Application of NT5M in the preparation of drugs for treating pancreatic cancer

Abstract

This invention belongs to the field of biopharmaceutical technology and specifically relates to the use of NT5M in the preparation of a drug for the treatment of pancreatic cancer. The invention reveals that NT5M is a key metabolic regulator in PAAD, influencing tumor progression, immune regulation, and drug sensitivity. Low NT5M expression is associated with poor prognosis, while its overexpression inhibits cell migration, invasion, and PD-L1-mediated immune evasion, highlighting its potential as a tumor suppressor in PAAD. It can serve as both a prognostic biomarker and a potential therapeutic target, providing a new treatment strategy for PAAD patients.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and particularly relates to an application of NT5M in preparing a medicine for treating pancreatic cancer. Background Art

[0002] Pancreatic adenocarcinoma (PAAD) is a malignant tumor of the digestive system characterized by a high mortality rate and an increasing annual prevalence. Improved treatment strategies are urgently needed. The insidious onset of PAAD and its nonspecific symptoms, such as abdominal pain and weight loss, pose diagnostic challenges. The pancreas's retroperitoneal location complicates the detection of early-stage lesions, while the rapid progression of the disease often leads to late diagnosis, low surgical resection rates, high recurrence rates, and frequent metastases. Furthermore, the median survival rate for patients with advanced disease remains low. This makes research on uncovering the molecular drivers of PAAD pathogenesis and identifying biomarkers for early detection, targeted therapy, and prognostic stratification crucial.

[0003] Recent studies have revealed that dysregulated nucleotide metabolism—a fundamental process controlling DNA / RNA synthesis and cellular energy homeostasis—is a key driver of tumor progression and therapeutic resistance. Driven by uncontrolled proliferation, cancer cells exhibit an elevated nucleotide demand, making metabolic enzymes such as NT5M (cytoplasmic 5′-nucleotidase) potential therapeutic targets. NT5M catalyzes the dephosphorylation of nucleoside monophosphates, regulating not only the nucleotide salvage pathway but also mitochondrial function and redox homeostasis. However, the role of NT5M in PAAD remains poorly understood.

[0004] Programmed death ligand 1 (PD-L1) is an immune checkpoint protein that binds to PD-1 on T cells, inhibiting anti-tumor immune responses. In PAAD, PD-L1 expression contributes to a highly immunosuppressive tumor microenvironment and is associated with a poor prognosis. Recent industry research has focused on overcoming PD-L1-mediated immune evasion through combination therapies, including immune checkpoint inhibitors with chemotherapy or targeted agents. Several genes, such as OBSCN, CXCL8 / IL-8, and CT83, have been reported to regulate PD-L1 expression, providing a potential strategy to counteract cancer immune evasion. However, the response of pancreatic cancer to anti-PD-L1 monotherapy remains limited, highlighting the need for better predictive biomarkers and novel combination therapies to improve treatment efficacy.

[0005] Therefore, it is necessary to further identify biomarkers for early detection, targeted treatment, and prognostic stratification based on the molecular drivers of PAAD pathogenesis for the development of PAAD drugs. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention aims to provide a use of NT5M in the preparation of a drug for the treatment of pancreatic cancer. After research, it was found that NT5M regulates PD-L1 expression through the NT5M / CXCL8 / PD-L1 axis, thereby reversing the immune evasion of pancreatic cancer. It was also established that NT5M is both a valuable prognostic indicator of tumor progression and a promising therapeutic target for PAAD, providing a new strategy for enhancing the efficacy of immune checkpoint blockade therapy for pancreatic cancer.

[0007] The first object of the present invention is to provide a use of NT5M in screening pancreatic cancer prognostic biomarkers.

[0008] The second object of the present invention is to provide an application of NT5M in screening drug targets for treating pancreatic cancer.

[0009] The third object of the present invention is to provide a use of NT5M in preparing a drug for treating pancreatic cancer.

[0010] Furthermore, the NT5M gene is located on chromosome 17 (Gene ID: 56953) and is composed of 38% α-helix, 22% β-sheet, and 40% random coil. Specifically, the NT5M gene (also known as mdN, dNT2, or dNT-2) is located on chromosome 17p11.2, within the Smith-Magenis syndrome critical region (chr17: 17,083,101-17,102,287, GRCh38 / hg38). The α-helix is ​​primarily located at the N-terminus, the β-sheet forms the core catalytic domain, and the random coil is primarily located in the flexible loop region.

[0011] The fourth object of the present invention is to provide an inhibitor comprising a substance that inhibits NT5M gene overexpression or a substance that promotes NT5M gene overexpression.

[0012] The fifth object of the present invention is to provide a use of the above inhibitor, which is used to inhibit the proliferation, migration and invasion of pancreatic cancer cells.

[0013] The sixth object of the present invention is to provide a use of the above inhibitor, wherein the inhibitor is used to inhibit the reverse immune escape of pancreatic cancer cells.

[0014] Furthermore, the reversal of immune escape reverses pancreatic cancer immune escape through the NT5M / CXCL8 / PD-L1 axis.

[0015] A seventh objective of the present invention is to provide a method for screening pancreatic cancer prognostic biomarkers, the method comprising: using bioinformatics methods to analyze genes related to nucleotide metabolism in the GeneCards database as initial candidate genes, then performing differential expression analysis and univariate regression analysis, combining PPI and GeneMANIA to screen out genes with prognostic value, then performing single-cell analysis on the genes with prognostic value, analyzing expression patterns based on cell maps, and screening out genes with reduced specificity in malignant cells as final prognostic genes, i.e., pancreatic cancer prognostic biomarkers.

[0016] An eighth objective of the present invention is to provide a method for screening drug targets for treating pancreatic cancer, the method comprising: using bioinformatics methods to analyze genes related to nucleotide metabolism in the GeneCards database as initial candidate genes, then performing differential expression analysis and univariate regression analysis, combining PPI and GeneMANIA to screen out quasi-candidate genes, then using cancer drug sensitivity genomics as a training set to predict the sensitivity of different chemotherapy drugs between the gene expression groups to thereby screen sensitive chemotherapy drug genes, and simultaneously using TIDE and immune checkpoint-related scores to evaluate the patient's response to immunotherapy based on the gene expression. If the gene is highly expressed in immune cells and stromal cells, the quasi-candidate gene is a drug target for treating pancreatic cancer.

[0017] Advantages compared to existing technologies:

[0018] This study used an integrated approach combining multi-omics bioinformatics analysis with systematic in vitro functional assays to investigate the multifaceted roles of NT5M in PAAD. The study identified NT5M as a key metabolic regulator in PAAD, influencing tumor progression, immune regulation, and drug sensitivity. Low NT5M expression was associated with poor prognosis, while its overexpression inhibited cell migration, invasion, and PD-L1-mediated immune evasion, highlighting its potential as a tumor suppressor in PAAD. It could serve as both a prognostic biomarker and a potential therapeutic target.

[0019] The present study found that, in terms of mechanism, NT5M can regulate the expression of PD-L1 through the NT5M / CXCL8 / PD-L1 axis, providing new insights into the intersection of nucleotide metabolism and immune regulation, indicating that targeting NT5M can enhance the efficacy of immune checkpoint blockade therapy and provide a new treatment strategy for PAAD patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1Partial analysis of the most potential prognostic genes for PAAD screened in this invention, where A is a volcano plot; B is a univariate Cox regression analysis of 126 DEGs; C is a protein-protein interaction (PPI) network, in which genes with a higher degree of interaction are represented in dark red, indicating stronger connectivity, and the histogram quantifies the interaction score.

[0021] Figure 2 Partial analysis diagram for screening the most promising prognostic genes in PAAD in the present invention, where A is a GeneMANIA network analysis visualization of the interactions and functional associations between prognosis-related genes; B is a Lasso regression analysis diagram; and C is a comprehensive analysis diagram combining the three selection criteria.

[0022] Figure 3 Figure 3 is a partial analysis of single cells of NT5M and NT5C3B of the present invention, where A is a PAAD cell atlas constructed from the GSE111672 dataset, in which cells are divided into 19 different clusters; B is an annotation of the cell atlas, in which clusters are assigned to 11 different cell types; C is a scatter plot depicting marker genes associated with different cell types; D is a ratio diagram of different cell types in three patients.

[0023] Figure 4 These are partial analysis diagrams of single cells of NT5M and NT5C3B of the present invention, wherein A is the cell map and violin plot of NT5C3B; B is the cell map and violin plot of NT5M.

[0024] Figure 5 : These are expression pattern analysis diagrams of NT5M of the present invention, wherein A is a paired differential expression analysis diagram of NT5M mRNA level between tumor and normal tissues; B is a histological examination HE staining diagram of normal pancreatic tissue and pancreatic cancer tissue.

[0025] Figure 6 Figure 1 is a diagram of the chromosomal location and structural characteristics of NT5M in the present invention, wherein A is a pan-cancer analysis diagram of the prognostic risk associated with NT5M expression; B is a comparison diagram of NT5M mRNA expression in PAAD patients stratified by TP53 mutation status; and C is a structural characterization diagram of NT5M using protein database resources.

[0026] Figure 7The figures are correlation analysis diagrams of NT5M expression and clinical characteristics of the present invention, wherein A is a correlation heat map between NT5M mRNA expression and six clinical characteristics (sex, stage, age, TNM stage); B is a histogram of NT5M mRNA expression levels in different sexes (female, male, p=0.36); C is a histogram of NT5M mRNA expression levels in different grades (grade 1-2, grade 3-4, p=0.59); D is a histogram of NT5M mRNA expression levels in different stages (stage I, stage II-IV, p=0.0012); E is a histogram of NT5M mRNA expression levels in different T stages (T1-2, T3-4, p=0.003); F is a histogram of NT5M mRNA expression levels in different N stages (N0, N1, p=0.27); and G is a histogram of NT5M mRNA expression levels in different M stages (M0, M1, p=0.73).

[0027] Figure 8 Figure 1 shows a partial analysis of NT5M as an independent prognostic factor for PAAD in the present invention, where A and B are the overall survival (OS) and disease-free survival (DFS) curves comparing patients with high and low NT5M expression in the TCGA database; C and D are the OS and DFS curves comparing patients with high and low NT5M expression in the GEPIA database.

[0028] Figure 9 The figures are partial analysis of NT5M as an independent prognostic factor for PAAD in the present invention, wherein A is a multivariate Cox regression analysis to evaluate the association between NT5M expression and clinical characteristics; B is a receiver operating characteristic (ROC) curve showing the prediction accuracy of NT5M (AUC=0.655); C is a calibration curve for evaluating the consistency between the predicted results and the observed results based on NT5M expression and clinical characteristics; D is a nomogram integrating NT5M expression and clinical characteristics to predict patient prognosis analysis (*, p<0.05; **, p<0.01; ***, p<0.001).

[0029] Figure 10 Figure 1 is a partial enrichment analysis diagram of NT5M-related DEGs in the present invention, where A is BP enrichment analysis of DEGs using GO; B is CC enrichment analysis of DEGs using GO; C is MF enrichment analysis of DEGs using GO; D is KEGG pathway enrichment analysis of DEGs.

[0030] Figure 11Figure 1 is a partial relationship analysis diagram between NT5M expression and immune infiltration in PAAD of the present invention, where A is the single sample gene set enrichment analysis (ssGSEA) illustrating the correlation between NT5M expression and the infiltration of 22 immune cell types; B is the immune score (ImmuneScore), stromal score (StromalScore) and ESTIMATE score (ESTIMATEScore) of PAAD samples stratified by NT5M expression level, reflecting the tumor immune microenvironment; C is the immunophenotypic score (IPS) analysis predicting potential immune responses based on NT5M expression.

[0031] Figure 12 Figure 1 is a partial relationship analysis diagram between NT5M expression and immune infiltration in PAAD of the present invention. A is a relationship diagram between NT5M expression and different immune cell populations as demonstrated by multi-cellular immune infiltration analysis; BF are correlation scatter plots.

[0032] Figure 13 This is a heat map showing the relationship between NT5M expression and key immune checkpoint molecules.

[0033] Figure 14 Figures 2 and 3 show the proliferation analysis of PAAD cells regulated by NT5M of the present invention, wherein A is qRT-PCR analysis used to verify the overexpression efficiency of NT5M in PANC-1 and BxPC-3 cells; B is WB analysis confirming the overexpression efficiency of NT5M in PANC-1 and BxPC-3 cells; C and D are EdU incorporation assays and their histograms; E and F are Ki-67 immunofluorescence staining and their histograms (*, p<0.05; **, p<0.01; ***, p<0.001).

[0034] Figure 15 Figure 3 is an analysis of the proliferation, migration and invasion of PAAD cells regulated by NT5M in the present invention, wherein A is the result of CCK-8 assay; B and C are the results of colony formation assay; D and E are wound healing assays comparing the migration rates between the NT5M overexpression group and the control (NC) group in PANC-1 and BxPC-3 cells; F and G are Transwell migration and invasion assays demonstrating the effect of NT5M overexpression on the migration and invasion abilities of PANC-1 and BxPC-3 cells (*, p<0.05; **, p<0.01; ***, p<0.001).

[0035] Figure 16Figure 3 is an analysis of the immune escape of PAAD cells regulated by NT5M through the NT5M / CXCL8 / PD-L1 axis in the present invention, wherein A is a CCK-8 assay to evaluate the cell proliferation of PANC-1 and BxPC-3 cells after co-culture with PBMC; BC is an ELISA quantification of granzyme B (GZMB) and perforin (PFN) levels in the co-culture supernatant; D is a Western blot (WB) analysis of PD-L1 and cyclin B1 expression (*, p<0.05; **, p<0.01; ***, p<0.001).

[0036] Figure 17 Figure 3 is an analysis of the immune escape of PAAD cells regulated by NT5M through the NT5M / CXCL8 / PD-L1 axis in the present invention, where A is a heat map of DEGs; B is a volcano plot of DEGs (|log2FC|>0.5, FDR<0.05); C is qRT-PCR verification of CXCL8 and PD-L1 mRNA levels in BxPC-3 (G) and PANC-1 (H) cells under the three conditions of control, NT5M-OE, and NT5M-OE+CXCL8 rescue; D is Western blot analysis of CXCL8 and PD-L1 protein expression in CXCL8-OE and control cells; E is Western blot analysis of NT5M and PD-L1 protein expression under the three conditions of control, NT5M-OE, and NT5M-OE+CXCL8 rescue. DETAILED DESCRIPTION

[0037] The above-mentioned technical features of the present invention and the technical features specifically described below (such as embodiments) can be combined with each other to form new or preferred technical solutions, but the present invention is not limited to these embodiments, and these embodiments do not limit the present invention in any way.

[0038] The experimental methods in the following examples are conventional methods unless otherwise specified. The preparations involved in the following examples are common commercial products and can be purchased from the market unless otherwise specified.

[0039] This study elucidates the dual role of NT5M in PAAD through its unique location at the intersection of nucleotide metabolism and immune regulation. While adenosine-mediated immunosuppression via CD73 / NT5E has been well characterized in PAAD, NT5M is identified as a novel metabolic regulator with distinct biological functions. Unlike its paralog NT5E, which produces extracellular adenosine, NT5M's cytoplasmic localization and specific substrate preference (deoxyribonucleotides) suggest a specialized role in maintaining nucleotide pools while shaping immune responses.

[0040] Two key findings, taken together, confirmed the pathophysiological significance of NT5M: First, in terms of prognostic stratification, multi-cohort validation (TCGA, GEO, GEPIA) consistently associated low NT5M expression with advanced T stage (p=0.003) and stage grade (p=0.0012) and decreased survival (median OS: 20 vs 45 months, p<0.001), indicating it is a new prognostic marker (AUC=0.655). This is consistent with the metabolic enzyme being a survival predictor in gastrointestinal cancer. Second, in terms of axis validation, the NT5M→CXCL8→PD-L1 cascade was mechanistically confirmed by: inhibition of PD-L1 and CXCL8 after NT5M overexpression (r=-0.72, p=0.008); upregulation of PD-L1 after CXCL8 overexpression (r=-0.72, p=0.008); and rescue of PD-L1 expression by CXCL8 reconstitution (2.3-fold increase, p=0.003).

[0041] Materials and methods

[0042] 1.1 Data Collection and Preprocessing

[0043] Gene expression profiles and clinical data for pancreatic adenocarcinoma (PAAD) were obtained from The Cancer Genome Atlas (TCGA-PAAD) and the Gene Expression Omnibus (GEO) datasets (GSE15471, GSE111672). For microarray datasets, raw data were normalized using RMA (Robust Multiarray Average), while RNA-seq data were normalized using TPM (transcripts per million). Batch effects were adjusted using the ComBat algorithm in the sva R package.

[0044] 1.2 Differential expression and prognostic screening

[0045] Differentially expressed genes (DEGs) between tumor and normal tissues were identified using the limma package (|log2FC| > 1, adjusted p < 0.05) and visualized using heatmaps and volcano plots. Univariate Cox regression was used to assess the prognostic relevance of DEGs. Key genes were further filtered using lasso regression (glmnet package, 10-fold cross-validation) and an integrated analysis combining GeneMANIA interaction scores, PPI network degree centrality (STRING database), and lasso-selected genes.

[0046] 1.3 Single-cell RNA sequencing analysis

[0047] Single-cell transcriptome data from GSE111672 were processed using Seurat (v4.0). Cells were filtered (minimum genes / cell = 200, maximum percentage of mitochondrial genes = 10%), normalized using SCTransform, and clustered using PCA and UMAP (resolution = 0.5). Cell types were annotated based on marker genes, and cell-cell interaction (CCI) analysis was performed using CellPhoneDB (v3.0).

[0048] 1.4 Survival and clinical correlation analysis

[0049] Kaplan-Meier survival curves (log-rank test) were generated using the survival and survminer R packages. The independent prognostic value of NT5M was assessed by multivariate Cox regression. Receiver operating characteristic (ROC) curves (pROC package) and nomograms (rms package) were constructed to assess predictive accuracy. The correlation between NT5M expression and clinical characteristics (such as TNM stage) was analyzed using the Wilcoxon rank sum and / or Kruskal-Wallis tests.

[0050] 1.5 Functional enrichment and immunoassay analysis

[0051] Gene Ontology (GO) and KEGG pathway enrichment analysis were performed using clusterProfiler. Gene set enrichment analysis (GSEA) was performed using the MSigDB collections (C2 and C7). Immune infiltration scores (CIBERSORT and ssGSEA) and immune checkpoint associations (Spearman's ρ) were calculated using the ESTIMATE and GSVA packages.

[0052] 1.6 Drug Sensitivity Prediction

[0053] Drug response data (20 drugs) were retrieved from the GDSC database, and IC50 values ​​were predicted using the pRRophetic package. Differences in drug sensitivity between the NT5M high and NT5M low groups were assessed by Student's t test.

[0054] 2. In vitro experiments

[0055] 2.1 Cell culture

[0056] Human pancreatic cancer cell lines PANC-1 and BxPC-3 were purchased from Wuhan Service Biotechnology Co., Ltd. PANC-1 cells were maintained in Dulbecco's modified Eagle's medium (DMEM; Gibco, Thermo Fisher Scientific, Waltham, MA, USA; Catalog No. 11995065) supplemented with 10% fetal bovine serum (FBS; Cell Kit; Catalog No. Aus-01s-02) and 1% penicillin / streptomycin (Gibco; Catalog No. 15140122). BxPC-3 cells were maintained in RPMI-1640 medium (Gibco; Catalog No. 11875093) containing the same serum and antibiotic concentrations. All cells were incubated at 37°C in a humidified atmosphere with 5% CO2 and routinely tested for mycoplasma contamination using the MycoAlert PLUS kit (Lonza, Basel, Switzerland; Catalog No. LT07-710).

[0057] 2.2 Plasmid transfection and overexpression experimental protocol

[0058] NT5M and CXCL8 were overexpressed using commercial plasmids (Hanbio Biotech (Shanghai) Co., Ltd.) using the following optimized procedure:

[0059] Cell seeding: 2 × 10 cells were seeded in a 6-well plate (Corning®). 5 PAAD cells / well were cultured overnight in complete medium (RPMI-1640 / DMEM + 10% FBS) at 37°C and 5% CO2.

[0060] Transfection complex preparation: Dilute 2.5 μg of plasmid (experimental / empty vector control) in 125 μL OPTI-MEM™ (Gibco, Cat. No. 31985070); mix with 7.5 μL Lipofectamine™ 3000 + 5 μL P3000™ Enhancer (ThermoFisher, L3000015) in 125 μL OPTI-MEM™; incubate at RT for 15 minutes to allow complex formation.

[0061] Transfection: Replace medium with 2 mL of fresh OPTI-MEM™; add transfection complexes dropwise; replace with complete medium after 6 hours;

[0062] Sample collection: RNA extraction: 48 hours after transfection;

[0063] Protein extraction: 72 hours after transfection.

[0064] 2.3 Proliferation assay

[0065] CCK-8: cells (1 × 10 3 / well) and CCK-8 reagent (TransDtect ® ; Product No. FC101-02) at 37°C for 2 hours. The absorbance (450 nm) was measured at 24, 48, and 72 hours.

[0066] EdU detection: EdU (Uelandy ® ) labeled cells, fixed (4% paraformaldehyde), and stained with Alexa Fluor 488 (Click-iT EdU Kit). EdU was detected by fluorescence microscopy. + Cells were quantified.

[0067] Colony formation: Cells (500 / well) were cultured for 14 days, fixed (4% paraformaldehyde) for 30 minutes, and stained with 0.1% crystal violet for 15 minutes.

[0068] 2.4 Migration and invasion assays

[0069] Wound healing: 8×10 5 Cells / well were seeded in 6-well plates (Corning ® ) in complete medium overnight until 100% confluence; sterile 200 μL pipette tips (Corning ® ) to create uniform wounds; washed × 3 with PBS to remove debris; maintained in serum-free medium (Gibco™, 11058021); initial wound image (0 h) captured using a phase-contrast microscope (Nikon Eclipse Ti); wound closure monitored every 24 h; wound area measured using ImageJ v1.53t with the Wound Healing Tool plugin (NIH); and wound edge movement distance calculated.

[0070] Transwell: cells (1 × 10 4 ) were seeded into Matrigel-coated (invasion) or uncoated (migration) chambers. Migrated / invaded cells were stained with 0.1% crystal violet and counted.

[0071] 2.5 Immune escape detection

[0072] Peripheral blood mononuclear cells (PBMCs) were obtained from ZEYI Primary Cells (Catalog No. PB00010C; Lot No. 2024071201) with a cell line authentication certificate. All experimental procedures involving human biological material were performed in accordance with the Declaration of Helsinki, and written informed consent was obtained from the donors before blood collection. PBMCs were cocultured with PAAD cells for 24 hours. Proliferation was assessed using CCK-8 assay, and PD-L1 expression after coculture was analyzed by Western blot (anti-PD-L1 antibody, 1:1000).

[0073] Western blot analysis

[0074] SDS-PAGE electrophoresis:

[0075] Gel preparation: 10% SDS-PAGE gel: Load 20 μg of protein lysate per lane for the following groups: empty vector control (NC), NT5M overexpression (NT5M-OE), and NT5M-OE + CXCL8-OE recovery group; and 4 μL of prestained protein marker (Thermo Fisher, catalog #26616).

[0076] 15% SDS-PAGE gel: 20 μg protein lysate was loaded into each lane and divided into two groups: empty vector control (NC) and CXCL8 overexpression (CXCL8-OE) groups; 4 μL prestained protein marker (G-CLONE, Cat# MP3245) was added.

[0077] Electrophoresis conditions: initial voltage: 80 V for 30 min, then 120 V for 90 min.

[0078] Protein transfer: Proteins were transferred from the gel to a 0.22 μm PVDF membrane using a semi-dry transfer system.

[0079] Transmission conditions: constant current 300mA for 60 minutes.

[0080] Blocking: Block the membrane with 5% BSA (bovine serum albumin) in TBST for 1 hour at room temperature. Antibody incubation: anti- (All purchased from Wuhan Tri-Eagle Pharmaceuticals): anti-NT5M primary antibody (dilution: 1:500, Cat No. 20765-1-AP); anti-CXCL8 primary antibody (dilution: 1:3,000, Cat No. 98137-1-RR); anti-PD-L1 primary antibody (dilution: 1:4,000, Cat No. 66248-1-Ig); and anti-α-tubulin primary antibody (dilution: 1:5,000, loading control, Cat Nos. 39527, 39528). The membrane was incubated with the primary antibodies overnight at 4°C, followed by three 5-minute washes in TBST. The secondary antibody: HRP-conjugated goat anti-rabbit IgG (UE Biologicals, 1:5,000) was incubated for 1 hour at room temperature.

[0081] Signal detection: Protein bands were detected using ECL Plus chemiluminescent substrate. Images were acquired using the ChemiDoc imaging system.

[0082] Membrane stripping and reprobing: Stripping buffer: Strip the membrane with Kanglin Western Blot Stripping Buffer for 20 minutes at room temperature with gentle agitation.

[0083] Wash: Wash the membrane 3 x 5 min with TBST to remove residual stripping buffer.

[0084] Verification of complete stripping: Reincubate the membrane with HRP-conjugated secondary antibody (UE Biologicals, 1:5,000) for 1 hour at room temperature; expose the membrane to ECL substrate and image to confirm the absence of residual chemiluminescent signal.

[0085] Reblock and reprobing: Block the membrane again with 5% BSA in TBST for 1 hour; repeat the primary antibody incubation conditions as above.

[0086] 2.7 Real-time quantitative PCR (qPCR)

[0087] RNA extraction and reverse transcription:

[0088] RNA Isolation: Total RNA was extracted from PANC-1 and BxPC-3 using the Beyotime RNAiso Plus Total RNA Isolation Kit (Beyotime Biotech) according to the manufacturer's instructions. RNA concentration and purity were determined spectrophotometrically (A260 / A280 ratio >1.8).

[0089] Reverse transcription: First-strand cDNA was synthesized from 1 μg of total RNA using the Goldenstar RT6 cDNA Synthesis Kit (Beijing Qingke Biotechnology Co., Ltd.), which integrates genomic DNA removal and reverse transcription in a single reaction.

[0090] The reaction conditions were: 42°C for 15 min (reverse transcription) followed by 85°C for 5 s (enzyme inactivation).

[0091] qPCR amplification:

[0092] Primers: Gene-specific primers were designed using Primer-BLAST (NCBI) and synthesized by Qingke Biotechnology [NT5M: 87 bp-F—AAGGCCATCAGCATTTGGGA (SEQ ID NO: 1), 87 bp-R—GGCCATCTCCTTGACAGCTT (SEQ ID NO: 2); IL-8: 170 bp-F—TGGCAGCCTTCCTGATTTCT (SEQ ID NO: 3), 170 bp-R—AATTTCTGTGTTGGCGCAGT (SEQ ID NO: 4); PDL1: 120 bp-F—ATTTGCTGAACGCCCCATAC (SEQ ID NO: 5), 120 bp-R—TCCAGATGACTTCGGCCTTG (SEQ ID NO: 6); GAPDH: 158 bp-F—GTCAAGGCTGAGAACGGGAA (SEQ ID NO: 7), 158 bp-R—AAATGAGCCCCAGCCTTCTC (SEQ ID NO: 8)]. NO: 8)], and primer specificity was confirmed by melting curve analysis.

[0093] Reaction setup: qPCR was performed on a Bio-Rad CFX96 Real-Time PCR System using TB Green Premix ExTaq II (Takara Bio, Japan, catalog #RR820A). Each 20 μL reaction contained: 10 μL TB Green premix (2×); 0.8 μL forward primer (10 μM); 0.8 μL reverse primer (10 μM); 2 μL cDNA template (1:10 dilution); and 6.4 μL nuclease-free water.

[0094] Thermal cycling conditions: initial denaturation: 95°C, 30 seconds, 40 cycles; denaturation: 95°C, 5 seconds; annealing / extension: 60°C, 30 seconds (plate reading); melting curve analysis: 65°C to 95°C, increasing by 0.5°C every 5 seconds.

[0095] Data Analysis: Threshold cycle (Ct) determination: Automatic baseline and threshold settings were applied using Bio-Rad CFX Maestro software (v2.3).

[0096] Normalization: Target gene expression was normalized to the geometric mean of two housekeeping genes (e.g., GAPDH and β-actin) using the ΔΔCt method.

[0097] Statistical analysis: Data are presented as mean ± SD of triplicate biological replicates.

[0098] 2.7 ELISA quantification of granzyme B and perforin

[0099] To assess CD8+ T cell activation, granzyme B (GZMB) and perforin (PFN) levels were measured in co-culture supernatants using commercial ELISA kits according to the manufacturer's protocol. Briefly, PBMCs isolated from healthy donors were co-cultured with PANC-1 or BxPC-3 cells (effector:target ratio 10:1) in RPMI-1640 medium supplemented with 10% FBS for 48 hours. Supernatants were collected after centrifugation (1,000 × g, 10 minutes, 4°C) and stored at -80°C until analysis. For GZMB detection, 100 μL of supernatant was added to pre-coated wells and incubated with the detection antibody for 2 hours at 37°C. PFN levels were quantified similarly, with absorbance measured at 450 nm using a SpectraMax i3x microplate reader. A standard curve was generated using the recombinant human protein provided in the kit, and concentrations were normalized to total protein content quantified by BCA assay. All samples were analyzed in triplicate, with interassay variability <10%.

[0100] 3. Statistical Analysis

[0101] Data are presented as mean ± SD. Statistical significance was determined using a two-tailed Student's t-test (two groups) or one-way analysis of variance (multiple groups). Survival analysis was performed using the log-rank test and the Cox proportional hazards model. P < 0.05 (*), P < 0.01 (**), and P < 0.001 (***) were considered significant. All analyses were performed in R v4.1.2 or GraphPad Prism 9.

[0102] The present invention is described in further detail below in conjunction with specific embodiments:

[0103] Example 1: Screening for the most potential prognostic genes in PAAD

[0104] We first identified 356 genes related to nucleotide metabolism from the GeneCards database and analyzed their differential expression in PAAD and normal tissues using the GSE15471 dataset. Among them, 126 genes showed significant differential expression in PAAD, of which 77 genes showed upregulation and 49 genes showed downregulation (e.g. Figure 1 This provides insights into the underlying molecular mechanisms of dysregulated nucleotide metabolism in PAAD and may help identify new therapeutic targets.

[0105] Then, UniCox analysis was performed to identify independent prognostic factors among these 126 differentially expressed genes. Using a p-value threshold of <0.05, 45 genes were identified that were significantly associated with patient prognosis (e.g. Figure 1These findings highlight the potential prognostic value of nucleotide metabolism-related genes in PAAD and provide a basis for further investigation of their clinical relevance.

[0106] Subsequently, GeneMANIA was used to explore the interactions between prognosis-related genes at the gene level. The analysis showed that most of these genes exhibited physical interactions and co-expression relationships, indicating potential functional associations. In addition, GeneMANIA results showed that the differentially expressed prognosis-related genes and their related molecules were mainly involved in nucleoside metabolism, pyrimidine-containing compound metabolism, glycosylation, nucleobase-containing small molecule catabolism, nucleoside catabolism, and glycosylation (e.g., Figure 2 These findings demonstrate that the identified genes are indeed related to nucleotide metabolism and participate in key metabolic pathways, further supporting their biological relevance in PAAD and highlighting their potential as therapeutic and prognostic biomarkers.

[0107] To further investigate the functional interactions between these prognosis-related genes, protein-protein interaction (PPI) analysis was performed. The results revealed an interaction network in which genes were ranked according to their degree of interaction. Genes with higher degrees of interaction were displayed in dark red, indicating they had stronger interaction potential. Furthermore, detailed interaction degrees were further visualized using histograms. This analysis provided additional insights into the molecular mechanisms of PAAD, and the top 10 genes were selected as potential therapeutic targets (e.g., Figure 1 (as shown in C in the figure).

[0108] Finally, Lasso regression analysis was performed on these prognosis-related genes, and 14 key genes were identified for further study (e.g. Figure 2 (B in Figure 3). Lasso regression (Least Absolute Shrinkage and Selection Operator) helps eliminate redundant or irrelevant genes while retaining genes with the highest predictive value. The 14 genes identified are key candidate genes for the underlying mechanisms of PAAD and may serve as potential prognostic biomarkers or therapeutic targets.

[0109] To identify the most likely prognostic genes, a comprehensive analysis was performed by taking the intersection of three key gene sets (e.g. Figure 2Figure 3 (C): (1) Top 10 genes ranked by interaction score in the GeneMANIA network; (2) Top 10 genes with the highest degree of interaction in the PPI network; (3) 14 key genes identified by Lasso regression analysis. The results showed that two genes (NT5M and NT5C3B) appeared consistently in all three selection criteria, indicating their key role in PAAD progression and their potential as robust prognostic biomarkers or therapeutic targets.

[0110] Example 2: Single-cell analysis of NT5M and NT5C3B

[0111] To further identify the best candidate genes, single-cell analysis of NT5M and NT5C3B was performed. First, the GSE111672 dataset was used to construct a cell atlas of PAAD. By applying unsupervised clustering, the cells were divided into 19 different clusters (such as Figure 3 ), which were subsequently annotated into 11 different cell types (as shown in A in Figure 3 The scatter plots were then used to visualize marker genes associated with different cell types, providing insights into the distinct molecular characteristics of each cell population (e.g., Figure 3 In addition, for a more intuitive view, the proportions of different cell types in the three patients were analyzed and visualized. Among them, ductal cells accounted for the largest proportion, followed by malignant cells (as shown in C). Figure 3 (D in Figure 3). To systematically investigate the dynamics of intercellular communication, different cell clusters (X-axis: receptor cells; Y-axis: ligand cells) were analyzed. Quantitative cell-cell interaction analysis revealed a distinct unidirectional communication pattern: fibroblast cluster C17 (Y-axis: ligand cells) exhibited significantly increased interaction frequency with malignant clusters C2, C6, C8, and C9 (X-axis: receptor cells), accounting for 82% of total fibroblast-malignant cell interactions. Notably, reciprocal interactions between these malignant clusters, with fibroblast C17 acting as a ligand source (X-axis) for the receptor cell (Y-axis), were barely detectable (<5% of baseline levels). This asymmetric signaling hierarchy suggests that fibroblasts primarily initiate pro-tumor signaling through specific ligand-receptor pairs (e.g., CXCL12-CXCR4), while malignant cells exhibit a limited ability to reciprocally regulate stromal behavior.

[0112] Since TME consists of three main components: cancer cells, immune cells, and stromal cells, in order to understand the relationship between candidate genes and TME, it is necessary to focus on analyzing these cell types. Therefore, the clusters were re-annotated into four categories: immune cells, malignant cells, stromal cells, and other cells. Then, the expression patterns of NT5M and NT5C3B in these cell types were examined using the cell atlas (e.g., Figure 4 Results showed that NT5M expression was significantly lower in malignant cells but higher in immune and stromal cells, suggesting a possible role in immune regulation or cancer prognosis. In contrast, NT5C3B expression remained relatively consistent across all three cell types, with the exception of stromal cells. To investigate the mechanisms underlying the prevalent low expression of NT5M in PAAD, mutational landscape analysis of NT5M and its NT5C3B counterpart was performed. Results revealed that only 2 of 178 samples (1.1%) harbored missense mutations in either NT5M or NT5C3B, all of which were classified as variants of uncertain significance (VUS) by the ClinVar database. This extremely low mutation frequency suggests that the low expression of NT5M is more likely attributable to non-mutational mechanisms, such as epigenetic modifications including microRNA regulation or promoter hypermethylation. Given its limited research and demonstrated potential as a prognostic biomarker for PAAD, NT5M was selected as the final candidate gene for further investigation.

[0113] Example 3: Determination of the expression pattern, chromosomal location and structural characteristics of NT5M

[0114] According to univariate Cox regression analysis (such as Figure 1 As shown in Figure 3B, NT5M was identified as a protective factor in PAAD (HR = 0.783, 95%, CI: 0.679–0.903, p < 0.001). In addition, according to the GSE15471 dataset, NT5M expression in tumor tissue mRNA was significantly decreased compared with normal tissue in PAAD (p < 0.001) (as shown in Figure 3B). Figure 5 Histological examination (HE staining) showed that NT5M was expressed in normal pancreatic tissue, but no expression was detected in pancreatic cancer tissue (as shown in Figure 2A). Figure 5 These results support the view that NT5M plays a protective role in PAAD, with low expression associated with poor prognosis, suggesting that tumors may implement some mechanisms to suppress NT5M expression.

[0115] For the pan-cancer analysis, NT5M was specifically associated with a reduced risk of PAAD, as the p-value for PAAD was the only one < 0.05 among the analyzed cancers. In addition, NT5M showed different effects in various other cancer types, including UVM, COAD, KIRC, SKCM, and BLCA (e.g., Figure 6 In addition, NT5M expression was significantly decreased in TP53 mutant PAAD patients compared with TP53 non-mutant PAAD patients (p < 0.001) (as shown in Figure 5A). Figure 6 Figure 3 (B), indicating that NT5M expression may be decreased with TP53 mutation.

[0116] To describe the genomic structure of NT5M, the RCircos package (version 3.8) was used to visualize its chromosomal location. Analysis showed that NT5M (also known as mdN, dNT2, or dNT-2) is genomically located at 17p11.2, within the Smith-Magenis syndrome critical region (chr17: 17,083,101-17,102,287, GRCh38 / hg38). The predicted molecular weight of the mature protein is approximately 25.9 kDa and it functions as a monomeric enzyme with no known multimerization, distinguishing it from the dimeric structure of membrane-bound NT5E / CD73. Structurally, NT5M is composed of 38% α-helix (mainly distributed at the N-terminus), 22% β-sheet (forming the core catalytic domain), and 40% random coil (mainly in the flexible loop region) (e.g. Figure 6 The narrow substrate channel (diameter ~8 Å) restricts its binding to deoxynucleotides (such as dTMP and dUMP), distinguishing its function from ribonucleotidases such as NT5C. In addition, the surface-exposed basic residue cluster (Lys 37 、Lys 41 and Arg 65 ) may contribute to its participation in the mitochondrial matrix protein interaction network.

[0117] Example 4: Evaluation of the association between NT5M expression and clinical characteristics

[0118] Using data from the TCGA database, we investigated the relationship between NT5M expression and clinical characteristics of PAAD. The correlation heatmap showed that NT5M expression was significantly correlated with tumor stage and T classification, while no significant differences were observed in age, sex, or M classification (e.g., Figure 7 These results were further confirmed by histogram analysis (see Figure A). Figure 7 (As shown in BG in the figure). Specifically, NT5M expression was significantly elevated in stage I and lower in stages II-IV (p=0.0012). Furthermore, NT5M expression was elevated in T1-2 tumors and decreased in T3-4 tumors (p=0.003). These findings indicate that NT5M is highly expressed in early-stage tumors and downregulated in late-stage tumors, further supporting its role as a tumor suppressor gene in PAAD.

[0119] To evaluate the clinical significance of NT5M, we first analyzed its prognostic value using TCGA-PAAD cohort data (n=178). Kaplan-Meier survival analysis showed that patients with higher NT5M expression showed significantly longer overall survival (OS; p=4×10 -4 ) and disease-free survival (DFS; p = 0.00023) (e.g. Figure 8These results indicate that decreased NT5M expression is closely associated with poor clinical outcomes in pancreatic cancer patients. To validate this observation, external validation was performed using the GEPIA database. Consistent with the TCGA analysis, GEPIA data showed that decreased NT5M expression was significantly associated with decreased OS (p=0.00016) and DFS (p=0.0088) (see Figure 2). Figure 8 Collectively, these multi-database analyses establish NT5M as a robust prognostic biomarker in pancreatic cancer, with low expression levels consistently predicting poor patient outcomes.

[0120] To identify NT5M as an independent prognostic factor for PAAD, a multivariate Cox regression analysis was performed using the TCGA cohort. Figure 9 As shown in Figure A, multivariate Cox regression analysis showed that NT5M expression (HR=0.785, 95%, CI=0.672–0.918 p=0.0024) and age (HR=1.027, 95%, CI=1.0058–1.048, p=0.0118) were independent predictors of OS. Notably, compared with age, NT5M showed a stronger prognostic value (lower HR and tighter confidence interval), indicating that it has a stronger predictive ability for the prognosis of PAAD patients. To fully evaluate the prognostic value of NT5M, ROC curve analysis was performed, which showed a moderate predictive ability with an AUC of 0.655 (as shown in Figure A). Figure 9 Based on this, a clinical nomogram was developed that combined NT5M expression with key prognostic features to predict survival in patients with PAAD. This model demonstrated strong predictive accuracy, with 1-, 2-, and 3-year survival probabilities of 0.785, 0.366, and 0.262, respectively. This was further supported by the calibration curves, which showed good agreement between predicted and observed outcomes across all time intervals and were closely aligned with the 45° reference line (see Figure 2). Figure 9 (as shown in the CD in the ).

[0121] These results establish NT5M as both a reliable prognostic biomarker and an independent predictor of survival outcomes in PAAD. The consistent association between decreased NT5M expression and worse clinical outcomes, coupled with its superior predictive performance compared to conventional clinical parameters, highlights its potential clinical utility in risk stratification and treatment decision-making.

[0122] Example 5: Identification of DEGs between high and low NT5M expression

[0123] PAAD samples from the TCGA cohort were divided into two groups for analysis based on the median NT5M expression: high NT5M expression and low NT5M expression. A total of 818 differentially expressed genes (DEGs) were identified, including 709 upregulated and 109 downregulated genes, achieving statistical significance (adjusted p-value < 0.05, |log2FC| > 1). A heat map displays the top 100 differentially expressed genes (DEGs), including 50 upregulated and 50 downregulated genes. A volcano plot provides a global overview of all DEGs, specifically annotating NT5M.

[0124] To further understand the molecular mechanism of NT5M, the above DEGs were subjected to gene ontology (GO) and KEGG analysis using the 'clusterProfiler' R package. GO analysis showed that these DEGs were significantly enriched in multiple biological processes (BPs), including regulation of chemical synaptic transmission, regulation of transsynaptic signaling, regulation of membrane potential, signal release, regulation of single atomic ion transmembrane transport, insulin secretion, and synaptic organization (e.g. Figure 10 In terms of cellular components (CC), these genes are mainly related to distal axons, neuronal cell bodies, synaptic membranes, neuronal projection terminals, postsynaptic specializations, cation channel complexes, and axon terminals (e.g. Figure 10 Regarding molecular function (MF), these genes were significantly enriched in ion transmembrane transporter activity, single-atom ion-gated channel activity, gated channel activity, voltage-gated single-atom cation channel activity, single-atom cation channel activity, voltage-gated single-atom ion channel activity, voltage-gated channel activity, and asymmetric synapses (e.g. Figure 10 In addition, KEGG pathway analysis showed that these genes were significantly involved in pancreatic secretion, insulin secretion; adrenergic signaling in cardiomyocytes; protein digestion and absorption; neuroactive ligand-receptor interaction; dopaminergic synapse; cAMP signaling pathway; circadian rhythm entrainment, etc. (e.g. Figure 10 (as shown in D in the figure).

[0125] Additionally, gene set enrichment analysis (GSEA) was performed using DEGs between the high and low NT5M expression groups to identify significantly enriched GSEA-GO terms. Normalized enrichment scores (NES) were used to select the most significantly enriched terms. For the highly expressed DEGs, the top five most significantly enriched GSEA-GO terms were related to cation channel complexes, neuronal projection terminals, postsynaptic membranes, single-atom cation channel activity, and voltage-gated single-atom cation channel activity. These findings suggest that high NT5M expression may play a role in neuronal signaling and synaptic transmission, consistent with its involvement in synapse-related functions and ion transport. In contrast, DEGs with low NT5M expression were significantly enriched in biological processes such as digestion, terpenoid metabolism, xenobiotic metabolism, brush boundaries, and actin-based cell projection clusters. These processes are crucial for metabolic regulation and cell morphology, suggesting that low NT5M expression may be associated with metabolic pathways and cytoskeletal regulation.

[0126] In addition, GSEA analysis was performed using C2 terms and key pathways associated with highly expressed DEGs were identified. These DEGs were significantly enriched in pathways such as calcium signaling, dilated cardiomyopathy, neuroactive ligand-receptor interaction, primary immunodeficiency, and type 2 diabetes. These suggest that high NT5M expression may be involved in cardiovascular health, neurotransmission, and immune response, and may contribute to the pathophysiology of cardiovascular and metabolic diseases. On the other hand, DEGs with low NT5M expression were enriched in pathways related to cytochrome P450, steroid biosynthesis, and xenobiotic metabolism. These suggest that low NT5M expression may affect pharmacological processes and endocrine regulation, with potential effects on drug metabolism and hormone biosynthesis.

[0127] In the C7 immune-related pathway set, DEGs were enriched only in high expression: naive vs day 3 effector CD8+ T cells (up), naive B cells vs monocytes (up), B cells vs MDCs day 7 influenza vaccine (up), classically activated vs type 2 activated macrophages (down), and untreated vs galectin-1 treated DCs (down). These results suggest that high NT5M expression may be associated with immune cell differentiation, activation, and response to infection or inflammation.

[0128] Example 6: Evaluation of the relationship between NT5M and drug sensitivity

[0129] Since GSEA analysis suggested that low NT5M expression may affect drug metabolism, to further demonstrate this, we applied the "oncoPredict" R package and used the Genomics of Drug Sensitivity in Cancer (GDSC) as a training set to predict drug sensitivity to various chemotherapy drugs in patients with high and low NT5M expression. The results showed that NT5M expression was closely associated with drug sensitivity in pancreatic cancer patients, affecting the drug sensitivity of 20 drugs to varying degrees. Higher scores correlated with lower drug sensitivity. Based on this, it can be seen that the high NT5M expression group will increase the sensitivity of 16 drugs (Vorinostat, Mirin, RO-3306, AZD8055, BMS-536924, GSK269962A, AZD1208, AZD1332, Linsitinib, JAK1_8709, Sabutoclax, KRAS (G12C) Inhibitor-12, GSK2578215A, Telomerase Inhibitor IX, NVP-ADW742, BMS-754807), and reduce the sensitivity of 4 drugs (SCH772984, afatinib, sapitinib, trametinib). These data can provide guidance for the clinical application of pancreatic cancer.

[0130] Example 7: Evaluation of the relationship between NT5M expression, TME, and immunotherapy

[0131] Based on GSEA analysis of DEGs in the C7 immune-related pathway set, it was speculated that NT5M may be associated with the TME and immunity. The TME, composed of tumor cells, infiltrating immune cells, stromal cells, extracellular matrix molecules, and proinflammatory cytokines, plays an important role in tumor initiation, progression, and metastasis. Immune cells play a key role in the TME by participating in tumor-associated immune responses, which can significantly impact tumor development and patient prognosis. Therefore, CIBERSORT-based immune infiltration analysis was performed to describe the relative proportions of various immune cell subtypes in TCGA samples. The y-axis represents the relative percentage of immune cell type, while the x-axis corresponds to individual TCGA samples. Each bar in the figure is segmented to represent the fractional contribution of different immune cell subsets, providing a comprehensive overview of the immune landscape in the analyzed cohort. Subsequently, the "ssGSEA" R package and Spearman test were applied to analyze the association between NT5M expression and the infiltration of 22 immune cell types. The results showed that the low NT5M expression group had higher scores for immature B cells, T cells, and regulatory T cells. This suggests that low NT5M may be associated with immunosuppression and immune escape in the TME (e.g. Figure 11Then, the “estimate” R package was used to calculate the immuneScore, StromalScore, and ESTIMATEScore of PAAD samples. The results showed that the StromalScore and ESTIMATEScore of the high NT5M expression group were significantly higher than those of the low NT5M expression group (p < 0.05) (as shown in Figure 2). Figure 11 In addition, multicellular immune infiltration analysis showed that NT5M was significantly positively correlated with anti-tumor immune cells such as T cell CD8+ (p=0.040), B cell naive (p<0.001), and T cell follicular helper (p=0.009), while it was negatively correlated with resting dendritic cells (p=0.034) and macrophage M1 (p=0.003). Figure 12 As shown in A). This indicates that NT5M is associated with immune cell activation. Spearman test again provided this result: B cell naive (R = 0.29, p = 9.3*10-5); follicular helper T cells (R = 0.20, p = 0.0088); T cell CD8+ (R = 0.15, p = 0.04); dendritic cell resting (R = 0.16, p = 0.034; macrophage M1 (R = -0.22, p = 0.0028) (as shown in A). Figure 12 To explore how NT5M affects immunotherapy (mainly regarding CTLA4 and PDL1), the immunophenotypic score (IPS) was used to predict immune response, and it was found that low NT5M expression was associated with negative CTLA4 and PDL1 treatment (as shown in Figure BF). Figure 11 Then, the correlation heat map between NT5M and some conventional immune checkpoints is given (as shown in C in Figure 2). Figure 13 ), NT5M was found to be positively correlated with LAG3, TNFRSF4, BTLA, and IDO2, and negatively correlated with CD40, CD47, CD80, CD86, and HAVCR2.

[0132] Example 8: Evaluation of the ability of NT5M to regulate proliferation, migration and invasion of PAAD cells

[0133] To further investigate the role of NT5M in PAAD, its functional effects on PAAD cells were evaluated in vitro. NT5M mRNA expression levels in different PAAD cell types were obtained from the Human Protein Atlas database (Cell Atlas). Given the relatively low expression of NT5M in PAAD, PANC-1 and BxPC-3 cells were selected in this example for NT5M overexpression and functional experiments. NT5M-cDNA was constructed, and the overexpression efficiency was verified using quantitative real-time PCR (qRT-PCR) and Western blot (WB) analysis (e.g., Figure 14 To evaluate the effect of NT5M overexpression on cell proliferation, 5-ethynyl-2′-deoxyuridine (EdU) incorporation assay, cell counting kit-8 (CCK-8) assay, Ki-67 immunofluorescence staining, and plate colony formation assay were used.

[0134] EdU incorporation assay: Actively proliferating cells were labeled with EdU, and the nuclei were stained with DAPI to determine the total cell number. The proliferation rate was calculated as the ratio of EdU-positive cells to the total number of cells. The results showed that NT5M overexpression significantly reduced the proliferation rate of PANC-1 and BxPC-3 cells (e.g., Figure 14 CD), indicating that NT5M overexpression has a potential inhibitory effect on the growth of PAAD cells.

[0135] Ki-67 immunofluorescence staining: Ki-67 is a well-known cell proliferation marker and was stained with red fluorescence, while DAPI was used to mark the cell nucleus and the mean gray value was used to quantify Ki-67 expression. The results showed that NT5M overexpressing cells showed a significant decrease in Ki-67 expression, indicating a slower proliferation rate (e.g. Figure 14 EF in Figure 1).

[0136] CCK-8 assay: CCK-8 assay, which measures cell viability and proliferation, further confirmed that NT5M overexpression significantly inhibited PAAD cell growth in both PANC-1 and BxPC-3 cells (e.g., Figure 15 (as shown in A in the figure).

[0137] Colony formation assay: PAAD cells overexpressing NT5M formed significantly fewer colonies, further supporting the finding that NT5M inhibits PAAD cell proliferation (e.g. Figure 15 BC in Figure 1).

[0138] Results showed that NT5M overexpression significantly inhibited PAAD cell proliferation. Reduced EdU incorporation and Ki-67 expression indicated decreased DNA synthesis and cell cycle progression, while CCK-8 and colony formation assays further confirmed the growth inhibitory effect of NT5M in PAAD cells. These results suggest that NT5M may function as a tumor suppressor in PAAD and may provide a new therapeutic target for intervention.

[0139] Furthermore, wound healing and Transwell assays were performed to evaluate its effects on cell migration and invasion. Wound healing assay: Wound healing assay was performed to evaluate the migration ability of PANC-1 and BxPC-3 cells after NT5M overexpression. The results showed that the migration rate of NT5M-overexpressing cells was significantly reduced compared with the control group, indicating that NT5M inhibits PAAD cell motility (e.g., Figure 15 DE in Figure 1).

[0140] Transwell migration and invasion assay: To further evaluate the effect of NT5M on cell motility, Transwell assays with and without Matrigel coating were performed. The results showed that the migration and invasion abilities of PAAD cells overexpressing NT5M were significantly lower than those of control cells (e.g. Figure 15 FG), indicating that NT5M overexpression inhibits the migration and invasion behaviors of PAAD cells.

[0141] These findings suggest that NT5M inhibits PAAD cell migration and invasion, potentially acting as a negative regulator of tumor progression. Given that metastasis is a key contributor to PAAD-related mortality, understanding the underlying mechanisms of NT5M-mediated inhibition of tumor cell motility may provide new insights into potential therapeutic strategies. Further studies are needed to elucidate the molecular pathways by which NT5M exerts its anti-migratory and anti-invasive effects in PAAD.

[0142] Example 9: NT5M reverses pancreatic cancer immune escape via the NT5M / CXCL8 / PD-L1 axis

[0143] The study of the role of NT5M in regulating immune evasion mechanisms in PAAD has revealed key functional interactions between tumor cells and immune components. In this example, by co-culturing PANC-1 and BxPC-3 cells with peripheral blood mononuclear cells (PBMCs), it was found that NT5M overexpression profoundly altered tumor immune dynamics. CCK-8 viability assays showed that under conditions of high NT5M expression, cancer cell mortality almost doubled (77.1% vs 48.2% in PANC-1, p<0.01; 92.7% vs 56.3% in BxPC-3, p<0.001), directly indicating that NT5M reversed immune evasion (e.g., Figure 16This enhanced cytotoxicity was associated with robust CD8+ T cell activation, as evidenced by elevated levels of granzyme B (GZMB) and perforin (PFN) in the co-culture supernatant (Figure ​Figure4A). Figure 16 To characterize the molecular basis of the immunomodulatory effects of NT5M, RNA sequencing was performed to compare NT5M overexpressing (OE) and control cells, and 49 differentially expressed genes enriched in immune checkpoint and cytokine signaling pathways (FDR < 0.05) were identified (see Figure 2). Figure 17 Among the top DEGs, CXCL8 emerged as a key candidate gene potentially associated with immune evasion. This finding is consistent with recent evidence demonstrating the ability of CXCL8 to regulate PD-L1 expression and reverse macrophage-mediated immunosuppression in non-small cell lung cancer. Given the established role of PD-L1 as a key immune checkpoint molecule, NT5M / CXCL8 / PD-L1 is hypothesized to be a regulatory axis. Western blot analysis confirmed that NT5M overexpression significantly downregulated CXCL8 and PD-L1 protein levels in PANC-1 and BxPC-3 cell lines (see Figure 2). Figure 16 D and Figure 17 Complementary qRT-PCR showed a parallel decrease in transcript levels (as shown in E). Figure 17 C), demonstrating the bidirectional regulation of NT5M on this pathway.

[0144] Determine causality:

[0145] CXCL8 gain-of-function: Overexpression of CXCL8 in NT5M-OE cells rescued PD-L1 expression at both mRNA and protein levels (e.g. Figure 17 (as shown in D in the figure).

[0146] CXCL8 rescue experiment: To determine causality, CXCL8 expression was restored in NT5M-OE cells using an overexpression plasmid. This intervention reversed the suppression of PD-L1 at both mRNA (3.2-fold increase in PANC-1 and 2.6-fold increase in BxPC-3 compared to NT5M-OE alone) and protein levels (e.g., Figure 17 C and E in the figure).

[0147] These complementary experiments ultimately demonstrated that NT5M regulates PD-L1 through CXCL8-mediated signaling. The identified NT5M / CXCL8 / PD-L1 axis provides mechanistic insights into how NT5M overexpression reverses tumor immune evasion and provides a potential therapeutic target for pancreatic cancer immunotherapy.

[0148] Finally, it should be emphasized that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Application of NT5M in the preparation of drugs for treating pancreatic cancer.

2. The use according to claim 1, characterized in that The NT5M is located on chromosome 17 and is composed of 38% α-helix, 22% β-sheet and 40% random coil.

3. The use according to claim 1, characterized in that The medicine comprises a substance causing overexpression of the NT5M gene.

4. The use according to claim 1, characterized in that The drug can inhibit the proliferation, migration and invasion of pancreatic cancer cells.

5. The use according to claim 1, characterized in that The drug can inhibit the reverse immune escape of pancreatic cancer cells.

6. Use of a reagent for detecting NT5M gene expression level in the preparation of a pancreatic cancer prognosis detection product.

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