Biomarker and kit for precise stratification of gastric cancer as well as application and preparation of biomarker and kit

Abstract

The invention provides a biomarker for precise stratification of gastric cancer, a kit and application and a preparation thereof, and the biomarker for precise stratification of gastric cancer is a combination of serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2. Researches find that a plurality of splicing variants (V1, V2, V3 and V4) expressed by PRSS3 co-express V1 and V2 in gastric cancer, although both inhibit tumor growth, V1 inhibits tumor malignant metastasis and V2 promotes tumor invasion and metastasis; if the whole PRSS3 is used as a marker, prognosis cannot be realized (gastric cancer survival is distinguished), and the difference of V1-V2 or the ratio of V1 to V2 can be well prognosed, so that the difference of splicing variants which are expressed in the gene and have functional activity or the difference expression of the ratio substituted gene is put forward to be used as a more accurate target.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and more specifically to biomarkers, reagent kits, and their applications and formulations for precise stratification of gastric cancer. Background Technology

[0002] Gastric cancer (GC) is one of the most common gastrointestinal malignancies, exhibiting significant cellular heterogeneity, characterized by increasingly well-defined intratumor heterogeneity (ITH) at both the genetic and epigenetic levels. ITH plays a crucial role in enhancing cellular phenotypic diversity, driving tumor progression, and influencing treatment resistance and recurrence. It also presents a significant challenge in identifying potential targeted biomarkers for predictive diagnosis, prognostic assessment, patient stratification, and personalized treatment and prevention of gastric cancer.

[0003] Alternative splicing (AS) is a crucial process that generates multiple mRNA transcript variants (SVs) through splicing. Most human genes generate different protein isoforms through alternative splicing. These isoforms may exhibit related or even completely opposite biological functions, thereby enhancing cellular phenotypic plasticity and functional diversity. Alternative splicing is generally regulated by epigenetic mechanisms, including the association between aberrant splicing processes and DNA methyltransferase 1 (DNMTs) and other epigenetic regulators, such as ubiquitin-like protein 1 (UHRF1) containing PHD and a ring finger domain, an association that has been shown to be associated with tumorigenesis. Although the increased number of splice variants resulting from aberrant alternative splicing is considered another important characteristic of cancer, the role of gene splice isoforms in the heterogeneity of functional tumors remains unclear, hindering their clinical translation within the framework of prediction, prevention, and precision medicine.

[0004] Trypsinogen, the precursor of human trypsin, is a serine protease that plays a crucial role in proteolytic processes and diseases. Protease-based research methods have become an attractive tool in the field of precision medicine. Trypsinogen primarily participates in pathological processes such as inflammation and tumorigenesis through its protease activity, specifically by increasing permeability and degrading extracellular matrix proteins, thereby promoting tumor invasion and metastasis. Unlike the two main forms of trypsinogen—serine protease 1 and 2 (PRSS1 and PRSS2)—serine protease 3 (PRSS3), although present in lower amounts, is an indispensable subtype of trypsin due to its resistance to classical trypsin inhibitors.

[0005] PRSS3 contains eight exons and can produce four different isoforms (PRSS3-V1, -V2, -V3, -V4) through alternative splicing or transcription with a selective promoter. The earliest cloned transcript... PRSS3-V2 Encoding pancreatic trypsinogen-3 (also known as intermediate trypsinogen, MTG), this protein is deduced to contain 247 amino acids (aa) and is mainly found in human pancreatic tissue and pancreatic juice. PRSS3-V1 was named extrapancreatic trypsinogen 4 (TRY4), and because it was first discovered in human brain tissue, it is also known as the brain type or isoform A. PRSS3-V1 The splicing variant expressed PRSS3 isoform 1 (PRSS3-V1) / TRY4 contains an unconventional leader sequence of 304 amino acids. PRSS3-V3 and PRSS3-V1 While sharing the same transcription start site (TSS), PRSS3-V3 (isoform B / trypsinogen IV) contains an in-frame substitution exon following the first exon and a downstream start codon, thus encoding a protein of 261 deduced amino acids. PRSS3-V4 (a novel isoform or trypsinogen 5), derived from a keratinocyte clone, is a restricted-expression isoform containing 240 amino acids and has been shown to participate in terminal differentiation of keratinocytes. Although numerous experiments have annotated PRSS3-V1–V4, their isoform-specific characteristics have not been systematically elucidated. Previous studies on PRSS3 have yielded conflicting and ambiguous results, suggesting that it may exist in a complex form involving the co-expression of multiple splice variants during inflammation and tumorigenesis.

[0006] Driven by chronic inflammatory pathways, PRSS3, with trypsin as its core, plays a role in the pathogenesis of gut-brain interaction disorders (including irritable bowel syndrome (IBS), pancreatitis, and inflammatory bowel disease (IBD)) and cancer. Its function is closely related to the protease-activated receptor (PARs) / G protein signaling pathway or the nuclear factor-κB (NF-κB) pathway. However, due to the high sequence similarity among PRSS family members, elucidating the specific role of PRSS3 in complex microenvironments presents challenges. For example, its association with cetuximab resistance, phosphorylation-mimicking KRAS-related cell invasion, and intestinal epithelial cell differentiation in colorectal cancer makes it difficult to distinguish between different PRSS subtypes. Furthermore, the lack of reliable splice-specific detection methods and isoform-specific antibodies makes it difficult to accurately distinguish different transcripts and related protein isoforms of PRSS3 splice variants (PRSS3-SVs). For example, studies have reported that upregulation of PRSS3 is associated with metastasis in pancreatic and prostate cancers; similarly, in lung adenocarcinoma (LUAD), PRSS3 expression has been shown to promote tumor cell invasion and growth (analyzed to be possibly related to PRSS3 / MTG), predict metastasis in non-small cell lung cancer (NSCLC) (analyzed to be PRSS3 / trypsinogen IVb), and promote cell proliferation and survival in esophageal adenocarcinoma (analyzed to be PRSS3 / trypsin 3). Furthermore, studies have shown that specific PRSS3 transcripts, such as PRSS3-V1 / Try4, are associated with gastric cancer cell metastasis or more readily promote the migration of tumor endothelial cells derived from ovarian and kidney tumors, compared to PRSS3-V2 / MTG. In these studies, expression levels were detected either at the mRNA level using quantitative polymerase chain reaction (qPCR) with universal primers targeting all transcripts, or through protein detection using pan-antibodies targeting all PRSS3 isoforms. Conversely, epigenetic silencing of PRSS3 has been observed in non-small cell lung cancer (NSCLC), hepatocellular carcinoma (HCC), bladder cancer, esophageal adenocarcinoma, and gastric cancer. Therefore, the expression pattern of the PRSS3 transcript appears to be differentially regulated, leading to functional heterogeneity. The expression and function of the PRSS3 transcript remain unclear, hindering translational research aimed at its application in irritable bowel syndrome, pancreatitis, and precision cancer medicine. Summary of the Invention

[0007] To address the aforementioned issues, this invention provides a biomarker for precise stratification of gastric cancer, wherein the biomarker is a combination of serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2.

[0008] In one embodiment, the biomarker is a biomarker used for the prognosis of gastric cancer patients.

[0009] In one embodiment, the present invention provides a kit for precise stratification of gastric cancer, the kit comprising reagents for detecting the level of a biomarker in the tissue to be tested, the biomarker being a combination of serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2.

[0010] In one embodiment, the kit is a kit for detecting the difference and / or ratio of expression levels of serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2 in a test tissue.

[0011] In one embodiment, the kit is a kit for the prognosis of gastric cancer patients.

[0012] In one embodiment, the present invention provides the use of a reagent for detecting a combination of serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2 in the preparation of a kit for precise stratification of gastric cancer.

[0013] In one embodiment, the present invention provides an agent for treating and preventing gastric cancer, said agent being able to increase the expression ratio and / or difference between serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2 in the human body.

[0014] Numerous studies have demonstrated the controversial role of differentially expressed genes (DEGs) in tumorigenesis. However, the impact of splice isoforms on phenotype remains largely unexplained. This study, using serine protease 3 (PRSS3) as a model, aimed to reveal how the epigenetic regulation of splice isoforms influences phenotypic plasticity in gastric cancer (GC) cells. Comprehensive analysis revealed unique co-expression patterns and functional roles of different PRSS3 isoforms in GC; furthermore, region-specific methylation of tumor-associated PRSS3 transcripts was significantly associated with metastasis and adverse clinical outcomes in GC patients. This study provides new evidence for the association between regulatory mechanisms and specific phenotypic alterations, and suggests that splice isoforms hold greater potential for optimizing precision medicine strategies compared to differentially expressed genes.

[0015] Currently, the debate surrounding the expression and functional correlation of mRNA isotypes continues. Due to the high sequence similarity among different splicing variants, many studies suggest that they are unlikely to produce functionally diverse isotypes. Furthermore, a widely accepted view is that in specific tissue types, only one splicing variant plays a major role. Therefore, to date, most studies have focused on single isotypes, without systematically analyzing the co-expression patterns among multiple transcripts or isotypes of the same gene. In recent years, advancements in multi-omics technologies have not only enabled computational prediction of gene splicing isotypes but also provided experimental support for verifying their biological functions. These studies have found that aberrant splicing can produce functionally aberrant isotypes, some of which exhibit opposite phenotypic effects in cancer cells. PRSS3 is a typical example; its expression and functional significance in cancer remain controversial: some studies suggest it is upregulated as an oncogene, while others find it is epigenetically silenced. This contradiction may stem from insufficient research on the overall expression profile of PRSS3 (especially its splicing isotypes). By analyzing the Encyclopedia of Cancer Cell Lines (CCLE) and The Cancer Genome Atlas (TCGA) datasets, we found differential co-expression patterns of different PRSS3 subtypes in gastric cancer, and further validated these results using splice-specific quantitative polymerase chain reaction (qPCR) and subtype-specific antibodies.

[0016] This study is the first to compare the functions of four PRSS3 transcripts in gastric cancer. The results showed that although all PRSS3 transcripts exerted anti-proliferative effects on gastric cancer cells by inhibiting the nuclear factor-κB (NF-κB) signaling pathway (an inflammatory pathway often associated with tumorigenesis), their effects on the migration ability of gastric cancer cells were diametrically opposed, and this difference was correlated with differences in the expression of matrix metalloproteinases (MMPs)—PRSS3-V1 or PRSS3-V3 showed inhibitory effects, while PRSS3-V2 or PRSS3-V4 showed promoting effects. Matrix metalloproteinases and serine proteases play key roles in cancer invasion and metastasis because they can degrade the extracellular matrix, thereby promoting the spread of cancer cells to surrounding tissues. Our results indicate that these proteases can significantly participate in extracellular matrix remodeling and influence key biological processes such as cell migration, invasion, and metastasis through interaction with matrix metalloproteinases; furthermore, they may also promote the release of endogenous factors, thereby supporting angiogenesis and metastatic colonization. In this process, the anti-proliferative properties of PRSS3-V2 may help with early tumor metastasis and colonization, but its abnormal expression in late-stage tumors may drive the metastasis process through cancer cells themselves or adjacent stromal cells.

[0017] Further analysis of the TCGA dataset revealed that PRSS3-V4 was absent and PRSS3-V3 expression was low in gastric cancer samples. PRSS3 expression was primarily determined by the co-expression of PRSS3-V1 and PRSS3-V2. Although the expression patterns of PRSS3 transcripts in gastric cancer tumors are diverse during clinical progression, the tumor suppressor subtype PRSS3-V1 and the metastasis-associated subtype PRSS3-V2 exhibit a dynamic co-expression pattern. Due to the co-expression of the tumor suppressor subtype PRSS3-V1 and the metastasis-associated oncogenic subtype PRSS3-V2 in gastric cancer patient samples, no significant correlation was found between PRSS3-V1 expression (including PRSS3-V2 expression) or PRSS3-V2 expression (including PRSS3-V1 expression) and clinical outcomes. This suggests that simultaneous high expression of PRSS3-V1 (PRSS3-V1) and high expression of PRSS3-V2 (PRSS3-V2) are not significant. High ) and PRSS3-V2 high expression (PRSS3-V2 High There may be effects that ultimately cancel each other out. However, PRSS3-V1 is highly expressed and PRSS3-V2 is lowly expressed (PRSS3-V1...). High V2 Low The subgroup of patients with high PRSS3-V2 expression (equivalent to PRSS3-V1 minus the effect of functional PRSS3-V2) had significantly better clinical outcomes; conversely, the subgroup with high PRSS3-V2 expression and low PRSS3-V1 expression (PRSS3-V1) had significantly better clinical outcomes. Low V2 High The subgroup of patients who expressed only PRSS3-V1 had significantly worse clinical outcomes. These results not only highlight the prognostic value of the relative expression levels of these two subtypes, but also indicate that the expression difference value (V1-V2) between the two provides a more precise distinguishing ability for gastric cancer stratification.

[0018] In summary, under the regulation of the tumor microenvironment, functional subtypes endow proteases with pleiotropic effects on cancer cells. However, the complex synergistic interactions between protease subtypes and the simultaneous release of matrix metalloproteinases pose challenges to assessing PRSS3-regulated metastatic processes in vitro (and even in vivo). This study confirms that multiple splicing variants of PRSS3 (V1, V2, V3, and V4) co-express V1 and V2 in gastric cancer. Although both inhibit tumor growth, V1 inhibits malignant metastasis while V2 promotes tumor invasion and metastasis. Using PRSS3 as a whole as a marker cannot predict (distinguish gastric cancer survival), while the difference between V1 and V2 or the V1 / V2 ratio can provide a good prognosis. Therefore, we propose using the difference and / or ratio of functionally active splicing variants expressed in genes as a more precise target instead of differential gene expression.

[0019] Furthermore, we discovered that the differential expression of PRSS3 splice variants is the molecular basis for the inconsistent expression and function in different tumors and even within the same tumor. This molecular basis (differential expression of splice variants) is tissue-specific and is epigenetically regulated (by differential methylation of its DMR). This study reveals that UHRF1 / DNMT1-mediated differential methylation of the differential methylation region (DMR) is a regulatory mechanism of the PRSS3 transcript, with intragenomic differential methylation of the differential methylation region (iDMR) having a particularly significant silencing effect on PRSS3-V1. Notably, this is related to the low expression of PRSS3-V1 (PRSS3-V1...). Low iDMR methylation associated with PRSS3-V1 was significantly associated with poor patient survival and higher metastatic potential, suggesting that methylation-driven alternative splicing plays a role in gastric cancer development. Importantly, 5-azacytidine (5-Aza-CR) treatment significantly and effectively restored PRSS3-V1 expression; while diallyl trisulfide (DATS) induced iDMR demethylation by reducing the binding affinity of the UHRF1 / DNMT1 complex in gastric cancer cells (possibly through downregulation of UHRF1 / DNMT1 expression), thereby promoting broader expression of the PRSS3 transcript. UHRF1 recruits DNMT1 to form the UHRF1 / DNMT1 complex, which is essential for DNA methylation maintenance and de novo methylation. Interestingly, the combined results of DNA methylation assays and CpG site-specific methylation knockout experiments showed that iDMR methylation selectively regulates PRSS3-V1 expression, highlighting the importance of the first intron in regulating alternative splicing and gene expression. Conversely, PRSS3-V2 is more broadly regulated by DMR, suggesting a possible distant enhancer-promoter regulatory mechanism. Given the early onset and potential reversibility of gene methylation, it holds promise as a target for early detection and clinical management. These findings indicate that tissue-specific epigenetic abnormalities of PRSS3 isoforms drive the formation of functional heterogeneity in gastric cancer cells, suggesting that these isoforms may serve as biomarkers for predicting prognosis and stratification in gastric cancer patients, thus contributing to the precision management of gastric cancer. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1This is a graph showing the expression of PRSS3 transcript variants in human gastric cancer, with a comparative analysis of the mRNA expression levels of the PRSS3 gene and its transcript variants in paired gastric cancer (GC) and normal liver tissue (n=32). The expression level of the PRSS3 transcript is presented in log2(TPM+1) form, and the dashed line represents X=Y (i.e., the reference line where the expression levels are equal). Figure 2 This is a graph showing the clinical relevance of trypsinogen 3 (PRSS3) transcripts in gastric cancer (GC), PRSS3-V1. High V2 Low The overall survival of patients in the group was significantly better than that of PRSS3-V1. Low V2 High Group of patients; Figure 3 This is a graph showing the clinical relevance of trypsinogen 3 (PRSS3) transcripts in gastric cancer (GC) tumor samples (n=372). Cox multivariate risk analysis showed that the difference in expression between PRSS3-V1 and PRSS3-V2 in gastric cancer (GC) tumors (i.e., V1 expression level minus V2 expression level, denoted as V1–V2) was significantly associated with improved patient survival. Figure 4 This study analyzed the association between PRSS3 differentially methylated region (DMR) methylation status and gastric cancer (GC) metastasis and clinical outcomes. PRSS3-V1 high expression / V2 low expression (PRSS3-V1...) High V2 Low Sixty-eight gastric cancer patients (n=1) were divided into hypermethylated and hypomethylated subgroups based on their CpG site methylation status. Kaplan-Meier analysis was used to analyze overall survival. The results showed a correlation between the expression of CpG_D and CpGp_E sites in the differentially methylated region (iDMR) of the PRSS3 genome and PRSS3-V1, suggesting that iDMR is associated with PRSS3-V1 expression. High V2 Low The expression regulation; Figure 5 This image shows the results of PRSS3 DMR methylation detection in gastric cancer tissue. In the validation cohort (n=243) of gastric cancer tissue, MSP analysis covered CpG_D iDMR methylation. Agarose gel electrophoresis showed a methylation frequency of 77% in gastric cancer tissue, confirming iDMR methylation of PRSS3-V1. High V2 Low Expression regulation. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions in this application, the present invention will be further described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application. The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] The following abbreviations are used in this application: AS: alternative splicing; CCLE: Cancer Cell Line Encyclopedia; ChIP: chromatin immunoprecipitation; DepMap: Cancer Dependency Map; DMR: differential methylation region; DATS: diallyltrisulfide; DNMT1: DNA methyltransferase 1; GC: gastric cancer; iDMR: intragenic differential methylation region; ITH: intratumor heterogeneity; MeDIP-qPCR: methylated DNA immunoprecipitation-qPCR; MMPs: matrix metalloproteinases; MS-qPCR: methylation-specific quantitative polymerase chain reaction. qPCR); MTG: trypsin-3 isoform 2 / mesotrypsinogen; PRSS3: serine protease 3; PRSS3High or PRSS3Low: high or low expression of PRSS3; PRSS3-V1 to PRSS3-V4: PRSS3 transcript variant 1 to 4; TCGA: The Cancer Genome Atlas; and UHRF1: ubiquitin-like protein 1 with PHD and ring finger domain 1.

[0024] I. Experimental Materials and Methods

[0025] The publicly available datasets used in this study were obtained from the following websites: - The Cancer Genome Atlas (TCGA, website: https: / / portal.gdc.cancer.gov); - The Cancer Dependency Map (DepMap) portal from the Cancer Cell Line Encyclopedia (CCLE) (URL: https: / / depmap.org / portal / download / all / , dataset: DepMap Public 20Q3 & CCLE2019). - The Broad Genome Data Analysis Center manages the Firehouse server for the Cancer Genome Atlas (TCGA) data (website: http: / / gdac.broadinstitute.org).

[0026] The oligonucleotide sequences and cDNA cloning information used to construct the recombinant plasmids in this application are shown in Table 1.

[0027] Table 1

[0028] Human immortalized gastric mucosal epithelial cell line GES-1 (ATCC number: 28200) and gastric cancer cell line NCI-N87 (ATCC number: CRL-5822) were purchased from the American Type Culture Collection (ATCC, USA). Gastric cancer cell lines BGC823, SGC7901, and MGC803, established in China, were purchased from the Shanghai Center for Biochips Tissue Bank (Shanghai, China).

[0029] Cells were cultured in a medium containing 90% DMEM (catalog number: 8122082, Gibco) and 10% fetal bovine serum (Fetal Bovine Serum, FBS; catalog number: 10099141, Gibco). TransSafe™ Mycoplasma Prevention Kit (catalog number: FM501-01, TransGene) was used to prevent mycoplasma contamination.

[0030] Cells were first cultured overnight to a low density (30% confluence), then treated with 5 μM 5-aza-2'-deoxycytidine (5-Aza-CR) (catalog number: 189825, Sigma) for 72 hours, with the medium changed every 24 hours. When the cell confluence reached approximately 60%, the cells were treated with diallyl trisulfide (DATS) (Shanghai Hefeng Co., Ltd., Shanghai, China).

[0031] The constructs of PRSS3-V1 to PRSS3-V4 were transfected into BGC823, SGC7901, and MGC803 cells, and then screened for 2 weeks with 10 μg / mL blastomycin (Catalog No.: A1113903, Invitrogen) or 0.5 mg / mL G418 (Catalog No.: 11811031, Invitrogen) to achieve stable overexpression of the PRSS3 transcript in these cells.

[0032] In vitro transfection was performed using Lipofectamine 3000 reagent (catalog number: L3000015, Invitrogen), following the manufacturer's instructions.

[0033] This study, approved by the General Hospital of the Chinese People's Liberation Army (Approval No.: 20090701-015), included a validation cohort of 243 consecutive gastric cancer patients, of whom 66 had follow-up information. The follow-up period began on the date of diagnosis and ended on the date of death or May 1, 2017 (if no endpoint event was recorded). All patients signed written informed consent forms to participate in this study.

[0034] Western blotting was performed, with proteins extracted using RIPA lysis buffer (catalog number: P0013B, Beyotime Biotechnology Co., Ltd.) and protein concentration determined at 820 nm using an ultra-micro spectrophotometer (catalog number: ND2000C, Thermo Scientific).

[0035] Proteins were separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) and transferred to a polyvinylidene fluoride (PVDF) membrane using a Bio-Rad MiniPROTEAN 3 system (Bio-Rad). The membrane was blocked with phosphate-buffered saline (PBS) containing 5% milk and 0.1% Tween-20, followed by immunoblotting with primary antibody, and then incubated with horseradish peroxidase (HRP)-conjugated anti-mouse or anti-rabbit secondary antibody (Supplementary Table 2, ST2). Immunoblotting bands were visualized using the Amersham ECL Protein Blot Detection Kit according to the manufacturer's instructions. β-actin was used as an internal control.

[0036] Immunofluorescence (IF) was performed by fixing cells with 4% paraformaldehyde at room temperature for 10 minutes, followed by washing and blocking. Primary antibodies (Supplementary Table 2, ST2) were incubated with cells overnight at 4 °C, followed by incubation for 1 hour with secondary antibodies conjugated with CoraLite 488 or CoraLite 549. Nuclear staining was performed using 4',6-diamidino-2-phenylindole (DAPI) (10 μg / ml in PBS, Invitrogen, Life Technologies). Images were acquired using a laser confocal microscope (Olympus FV1000 laser scanning confocal microscope).

[0037] Cell viability was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay kit (catalog number: ab211091, Abcam). Gastric cancer cell viability was detected in 96-well plates, with 4 × 10³ cells seeded per well. Assays were performed daily. Absorbance at 490 nm was measured using a microplate reader.

[0038] For the colony formation assay, cells were seeded at a density of 0.3 × 10³ cells per well in 6-well cell culture plates. After culturing for 1 week, colonies containing more than 50 cells were counted. Cells were fixed with 75% ethanol for 30 minutes and then stained with 0.2% crystal violet (catalog number: C0121, Beyotime Biotechnology Co., Ltd., China) for 20 minutes.

[0039] Cell migration assay (Transwell assay): The Transwell assay used an 8 μm pore size membrane (catalog number: 3115, Corning). Different numbers of cells were seeded in the upper chamber and incubated for different times: SGC7901 cells (2 × 10⁻⁶) 4 (4 cells / well, incubated for 20 hours), BGC823 cells (4 × 10⁻⁶) 4 Cells / well, incubated for 24 hours), MGC803 cells (2×10⁶ cells / well, incubated for 24 hours), 4 Cells / well, incubated for 24 hours), NCI-N87 cells (10×10⁶ cells / well, incubated for 24 hours), 4 (One cell / well, incubated for 40 hours).

[0040] The scratch assay involved seeding cells at 80% confluence into 6-well cell culture plates, with three replicates per sample. A straight line was drawn on the cell layer using a pipette tip, and images were acquired at 0 and 24 hours. The area of ​​the scratched region was analyzed using ImageJ software.

[0041] RNA extraction and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) were performed. RNA was extracted using TRIzol reagent (catalog number: 15596018, Invitrogen), and first-strand cDNA was synthesized using the Superscript first-strand synthesis system (catalog number: AT311-02, TransGene Biotechnology Co., Ltd.).

[0042] mRNA expression levels were analyzed using 2×SYBR Green real-time PCR reagent on an ABI 7500 real-time PCR instrument (Applied Biosystems). The 2-ΔΔCt method was used to normalize the target gene expression level to the expression level of the internal reference gene in the same cDNA, and then the relative expression level was calculated by comparing it with the control group.

[0043] Genome-wide DNA methylation was detected using the QIAamp DNA Mini Kit (catalog number: 51304, QIAGEN). Genomic DNA was extracted using the Genome-wide DNA Methylation Detection Kit (5-methylcytosine, colorimetric method, catalog number: ab233486, Abcam). The methylation level of genome-wide DNA was quantified using the kit (5-methylcytosine, colorimetric method, catalog number: ab233486, Abcam).

[0044] The DNA and DNA binding solution were incubated at 37 °C for 60 minutes. After washing three times with washing buffer, 5-methylcytosine (5mC) antibody, signal indicator and enhancer solution were added and incubated at room temperature for 1 hour. After washing thoroughly with washing buffer, chromogenic solution was added and incubated at room temperature for 3 minutes until the positive control turned blue. Then, stop solution was added to each well and incubated for 2 minutes to terminate the enzymatic reaction. The absorbance value at 450 nm was measured.

[0045] DNA analysis, methylation-specific PCR / quantitative PCR (MSP-PCR / qPCR), and MassARRAY assays were performed. Genomic DNA was extracted using the QIAamp DNA Mini Kit, and DNA concentration was determined using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific). DNA was bisulfite modified using the Zymo DNA Methylation Kit (catalog number: D5006, Zymo Research), followed by analysis using methylated or unmethylated primer pairs via PCR or qPCR. Positive and negative controls were obtained from the Human Methylated and Unmethylated DNA Kit (catalog number: D5013, Zymo Research).

[0046] The relative levels of PRSS3 methylation and non-methylation obtained by qPCR were normalized using the 2-ΔΔCt method with β-actin as an internal control. Furthermore, the MassARRAY Compact system (Sequenom) provided by Beijing Ouyi Biotechnology Co., Ltd. was used to detect the methylation level of PRSS3 at CpG sites. Primer pairs used for amplifying the target region are detailed in Table 1. Data analysis was performed using the MassARRAY EpiTYPER (Sequenom) to obtain quantitative results for each CpG site or a summary result for multiple CpG sites.

[0047] Genomic DNA was extracted from gastric cancer cells that had been transfected or treated with DATS (40 μM, 12 h) using methylated DNA immunoprecipitation (MeDIP) and chromatin immunoprecipitation (ChIP). Fragmented DNA obtained from sonication was incubated overnight at 4 °C with anti-5-methylcytidine (anti-5mC) monoclonal antibody or non-specific IgG for immunoprecipitation. Both the input control (10% of sonicated DNA) and the immunoprecipitated DNA fragments were released by proteinase K digestion and then purified using a QIAquick purification kit (catalog number: 28104, QIAGEN). The enrichment of the precipitated DNA was determined by qPCR using the primer pairs in Table 1. Normalization was performed using genomic DNA extracted with non-specific IgG as a control, and the result is expressed as "% of input".

[0048] Chromatin immunoprecipitation (ChIP) was performed as previously described: chromatin was prepared by sonicating cell lysates and pre-cleaned with protein A beads; a portion of the pre-cleaned chromatin solution (referred to as the IP fraction) was taken, and 2 μg of anti-DNMT1 antibody, anti-UHRF1 antibody, or pre-immunized rabbit IgG was added, and the mixture was incubated overnight on a rotating platform at 4 °C; 1% of the IP fraction was used as the ChIP input control. Antibody-enriched protein-DNA complexes were precipitated from the IP fraction using protein A beads; DNA fragments were released by reverse cross-linking and purified using a QIAquick purification kit (catalog number: 28104, QIAGEN); the immunoprecipitated DNA fraction was analyzed by qPCR.

[0049] Statistical analysis was performed, and data were expressed as mean ± standard deviation (mean ± SD) from at least three independent trials. Two-tailed Wilcoxon rank-sum test or one-way ANOVA combined with Tukey's post-hoc test was used to analyze PRSS3 transcript expression levels and epigenetic alterations. The chi-square exact test (χ² exact test) was used to analyze the relationship between PRSS3 splice variants and clinicopathological features, and Fisher's exact test was used to analyze clinicopathological correlation. Kaplan-Meier method was used to assess overall survival, cancer-related survival, and prognostic value, and log-rank test was used for inter-group comparisons. A p-value < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS version 23.0 (IBM, RRID: SCR_002865) and R software (R Studio).

[0050] II. Experimental Results

[0051] 1. Differentially spliced ​​transcripts of PRSS3 in gastric cancer First, CCLE RNA-seq data were analyzed, revealing differences in PRSS3 expression among 36 gastric cancer cell lines (see Table 2). Table 2 shows the expression of PRSS3 and its transcript variants in human gastric cancer cell lines. Further analysis was conducted using reverse transcription-quantitative polymerase chain reaction (RT-qPCR), Western blotting, and immunofluorescence (IF) techniques. Using the immortalized gastric mucosal epithelial cell line GES-1 as a control, gastric cancer cells were divided into a PRSS3 low-expression group (PRSS3Low) (including BGC823, MGC803, and SGC7901 cells) and a PRSS3 high-expression group (PRSS3High) (including NCI-N87 cells).

[0052] Table 2

[0053] Note: RNA sequencing (RNA-seq) data are sourced from the Cancer Dependency Map (DepMap) website (https: / / depmap.org / portal / download / ). The relative expression levels of the PRSS3 gene (gene ID: ENSG00000010438.12) and its transcript variants are normalized to per million transcripts (TPM) and displayed on a log2 (TPM+1) scale. Expression levels above the median are highlighted in gray.

[0054] Analysis of TCGA-STAD RNA-seq data from FIREHOSE Broad GDAC (hereinafter referred to as the "TCGA cohort") (Table 3) showed that the expression level of PRSS3 in gastric cancer tissue samples (n=415) was higher than that in non-tumor tissues (n=35); comparative analysis of paired gastric cancer tumor-normal tissue samples (n=32) further validated this result. Furthermore, in CCLE gastric cancer cells, PRSS3-V1 and / or PRSS3-V2 mRNA expression was dominant, while PRSS3-V3 and / or PRSS3-V4 expression was extremely low or rare; Table 3 shows the correlation results between PRSS3 expression and clinicopathological parameters in gastric cancer (GC).

[0055] Table 3

[0056] Note: RNA sequencing data and clinical parameters for gastric cancer were obtained from the FIREHOSE database. After excluding 35 cases of primary solid normal tissue (median TPM: 68.30; TPM range: 1.19-299.00), this dataset contains 415 cases of primary solid gastric cancer tissue. PRSS3 gene RNA expression levels are expressed in transcripts per million (TPM). Based on the median PRSS3 gene TPM value, the samples were divided into a PRSS3 high expression group (PRSS3High) and a PRSS3 low expression group (PRSS3Low), where PRSS3High / PRSS3Low represent high / low expression of the PRSS3 gene in tumor tissue, respectively. The chi-square test (χ² test) was used to analyze the association between the PRSS3 high expression group and the low expression group.

[0057] RT-qPCR analysis using transcript-specific primers confirmed that PRSS3-V1 mRNA was predominantly expressed in both gastric cancer cells and GES-1 cells, consistent with protein level results obtained by Western blotting using PRSS3 isoform-specific polyclonal antibodies. Immunofluorescence experiments further revealed that different PRSS3 isoforms were primarily localized in the cytoplasm of gastric cancer cells, with PRSS3-V1 and PRSS3-V2 exhibiting particularly pronounced cytoplasmic localization characteristics.

[0058] Analysis of the TCGA-STAD RNA-seq dataset showed differentially elevated expression of PRSS3-V1, PRSS3-V2, and PRSS3-V3 in gastric cancer tissue samples, while data for PRSS3-V4 were missing. Comparative analysis of tumor samples (n=32) with their paired non-tumor tissues confirmed that differential PRSS3 expression was associated with dominant expression of PRSS3-V1 and / or PRSS3-V2 and low-level expression of PRSS3-V3. Figure 1 (See Table 4). Table 4 shows the expression of the PRSS3 gene and its transcript variants in 32 human gastric cancer specimens and their paired normal tissues from the Cancer Genome Atlas (TCGA) cohort. The data indicate that altered PRSS3 expression in gastric cancer is primarily due to the co-expression of its splice isoforms (especially PRSS3-V1 and PRSS3-V2).

[0059] Table 4

[0060] Note: RNA sequencing (RNA-seq) data were obtained from The Cancer Genome Atlas (TCGA) and analyzed by the Firehouse server (FIREHOSE database) of the Broad Genome Data Analysis Center (http: / / gdac.broadinstitute.org). Relative expression levels of the PRSS3 gene and its transcript variants are normalized to per million transcripts (TPM). Expression levels above the median are highlighted in gray, and the two expression levels closest to the median are marked in red.

[0061] 2. Pleiotropic effects of PRSS3 transcripts on gastric cancer cells Differential expression of PRSS3 splice variants (PRSS3-SVs) may lead to their different functions in gastric cancer. To elucidate the biological role of PRSS3-SVs, we ectopically overexpressed PRSS3-V1 to PRSS3-V4 in gastric cancer cells and conducted functional assessment experiments. The results showed that overexpression of these splice variants significantly inhibited the proliferation of gastric cancer cells (Table 5, which shows the effect of PRSS3-SV overexpression on cell proliferation in gastric cancer cells as detected by the MTT assay) and colony formation ability (Table 6, which shows the effect of PRSS3-SV overexpression on cell proliferation in gastric cancer cells as detected by the colony formation assay). These results indicate that overexpression of PRSS3-SVs promotes cell proliferation in gastric cancer cells.

[0062] Table 5

[0063] Table 6

[0064] This inhibitory effect is accompanied by a weakening of the nuclear factor-κB (NF-κB) signaling pathway, specifically manifested as an increase in the content of the NF-κB subunit p65 in the cytoplasm and a decrease in the content of p65 in the nucleus. Furthermore, treatment with diallyl trisulfide (DATS) upregulated the expression of IκB-α (NFKBIA) and downregulated the expression of its phosphorylated form (p-IκB-α); previous studies have confirmed that DATS, as an epigenetic regulator, can inhibit the NF-κB pathway. Increased IκB-α levels and decreased p-IκB-α levels can inhibit NF-κB activation by retaining NF-κB in the cytoplasm.

[0065] Surprisingly, as shown in Table 7 (Table 7 shows the results of the cell scratch assay to detect the effect of PRSS3-SVs overexpression on the migration ability of gastric cancer cells; Table 7 results show that PRSS3-SVs overexpression had opposite effects on cell migration ability (V1 and V3 inhibited while V2 and V4 promoted); Table 8 shows the results of the Transwell assay to detect the effect of PRSS3-SVs overexpression on the invasive ability of gastric cancer cells; Table 8 results show that PRSS3-SVs overexpression had opposite effects on cell invasive ability (V1 and V3 inhibited while V2 and V4 promoted). PRSS3-SVs overexpressed in gastric cancer cells had opposite effects on cell migration and invasion. This differential effect was correlated with the protein expression levels of matrix metalloproteinases (MMPs): specifically, PRSS3-V1 and PRSS3-V3 exhibited inhibitory effects, while PRSS3-V2 and PRSS3-V4 exhibited promoting effects, and these effects were correlated with the expression of MMP2 and MMP7, respectively. These findings suggest that differentially expressed PRSS3 transcripts may contribute to functional intratumoral heterogeneity in gastric cancer, hinting at its potential application in precise stratification of gastric cancer.

[0066] Table 7

[0067] Table 8

[0068] 3. Clinical relevance of PRSS3 transcripts in gastric cancer To further validate the function, the clinical significance of PRSS3 transcripts in gastric cancer stratification was investigated using the TCGA-STAD cohort (Table 9 shows the expression results of PRSS3 transcript variants in human gastric cancer tissues). Compared with control tissues, the expression levels of PRSS3 and its transcripts showed a trend of first increasing and then gradually decreasing in gastric cancer tumors at different clinical stages and pathological grades, indicating that differentially expressed PRSS3 transcripts dynamically participate in the progression of gastric cancer.

[0069] Table 9

[0070] To further investigate this phenomenon, we divided gastric cancer patients into a PRSS3 high expression group (PRSS3...). High ) and PRSS3 low expression group (PRSS3 Low The results showed statistically significant differences between the two groups of patients in stage II and III cancer. High expression of PRSS3 (PRSS3) was observed in gastric cancer tumors. High ) or its variants PRSS3-V1, PRSS3-V3 highly expressed (PRSS3-V1 / V3)High Patients with PRSS3-V1 showed longer overall survival, and the survival of patients with early-stage cancer was also significantly prolonged. Furthermore, PRSS3-V1 was highly expressed in high-grade tumors. High It is associated with prolonged survival in gastric cancer patients.

[0071] PRSS3 High and PRSS3-V1 / V3 High The favorable survival outcomes of patients were consistent with in vitro experimental results, suggesting that these splice variants play a tumor-suppressive role in gastric cancer. However, the prognostic effect of PRSS3-V2 on gastric cancer patients varies depending on cancer stage (PRSS3-V2). High (Poor prognosis) and tumor grade (PRSS3-V2) Low The varying prognostic outcomes (differential rates) suggest that PRSS3-V2 plays a dual role in gastric cancer development. Therefore, the dynamic changes in differential co-expression patterns among PRSS3 transcripts serve different functions during cancer progression, potentially leading to functional intratumoral heterogeneity and phenotypic plasticity in gastric cancer cells.

[0072] Although PRSS3-V1 and PRSS3-V2 perform different functions, their co-expression may affect the clinical outcomes of patients in stratified groups. Therefore, we further divided gastric cancer patients into two subgroups: a PRSS3-V1 high-expression and a PRSS3-V2 low-expression group (PRSS3-V1...). High V2 Low That is, PRSS3-V1 High The group with low PRSS3-V1 expression and high PRSS3-V2 expression (n=68) and the group with low PRSS3-V1 expression and high PRSS3-V2 expression (PRSS3-V1) Low V2 High PRSS3-V2 High Groups, n=67). Results are as follows: Figure 2 The display shows PRSS3-PRSS3-V1 High V2 Low The overall survival of patients in the group was significantly better than that of PRSS3-V1. Low V2 High Group of patients.

[0073] Furthermore, hazard ratio analysis showed that in gastric cancer tumors (n=372), an increased difference in expression between PRSS3-V1 and PRSS3-V2 (V1–V2) was positively correlated with prolonged patient survival. The results were as follows: Figure 3As shown, this suggests that the V1-V2 expression ratio has independent prognostic significance for gastric cancer stratification. These findings indicate that V1–V2 can serve as an independent prognostic factor for assessing the occurrence and development of gastric cancer and stratifying patients. They also confirm that spliced ​​transcripts have a more significant advantage than differentially expressed genes (DEGs) in predictive, preventive, and precision medicine systems.

[0074] 4. Epigenetic regulation of DMR methylation in PRSS3 transcripts Given that epigenetic-driven tissue-specific gene expression can serve as a non-invasive biomarker for early prediction, precise stratification, and targeted prevention within the framework of Predictive, Preventive, and Precision Medicine (PPPM), we aimed to investigate the regulatory mechanisms of differential dynamic expression of PRSS3 splice variants (PRSS3-SVs) in gastric cancer (GC). Different transcription start sites (TSS) lead to differences in the 5' region of PRSS3 transcripts. PRSS3-V1 and PRSS3-V3 (TSS_V1 / V3) share a transcription start site (defined as cTSS), which is close to the transcription start site of PRSS3-V4 (TSS_V4, +356 nt from cTSS) and far from the transcription start site of downstream PRSS3-V2 (TSS_V2, +45,296 nt from cTSS).

[0075] PRSS3 methylation data from gastric cancer cells obtained from the CCLE dataset covers the upstream region (from -1,000 nt to cTSS) and three CpG islands (CpGI_1-3) on the PRSS3 sequence. CpGI_1 (from -1749 to -180 nt to cTSS) and CpGI_2 (from -89 to -63 nt to cTSS) overlap with cTSS, while CpGI_3 is 509 to 653 nt away from cTSS.

[0076] Association analysis showed that upstream region methylation was significantly associated with decreased PRSS3 expression in CCLE gastric cancer cell lines, particularly in PRSS3-V1 and PRSS3-V2, but not in PRSS3-V3 and PRSS3-V4. The study found a stronger negative correlation between CpGI3 methylation (followed by CpGI2 methylation) and PRSS3-V1 and PRSS3-V2 expression (Tables 10-11). Table 10 shows the expression and methylation of PRSS3 transcripts in CCLE gastric cancer cell lines, and Table 11 shows the Spearman correlation analysis results of PRSS3 transcript expression and CpG island methylation in human gastric cancer cell lines. This indicates that genomic regions containing CpGI2 and CpGI3 play a more crucial role in the epigenetic regulation of PRSS3 transcripts in CCLE gastric cancer cells.

[0077] Table 10

[0078] Note: RNA sequencing (RNA-seq) data and PRSS3 gene CpG island (CCpGIs) methylation data were obtained from the Cancer Dependency Map (DepMap) portal of the Cancer Cell Line Encyclopedia (CCLE) (https: / / depmap.org / portal / download / ). Methylation data of the upstream 1kb region was also extracted from DepMap (https: / / depmap.org / portal / download / ).

[0079] Table 11

[0080] The TCGA-STAD dataset provides a methylation map of PRSS3 from 170 nt upstream to 34,654 nt downstream of the cTSS genome, encompassing six CpG sites (CpG_A to CpG_F). CpG_A is located upstream in CpGI_1, CpG_B and CpG_C in exon 1, CpG_D and CpG_E are adjacent to CpGI_3, and CpG_F is located further downstream. Correlation analysis showed a strong negative correlation between PRSS3 transcript expression and methylation status at these specific sites, stably dividing patients into two distinct subgroups: a low-expression group of hypermethylated PRSS3 splice variants (mPRSS3Low) and a high-expression group of hypomethylated PRSS3 splice variants (umPRSS3High). This grouping was particularly pronounced at CpG_A to CpG_E sites, indicating that the region covering CpG_A to CpG_E is a differentially methylated region (DMR). Aberrant methylation in this region may interfere with the expression of PRSS3 transcripts in gastric cancer.

[0081] To verify these findings, we used methylation-specific quantitative PCR (MSP-qPCR) to analyze the genomic region containing CpGI_3 in PRSS3 (this fragment was named the intragene differentially methylated region, iDMR). The results showed that PRSS3 was lowly expressed (PRSS3... Low Partial methylation was observed in SGC7901, MGC803, and BGC823 cells, while PRSS3 was highly expressed (PRSS3...). High The methylation level in NCI-N87 cells was low, consistent with bisulfite sequencing (BS) results. Furthermore, we used MassARRAY technology to quantify the methylation status of each CpG site relative to cTSS, ranging from 57 nt to 784 nt, and compared the average CpG site methylation level in this region across different cell lines; both results were consistent with MSP-qPCR and BS results.

[0082] Further research revealed that treatment with the DNA methyltransferase inhibitor 5-aza-2'-deoxycytidine (5-Aza-CR) increased PRSS3 expression in PRSS3Low gastric cancer cells, and this increase was mainly achieved by upregulating PRSS3-V1 expression, without affecting PRSS3-V2 expression levels. These results indicate that methylation of PRSS3 in the DMR region (DMRm) regulates the expression of its splice transcripts through epigenetic silencing, and the regulation of PRSS3-V1 is particularly crucial in gastric cancer.

[0083] 5. UHRF1 / DNMT1-mediated DMR methylation regulates PRSS3 transcripts Among the epigenetic regulators of DNA methylation, the expression of DNA methyltransferase 1 (DNMT1) and its adaptor, ubiquitin-like protein 1 (UHRF1) containing PHD and ring finger domains, was significantly upregulated. These proteins have clinicopathological significance and are associated with favorable clinical outcomes. Subsequently, we investigated the effect of the UHRF1 / DNMT1 complex on DMRm. Our study found that in gastric cancer cells, the co-expression of UHRF1 / DNMT1 was negatively correlated with the expression of the PRSS3 transcript.

[0084] Ectopic expression of UHRF1 or DNMT1 in SGC7901 cells (constructing UHRF1-SGC7901 or DNMT1-SGC7901 cells) resulted in decreased expression of the PRSS3 transcript and its isoforms. Chromatin immunoprecipitation-quantitative PCR (ChIP-qPCR) experiments using iDMR-specific primers showed increased enrichment of DNA fragments binding to anti-UHRF1 or anti-DNMT1 antibodies in UHRF1-SGC7901 or DNMT1-SGC7901 cells.

[0085] Conversely, downregulation of UHRF1 or DNMT1 expression in BGC823 cells significantly upregulated the expression of the PRSS3 transcript and its protein isoforms, while reducing the DMR fragments obtained by immunoprecipitation with anti-UHRF1 or anti-DNMT1 antibodies, respectively. These findings suggest that the UHRF1 / DNMT1 complex affects PRSS3 transcript expression through DMRm mediation.

[0086] To verify this hypothesis, we found that treatment of gastric cancer cells with diallyl trisulfide (DATS) has a demethylating effect. Specific evidence includes: a reduction in DNA fragments enriched from DATS-treated BGC823 cells and bound to anti-5-methylcytosine (anti-5mC) antibodies via MeDIP assays; and a reduction in enriched DNA fragments via ChIP assays using anti-UHRF1 or anti-DNMT1 antibodies. Furthermore, the expression levels of UHRF1 or DNMT1 were decreased in DATS-treated gastric cancer cells, and downregulation of UHRF1 or DNMT1 expression further enhanced this reduction effect. Additionally, the ability of DATS to attenuate DNMT1 / UHRF1-mediated DMRm is associated with its ability to upregulate PRSS3 transcript expression in gastric cancer cells. Therefore, differential expression of PRSS3 transcripts in gastric cancer cells is regulated by UHRF1 / DNMT1-mediated DMR methylation, suggesting that this regulatory mechanism is a potential epigenetic target.

[0087] 6. DMR methylation of PRSS3 transcripts is associated with gastric cancer metastasis and suggests a poor prognosis. Early and reversible epigenetic alterations make them promising non-invasive, targeted biomarkers for precision oncology. Based on this, we used data from the TCGA-STAD cohort to investigate the impact of DMRm on PRSS3 transcripts and its correlation with clinical indicators in gastric cancer patients.

[0088] Studies have observed a dynamic pattern in CpG methylation: upstream CpG_A sites (mCpG_A) exhibit higher methylation levels across different cancer stages and tumor grades; while from CpG_B to CpG_F sites, methylation levels gradually increase but remain generally low. Methylation levels at most CpG sites gradually increase with disease progression, which is negatively correlated with PRSS3 transcript expression in gastric cancer. Importantly, analysis of site-specific CpG methylation showed that gastric cancer patients with high methylation at CpG_B, CpG_C, CpG_D, and CpG_E sites had poorer prognoses.

[0089] Further research on iDMRs containing CpG_B to CpG_E revealed that iDMR hypermethylation was associated with shortened overall survival and could predict adverse clinical outcomes in patients with advanced or low-grade gastric cancer.

[0090] In gastric cancer tissues from the validation cohort, we performed MSP analysis on the iDMR methylation status covering the CpG_D site, revealing a methylation frequency of 77% at this site. Figure 5 As shown in Table 12, after dividing the gastric cancer patients in this cohort into a hypomethylation group and a hypermethylation group, it was found that iDMR methylation was significantly associated with metastatic potential (P<0.001) (Table 12 shows the relationship between PRSS3 gene methylation and clinicopathological parameters of gastric cancer in the validation cohort). Furthermore, in the subgroup of patients with follow-up data in the validation cohort (n=66), 88% of cases showed metastasis-related iDMR methylation.

[0091] Table 12

[0092]

[0093] The above findings suggest that iDMR methylation promotes gastric cancer metastasis by altering the PRSS3 transcript (specifically, downregulating the expression differences between V1 and V2). To verify this, we selected cells with high PRSS3-V1 expression and low V2 expression (PRSS3-V1...). High V2 Low In patients with predominantly PRSS3-V1 expression, the methylation status of CpG_D and CpG_E sites in their iDMR was analyzed to reduce the mutual interference caused by co-expression of PRSS3 transcripts in gastric cancer. Figure 4As shown, PRSS3-V1 only shows low methylation at CpG_D or CpG_E sites in tumor tissue. High V2 Low Patients only achieve good survival outcomes after these procedures. These results indicate that UHRF1 / DNMT1-mediated PRSS3 DMR methylation may regulate the expression of specific PRSS3 transcripts in gastric cancer, with particularly significant regulation of PRSS3-V1. Gastric cancer tissues exhibit low expression of DMRm-associated PRSS3-V1 and high expression of V2 (PRSS3-V1...). Low V2 High The phenotype suggests that the tumor has metastatic potential and can predict adverse progression in gastric cancer patients.

[0094] In summary, methylation-driven differentially spliced ​​PRSS3 transcripts can classify gastric cancer into two subtypes: a subtype with low expression of methylated PRSS3 splice variants and a subtype with high expression of non-methylated PRSS3 splice variants. Methylation-associated PRSS3-V1 Low V2 High Gastric cancer subtypes have the potential to predict gastric cancer metastasis and therefore can serve as potential targeted biomarkers for precise stratification, prediction, and personalized treatment and prevention of gastric cancer.

[0095] It should be understood that the disclosed invention is not limited to the specific methods, schemes, and substances described, as these are all subject to variation. It should also be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the scope of the invention, which is limited only by the appended claims.

[0096] Those skilled in the art will also recognize, or be able to identify, many equivalents of the specific embodiments of the invention described herein using no more than conventional experiments. These equivalents are also included in the appended claims.

Claims

1. A biomarker for precise stratification of gastric cancer, characterized in that, The marker is a combination of serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2.

2. The biomarker according to claim 1, characterized in that, The biomarkers mentioned are used for the prognosis of gastric cancer patients.

3. A reagent kit for precise stratification of gastric cancer, characterized in that, The kit contains reagents for detecting marker levels in the tissue to be tested, the marker being a combination of serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2.

4. The reagent kit according to claim 3, characterized in that, The kit is used to detect the ratio and / or difference in the expression levels of serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2 in the test tissue.

5. The reagent kit according to claim 3, characterized in that, The kit is for the prognosis of gastric cancer patients.

6. The use of a reagent for detecting a combination of serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2 in the preparation of a kit for precise stratification of gastric cancer.

7. The application according to claim 6, characterized in that, The kit is used to detect the ratio and / or difference in the expression levels of serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2 in the test tissue.

8. The application according to claim 6, characterized in that, The kit is for the prognosis of gastric cancer patients.

9. An agent for treating and / or preventing gastric cancer, characterized in that, The formulation can increase the expression ratio and / or difference between the serine protease 3 transcript variants PRSS3-V1 and PRSS3-V2 in the human body.

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