Biomarker for diagnosing and prognostically predicting diabetes accompanied by pancreatic cancer and application of biomarker

Abstract

The present invention relates to a biomarker for diagnosing and prognostically predicting diabetes mellitus associated with pancreatic cancer and a use thereof, and more particularly, to a biomarker composition for diagnosing and prognostically predicting diabetes mellitus associated with pancreatic cancer, a kit and an information providing method, which comprise a preparation for determining the expression level of REG4 (REGEN 4) protein. Furthermore, the present invention relates to an information providing method for providing necessary information for the diagnosis and prognosis of diabetes-associated pancreatic cancer by measuring the level of REG4 in a biological sample isolated from a subject suffering from diabetes-associated pancreatic cancer, and for determining the prognosis of diabetes-associated pancreatic cancer by patient queue analysis, single-cell RNA sequencing (scRNAseq) analysis, and organoid methods. Provided is a complex prognostic prediction of REG4 against diabetes accompanied by pancreatic cancer. Therefore, an individualized treatment method can be provided for a patient, and prognosis prediction and treatment method determination can be more reasonably carried out on an object with diabetes mellitus accompanied by pancreatic cancer.

Description

Technical Field

[0001] This invention relates to biomarkers for the diagnosis and prognostic prediction of diabetes mellitus with pancreatic cancer and their uses, specifically to a biomarker composition, kit, and information delivery method for the diagnosis and prognostic prediction of diabetes mellitus with pancreatic cancer, comprising a formulation for measuring the expression level of REG4 (Regeneration Gene 4) protein. Background Technology

[0002] The pancreas is an organ with both exocrine and endocrine functions. Its exocrine function involves secreting digestive enzymes that break down carbohydrates, fats, and proteins from ingested food, while its endocrine function involves secreting hormones such as insulin and glucagon that regulate blood sugar.

[0003] Pancreatic cancer is a mass (tumor mass) formed by cancerous cells in the pancreas. The term "pancreatic cancer" usually refers to pancreatic duct adenocarcinoma, but it also includes cystic carcinomas (cystic adenocarcinomas), endocrine tumors, etc. Because pancreatic cancer lacks specific early symptoms, it is difficult to detect early. The pancreas is about 2 cm thick, with a thin structure and is only enclosed by a capsule. It is also closely adjacent to the superior mesenteric artery, which supplies oxygen to the small intestine, and the portal vein, which transports nutrients absorbed by the intestine to the liver. Therefore, cancer infiltration is likely to occur. Furthermore, the nerve bundles and lymph nodes behind the pancreas are also prone to early metastasis. In particular, pancreatic cancer cells grow relatively quickly. The average survival at onset is 4 to 8 months, with a poor prognosis. Even with surgical intervention, the 5-year survival rate is low, approximately 17-24%.

[0004] Among the various drug treatments used to treat pancreatic cancer, gemcitabine has been considered the most commonly used treatment for pancreatic cancer patients over the past 20 years. Although gemcitabine has been used in combination with multiple drugs in pancreatic cancer, its efficacy has been limited and has not significantly increased survival rates. FOLFIRINOX, developed subsequently, consists of a combination of 5-FU (5-fluorouracil), leucovorin, irinotecan, and oxaliplatin, which improved median survival in pancreatic cancer compared to gemcitabine alone. Similarly, the combination of albumin-bound paclitaxel and gemcitabine has also shown improved median survival in pancreatic cancer compared to gemcitabine alone. However, the use of the aforementioned therapeutic agents (anticancer agents) may induce resistance in pancreatic cancer patients. Therefore, there is an urgent need for the diagnosis of resistance to the use of therapeutic agents (anticancer agents) and appropriate combination therapy.

[0005] Among the risk factors for pancreatic cancer, new-onset diabetes is considered to significantly increase the risk of developing pancreatic cancer, and the increased incidence of cancer caused by diabetes has been shown to be most pronounced in the pancreas and liver. Therefore, it is necessary to develop appropriate biomarkers for the diagnosis and prognostic prediction of pancreatic cancer in patients with diabetes. Summary of the Invention

[0006] The technical problem that the invention aims to solve

[0007] The present invention addresses the aforementioned problems and aims to provide a biomarker composition for the diagnosis and prognostic prediction of diabetes mellitus with pancreatic cancer, comprising a formulation for measuring the expression level of REG4 (Regeneration Gene 4) protein.

[0008] Another object of the present invention is to provide a kit for diagnosing and predicting the prognosis of diabetes mellitus with pancreatic cancer, comprising the biomarker composition.

[0009] Another object of the present invention is to provide an information provision method for diagnosing diabetes mellitus complicated with pancreatic cancer, comprising the following steps: (a) obtaining a biological sample from a subject; (b) obtaining data on the expression level of REG4 protein by measuring the expression level of REG4 (Regeneration Gene 4) protein in the sample; and (c) comparing the REG4 protein expression level of the subject obtained in step (b) with the REG4 protein expression level of a control group sample.

[0010] means for solving problems

[0011] To achieve the above objectives, the present invention provides a biomarker composition for the diagnosis and prognostic prediction of diabetes mellitus with pancreatic cancer, comprising a formulation for measuring the expression level of REG4 (Regeneration Gene 4) protein.

[0012] Furthermore, the present invention provides a kit for diagnosing and predicting the prognosis of diabetes mellitus with pancreatic cancer, comprising the aforementioned biomarker composition.

[0013] Furthermore, the present invention provides a method for providing information for diagnosing diabetes mellitus accompanied by pancreatic cancer, comprising the following steps: (a) obtaining a biological sample from a subject; (b) obtaining data on the expression level of REG4 protein by measuring the expression level of REG4 protein in the sample; and (c) comparing the REG4 protein expression level of the subject obtained in step (b) with the REG4 protein expression level of a control group sample, thereby providing a method for providing information for predicting the prognosis of pancreatic cancer.

[0014] The effects of the invention

[0015] This invention relates to an information provision method that provides necessary information for the diagnosis and prognostic prediction of diabetes-associated pancreatic cancer by measuring REG4 levels in biological samples isolated from individuals with diabetes and pancreatic cancer. Furthermore, it provides complex prognostic predictions for REG4 in diabetes-associated pancreatic cancer through patient cohort analysis, single-cell RNA sequencing (scRNA-seq) analysis, and organoid methods. Therefore, this invention can provide personalized treatment methods for patients and enable more rational prognostic prediction and treatment determination for individuals with diabetes and pancreatic cancer. Attached Figure Description

[0016] Figure 1 shows the biomarker identification results for PCDM. (a) Reanalysis of publicly available single-cell genomic analysis data of pancreatic cancer (GSE169321 data from the GEO database) confirmed the results based on cell distribution with or without diabetes; (b) Based on the expression level of gene markers with or without diabetes, REG4 was selected as the gene with the highest expression in pancreatic cancer with diabetes; (c)(d) In single-cell genomic analysis data, it was confirmed that REG4 expression was increased in the pancreatic duct adenocarcinoma cell clusters with diabetes compared to those without diabetes; (e) Kaplan-Meier survival curve analysis results, confirmed by analyzing publicly available TCGA data, showed that the survival rate of pancreatic cancer patients was reduced in the REG4 high-expression group.

[0017] Figure 2 shows the pathway analysis of PCDM associated with REG4. (a) Differentially expressed genes associated with diabetes were identified by pseudotime analysis; (b) The expression distribution of differentially expressed genes in pancreatic cancer cell clusters with diabetes was identified by pseudotime analysis; (c) Gene ontology pathways upregulated and downregulated in pancreatic cancer cell clusters with or without diabetes were identified by gene set enrichment analysis (GSEA); (d) Cell death regulation and intrinsic apoptosis signaling pathways downregulated in pancreatic cancer cell clusters with diabetes were identified by gene set enrichment analysis (GSEA).

[0018] Figure 3 shows the enzyme-linked immunosorbent assay (ELISA) data. (a) Serum REG4 concentration in pancreatic cancer patients with or without diabetes; (b) Correlation between HbA1c, an indicator of diabetes severity, and serum REG4; (c) Serum REG4 concentration in pancreatic cancer patients with and without diabetes according to pancreatic cancer stage; (d) Survival curves of pancreatic cancer patients among the quartiles of serum REG4 concentration; (e) Survival curves of patients with high and low REG4 concentrations according to duration of diabetes; (f) Distribution of serum REG4 concentrations based on anticancer agent responsiveness in newly diagnosed pancreatic cancer with diabetes, persistent pancreatic cancer, and pancreatic cancer without diabetes.

[0019] Figure 4 shows the laboratory test results and survival rates based on REG4 levels. (a) Correlation between serum REG4 concentration and the concentration of CA19-9, a tumor marker in pancreatic cancer; (b) Survival curves for high and low REG4 concentration groups in pancreatic cancer patients without diabetes.

[0020] Figure 5 shows the replication results of the 2D / organoid experiments. (a) Principal component analysis (PCA) results after RNA sequencing (RNA-seq) of pancreatic cancer organoids from newly diagnosed diabetes mellitus with pancreatic cancer, persistent diabetes mellitus with pancreatic cancer, and pancreatic cancer without diabetes mellitus; (b) Expression levels of REG1A, REG1B, and REG3A, as well as expression levels of genes constituting the intrinsic apoptotic signaling pathway and cell death pathway, in pancreatic cancer organoids from newly diagnosed diabetes mellitus with pancreatic cancer, persistent diabetes mellitus with pancreatic cancer, and pancreatic cancer without diabetes mellitus; (c) Upregulated and downregulated pathways obtained from gene ontology analysis of differentially expressed genes between newly diagnosed diabetes mellitus with pancreatic cancer and persistent diabetes mellitus with pancreatic cancer; (d) WNT signaling pathway in the AsPC-1 cell line, based on whether glucose stimulation was performed and the duration of stimulation. (e) Expression levels of genes constituting pathways; (f) Expression levels of REG4 and CD44 proteins in the AsPC-1 cell line based on whether glucose stimulation was performed and the duration of stimulation; (g) Expression levels of the WNT1 gene in pancreatic cancer organoids with newly diagnosed diabetes, persistent diabetes, and pancreatic cancer without diabetes; (c) IC50 curve analysis of the anticancer agent FOLFIRINOX in the AsPC-1 cell line based on whether glucose stimulation was performed and the duration of stimulation.

[0021] Figure 6 shows the REG4 expression results in pancreatic ductal adenocarcinoma (PDAC) organoids according to diabetes status. (a) REG4 gene expression levels in pancreatic cancer organoids without diabetes and those with diabetes; (b) Survival rates in pancreatic cancer organoids without diabetes and those with diabetes treated with FOLFIRINOX. Detailed Implementation

[0022] In this invention, unbiased analysis was performed on single-cell RNA sequencing (scRNA-seq) data to identify biomarkers for diabetes mellitus-associated pancreatic cancer. To identify key molecules regulating the pathogenesis of diabetes mellitus-associated pancreatic cancer, prognostic biomarkers were selected using unbiased methods including trajectory analysis, gene ontology pathway analysis, and overall survival assessment via Kaplan-Meier curves. Furthermore, the diagnostic and prognostic functions of key regulatory factors were evaluated by measuring the concentrations and expression levels of novel biomarkers in serum samples and organoids from patients with diabetes mellitus-associated pancreatic cancer.

[0023] Comparative analysis of scRNA-seq databases confirmed REG4 as a key molecule indicating a specific biomarker for diabetes mellitus associated with pancreatic cancer. In pancreatic cancer samples, cell death and cell death signaling pathways were downregulated with the progression of diabetes. Circulating serum REG4 concentrations were significantly increased in diabetic patients at all stages of pancreatic cancer. Decreased survival and chemotherapy resistance were observed only in patients with long-term diabetes mellitus and pancreatic cancer who exhibited high REG4 expression. Pancreatic cancer organoids and AsPC-1 cells derived from diabetic-associated pancreatic cancer tissues, under chronic glucose stimulation conditions, showed an increased FOLFIRINOX resistance pattern compared to acute conditions, with upregulation of REG4 / Wnt-1.

[0024] The present invention will now be described in more detail.

[0025] This invention provides a biomarker composition for the diagnosis and prognostic prediction of diabetes mellitus with pancreatic cancer, comprising a formulation for measuring the expression level of REG4 (Regeneration Gene 4) protein.

[0026] In this invention, the term "diabetes-associated pancreatic cancer" refers to pancreatic cancer associated with diabetes, specifically pancreatic cancer associated with diabetes that has progressed for more than 3 years or has been present for a long time.

[0027] In this invention, the formulation capable of measuring the REG4 protein expression level can be any one or more selected from the group consisting of antibodies, peptides, aptamers, and compounds that specifically bind to the REG4 protein.

[0028] In this invention, the term "antibody" is a well-known term in the art, referring to a specific protein molecule targeting an antigenic site. To achieve the objectives of this invention, the antibody refers to an antibody capable of specifically binding to the REG4 protein of this invention. This antibody can be obtained by cloning the genes into an expression vector using conventional methods and preparing the REG4 protein using conventional methods.

[0029] The form of the antibody is not particularly limited. Any polyclonal antibody, monoclonal antibody, or fragment thereof with antigen-binding properties is included within the scope of the antibodies of this invention, and includes all immunoglobulin antibodies. Furthermore, the antibodies of this invention also include special antibodies such as humanized antibodies. The antibodies against the REG4 protein of this invention can be any antibody that can be prepared by methods known in the art. For example, the antibody can be not only in its complete form having two full-length light chains and two full-length heavy chains, but can also include functional fragments of the antibody molecule. The functional fragment of the antibody molecule refers to a fragment with at least antigen-binding function, and can be Fab, F(ab'), F(ab')2, Fv, etc., but is not limited to these.

[0030] In this invention, the determination includes quantitative and / or qualitative analysis, including determination of presence or absence and determination of expression levels. These methods are known in the art, and those skilled in the art can choose appropriate methods to implement this invention.

[0031] In this invention, the prognosis refers to the estimation of medical outcomes (such as the probability of long-term survival, disease-free survival rate, etc.), including positive prognosis (positive prognosis) or negative prognosis (negative prognosis), wherein the negative prognosis includes disease progression or mortality such as recurrence, tumor growth, metastasis, drug resistance, etc., and the positive prognosis includes improvement of disease such as disease-free state, improvement or stabilization of disease such as tumor regression.

[0032] In this invention, the prediction refers to the preliminary judgment of medical outcomes. For the purposes of this invention, it refers to the preliminary prediction of the course of disease (disease progression, improvement, recurrence of diabetes with pancreatic cancer, tumor growth, and responsiveness to therapeutic agents) of a patient diagnosed with diabetes mellitus complicated by pancreatic cancer.

[0033] In this invention, the diagnosis and prognostic prediction can be based on responsiveness to therapeutic agents.

[0034] In this invention, the therapeutic agent may be any one or more selected from the group consisting of FOLFIRINOX, 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin.

[0035] In this invention, the expression of the REG4 (Regeneration Gene 4) protein can upregulate Wnt-1 signaling.

[0036] In this invention, the REG4 protein can be detected in pancreatic cancer cells and in the blood of patients.

[0037] Furthermore, the present invention provides a kit for diagnosing and predicting the prognosis of diabetes mellitus with pancreatic cancer, comprising the aforementioned biomarker composition.

[0038] In this invention, the kit can be a protein chip kit.

[0039] In this invention, the kit may include an antibody that recognizes the REG4 protein, as well as a composition, solution, or device containing one or more other components suitable for the analytical method.

[0040] In this invention, the kit for determining protein expression levels may include a matrix for antibody immunological detection, a suitable buffer solution, a secondary antibody labeled with a chromogenic enzyme or fluorescent substance, and a chromogenic substrate. The matrix may be a nitrocellulose membrane, a 96-well plate synthesized from polyethylene resin, a 96-well plate synthesized from polystyrene resin, or a glass slide, etc. The chromogenic enzyme may be peroxidase or alkaline phosphatase, the fluorescent substance may be FITC, RITC, etc., and the chromogenic substrate solution may be ABTS (2,2'-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid)), OPD (o-phenylenediamine), or TMB (tetramethylbenzidine), etc.

[0041] In this invention, the method for determining the expression level of REG4 protein can be selected from any one or more of the following: Western blot, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, flow cytometry (FACS), and protein chip.

[0042] In this invention, the kit can be used to overcome therapeutic resistance. The therapeutic agent can be FOLFIRINOX, which is selected from, but is not limited to, one or more of the group consisting of 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin.

[0043] Furthermore, the present invention provides a method for providing information for diagnosing diabetes mellitus accompanied by pancreatic cancer, comprising the following steps: (a) obtaining a biological sample from a subject; (b) obtaining data on the expression level of REG4 protein by measuring the expression level of REG4 protein in the sample; and (c) comparing the REG4 protein expression level of the subject obtained in step (b) with the REG4 protein expression level of a control group sample, thereby providing a method for providing information for predicting the prognosis of pancreatic cancer.

[0044] In this invention, the subject is an individual with diabetes mellitus accompanied by pancreatic cancer, including humans whose REG4 expression may be elevated or has been elevated after the onset of diabetes mellitus accompanied by pancreatic cancer, primates including chimpanzees, pets such as dogs and cats, livestock such as cattle, horses, sheep and goats, mammals such as rodents such as mice and rats, and farmed fish, etc., without limitation.

[0045] In this invention, the biological samples include biological samples such as tissues, cells, blood, serum, plasma, saliva, and urine that can confirm the REG4 protein associated with diabetes and pancreatic cancer.

[0046] In this invention, the prognostic prediction refers to the responsiveness to therapeutic agent resistance, and may further include the following step: (d) if the REG4 protein expression level of the subject measured in step (c) is higher than the REG4 protein expression level of the control group sample, then the step of determining the presence of therapeutic agent resistance.

[0047] In this invention, the biological sample can be any one or more of the group consisting of tissues, cells, blood, serum, plasma, saliva, and urine, but is not limited thereto.

[0048] In this invention, the control group refers to an individual who does not have diabetes with pancreatic cancer, and the control group refers to an individual who has pancreatic cancer without diabetes.

[0049] In this invention, the determination of the protein expression level refers to the process of confirming the presence and expression level of REG4 protein to confirm the progression of pancreatic cancer associated with diabetes, achieved by measuring the protein content. Therefore, the method used to determine the REG4 protein expression level can be any one or more of the following groups, but is not limited to: Western blot, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, flow cytometry (FACS), and protein chip.

[0050] The following detailed description, through embodiments, aims to aid in understanding the present invention. It should be noted that the embodiments described below are merely illustrative and not intended to limit the scope of the invention. The embodiments of the present invention are intended to enable those skilled in the art to more fully understand the invention.

[0051] Example 1: Materials and Methods

[0052] 1.1 Patient Cohort

[0053] In this invention, two independent cohorts of pancreatic cancer patients were studied. To assess the impact of diabetes on the clinical characteristics of pancreatic cancer patients, a total of 97 patients with a history of diabetes, examination results, and detailed clinical information on pancreatic cancer diagnosis were included (cohort 1), and patient-derived pancreatic cancer organoids were available in this cohort. Serum samples from a biobank of 100 pancreatic cancer patients (cohort 2), along with 100 age / sex-matched control participants, were used for ELISA assays to analyze content related to history of diabetes and survival. This invention is conducted in accordance with the Declaration of Helsinki and has been approved by the Institutional Review Board of Yonsei University Health System (IRB No. 4-2021-1395).

[0054] 1.2 Definition of Clinical Parameters

[0055] To clinically define type 2 diabetes, laboratory test results, including HbA1c and fasting blood glucose measured by automated analyzers, were interpreted according to the American Diabetes Association (ADA) methodology. To identify the onset of diabetes, it was classified as newly diagnosed diabetes (NODM) or long-term diabetes mellitus (LSDM) based on diagnosis according to the International Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) code (E11) or self-reported findings confirmed during antidiabetic medication use. Based on the time of pancreatic cancer diagnosis, patients diagnosed with diabetes within the last 3 years were classified as newly diagnosed diabetes (NODM), while those diagnosed more than 3 years prior were classified as long-term diabetes mellitus (LSDM). Overall survival (OS) was assessed based on the date of medication initiation and the last follow-up. Response to chemotherapy was determined using the RECIST (response evaluation criteria in solid tumors) v1.1 criteria. Overall, a good response was defined as a partial response (PR) or stable disease (SD) at any point in time during chemotherapy, while an adverse response was defined as disease progression (PD) or relapse or metastasis during follow-up.

[0056] 1.3 Cell and organoid culture

[0057] The AsPC-1 pancreatic ductal adenocarcinoma (PDAC) cell line, obtained from a Korean cell bank, was maintained in Roswell Park Memorial Institute (RPMI) 1× medium supplemented with 10% fetal bovine serum (FBS) (Welgene #S101-01, Republic of Korea) and 1% 100× antibiotic-antifungal agent (100 units / mL penicillin, 100 units / mL streptomycin, 250 ng / mL amphotericin B) (Gibco #15240062, Carlsbad, CA, US). The medium contained 2.05 mM L-glutamine, 25 mM HEPES, 15 mg / L L-methionine, 2000 mg / L sodium bicarbonate, and 5 mg / L phenol red (Welgene #LM011-05, Republic of Korea). Cells were cultured at 37°C in a 5% CO2 incubator.

[0058] The isolation and culture of pancreatic cancer organoids were performed using standard methods. Human pancreatic cancer organoids were cultured in a culture system containing the following components: GlutaMAX (Thermo Fisher Scientific), penicillin / streptomycin (Thermo Fisher Scientific), B27 (Thermo Fisher Scientific), N-acetyl-L-cysteine ​​(1 mM, Sigma-Aldrich), Wnt3a conditioned medium (50% v / v), RSPO1 conditioned medium (10% v / v, R&D Systems), recombinant noggin protein (100 ng / mL, PeproTech), recombinant epidermal growth factor protein (EGF, 50 ng / mL, PeproTech), gastrin (10 nM, Sigma-Aldrich), recombinant fibroblast growth factor 10 protein (FGF10, 100 ng / mL, PeproTech), nicotinamide (10 mM, Sigma-Aldrich), and A83-01 (0.5 μM, Tocris, Bristol, UK). RNA collection or cytotoxicity testing was performed 48 hours after organoid culture.

[0059] 1.4. Glucose Stimulation Simulation in Diabetes

[0060] To simulate the diabetic microenvironment, ASPC-1 cells were cultured in three different media: normal (11 mM glucose), high-concentration glucose (25 mM), and very high-concentration glucose (50 mM). To establish different durations of diabetes, ASPC-1 cells were continuously exposed for 2 cycles (short-term) and 6 cycles (long-term). Patient-derived organoids were exposed to high-concentration glucose (50 mM) medium for 3 days (short-term) and 6 days (long-term), respectively.

[0061] 1.5 Chemical reagents and immunofluorescence (IF) staining

[0062] Oxaliplatin (Sigma-Aldrich #O9512), irinotecan hydrochloride (Sigma-Aldrich #1347609), fluorouracil (Sigma-Aldrich #F0250000), calcium folic acid hydrate (Sigma-Aldrich #F7878), and dimethyl sulfoxide (DMSO) (Sigma-Aldrich #D2650) were commercially available.

[0063] For whole-body immunofluorescence staining, pancreatic cancer organoids were cultured in 8-well plates. Organoids embedded in Matrigel were washed with PBS and fixed with cold 4% PFA on a horizontal shaker. The fixed organoids were washed three times with PBS and blocked with 5% BSA and 0.1% Triton X-100 for 1 hour. Primary antibody was added to antibody dilution buffer (DAKO #S3022), and the plates were incubated at 4°C on a horizontal shaker for 48 hours. After washing three times with PBS, secondary antibody was added and the plates were incubated at 4°C for 4 hours, followed by three more washes with PBS. Nuclear staining was performed using Hoechst 33342 (Invitrogen #H3570). The primary antibodies used were REG4 (R&D #AF1379) and CD44 (biogems #065111), and the secondary antibodies were Alexa Fluor anti-goat (Abcam #ab6881) and Alexa Fluor anti-rat (Thermo Fisher Scientific #A21434). Fluorescence images were acquired using a Zeiss LSM 700 laser scanning confocal microscope and analyzed using ImageJ software (NIH).

[0064] 1.6 WST-8 Cytotoxicity Detection and IC50 Calculation

[0065] The FOLFIRINOX regimen used in in vitro experiments followed clinical practice, with each component's 1× concentration set at 0.46 μM oxaliplatin (DMSO), 5.7 μM irinotecan (DMSO), 6.1 μM leucovorin (H2O), and 46.3 μM fluorouracil (DMSO). The FOLFIRINOX regimen was serially diluted from 100× concentration using oxaliplatin (46 μM), irinotecan (570 μM), fluorouracil (4630 μM), and leucovorin (610 μM). These four drugs were serially diluted 10-fold in the pancreatic ductal adenocarcinoma AsPC-1 cell line. Cells were initially seeded at 5 × 10^4 cells / well in 96-well plates. After removing the culture medium, the medium was replaced with FOLFIRINOX-containing medium after 24 hours. Cell viability was assessed using the Cell Counting Kit-8 (CCK-8, also known as WST-8) (Sigma-Aldrich #96992, Inc., St. Louis, MO, USA). After culturing in FOLFIRINOX for 72 hours, 10 μL of CCK-8 was added to each well for detection. Readings were taken at 450 nm / 650 nm after culturing at 37°C for 3 hours. For organoid cell viability assays, after FOLFIRINOX treatment, 100 μL of CellTiter-Glo® 3D (Promega #G9681) was added to each well. Following the manufacturer's instructions, the organoids were shaken to mix and then incubated at room temperature for 30 minutes. Luminescence signals were measured using a VARIOSKANLUX plate reader (Thermo Fisher Scientific). The IC50, IC70, and IC80 values ​​of cells were calculated using GraphPad Prism (GraphPad Software, Inc., La Jolla, CA, USA) software, based on a linear approximate regression between drug concentration and survival rate.

[0066] 1.7 Reverse transcription (RT-PCR) and quantitative PCR analysis (qPCR)

[0067] Total RNA was extracted from passage 5 cultured pancreatic cancer organoids using the RNeasy micro-kit (Qiagen #74004, QIAGEN GmbH, Verogen, Inc.). To reverse transcribe the RNA into cDNA, the purified RNA sample was mixed with the RNA-cDNA EcoDry premix (Takara Bio Inc. #639549, Shiga, Japan). The mixture was incubated at 42°C for 1 hour, followed by incubation at 70°C for 10 minutes.

[0068] qPCR was performed using the Applied Biosystems StepOne System (Applied Biosystems, Forster City, CA, USA). Real-time PCR was measured by detecting the binding of fluorescent SYBR Green dye to double-stranded DNA. For PCR amplification, 100 ng cDNA, 2 μL primer set, 10 μL 2× SYBR premix Ex Taq, and 0.4 μL 50× ROXreference dye (Takara #RR420L) were mixed and the total reaction volume was adjusted to 20 μL with RNase-free water. Amplification was performed under the following cycling conditions: 95°C for 15 min, followed by 40 cycles of 95°C for 15 s and 60°C for 40 s. Each cDNA was analyzed in triplicate. Relative mRNA gene expression was normalized to the housekeeping gene GAPDH, and the ΔCt value was calculated as follows: ΔCt = Ct(GAPDH) - Ct(target gene). Subsequently, the average ΔCt under each experimental condition was subtracted to obtain the ΔΔCt value. The fold change in gene expression was calculated relative to the control group by 2^-ΔΔCt after normalization with GAPDH. The primers used are listed in Table 1.

[0069] Table 1

[0070] 1.8 Single-cell RNA-seq

[0071] For scRNA-seq analysis, a publicly available dataset (Genome Sequence Archive PRJCA001063) was used, along with annotations of clinical datasets provided by Peng et al. in existing reports. Cellranger (v3.1) software was used for sequencing read trimming and alignment with the GRCh38-3.0.0 reference genome. For filtering, normalization, and clustering, the standard analysis workflow of the R package Seurat (v4.0.3) was followed. Specifically, cells expressing fewer than 200 genes, more than 10,000 genes, or mitochondrial gene expression exceeding 20% ​​were filtered. After cell cycle regression, the filtered data were normalized using the Seurat function SCTransform. Cell type annotation was performed using the Seurat functions FindMarkers and PanglaoDB. Annotated T cell populations were reanalyzed using the DatabaseImmune CellExpression Data in the R package SingleR (v1.4.1). To investigate the plasticity between CIC and differentiated ductal carcinoma cells, trajectory analysis was performed in PDAC clusters using the R package Monocle (v2.18.0). t-tests were used to identify differentially expressed genes with statistical significance across different states. Gene ontology enrichment analysis was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID).

[0072] 1.9 Analysis of TCGA and GTEX datasets

[0073] The pancreatic adenocarcinoma (PAAD) dataset from The Cancer Genome Atlas (TCGA) and the normal pancreatic dataset from Genotype-Tissue Expression (GTEX) were analyzed using the R packages recount3 and TCGAbiolinks. Overall survival (OS) of TCGA was analyzed using Gene Expression Profiling Interactive Analysis (GEPIA).

[0074] To verify the transcriptomic differences between normal pancreatic and tumor samples, machine learning techniques, specifically the R package caret, were used for analysis. Normal and tumor samples were divided into a training set (75% of the samples) and a test set (25% of the samples) using the createDataPartition function, and an Elastic Net model was trained accordingly.

[0075] 1.10 ELISA Analysis

[0076] The concentration of REG4 in serum samples from patients and controls was measured using a commercial ELISA kit (Sino Biological #SEK11186). Linearity was validated using patient samples within the detection range of 3.91–250 pg / mL, according to CLSI guideline EP09-A3.

[0077] 1.1 Statistical Data Analysis

[0078] Results from multiple experiments are expressed as mean ± standard error of the mean (SEM). Statistical analysis was performed using one-way ANOVA, with Tukey multiple comparison tests performed where appropriate using GraphPad Prism 8 (GraphPad Software, Inc., La Jolla, CA, USA). p < 0.05 was considered statistically significant. Kaplan-Meier curves were compared between groups using the log-rank test for estimates of overall survival (OS) and progression-free survival (PFS).

[0079] Example 2 Experimental Results

[0080] 2.1. REG4 was identified as a biomarker for pancreatic cancer associated with diabetes mellitus through analysis of publicly available scRNA-seq databases.

[0081] To identify differentially expressed biomarkers in diabetic pancreatic cancer compared to non-diabetic pancreatic cancer, previously reported scRNA-seq data were filtered and analyzed using strict quality control (QC) standards. A total of 3187 unique single-cell transcriptomes were obtained, with an average of 1266 genes and 5733 transcripts per cell, and were clustered into 11 clusters (…). Figure 1a Although the proportions of the 11 clusters were not different, REG4 was identified as one of the genes highly expressed in pancreatic cancer (PC) cell clusters in the diabetic-associated pancreatic cancer group compared to the non-diabetic pancreatic cancer group. Figure 1b REG4 is a specific and unique biomarker expressed only in the pancreatic cancer cluster and not in the other 10 clusters. Figure 1c -d). Using the TCGA dataset from the PDAC cohort, despite the differences between basal and classical patterns and the inability to use diabetes factors in the analysis, overexpression of REG4 in whole tissue RNA sequencing still showed reduced overall survival ( Figure 1e ).

[0082] 2.2 Correlation between downregulation of cell death and apoptosis signaling pathways in pancreatic cancer and diabetes.

[0083] To investigate key pathways associated with diabetes severity in pancreatic cancer cell populations, a gene set including REG4 was identified in a series of pseudo-time trajectory analyses. Figure 2a -b). Gene ontology (GO) analysis of differentially expressed genes (DEGs) identified in trajectory analysis showed that extracellular matrix (ECM) and tight junction components were upregulated, while the assembly of peptide antigens and MHC complexes was downregulated in pancreatic cancer cell clusters with the progression of diabetes. Figure 2c It is noteworthy that cell death is primarily regulated through the apoptosis signaling pathway, and this pathway is significantly downregulated in patients with pancreatic cancer who also have diabetes compared to those without. Figure 2d This indicates that patients with diabetes and pancreatic cancer have a poorer prognosis.

[0084] 2.3. Circulating REG4 concentration in pancreatic cancer patients only predicts survival in diabetic patients.

[0085] In clinical samples from PDAC patients, a statistically significant increase in serum circulating REG4 concentrations was observed in patients with diabetes mellitus and pancreatic cancer. Figure 3a The severity of diabetes at the time of sample collection was expressed as HbA1c and was positively correlated with REG4 concentration. Figure 3b The difference between the diabetes mellitus-associated pancreatic cancer group and the pancreatic cancer group without diabetes mellitus was more pronounced in the early stages of PDAC. Figure 3c However, the initial tumor marker CA19-9 level in patient serum did not show a proportional relationship with REG4 concentration. Figure 4a When analyzing the survival of patients with diabetes and pancreatic cancer, the highest quartile serum concentration of REG4 expression showed the worst survival trend, but this did not reach statistical significance. Figure 3d In the LSDM group with diabetes mellitus and pancreatic cancer, survival rates differed significantly based on REG4 expression levels, while this phenomenon was not observed in the NODM group with diabetes mellitus and pancreatic cancer without diabetes mellitus. Figure 3e , Figure 4b Furthermore, REG4 concentrations were also increased in chemotherapy-resistant pancreatic cancer patients with LSDM, but no significant differences were observed in patients with NODM and non-diabetic pancreatic cancer. Figure 3f ).

[0086] 2.4. Glucose stimulation induces REG4 in PDACs in pancreatic cancer that exhibit chemotherapy resistance by upregulating Wnt signaling. guide

[0087] Transcriptome analysis of pancreatic cancer organoids derived from diabetic patients with high REG4 RNA expression showed clustering in PCA and higher expression of genes associated with the "endogenous apoptosis signaling" pathway. Figure 5a -b, Figure 6a Furthermore, gene ontology (GO) terms related to Wnt signaling were upregulated, while responses to chemokines and granulocyte / leukocyte chemotactic GO terms were downregulated in diabetic organoids compared to controls, suggesting that glucose exposure plays a central role in the transcriptome pattern associated with chemotherapy resistance in PDAC organoids. Figure 5c Among the various ligands responsible for Wnt signaling, WNT1 was continuously upregulated in PDAC organoids after treatment under both short-term and long-term hyperglycemic stimulation, and showed higher expression of REG4 and CD44. Figure 5d -e). Compared with organoids derived from NODM patients, the fold change in WNT1 RNA was more significant in PDAC organoids derived from LSDM patients ( Figure 5f When analyzed using the AsPC-1 cell line, the IC50 value of FOLFIRINOX treatment showed a statistically significant increase only under prolonged glucose stimulation. Figure 5g When treated with the same concentration of FOLFIRINOX, PDAC organoids derived from diabetic patients showed significantly higher survival rates compared to control PDAC organoids. Figure 6b ).

[0088] The foregoing has described specific parts of the present invention in detail. It will be apparent to those skilled in the art that these specific descriptions are merely preferred embodiments and are not intended to limit the scope of the invention. That is, the essential scope of the invention is defined by the appended claims and their equivalents.

[0089] The following national research and development projects support this invention: [Project Unique Number] 1711180280 [Project Number] 2022R1A2C1013380 [Name of the competent authority] Ministry of Science, Technology and Information [Name of the organization responsible for project management (professional)] Korea Research Foundation [Research Project Title] Personal Basic Research (Ministry of Science, Technology and Information) [Research Topic Title] Development and Performance Evaluation of Customized Hydrogels for Pancreatic Cancer Organoids for In Vivo Tumor Microenvironment Construction and Clinical Chemotherapy Response Prediction [Project Undertaking Institution] Yonsei University [Research Period] 20230301~20240229.

Claims

1. A biomarker composition for the diagnosis and prognostic prediction of diabetes mellitus with pancreatic cancer, comprising a formulation for measuring the expression level of REG4 (Regeneration Gene 4) protein.

2. The biomarker composition for diagnosing and predicting the prognosis of diabetes mellitus complicated with pancreatic cancer according to claim 1, wherein, The formulation capable of measuring REG4 protein expression level is selected from one or more of the group consisting of antibodies, peptides, aptamers, and compounds that specifically bind to the REG4 protein.

3. The biomarker composition for diagnosing and predicting the prognosis of diabetes mellitus complicated with pancreatic cancer according to claim 1, characterized in that, The diagnosis and prognostic prediction are based on responsiveness to treatment resistance.

4. The biomarker composition for diagnosing and predicting the prognosis of diabetes mellitus complicated with pancreatic cancer according to claim 1, wherein, The therapeutic agent is selected from one or more of the group consisting of FOLFIRINOX, 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin.

5. The biomarker composition for diagnosing and predicting the prognosis of diabetes mellitus complicated with pancreatic cancer according to claim 1, characterized in that, The expression of the REG4 (Regeneration Gene 4) protein upregulates Wnt-1 signaling.

6. The biomarker composition for diagnosing and predicting the prognosis of diabetes mellitus complicated with pancreatic cancer according to claim 1, characterized in that, The REG4 protein was detected in pancreatic cancer cells and in the blood of patients.

7. The biomarker composition for diagnosing and predicting the prognosis of diabetes mellitus complicated with pancreatic cancer according to claim 1, wherein, The term "diabetes with pancreatic cancer" refers to pancreatic cancer that has accompanied diabetes for more than 3 years.

8. A kit for diagnosing and predicting the prognosis of diabetes mellitus with pancreatic cancer, comprising a biomarker composition according to any one of claims 1 to 7.

9. The kit for diagnosing and predicting the prognosis of diabetes mellitus complicated with pancreatic cancer according to claim 8, characterized in that, The kit is a protein chip kit.

10. The kit for diagnosing and predicting the prognosis of diabetes mellitus complicated with pancreatic cancer according to claim 8, characterized in that, The kit is designed to overcome therapeutic resistance.

11. The kit for diagnosing and predicting the prognosis of diabetes mellitus complicated with pancreatic cancer according to claim 10, characterized in that, The therapeutic agent is selected from one or more of the group consisting of FOLFIRINOX, 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin.

12. A method for providing information for diagnosing diabetes mellitus complicated with pancreatic cancer, comprising the following steps: (a) Steps for obtaining biological samples from an object; (b) The step of obtaining data on the expression level of REG4 protein by measuring the expression level of REG4 (Regeneration Gene 4) protein in the sample; and (c) The step of comparing the REG4 protein expression level of the subjects obtained in step (b) with the REG4 protein expression level of the control group samples.

13. The method for providing information for predicting the prognosis of pancreatic cancer according to claim 12, wherein, The prognostic prediction, which represents responsiveness to therapeutic agent resistance, further includes the following steps: (d) If the REG4 protein expression level of the subject measured in step (c) is higher than the REG4 protein expression level of the control group sample, then the step of determining the presence of therapeutic resistance is taken.

14. The method for providing information for predicting the prognosis of pancreatic cancer according to claim 12, characterized in that, The biological sample is selected from one or more of the following groups: tissues, cells, blood, serum, plasma, saliva, and urine.

15. The method for providing information for predicting the prognosis of pancreatic cancer according to claim 12, characterized in that, The control group consisted of individuals who developed pancreatic cancer without diabetes.

16. The method for providing information for predicting the prognosis of pancreatic cancer according to claim 12, characterized in that, The method for determining the REG4 protein expression level is selected from one or more of the following groups: Western blot, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, flow cytometry (FACS), and protein chip.

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