper row shows a group scatter plot showing the gene expression score of the DSC2 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the JAK2 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the PYGL gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the CBLN3 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the MS4A1 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the HIP1R gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the TMEM176B gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the MED20 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the SCML2 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the KITLG gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the ANXA5 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the S100A12 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the LRRK2 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the AMXA3 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the PTDSS2 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the EMC1 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the TRIM22 gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The upper row shows a group scatter plot showing the gene expression score of the NRGN gene in blood derived from the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The lower row shows the gene expression scores in the pancreatic cancer patient group and the IPMC patient group, and the ROC curve and sensitivity / specificity curve based on the gene expression score in the IPMN patient group. The tree diagram (upper) and heat map (lower) of the cluster analysis based on the expression level of the gene contained in the gene set 1 in the blood derived from the pancreatic cancer patient group and the IPMN patient group are shown. A group scatter plot of sample score values based on the expression level of gene set 1 in blood in a pancreatic cancer patient group and an IPMN patient group is shown. The ROC curve and the sensitivity / specificity curve based on the sample score value based on the expression level of gene set 1 in blood in the pancreatic cancer patient group and the IPMN patient group are shown. The expression level of gene set 1 in blood derived from pancreatic cancer patient group and IPMN patient group was measured, and the heat map (upper) showing the expression level of gene set 1 in each sample arranged in the order of sample value score and its sample value score. The graph (lower) is shown. The expression level of gene set 1 and the measurement results of the sample value score in the four additional samples are added to the heat map and the graph of the sample value score in FIG. 22. The tree diagram (upper) and heat map (lower) of the cluster analysis based on the expression level of the gene contained in the gene set 2 in the blood derived from the pancreatic cancer patient group, the IPMC patient group, and the IPMN patient group are shown. A group scatter plot of sample score values based on the expression level of gene set 2 in blood in a pancreatic cancer patient group, an IPMC patient group, and an IPMN patient group is shown. ROC curve and sensitivity based on the expression level of gene set 2 in blood in the pancreatic cancer patient group and the IPMC patient group, and the sample score value based on the expression level of gene set 2 in blood in the IPMN patient group. The specificity curve is shown. A heat map (upper) showing the expression level of gene set 2 in blood derived from pancreatic cancer patient group, IPMC patient group, and IPMN patient group, and showing the expression level of gene set 2 in each sample arranged in the order of sample value score. The graph (lower) of the sample value score is shown. The expression level of gene set 2 and the measurement results of the sample value score in the additional 4 samples are added to the heat map and the graph of the sample value score in FIG. 27. The tree diagram (upper) and heat map (lower) of the cluster analysis based on the expression level of the gene contained in the gene set 3 in the blood derived from the IPMC patient group and the IPMN patient group are shown. A group scatter plot of sample score values based on the expression level of gene set 3 in blood in the IPMC patient group and the IPMN patient group is shown. The ROC curve and sensitivity / specificity curve based on the sample score value based on the expression level of gene set 3 in blood in the IPMC patient group and the sample score value based on the expression level of gene set 3 in blood in the IPMN patient group. Shown. The expression level of gene set 3 in blood derived from the IPMC patient group and the IPMN patient group was measured, and the heat map (upper) showing the expression level of gene set 3 in each sample arranged in the order of the sample value score and the sample value score. The graph (lower) is shown.

1. 1. Method for detecting IPMC or pancreatic cancer using a marker gene

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 a healthy person or a patient diagnosed with IPMN using the expression level of a gene contained in a marker gene set as an index. Is. The marker gene set of the present invention is composed of genes selected from 43 kinds of gene groups, and by measuring the expression level of a specific gene in the gene group in a sample of a subject, a healthy person or a healthy person or The presence of IPMC and / or pancreatic cancer in the IPMN patient group can be detected, or a healthy person or IPMN can be distinguished from IPMC or pancreatic cancer.
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 be paraphrased as a method of assisting the differentiation of IPMC or pancreatic cancer.

1-2. Definition Intraductal papillary mucinous tumor (IPMN) refers to a pancreatic duct epithelial tumor characterized by dilation of the pancreatic duct due to mucus retention. IPMN has a series of lesions including hyperplasia, line type (Intraductal papillary-mucinous adenoma (IPMA)), non-invasive cancer, microinvasive cancer, and invasive cancer derived from IPMN. As used herein, the term "IPMN" means an IPMN in a low-to-medium atypia lesion that indicates lesions up to non-invasive cancer that does not show infiltration.

In addition, the term IPMN-derived pancreatic cancer (IPMC) as used herein refers to adenocarcinoma formed as a result of the progression of IPMN lesions. IPMCs herein include micro-invasive cancers and invasive cancers. On the other hand, the term pancreatic cancer as used herein means pancreatic cancer classified other than IPMC.

The distinction between IPMN and IPMC defined above is known, and those skilled in the art are non-invasive IPMN by diagnostic imaging (CT, MRI, endoscopic ultrasonography, etc.) based on the IPMN international medical care guidelines. It is possible to distinguish between IPMC and IPMC.

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 a healthy person or a patient diagnosed with IPMN. The expression level of each gene included in the marker gene set in the present invention relates to information on the expression of a transcript (mRNA), its cDNA or the protein encoded by each gene.

As used herein, the "gene expression score" is a score determined by the expression level of each gene or a plurality of genes included in the "marker gene set". There are no particular restrictions on the type of score or calculation method. In one embodiment, the gene expression score is 16 of the marker genes (CBLN3, HIP1R, PTDSS2, MS4A1, KITLG, EMC1, ENC1, GPR15, ZNF609, RPMRIP1, RABGGTB, ZC3HC1, USP37, MALSU1, EEF1B2, and LAMC1. The expression level of a gene) multiplied by -1 can be treated as a "gene expression score", and the remaining 27 genes can be treated as they are as a "gene expression score". In another embodiment, an expression level cutoff value is set for each gene, and the score is determined by comparison with the cutoff value (for example, if the expression level is equal to or higher than the cutoff value, the gene expression score is set. If it is set to "1" and less than the cutoff value, the gene expression score can be set to "-1").

In the present specification, the "sample score" is a score obtained by the expression level of a marker gene given for each test sample. The type of score and the calculation method are not particularly limited, but for example, the "sample score" can be treated as the sum of the gene expression scores of the marker genes in each test sample.

As used herein, the "measured value" is a value obtained by a method for measuring a gene expression level. The measured value may be an absolute value in which the amount of mRNA in the sample is expressed by weight such as ng (nanogram) or μg (microgram), or is expressed by the absorbance with respect to the control value, the fluorescence intensity of the labeled molecule, or the like. It may be a relative value.

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

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

1-3. Structure The term "marker gene set" as used herein refers to 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, and LRRK2. Gene, ANXA3 gene, PTDSS2 gene, EMC1 gene, TRIM22 gene, NRGN gene, MX1 gene, EPSTI1 gene, IFIT1 gene, IFI6 gene, MIR100HG gene, C17orf51 gene, FAM118A gene, SPATA33 gene, ENC1 gene, GPR15 gene, ZNF609 gene, 43 marker genes consisting of 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 Including.

In the present specification, the genes included in the "marker gene set" include genes consisting of a base sequence containing a degenerate codon encoding the same amino acid sequence, various variants of each gene, point mutant genes, and the like. Mutant genes and ortholog genes of other species such as chimpanzees are also included. Such genes include 70% or more (preferably 75% or more, 80% or more, 85% or more, more preferably 90% or more) with the base sequence of the gene specified by the GenBank accession number shown in the table below. It is a gene consisting of a base sequence having a base identity of 95% or more, 96% or more, 97% or more, 98% or more, or 99% or more), and includes a gene that retains 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 nucleotide sequence shown in SEQ ID NO: 1, and at this time, the DSC2 gene includes the nucleotide sequence shown in SEQ ID NO: 1 and 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 It is a gene consisting of a base sequence having, and includes a gene that retains the function of the DSC2 gene. In addition, in this specification, "base identity" means when two base sequences are aligned and a gap is introduced as necessary so that the degree of base matching between the two base sequences is the highest. Refers to the ratio (%) of the same number of bases in the base sequence of the nucleotide to be compared with respect to the total number of bases of the gene.

1-4. Measuring Method 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 contained in the marker gene set. More specifically, the method for detecting IPMC or pancreatic cancer of the present invention is the DSC2 gene, JAK2 gene, PYGL gene, CBLN3 gene, MS4A1 gene, HIP1R gene, TMEM176B gene, MED20 gene, SCML2 gene, KITLG in the test sample. 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, 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 It comprises the step of measuring the expression level of at least one gene in the group consisting of R3HDM4 genes.

One embodiment of the detection method of the present invention is a method for detecting IPMC in a test sample derived from IPMN, which is 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, it is a method of detecting IPMC by measuring the expression level of at least one gene in the group consisting of the R3HDM4 gene. According to this embodiment, it is possible to detect IPMC caused by the progression of IPMN lesions in an IPMN patient.

Hereinafter, the measurement method will be specifically described.

The "measurement step" is a step of measuring the expression level of a marker gene in a test sample and obtaining the measured value. As for the measurement of the expression level of the marker gene, it is preferable to measure the expression level per unit amount of each marker gene.

As the marker gene to be measured, at least one or more can be selected from the 43 genes included in the marker gene set. The number of genes to be selected may be any combination of 2 to 43. When the number of genes to be measured is increased, it is preferable in that the accuracy of detection of IPMC or pancreatic cancer is improved. When selecting two or more genes, the combination of genes is not limited.

When two or more genes are selected and measured, in one preferred embodiment, the combination of the genes has a p value of expression level of 0.05 or less, more preferably 0.01 in the healthy subject or IPMN group and the IPMC group or pancreatic cancer group. It can be selected as follows. In yet another embodiment, the gene combination has a sensitivity and specificity of 70% or higher, 75% or higher, 80 when the ROC curve is drawn between a healthy subject or IPMN group and the IPMC group or pancreatic cancer group. It can be selected so that the cutoff value of% or more, 85% or more, and 90% or more can be set.

In the present specification, the "test sample" is a sample collected from a subject. The “subject” is not particularly limited, but is preferably a healthy person or an IPMN patient for the purpose of detecting IPMC or pancreatic cancer. The subject may be an individual suspected of having pancreatic cancer or IPMC (hereinafter referred to as "pancreatic cancer or the like"), or an individual having a history of pancreatic cancer or the like. The term "individual with a history of pancreatic cancer or the like" as used herein includes a patient currently suffering from pancreatic cancer or the like and a person having a history of pancreatic cancer or the like who has suffered from pancreatic cancer or the like in the past. Further, in the present specification, the "subject" is a human individual who provides a sample and is subjected to a test. More preferably, the subject diagnosed with IPMN is the subject subject to the method of the present invention.

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

As used herein, the term "sample" refers to a sample collected from the subject and used in the discrimination method of this embodiment, and corresponds to, for example, a tissue, a cell, or a body fluid. The "tissue" and "cell" referred to here may be derived from any part of the subject, but preferably a sample collected by biopsy or excised by surgery, more specifically, pancreatic tissue or pancreas. Cell. Particularly preferably, it is a pancreatic tissue or cell collected by biopsy or a pancreatic tissue or cell suspected of having pancreatic cancer. In addition, these tissues or cells may be those embedded in paraffin after formalin fixation (FFPE: Formalin-Fixed Paraffin Embedded). The term "body fluid" as used herein refers to a liquid biological sample collected from a subject. Examples thereof include blood (including serum, plasma and interstitial fluid), cerebrospinal fluid (cerebrospinal fluid), urine, lymph, digestive juice, ascites, pleural effusion, perineural fluid, and extracts of each tissue or cell. .. Blood is preferred.

Samples may be obtained by biopsy or surgical removal by surgery if the tissue or cells are used. If it is a body fluid, it may be collected based on a known collection method in the art. For example, in the case of blood or lymph, a known blood collection method may be followed. The amount of sample required in the detection method of this embodiment is not particularly limited. For tissues or cells, at least 10 μg, preferably at least 0.1 mg is desirable. It may also be a biopsy material. For body fluids such as blood or lymph, a volume of at least 0.1 mL, preferably at least 1 mL, more preferably at least 10 mL may be sufficient. The sample can be prepared and processed as needed so that the expression level of the marker gene can be measured. For example, if the sample is a tissue or a cell, homogenization treatment, cytolysis treatment, removal of impurities by centrifugation or filtration, addition of a protease inhibitor, or the like can be mentioned. Details of these processes can be found in Green & Sambrook, Molecular Cloning, 2012, Fourth Ed., Cold Spring Harbor Laboratory Press for reference.

As used herein, the term "unit amount" refers to an arbitrarily determined amount of sample. For example, volume (represented by μL, mL) and weight (represented by μg, mg, g) are applicable. The unit amount is not particularly specified, but it is preferable that the unit amount measured by a series of detection methods is constant. For example, when comparing a sample derived from a subject and a sample derived from a patient such as pancreatic cancer, 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.

Hereinafter, a method for measuring a transcript of a gene will be specifically described. A method for measuring a transcript of a gene is known. Hereinafter, the description regarding the method for measuring a transcript or translation product of a gene in JP-A-2016-133081 will be referred to or cited. In the following, a method for measuring a transcription product or a translation product of a typical gene will be described, but the method is not limited to these methods, and a known measurement method can be used.

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

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

The nucleotide used as the primer or probe may be either the sense strand of each gene constituting the marker gene or the nucleotide consisting of the antisense strand.

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 later, it is at least 10 bases or longer to the full length of the gene, preferably 15 bases or longer to the full length of the gene, more preferably 30 bases or longer to the full length of the gene, and further preferably 50 bases or longer. The total length of the gene is from the base length or more, and if it is used for a microarray, it is 10 to 200 base length, preferably 20 to 150 base length, and more preferably 30 to 100 base length. Generally, 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, each of the forward primer and the reverse primer may be 10 to 50 bp, preferably 15 to 30 bp.

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

The measurement of the transcript of the marker gene may be performed by any known nucleic acid detection / quantification method, and is not particularly limited. For example, hybridization method, nucleic acid amplification method or RNA sequencing (RNA-Seq) analysis method can be mentioned.

The "hybridization method" uses 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 as a probe, and utilizes the base pairing between the nucleic acid and the probe. , A method for detecting and quantifying a target nucleic acid or a fragment thereof. In this embodiment, the target nucleic acid corresponds to mRNA or cDNA of each gene constituting a marker gene, or a fragment thereof. In general, the hybridization method is preferably performed under stringent conditions in order to eliminate unintended nucleic acids that hybridize non-specifically. High stringent conditions at low salt concentrations and high temperatures are more preferred. Several methods with different detection means are known for the hybridization method, and for example, Northern blotting (Northern hybridization method), microarray method, surface plasmon resonance method, or quartz crystal microbalance method is preferable. ..

"Northern blot method" is one of the methods for analyzing gene expression. Total RNA or mRNA prepared from a sample is separated by electrophoresis on an agarose gel or polyacrylamide gel under denatured conditions, and transferred to a filter (blotting). ), Then a probe having a base sequence specific to the target RNA is used to detect the target nucleic acid. By labeling the probe with a suitable marker such as a fluorescent dye or radioisotope, for example, a chemiluminescent analyzer (eg, Light Capture; Atto), a scintillation counter, an imaging analyzer (eg, FUJIFILM). : It is also possible to quantify the target nucleic acid using a measuring device such as BAS series). The Northern blot method is a well-known and well-known technique in the art, and for example, the above-mentioned Green, M.R. and Sambrook, J. (2012) may be referred to.

In the "microarray method", a nucleic acid fragment complementary to all or part of the base sequence of a target nucleic acid is arranged on a substrate as a probe at a high density in a small spot shape, and the target nucleic acid is contained in a solid-phased microarray or microchip. This is a method in which a sample is reacted and a nucleic acid hybridized to a base spot 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 fluorescence based on hybridization of a target nucleic acid or the like with a microplate reader or a scanner. From the measured fluorescence intensity, the amount of mRNA or cDNA or their abundance ratio to reference mRNA (reference mRNA) can be determined. The microarray method is also a well-known technique in the art. For example, the DNA microarray method (DNA microarray and latest PCR method (2000), supervised by Masaaki Muramatsu, Hiroyuki Nawa, Shujunsha) and the like may be referred to.

The "Surface Plasmon Resonance (SPR) method" is a surface plasmon resonance in which the reflected light intensity is significantly attenuated at a specific incident angle (resonance angle) when the incident angle of the laser beam irradiated to the metal thin film is changed. This is a method of detecting and quantifying adsorbates on the surface of a metal thin film with extremely high sensitivity by utilizing the phenomenon. In the present invention, for example, a probe having a sequence complementary to the base sequence of the target nucleic acid is immobilized on the surface of the metal thin film, the surface portion of the other metal thin film is blocked, and then the sample collected from the subject is subjected to the metal thin film. By circulating on the surface, a base pair of the target nucleic acid and the probe can be formed, and the target nucleic acid can be detected and quantified from the difference in the measured values before and after the sample is distributed. Detection and quantification by the surface plasmon resonance method can be performed using, for example, an SPR sensor commercially available from Biacore. This technique is well known in the art. See, for example, Kazuhiro Nagata and Hiroshi Handa, Real-time Analytical Experimental Method for Biological Interactions, Springer Fairlark Tokyo, Tokyo, 2000.

The "Quartz Crystal Microbalance (QCM) method" utilizes the phenomenon that when a substance is adsorbed on the surface of an electrode attached to a crystal oscillator, the resonance frequency of the crystal oscillator decreases according to its mass. This is a mass measurement method that quantitatively captures a very small amount of adsorbate according to the amount of change in the resonance frequency. Similar to the SPR method, the detection and quantification by this method also uses a commercially available QCM sensor, for example, a probe having a sequence complementary to the base sequence of the target nucleic acid fixed on the electrode surface and a sample collected from the subject. The target nucleic acid can be detected and quantified by base pairing with the target nucleic acid inside. This technology is well known in the art, for example, Christopher J. et al., 2005, Self-Assembled Monolayers of a Form of Nanotechnology, Chemical Review, 105: 1103-1169, Toyoe Morizumi, Takamichi Nakamoto, ( 1997) See Sensor Engineering, Shokodo.

The "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 a forward / reverse primer. For example, PCR method (including RT-PCR method), NASBA method, ICAN method, LAMP (registered trademark) method (including RT-LAMP method) can be mentioned. The PCR method is preferable. A quantitative nucleic acid amplification method such as a real-time RT-PCR method is used as a method for measuring a transcript of a gene using a nucleic acid amplification method. Further known real-time RT-PCR methods include an intercalator method using SYBR (registered trademark) Green and the like, a Taqman (registered trademark) probe method, a digital PCR method, and a cycling probe method. There may be. All of these are known methods and are also described in appropriate protocols in the art, so you can refer to them.

"RNA sequencing (RNA-Seq) analysis method" is the conversion of RNA into cDNA by reverse transcription reaction, and the next-generation sequencer (for example, HiSeq series (illumina)) and Ion Proton system (Thermo Fisher) It refers to a method of measuring the expression level of a gene by counting the number of reads using (although it exists, but is not limited to this). All of these are known methods and are also described in appropriate protocols in the art, so you can refer to them.

A method for quantifying gene transcripts by real-time RT-PCR will be briefly described below with an example. The real-time RT-PCR method is a reaction system in which the amplification product of PCR is specifically fluorescently labeled using the cDNA prepared by the reverse transcription reaction from the mRNA in the sample, and the fluorescence intensity derived from the amplification product is detected. This is a nucleic acid quantification method in which PCR is performed using a temperature cycler device equipped with a function. The amount of amplification product of the target nucleic acid in the reaction is monitored in real time, and the result is regression-analyzed by computer. Methods for labeling the amplification product include a method using a fluorescently labeled probe (for example, the TaqMan (registered trademark) PCR method) and an intercalator method using a reagent that specifically binds to double-stranded DNA. The TaqMan (registered trademark) PCR method uses a probe in which the 5'end is a quencher substance and the 3'end is a fluorescent dye. Normally, the 5'end quencher substance 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. As a result, the suppression of the quencher substance is released, and the fluorescence is emitted. The amount of fluorescence reflects the amount of amplification product. Since the number of cycles (CT) when the amplification product reaches the detection limit and the initial template amount are inversely related, the real-time measurement method quantifies the initial template amount by measuring CT. The absolute value of the initial template amount of an unknown sample can be calculated by measuring CT using several stages of known amounts of templates and preparing a calibration curve. As the reverse transcriptase used in RT-PCR, for example, M-MLV RTase, ExScript RTase (TaKaRa), Super Script II RT (Thermo Fisher Scientific) and the like can be used.

The reaction conditions for real-time PCR are generally based on known PCR methods, the base length of the nucleic acid fragment to be amplified, the amount of nucleic acid for the template, the base length and Tm value of the primer to be used, and the optimum reaction of the nucleic acid polymerase to be used. Since it varies depending on the temperature, optimum pH, etc., it may be appropriately determined according to these conditions. As an example, the denaturation reaction is usually carried out at 94 to 95 ° C. for 5 seconds to 5 minutes, the annealing reaction is carried out at 50 to 70 ° C. for 10 seconds to 1 minute, and the extension reaction is carried out at 68 to 72 ° C. for 30 seconds to 3 minutes. The elongation reaction can be repeated for about 15 to 40 cycles as one cycle. When using a kit commercially available from the manufacturer, in principle, the protocol attached to the kit may be followed.

Nucleic acid polymerases used in real-time PCR are DNA polymerases, especially heat resistant DNA polymerases. Various types of such nucleic acid polymerases are commercially available, and they can also be used. For example, Taq DNA polymerase attached to the Applied Biosystems TaqMan MicroRNA Assays Kit (Thermo Fisher Scientific) can be mentioned. In particular, such a commercially available kit is useful because it comes with a buffer or the like optimized for the activity of the attached DNA polymerase.

1-5. Detection method The method for detecting IPMC or pancreatic cancer of the present invention is based on the expression level of the marker gene measured as described above, when IPMC or pancreatic cancer is present in the body of the subject. Detect the presence.

A marker gene is a gene whose expression level fluctuates when it is transferred from IPMN to IPMC or when pancreatic cancer is complicated, and the subject is IPMC based on the expression profile of the marker gene or a marker gene set that combines multiple marker genes. Alternatively, it becomes possible to detect that the patient has pancreatic cancer.

Here, in one embodiment of the detection method of the present invention, the expression level of the marker gene measured in the measurement step and 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 Detects the presence or risk of healthy individuals, IPMN patients, IPMC patients or pancreatic cancer in the subject, including the step of comparing with. Here, the comparison of the expression levels of the marker genes includes not only the comparison of the expression levels of a single marker gene but also the comparison of the expression profiles obtained from the expression levels of two or more marker genes.

The expression level of the marker gene or the expression profile of each gene contained in the marker gene set of the sample derived from a healthy subject, IPMN patient, IPMC patient or pancreatic cancer patient to be compared with the test sample shall be measured in advance. It may be used, or a sample obtained by newly measuring the expression level of each gene contained in the marker gene set from a sample derived from a healthy subject, an IPMN patient, an IPMC patient or a pancreatic cancer patient may be used.

Therefore, in one embodiment, the detection method of the present invention comprises at least one marker gene included in a marker gene set for detecting IPMC or pancreatic cancer in a sample derived from a healthy subject, an IPMN patient, an IPMC patient or a pancreatic cancer patient. Further includes the step of measuring the expression level of. 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 the IPMC or pancreatic cancer patient to be detected can be compared. A sample derived from one individual may be used, or a sample derived from two or more individuals may be included. The larger the number of individuals derived from the sample, the more the individual difference of the sample can be averaged and the accuracy of detection is improved, which is preferable.

Here, the step of comparing the expression level of the marker gene obtained 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 is, for example, a subject. If the test sample has an expression level equivalent to the expression level of the corresponding marker gene in a sample derived from IPMC or pancreatic cancer, it is evaluated as having IPMC or pancreatic cancer, or derived from IPMC or pancreatic cancer patient. It can be evaluated that the patient does not have IPMC or pancreatic cancer when the expression level of the gene is different from the expression level of the corresponding marker gene in the sample of.

Here, "having an equivalent gene expression level or the like" means that the expression level or the like of each gene included in the marker gene set is the same or similar. Further, "having different gene expression levels and the like" means that the expression profiles of each gene contained in the gene set are not similar.

A known method can be adopted as a specific method for determining whether the expression level of each gene in the gene set is the same or different. For example, (i) a method of classifying a test sample into a group suffering from IPMC or pancreatic cancer or a group other than that (such as a healthy subject group) based on a hierarchical clustering analysis, (ii). Examples thereof include a method of evaluating by comparing the expression level of genes and the like, and a method of evaluating whether a test sample has IPMC or pancreatic cancer by setting a (iii) threshold.

(I) Hierarchical clustering analysis In one embodiment of the detection method of the present invention, the test sample can be classified by cluster analysis of the expression level of each gene contained in the marker gene set in the test sample. .. The data used for cluster analysis is preferably expression profiles for a plurality of genes included in the marker gene set.

More specifically, a 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 the sample derived from a patient such as pancreatic cancer. When classifying whether a subject who provides a test sample suffers from pancreatic cancer or the like by hierarchical clustering analysis, (i) expression of a marker gene set in the test sample so that a hierarchical cluster can be created. In addition to the profile and (ii) the expression profile of the gene set in the sample derived from a patient such as pancreatic cancer to be classified, (iii) the expression profile of the gene set in the sample derived from a healthy person or an IPMN patient is required. Become.

A known method can be adopted as the method of hierarchical cluster analysis. A particularly preferred embodiment of the present invention is cluster analysis by the group averaging method based on the Euclidean distance. Known software can be used for cluster analysis, and, for example, ExpressionView Pro software (MicroDiagnostic, Tokyo, Japan) can be used as commercially available software without being limited to the following.

By performing hierarchical cluster analysis, a hierarchical structure consisting of clusters belonging to patients with pancreatic cancer to be detected and clusters belonging to healthy subjects, IPMN patients, or patients with pancreatic cancer other than the classification target (hierarchical cluster analysis) You can draw a dendrogram). Thereby, the subject who provides the test sample can detect that he / she has pancreatic cancer or IPMC, or the risk thereof.

(Ii) Method of evaluating by comparing gene expression levels In one embodiment, the total value of the gene expression levels and the like included in the marker gene set in the test sample and the patients belonging to the pancreatic cancer and the like to be classified By comparing the expression level of the corresponding gene in the sample of origin and the like, it is possible to detect that the subject has IPMC or pancreatic cancer, or the risk thereof.

Although not limited to the following, to explain one form, the expression level is measured for a plurality of marker genes, and the total expression level of the genes included in the marker gene set in the affected patient group such as pancreatic cancer to be detected is calculated. Draw each as a group scatter diagram to see where the total gene expression level of the marker gene set in the test sample is plotted. The plotted positions can be used to assess whether the subject providing the test sample is likely to have pancreatic cancer or IPMC.

When using the total value of the gene expression level, the obtained expression level values of CBLN3, HIP1R, PTDSS2, MS4A1, KITLG and EMC1 are inverted and used. For example, when using the total expression level of each gene obtained by a method such as a microarray for the expression level of a gene, the expression level of CBLN3, HIP1R, PTDSS2, MS4A1, KITLG and EMC1 (for example, with respect to a common reference). Multiply (Log2 ratio) by -1 to obtain the inverted value, and calculate the total value of the inverted value and the expression level of other genes.

(Iii) Method of discrimination or classification by setting a threshold value Further, in one embodiment, the patient suffers from IPMC or pancreatic cancer by comparing the expression level of the marker gene set in the test sample with a predetermined threshold value. That, or its risk, can be detected.

Here, the "predetermined threshold value" refers to a predetermined cutoff value based on the expression level of a marker gene or the like in a sample derived from a patient group such as pancreatic cancer to be detected. The cutoff value can be set as follows, for example, but the cutoff value is not limited to this. That is, the expression level of the gene contained in the marker gene set was measured for the sample derived from the IPMC or pancreatic cancer patient group (detection target patient group) to be detected and the sample derived from a healthy person or IPMN patient, and the gene expression was measured for each sample. Calculate the level. Then, a predetermined cutoff value can be derived by creating a ROC curve from the value of the expression level of the obtained gene. By setting a cutoff value, it is possible to detect whether or not the patient is suffering from IPMC or pancreatic cancer depending on whether or not the expression level of the marker gene set obtained from the test sample exceeds the cutoff value. can do.

In the "ROC curve (Receiver Operating Characteristic curve)", the vertical axis is the true positive rate (TPF: True Position Fraction), that is, the sensitivity, and the horizontal axis is the false positive rate (FPF: False Position Fraction), that is. It is created by setting (1-specificity) and plotting by changing the threshold value of which value of the test result is judged to have a finding, that is, the cutoff point as a parameter. Specificity is the rate at which a negative person is accurately judged to be negative.

The method of setting the cutoff value from the created ROC curve can basically be set so as to increase both sensitivity and specificity (approach 1). For that purpose, the cutoff value should be set to a value that gives the point closest to the point (0,1) on the ROC curve. The most preferred embodiment is to have a clearly distinguishable cutoff value between a group of patients suffering from IPMC or pancreatic cancer and a sample from a group of healthy individuals or patients suffering from IPMN.

1-6. Effect According to the method for detecting the risk of developing IPMC or pancreatic cancer in this embodiment, the subject is suffering from IPMC or pancreatic cancer by examining the expression level of the marker gene in the sample collected from the subject. , Or its risk can be detected. The detection method of this embodiment, which has a high accuracy rate, makes it possible to make a diagnosis regarding the morbidity of IPMC or pancreatic cancer, and has an advantage that it can be dealt with in consideration of the judgment of the treatment method.

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

2-1. Overview Another aspect of the present invention is a reagent (differential reagent) for differentiating the risk of suffering from IPMC or pancreatic cancer. By applying the differentiating reagent of this embodiment to, for example, a sample derived from a healthy person or a subject diagnosed with IPMN, it is possible to distinguish which disease the subject has.

2-2. Configuration The detection kit of this embodiment includes measuring means for measuring the expression level of the marker gene group constituting the marker gene set. If the measurement of the expression level of a gene is the measurement of a transcript, ie mRNA or cDNA, the detection means comprises a probe or primer set. These specific configurations are described in the column of measurement process. Further, for example, when detecting transcripts of four marker gene groups constituting a marker gene set, the discrimination kit can include four probe groups capable of detecting transcripts of the corresponding genes.

When the measuring means of this embodiment is a probe as described above, the measuring means can also be provided in the form of a DNA microarray or a DNA microchip in which each probe is fixed on a substrate. The material of the substrate on which each probe is fixed is not limited, but a glass plate, a quartz plate, a silicon wafer, or the like is usually used. Examples of the size of the substrate include 3.5 mm × 5.5 mm, 18 mm × 18 mm, 22 mm × 75 mm, etc., which can be variously set according to the number of probe spots and the size of the spots. .. As the probe, 0.1 μg to 0.5 μg of nucleotides are usually used per spot. As a method for immobilizing a nucleotide, a method using the charge of the nucleotide to electrostatically bond it to a solid phase carrier surface-treated with a polycation such as polylysine, poly-L-lysine, polyethyleneimine, or polyalkylamine, or amino. Examples thereof include a method in which a nucleotide having a functional group such as an amino group, an aldehyde group, an SH group or biotin is covalently bonded to a solid phase surface into which a functional group such as a group, an aldehyde group or an epoxy group has been introduced.

The probe or marker for the marker gene set is not limited as long as it can be used to measure the expression level of the marker gene, and can be appropriately designed by those skilled in the art. Specific examples of the probe are not limited to the following, and for example, the probes described in the table below can be used.

2-3. Effect By applying the differential kit of this embodiment to a subject, it is possible to detect that the subject has IPMC or pancreatic cancer, or to evaluate the risk.
The detection kit of the present invention may include other reagents necessary for the detection of a marker gene, such as a buffer or a secondary antibody, and instructions used for detection and result identification.
Hereinafter, the present invention will be described in more detail 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 a living body. Specifically, total RNA was extracted from the collected blood using ISOGEN-LS (Nippon Gene Co., Ltd., Tokyo, Japan).
Human Universal Reference RNA Type II (MicroDiagnostic) was used as the human common reference RNA.

(Example 2. Comprehensive gene expression analysis)
As the DNA microarray for obtaining the gene expression profile, 14,400 kinds of synthetic DNA (80 mers) (MicroDiagnostic) corresponding to human-derived transcripts were arrayed on a slide glass with a custom layer.

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

Fluorescence intensity after hybridization with a DNA microarray was measured using a GenePix 4000B Scanner (Axon Instruments, Inc., Union city, CA, USA). In addition, the expression ratio (fluorescence intensity of sample-derived Cyanine-5-labeled cDNA / derived from human common reference RNA) was obtained by dividing the fluorescence intensity of the sample-derived Cyanine-5-labeled cDNA by the fluorescence intensity of the human-derived Cyanine-3-labeled cDNA. Fluorescence intensity of Cyanine-3 labeled RNA) was calculated. Furthermore, using GenePix Pro 3.0 software (Axon Instruments, Inc.,), the calculated expression ratio was multiplied by the normalization factor for normalization. Next, the expression ratio was 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 MDI gene expression analysis software package (MicroDiagnostic).

(Example 3. Useful marker 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 collected from pancreatic cancer patients (12 cases), blood collected from IPMC patients (4 cases), and blood collected from IPMN patients (24 cases).

Next, genes whose expression levels were below the detection limit and data could not be obtained in 2 or more of the pancreatic cancer patient group (12 samples) or 3 or more of the IPMN patient group (24 samples) were deleted. In addition, the average value of the expression level of each gene was calculated for each of the pancreatic cancer patient group and the IPMN patient group, and genes having an absolute difference of 0.5 or more between the two were extracted. Furthermore, a two-group comparison was performed between the pancreatic cancer patient group and the IPMN patient group by Student's t-test, and genes (13 genes) having a p value of less than 0.01 (hereinafter referred to as "gene set 1") were extracted (Table 1). ..

Next, data was obtained when the expression level was below the detection limit in 2 or more of the pancreatic cancer patient group (12 samples), 2 of the IPMC patient group (4 samples), or 3 or more of the IPMN patient group (24 samples). The genes that could not be obtained were deleted. In addition, the average value of the expression level of each gene was calculated for each patient group, and the average of the pancreatic cancer patient group and the IPMC patient group (hereinafter referred to as "pancreatic cancer-IPMC patient group") and the IPMN patient group. Genes with an absolute value difference of 0.5 or more were extracted. Furthermore, a two-group comparison was performed between the pancreatic cancer-IPMC patient group and the IPMN patient group by Student's t-test, and genes (13 genes) having a p value of less than 0.01 (hereinafter referred to as "gene set 2") were extracted.

Next, genes whose expression levels were below the detection limit and data could not be obtained in 2 or more of the IPMC patient group (4 samples) and 3 or more of the IPMN patient group (24 samples) were deleted. In addition, the average value of the expression level of each gene was calculated for each patient group, and genes in which the absolute value of the difference between the average value of the IPMC patient group and the average value of the IPMN patient group was 0.5 or more were extracted. Furthermore, a two-group comparison was performed between the IPMC patient group and the IPMN patient group by Student's t-test, and genes (25 genes) having a p value of less than 0.001 (hereinafter referred to as "gene set 3") were extracted.

(Example 4. Differentiation of pancreatic cancer risk by 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) were used as marker genes for IPMC or pancreatic cancer. When comparing the average expression level value of the pancreatic cancer patient group and the average expression level value of the IPMN patient group for each gene, the expression level value of the IPMN patient group is higher than the average expression level value of the pancreatic cancer patient group. When comparing the average value of the expression level of the gene with the larger average value and the expression level of the pancreatic cancer-IPMC patient group combined with the pancreatic cancer patient group and the IPMC patient group for each gene and the average value of the expression level value of the IPMN patient group. , Pancreatic cancer-Expression of genes whose average expression level in the IPMN patient group is higher than the average expression level in the IPMC patient group (total of 6 genes (CBLN3, HIP1R, PTDSS2, MS4A1, KITLG and EMC1)) The value obtained by multiplying the level value by -1 was defined as the "gene expression score", and for the remaining 12 genes, the respective expression level values were defined as the "gene expression score".

For DSC2, the average gene expression score of the IPMN patient group was 0.4561, the average gene expression score of the IPMC patient group was 1.0294, the average gene expression score of the pancreatic cancer patient group was 0.9785, and the IPMN patient group. The p-value was 0.0226 between the IPMC patient group and 3.12 × 10 ^-5 between the IPMN patient group and the pancreatic cancer patient group (Fig. 1). Furthermore, the ROC (Receiver Operating Characteristic curve) curve is used to determine the area under the curve (AUC) based on the gene expression score in the pancreatic cancer-IPMC patient group and the gene expression score in the IPMN patient group, and the sensitivity is determined.・ A specificity curve was created to obtain the optimum cutoff value. The ROC curve and group scatter plot are created using StatFlex ver.6.0 software (Artech Co., Ltd., Osaka, Japan), and the optimum cutoff value is set to maximize the sum of sensitivity and specificity. The values were used (hereinafter, the ROC curve, the sensitivity / specificity curve were created, and the optimum cutoff value was set in the same manner). For DSC2, the AUC was 0.8958, the cutoff value was 0.7568, and the sensitivity and specificity at this time were 83.3% (Fig. 1).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

(Example 5. Cluster analysis using gene set 1)
The gene expression level of the gene set 1 containing the 13 genes selected in Example 3 was measured (Tables 3A to D), and cluster analysis was performed. The cluster analysis was performed by the Euclidean distance group averaging method using ExpressionView Pro software (MicroDiagnostic). The result of the cluster analysis is shown in FIG. As shown in FIG. 19, when a hierarchical cluster analysis was performed based on the expression profiles of the extracted 13 genes, it was possible to classify into clusters mainly containing IPMN patients and clusters mainly containing pancreatic cancer patients.

(Example 5. Differentiation of pancreatic cancer risk by scoring gene set 1)
First, of the 13 genes contained in gene set 1, four genes, CBLN3, MS4A1, HIP1R, and KITLG, which have higher expression levels in the IPMN patient group than in the pancreatic cancer patient group, are expressed by multiplying the expression level value by -1. The score value was calculated. For the remaining 9 genes, the expression level value was used as it was as the gene expression score value. Further, the sum of the gene expression score values of 13 genes was defined as "sample score value (gene set 1)".

A group scatter plot of sample score values (gene set 1) was created for the IPMN patient group and the pancreatic cancer patient group (Fig. 20). The average sample score value of the IPMN patient group is 3.5644, the average sample score value (gene set 1) of the pancreatic cancer patient group is 11.40117, and the sample score value (gene set 1) is significantly different between the two. It was found that there was (p value = 6.66 × 10 ^-9).

Furthermore, the area under the curve (AUC) was calculated using the ROC (Receiver Operating Characteristic curve) curve (Fig. 21). In addition, a sensitivity / specificity curve was created to obtain the optimum cutoff value (FIG. 21). ROC curves and group scatter plots are created using StatFlex ver.6.0 software (Artech Co., Ltd., Osaka, Japan), and the optimum cutoff value is set to maximize the sum of sensitivity and specificity. It was set as a value (hereinafter, the setting of the optimum cutoff value was the same). In the ROC curve, the AUC was 0.9688, and in the sensitivity / specificity curve, the optimum cutoff value was set at 8.2499, at which time the sensitivity and specificity were 91.7% (Fig. 21).

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

Furthermore, 2 cases (2 samples each collected from the same patient at intervals) were adopted for verification. After the two measurements, the sample score values of the two cases were obtained, and the samples were rearranged in ascending order of the sample score values (gene set 1) (FIG. 23). For the first person, the sample score value (gene set 1) was 4.6174 when the first sample was collected (FMU # 12875), and the sample score value (gene set 1) was the sample score value (gene set 1) when the second sample was collected (FMU # 14156). It was 4.8639, and the sample score value (gene set 1) was increased. The pathological diagnosis of the first patient was IPMN when it was collected the first time, and intraductal papillary-mucinous adenoma (IPMA) when it was collected the second time. Next, for the second person, the sample score value (gene set 1) was 10.399 when the first sample was collected (FMU # 11788), and the sample score value (gene set) was 10.399 when the second sample was collected (FMU # 13478). 1) was 15.5487, which was higher in the second time than in the first time. The diagnosis of the second patient was that IPMC was suspected when it was collected the first time, and pancreatic cancer was when it was collected the second time.

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

(Example 6. Cluster analysis using gene set 2)
The gene expression level of the gene set 2 containing the 13 genes selected in Example 3 was measured (Tables 4A to D), and cluster analysis was performed. The cluster analysis was performed by the Euclidean distance group averaging method using ExpressionView Pro software (MicroDiagnostic). The result of the cluster analysis is shown in FIG. As shown in FIG. 24, when a hierarchical cluster analysis was performed based on the expression profiles of the extracted 13 genes, it was possible to classify into clusters mainly containing IPMN patients and clusters mainly containing pancreatic cancer patients.

(Example 7. Differentiation of pancreatic cancer risk by scoring gene set 2)
First, among the 13 genes contained in gene set 2, the expression level values of 6 genes, CBLN3, HIP1R, PTDSS2, MS4A1, KITLG, and EMC1, which are higher in the IPMC patient group than in the pancreatic cancer patient group, were set to -1. Multiplyed to calculate the gene expression score. For the remaining 7 genes, the expression level value was used as it was as the gene expression score value. Further, the sum of the gene expression score values of 13 genes was defined as "sample score value (gene set 2)".

A group scatter plot of sample score values (gene set 2) was created for the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group (Fig. 25). The average sample score value (gene set 2) of the IPMN patient group is 10.1070, the average sample score value (gene set 2) of the IMPC patient group is 17.9437, and the average sample score value (gene set 2) of the pancreatic cancer patient group is 17.7812. Met. In addition, the sample score value (gene set 2) of the IPMN patient group is p-value = between the sample score value of the IPMC patient group (gene set 2) and the sample score value of the pancreatic cancer patient group (gene set 2). It was confirmed that there was a significant difference at 0.0152 and p value = 5.9961 × 10 ^-7.

Furthermore, the area under the curve (AUC) was calculated using the ROC (Receiver Operating Characteristic curve) curve (Fig. 26). In addition, a sensitivity / specificity curve was created and the optimum cutoff value was obtained (FIG. 26). The optimum cutoff value was set to the value at which the sum of sensitivity and specificity was maximized (hereinafter, the optimum cutoff value was set in the same manner). In the ROC curve, the AUC was 0.9401, and in the sensitivity / specificity curve, the optimum cutoff value was set at 14.5626, at which time the sensitivity and specificity were 91.7% (Fig. 26).

In addition, when the samples were rearranged in ascending order of sample score value, it was found that pancreatic cancer patients and IPMN patients can be distinguished by setting an appropriate threshold value (Fig. 27).

Furthermore, 2 cases (2 samples each obtained from the same patient) were adopted for verification. For the first person, the sample score value was 12.8807 when the first sample was collected (FMU # 12875), and the sample score value was 10.1967 when the second sample was collected (FMU # 14156), and the sample score value decreased. Was there. The pathological diagnosis of the first patient was IPMN when it was collected the first time and IPMA when it was collected the second time. Next, for the second person, the sample score value was 17.4963 when the first sample was collected (FMU # 11788), and the sample score value was 19.2133 when the second sample was collected (FMU # 13478). In the pathological diagnosis of the second patient, IPMC was suspected when the first collection was made, and pancreatic cancer was the second collection (Fig. 28).

In the first case for verification, the lesion had transitioned from IPMN to IPMA during the two measurement periods, and in the second case, the lesion had progressed from suspected IPMC to pancreatic cancer during the two measurement periods. The sample score value of gene set 2 was consistent with the transition and progression of the pathological condition 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 level of the gene set 3 containing the 25 genes selected in Example 3 was measured (Tables 5A to D), and cluster analysis was performed.

The cluster analysis was performed by the Euclidean distance group averaging method using ExpressionView Pro software (MicroDiagnostic). The result of the cluster analysis is shown in FIG. As shown in FIG. 29, when hierarchical cluster analysis was performed based on the expression profiles of the extracted 25 genes, it was possible to classify into clusters mainly containing IPMN patients and clusters mainly containing IPMC patients.

Next, of the 25 genes contained in gene set 3, 10 genes of ENC1, GPR15, ZNF609, RPGRIP1, RABGGTB, ZC3HC1, USP37, MALSU1, EEF1B2, and LAMC1 whose expression levels are lower in the IPMC patient group than in the IPMN patient group. The gene expression score value was calculated by multiplying the expression level value by -1. For the remaining 7 genes, the expression level value was used as it was as the gene expression score value. Further, the sum of the gene expression score values of 25 genes was defined as "sample score value (gene set 3)".

A group scatter plot of sample score values (gene set 3) was created for the IPMN patient group, the IPMC patient group, and the pancreatic cancer patient group. The average sample score value (gene set 3) of the IPMN patient group is 9.3232, the average sample score value (gene set 3) of the IMPC patient group is 24.9817, and the average sample score value (gene set 3) of the pancreatic cancer patient group is 11.1527. Met. In addition, the sample score value (gene set 3) of the IPMC patient group is p value = the sample score value (gene set 3) of the IPMN patient group or the sample score value (gene set 3) of the pancreatic cancer patient group, respectively. It was confirmed that there was a significant difference between 9.0662 × 10 ^-14 and p value = 1.1930 × 10 ^{-13 (Fig. 30).}

Furthermore, the area under the curve (AUC) was calculated using the ROC (Receiver Operating Characteristic curve) curve. In addition, a sensitivity / specificity curve was created and the optimum cutoff value was obtained. The optimum cutoff value was set to the value at which the sum of sensitivity and specificity was maximized (hereinafter, the optimum cutoff value was set in the same manner). In the ROC curve, the AUC was 1.000, and in the sensitivity / specificity curve, the optimum cutoff value was set at 22.7082, at which time the sensitivity and specificity were 100% (Fig. 31).

In addition, when the samples were rearranged in ascending order of the sample score values, it was found that pancreatic cancer patients and IPMN patients can be distinguished by setting an appropriate threshold value (FIG. 32).

Claims (14)

A method for detecting IPMC or pancreatic cancer in a test sample.
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, 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. Detection method including steps. The detection method according to claim 1.
A detection method in which the test sample is a sample derived from an IPMN patient. The detection method according to claim 1 or 2.
The measurement step is a step of measuring the expression level of DSC2 gene, JAK2 gene, CBLN3 gene, MS4A1 gene, HIP1R gene, TMEM176B gene, KITLG gene, S100A12 gene, ANXA3 gene, PTDSS2 gene, EMC1 gene, TRIM22 gene and NRGN gene. Is the detection method. The detection method according to claim 1 or 2.
The measurement step measures the expression levels of 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, and LRRK2 gene. Detection method, which is the process of The detection method according to claim 1.
The above method is a method for detecting IPMC in a test sample derived from IPMN.
The measurement steps are 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, Detection, which is a step of measuring the expression level of at least one gene in the group consisting of LIAS gene, SMN2 gene, RABGGTB gene, LAMB3 gene, ZC3HC1 gene, USP37 gene, MALSU1 gene, EEF1B2 gene, LAMC1 gene, and R3HDM4 gene. Method. The detection method according to any one of claims 1 to 4.
A detection method further comprising 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, an IPMN patient, an IPMC patient or a pancreatic cancer patient. The detection method according to any one of claims 1 to 4.
The expression level of the gene obtained in the measurement step, the expression level of the corresponding gene in the sample derived from a healthy person or IPMN patient, and the expression level of the corresponding gene in the sample derived from IPMC patient or pancreatic cancer patient are compared by cluster analysis. A detection method that further comprises a step. The detection method according to any one of claims 1 to 4.
A detection method further comprising a step of comparing the expression level of a gene in the test sample obtained in the measurement step with a predetermined threshold value. The detection method according to any one of claims 1 to 8.
A detection method in which the test sample is blood. The detection method according to any one of claims 1 to 9.
A detection method in which the measurement of the expression level of the gene in the measurement step is the measurement of the mRNA expression level of the gene. A set of marker genes for detecting IPMC or pancreatic cancer, 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, FAM118A gene, SPATA33 gene, ENC1 gene, GPR15 gene, ZNF609 At least in the group consisting of genes, 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. A set of marker genes containing one gene. A kit for detecting IPMC or pancreatic cancer
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, FAM118A gene, SPATA33 gene, ENC1 gene, GPR15 gene, ZNF609 gene, TNFAIP8L3 gene, GRHL2 gene, RPGRIP1 gene, SUZ12 gene, LIAS A kit comprising measuring means for measuring the expression of at least one gene in the group consisting of a gene, SMN2 gene, RABGGTB gene, LAMB3 gene, ZC3HC1 gene, USP37 gene, MALSU1 gene, EEF1B2 gene, LAMC1 gene, and R3HDM4 gene. The kit according to claim 12.
The kit is a kit for detecting IPMC in a test sample derived from IPMN.
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 a measuring means for measuring the expression of at least one gene in the group consisting of R3HDM4 gene. The kit according to claim 12 or 13.
A kit in which the measuring means for measuring the expression of the gene is at least one means selected from the group consisting of primers, probes, or labels for the gene.

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