Application of GLMP in preparation of reagent for gastric cancer diagnosis, prognosis evaluation and treatment

Abstract

The invention discloses an application of a glycosylated lysosomal membrane protein (GLMP) in preparation of a reagent for gastric cancer diagnosis, prognosis evaluation and treatment. Tissue microarray (TMA) immunohistochemical detection, cancer genome map (TCGA) data verification and clinical pathological characteristic and survival analysis prove that the GLMP is remarkably and highly expressed in GC tissues, and the high expression of the GLMP is related to the M stage and the TNM stage of a GC patient and can be used as an independent prognosis risk factor for predicting the adverse overall survival rate of the patient. Functional enrichment analysis shows that GLMP participates in immune related processes and extracellular matrix tissues, and is closely related to M2 type macrophage infiltration in an immunosuppressive tumor microenvironment. In-vitro experiments prove that the proliferation, migration and invasion ability of GC cells can be inhibited by knocking out the GLMP, and meanwhile, the polarization direction and chemotactic ability of macrophages are regulated and controlled. The molecular mechanism of the GLMP in GC is defined, a novel gastric cancer prognosis biomarker is provided, a novel target spot and strategy are provided for diagnosis, prognosis evaluation and targeted therapy of gastric cancer, and important clinical application value is achieved.

Description

Technical Field

[0002] This invention belongs to the field of biomedical technology, specifically the application of glycosylated lysosomal membrane protein (GLMP) in the preparation of reagents for the diagnosis, prognosis assessment and treatment of gastric cancer. Background Technology

[0004] Gastric cancer (GC) is a malignant tumor with leading incidence and mortality rates worldwide. Its prominent clinical characteristics include low early diagnosis rates, limited treatment options, and extremely poor prognosis. Despite continuous innovation in surgical techniques and optimization of comprehensive treatment regimens such as systemic chemotherapy and targeted therapy in recent years, the overall survival rate of patients with advanced gastric cancer has not been fundamentally improved, posing significant challenges to clinical diagnosis and treatment. Therefore, there is an urgent need to discover novel biomarkers to achieve early screening and diagnosis of gastric cancer, accurately predict treatment response, and deeply elucidate the core molecular mechanisms driving disease progression, providing crucial support for developing efficient diagnostic and treatment strategies.

[0005] Glycosylated lysosomal membrane proteins (GLMPs), as membrane proteins related to lysosomal function, have gradually entered the research field of tumor biology in recent years. Existing studies have shown that GLMPs may participate in a variety of key cellular processes, such as autophagy regulation, metabolic reprogramming, and signal transduction, which are often dysregulated in cancer development and progression. Previous whole-cancer analyses have confirmed that GLMPs are significantly overexpressed in various malignant tumors compared to adjacent normal tissues, suggesting that they may have broad functional importance in tumorigenesis. Meanwhile, the tumor microenvironment (TME) has been confirmed as a key factor in regulating tumor progression. Tumor-associated macrophages (TAMs), especially M2 phenotype-polarized macrophages, significantly promote tumor growth, angiogenesis, and metastasis by constructing an immunosuppressive microenvironment. GLMPs have a clear function in maintaining lysosomal integrity, making them a potential role in regulating the tumor immune microenvironment (such as macrophage polarization), and thus a research direction worthy of in-depth exploration.

[0006] However, current research on GLMP still has significant shortcomings: First, the specific expression pattern of GLMP in gastric cancer, its correlation with clinicopathological features, and its prognostic predictive value have not been systematically elucidated, and there is insufficient clinical evidence to support its potential role as a biomarker for gastric cancer. Second, the specific molecular functions of GLMP in cancer, especially its mechanism of action in regulating the tumor immune microenvironment (such as M2 macrophage polarization), have not been thoroughly explored, and the core pathways of its tumor-promoting effects remain unclear. Third, no research has yet clearly demonstrated the potential of GLMP as a therapeutic target for gastric cancer, and related targeted intervention strategies have not yet been developed. These research gaps prevent the full realization of the application value of GLMP in the diagnosis and treatment of gastric cancer, necessitating systematic research to fill the technological gaps in this field. Summary of the Invention

[0008] Based on the shortcomings of the existing technology, the core objective of this invention is to provide the application of glycosylated lysosomal membrane protein (GLMP) in the preparation of reagents for gastric cancer diagnosis, prognostic assessment and treatment, to clarify the expression pattern, clinical significance and molecular mechanism of GLMP in gastric cancer, and thus provide a precise prognostic biomarker and potential therapeutic target for gastric cancer, providing a new technical solution for early screening, prognostic judgment and targeted therapy of gastric cancer, and ultimately improving the diagnosis and treatment effect and prognosis of gastric cancer patients. Attached Figure Description

[0010] Figure 1 shows the layout of the tissue microarray (TMA) (A) and representative immunohistochemical images of GLMP in gastric cancer tissue and adjacent normal tissue (B). Unpaired t test (C), paired t test (D), and TCGA data (GEPIA analysis) (E) confirmed that GLMP was overexpressed in gastric cancer tissue (both at the protein and mRNA levels). Kaplan-Meier survival analysis plots from the TMA cohort (F), TCGA-STAD cohort (G), and KM-plotter database (H) were also performed.

[0011] Figure 2. Forest plot showing the association between various clinicopathological variables and overall survival of gastric cancer patients through univariate and multivariate Cox proportional hazards regression analysis (TMA cohort and TCGA cohort).

[0012] Figure 3. The knockdown efficiency of GLMP in MKN45 and NUGC4 cells was verified by Western blot (A) and RT-qPCR (B). The knockdown of GLMP was confirmed by CCK-8 assay (C, D) and migration and invasion assay (E, F). GLMP knockdown can inhibit the proliferation, migration and invasion of these two gastric cancer cells.

[0013] Figure 4. Enrichment analysis of differentially expressed genes (DEGs) in gastric cancer tissues with high and low GLMP expression in the TCGA cohort: A is the differential gene volcano plot; BD is the gene ontology (GO) enrichment analysis, showing the significant enriched entries of cellular components (CC), biological processes (BP), and molecular functions (MF); E is the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis; FI is the gene set enrichment analysis (GSEA).

[0014] Figure 5. Correlation between GLMP expression and immune cell infiltration analyzed using multiple algorithms: A is the relative proportion of 22 tumor-infiltrating immune cells in the gastric cancer samples from the TCGA cohort estimated by the CIBERSORT algorithm; B is a heatmap showing the correlation between GLMP expression and the abundance of various immune cells in multiple cancer types in the TCGA database; C is the correlation coefficient between GLMP expression and the infiltration level of specific immune cell subsets in the gastric cancer cohort shown by ssGSEA analysis; D is the association between GLMP expression and immune cell infiltration in the gastric cancer tumor microenvironment analyzed by the CIBERSORT algorithm.

[0015] Figure 6. Schematic diagram of the regulatory effect of GLMP on macrophage polarization and chemotaxis in gastric cancer: A is a schematic diagram of the experimental procedure of GLMP knockdown conditioned medium (CM) acting on macrophages; BC shows that CM can upregulate the expression of M1 markers (TNFα, IL12A) and downregulate the expression of M2 markers (ARG1, CD163, MRC1) in macrophages by RT-qPCR detection; DG confirms by Transwell experiment and quantitative analysis that after GLMP knockdown, CM derived from MKN45 and NUGC4 cells can inhibit the chemotactic ability of M0 and M2 macrophages.

[0016] Figure 7 The correlation between GLMP expression and clinicopathological features in tissue microarray cohorts. GLMP expression was correlated with various clinicopathological features in gastric cancer patients, specifically age (A), sex (B), tumor size (C), tumor grade (D), T stage (E), N stage (F), M stage (G), TNM stage (H), P53 status (I), VEGFR expression (J), CD133 expression (K), and E-cadherin expression (L). Statistical significance was determined using Student's t-test, with P < 0.05 considered statistically significant.

[0017] Figure 8TCGA data were used to validate the correlation between GLMP expression and M2 macrophage infiltration. A. The GSVA algorithm was used to analyze the correlation between GLMP expression and immune cell infiltration in the gastric cancer tumor microenvironment; B. The Xcell algorithm was used to analyze the correlation between GLMP expression and immune cell infiltration in the gastric cancer tumor microenvironment. Detailed Implementation

[0019] The present invention will be described in detail below with reference to specific embodiments.

[0020] Tissue specimens and clinical data: Immunohistochemical (ihc) analysis was performed using a tissue microarray (TMA, HStm-Ade180Sur-05; Outdo Biotech Company, Shanghai, China). This microarray contained 83 GC tumor tissues and 76 pairs of adjacent non-cancerous tissues. All GC patients were clinically staged according to the 7th edition of the TNM staging system established by the American Joint Committee on Cancer (AJCC) and the International Union for Cancer Control (UICC). All patient data were anonymized, and the follow-up period was extended to July 2015. Prior to the experiment, the tissue microarray received approval from the company's ethics committee (YB M-05-02).

[0021] IHC Analysis: Immunohistochemical staining was performed to assess GLMP protein expression in TMA sections. 4 µm thick TMA sections were dewaxed in xylene and rehydrated in a series of fractionated ethanol solutions. Antigen retrieval was performed by microwave heating in citrate buffer (pH 6.0). Endogenous peroxidase activity was quenched by treating slides with 3% hydrogen peroxide for 15 minutes at room temperature. After washing with phosphate-buffered saline (PBS), sections were incubated overnight at 4°C with primary antibody against GLMP (Abmart, PK758711:500). Subsequently, sections were incubated for 1 hour at room temperature with secondary antibody conjugated to horseradish peroxidase (HRP). Antigen signal was observed using 3,3′-diaminobenzidine (DAB) followed by counterstaining with hematoxylin. Finally, sections were dehydrated in a series of fractionated ethanol solutions, cleaned with xylene, and fixed with synthetic resin. All section images were acquired using the OmnipathSlideCenter system. Semi-quantitative assessment was performed using a tissue score (H score), calculated by multiplying the staining intensity score by the percentage of positive cells. Negative controls were treated simultaneously by omitting the primary antibody. All stained sections were independently evaluated by two experienced pathologists who were unaware of the clinical data.

[0022] Datasets and Bioinformatics Analysis: This study utilized publicly available datasets and online analysis tools. RNA sequence data and corresponding clinical information for GCs were obtained from the Cancer Genome Atlas (TCGA) database (https: / / portal.gdc.cancer.gov / ). Gene expression analysis and initial survival analysis were performed using the GEPIA network (http: / / gepia.cancer-pku.cn / ). Kaplan-Meier survival analysis, particularly overall survival (OS) analysis, was further validated and generated using the KM plotter database (https: / / kmplot.com / analysis / ). The Xiantao Academic online platform (https: / / www.xiantao.love / ) was used for comprehensive bioinformatics analysis, including assessment of immune cell infiltration.

[0023] Cell Culture: Human cancer cell lines MKN45 and NUGC4 were purchased from Cas9X Biotechnology Co., Ltd. (Suzhou, China). Cells were maintained in RPMI-1640 medium supplemented with 10% fetal bovine serum and cultured in a humidified incubator at 37°C and 5% CO2. THP-1 cells were maintained in RPMI-1640 medium supplemented with 15% fetal bovine serum. To induce differentiation, cells were first treated with phorbol 12-myristate 13-acetate (PMA, 100 ng / mL) for 48 hours to polarize them to the M0 macrophage phenotype. Subsequently, M0 macrophages were stimulated with interleukin-4 (IL-4, 20 ng / mL) for an additional 48 hours to promote polarization towards the M2 phenotype.

[0024] Small interfering RNA (siRNA) transfection: GLMP-targeting siRNA was synthesized by Cas9X Biotechnology Co., Ltd. (Suzhou, China).

[0025]

[0026] According to the manufacturer's protocol, Lipofectamine 2000 (Thermo, cat.#11668019) was used for cell transfection, and subsequent experiments were performed 48 to 72 hours after transfection.

[0027] Cell migration and invasion assays: Cell migration and invasion assays were performed using a Transwell chamber. Cells were counted and resuspended in serum-free medium, then seeded into the upper chamber. The lower chamber was filled with serum-containing medium. After 24 hours of incubation, cells that had migrated or invaded the lower surface were stained with crystal violet, imaged, and counted under a microscope. For the invasion assay, the chamber was pre-coated with Matrigel, and all other steps were the same as for the cell migration assay.

[0028] Preparation of conditioned medium: After a 48-hour transfection period, the medium was removed and replaced with serum-free medium. After another 24 hours of incubation, the supernatant was collected and centrifuged to remove cell debris. Subsequently, the supernatant was sterilized by filtering through a 0.22µm filter. The resulting preparation, termed conditioned medium (CM), was aliquoted and stored for subsequent experiments.

[0029] Macrophage chemotaxis assay: Macrophage chemotaxis was assayed using a Transwell system. Macrophages were collected, counted, and resuspended in serum-free medium. The cell suspension was then seeded into the upper chamber. The lower chamber was filled with CM derived from cancer cells as a chemotactic attractant. After 48 hours of incubation, cells that had migrated to the submembrane surface were carefully removed. The migrating cells on the submembrane side were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and subsequently imaged and counted under a light microscope.

[0030] Cell Counting Kit-8 (CCK-8) Assay: After counting, cells were seeded into 96-well plates. At specified time points (days 1, 2, 3, 4, and 5), CCK-8 solution was added to each well, and the plates were incubated in a cell culture incubator for 1 hour. The absorbance at 450 nm was then measured using a microplate reader.

[0031] Western blot: Proteins were extracted using RIPA lysis buffer containing protease and phosphatase inhibitors. Protein concentration was determined using a BCA assay kit (Boster Biological Technology, China). Equal volumes of protein were subjected to SDS-PAGE and transferred to a PVDF membrane. After blocking with 5% skim milk, the membrane was incubated overnight at 4°C with primary antibodies against GLMP (Abmart Biotech Co. Ltd., PK75871, 1:500) and GAPDH (Proteintech Biotech Co. Ltd., Cat No. 60004-1-Ig, 1:1000). After incubation with HRP-conjugated secondary antibody, bands were observed using an enhanced chemiluminescence (ECL) detection system.

[0032] RT-qPCR: Total RNA was extracted and reverse transcribed into complementary DNA (cDNA). Quantitative real-time PCR (qPCR) was then performed using a SYBR Green master mix (Takara, JPN). Relative mRNA expression levels of the genes were calculated using the 2^(−ΔΔCt) method and normalized to GAPDH. Primers were synthesized by Sangon Biotech Ltd. (Shanghai, China).

[0033]

[0034] Statistical analysis: SPSS 22.0 and GraphPad Prism 9.0 were used for statistical analysis. Continuous data are expressed as mean ± standard deviation. Student's t-test was used for comparisons between two groups, and one-way ANOVA was used for comparisons among multiple groups. Categorical data were analyzed using chi-square test or Fisher's exact test as appropriate. Kaplan-Meier survival analysis was performed, and log-rank test was used for curve comparison. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. The correlation between GLMP expression and clinical parameters was assessed using appropriate statistical methods based on data characteristics. For all analyses, a two-sided p-value < 0.05 was considered statistically significant.

[0035] Results and Analysis

[0036] IHC analysis was performed on TMA containing 90 pairs of GCs and adjacent normal tissue specimens. A schematic layout of the TMA is shown below. Figure 1 As shown in Figure A, GLMP expression was significantly increased in GC tissue compared to normal tissue. Figure 1 B). Subsequent quantitative assessment of TMA confirmed that, through unpaired and paired t-tests, GLMP levels in cancer tissue were significantly elevated (B). Figure 1 C, D). Analysis of the Cancer Genome Atlas (TCGA) data also confirmed these findings and demonstrated a significant upregulation of GLMP in GC (C, D). Figure 1 E). Furthermore, stratified analysis showed that GLMP expression varied significantly with age, TNM stage, and VEGFR status, but was not significantly correlated with other clinicopathological parameters. Figure 7 ).

[0037] Survival analysis was performed using clinical data from the TMA, TCGA, and KM plotter online databases. For the TMA cohort, patients were divided into high-expression and low-expression groups (high GLMP and low GLMP) based on median GLMP expression. Results showed that patients with high GLMP expression had significantly lower overall survival compared to those with low expression. Figure 1 F). In the TCGA cohort, the cutoff value for GLMP expression was determined to be 3.988 by ROC analysis, thus dividing the samples into high-GLMP and low-GLMP groups. Consistent with the TMA data, analysis of the TCGA dataset showed that high GLMP expression was significantly associated with shorter overall survival. Figure 1 G). Similarly, analysis of the KM plotter database confirmed that high GLMP expression was associated with poor prognosis (G). Figure 1 H).

[0038] Univariate and multivariate Cox regression analyses were performed on the TMA and TCGA datasets. Univariate Cox analysis of the TMA cohort showed that high GLMP expression, tumor size, T stage, N stage, M stage, grade, TNM stage, and VEGFR expression were important risk factors for GC patients. Figure 2 A). Subsequent multivariate Cox analysis of the TMA data indicated that high GLMP expression and grade remained independent risk factors. Figure 2 B). Similarly, in the TCGA cohort, univariate analysis identified high GLMP expression, T phase, N phase, M phase, and TNM phase as significant risk factors. Figure 2 C). Multivariate Cox analysis of TCGA data further confirmed that high GLMP expression is an independent risk factor. Figure 2 D). These results suggest that elevated GLMP expression may be a predictor of poor prognosis in patients with GC.

[0039] The correlation between GLMP expression and clinicopathological features in GC patients was further evaluated. TMA data analysis showed that high GLMP expression was significantly associated with age, M stage, and TNM stage (Table 1). TCGA data analysis showed that GLMP expression was significantly associated with patient survival (Table 2). These findings suggest that elevated GLMP expression may be associated with the aggressive tumor characteristics of GC patients.

[0040] Table 1. Clinical relevance analysis of CLMP in TMA GC samples

[0041] Characteristics CLMP-low (%) CLMP-high (%) P value Gender 0.82 Male 14 (34.1%) 16 (38.1%) Female 27 (65.9%) 26 (61.9%) Age 0.028 ≤65 26 (63.4%) 16 (38.1%) >65 15 (36.6%) 26 (61.9%) Tumor size 0.177 ≤5cm 26 (66.7%) 21 (50.0%) >5cm 13 (33.3%) 21 (50.0%) T stage 0.547 T1,2 8 (19.5%) 5 (12.5%) T3,4 33 (80.5%) 35 (87.5%) N stage 0.443 N0 11 (26.8%) 8 (19.1%) N1,2,3 30 (73.2%) 34 (80.9%) M stage 0.015 M0 40 (97.6%) 33 (78.6%) M1 1 (2.4%) 9 (21.4%) Grade 0.379 G2 16 (39.0%) 21 (50.0%) G3,4 25 (61.0%) 21 (50.0%) TNM 0.041 Ⅰ-Ⅱ 21 (51.2%) 11 (26.8%) Ⅲ-Ⅳ 20 (48.8%) 30 (73.2%) Survival status 0.277 Alive 19 (43.2%) 14 (31.1%) Dead 25 (56.8%) 31 (68.9%) P53 0.819 ≤45 17 (47.2%) 20 (51.3%) ＞45 19 (52.8%) 19 (48.7%) Ki67 0.251 ≤40 21 (58.3%) 17 (43.6%) ＞40 15 (41.7%) 22 (56.4%) VEGFR 0.063 ≤142.5 24 (66.7%) 17 (43.6%) ＞142.5 12 (33.3%) 22 (56.4%) CD133 0.644 ≤142.5 21 (58.3%) 20 (51.3%) ＞142.5 15 (41.7%) 19 (48.7%) E-Cadherin 0.819 ≤60 18 (50.0%) 21 (53.8%) ＞60 18 (50.0%) 18 (46.1%)

[0042] Table 2. Clinical relevance analysis of CLMP in tissue microarrays.

[0043] Characteristics CLMP-low (%) CLMP-high (%) P value Gender 0.245 Femal 113 (37.2%) 45 (31.3%) Male 191 (62.8%) 99 (68.7%) T stage 0.252 1,2 80 (26.4%) 44 (32.1%) 3,4 223 (73.6%) 93 (67.9%) N stage 0.176 N0 84 (28.5%) 47 (35.1%) N1 211 (71.5%) 87 (64.9%) M stage 0.680 M0 272 (93.7%) 128 (92.7%) M1 18 (6.2%) 10 (7.2%) TNM stage 0.206 1,2 135 (45.9%) 68 (52.7%) 3,4 159 (54.1%) 61 (47.3%) Survival status 0.025 Alive 182 (63.2%) 68 (51.5%) Desd 106 (36.8%) 64 (48.5%)

[0044] GLMP expression was knocked down in GC cells, and its impact on malignant behavior was assessed. Initially, GLMP siRNAs (Si-1, Si-2) successfully downregulated GLMP expression levels in MKN45 and NUGC4 cells. Figure 3 AB). Analysis of CCK-8 on cell proliferation capacity showed that GLMP knockdown weakened cell proliferation (AB). Figure 3 Furthermore, Transwell assays showed that GLMP knockout significantly reduced the migration and invasion abilities of GC cells. Figure 3 These results collectively indicate that GLMP knockout suppresses the malignant phenotype in GC cells.

[0045] Enrichment analysis was performed using TCGA data, and the volcano plot showed differentially expressed genes. Figure 4A). Gene ontology (GO) enrichment analysis showed that, within the cellular component (CC) category, GLMP was associated with terms such as collagen-containing extracellular matrix and endoplasmic reticulum lumen. Figure 4 B). In the category of biological processes (BP), it relates to processes including the detection of chemical stimuli involved in sensory perception, and sensory perception itself. Figure 4 C). Regarding molecular function (MF), GLMP expression is related to olfactory receptor activity and endopeptidase activity. Figure 4 D). Kyoto Encyclopedia of Genetics and Genomes (KEGG) pathway analysis showed that GLMP expression was enriched in pathways such as olfactory transduction and neuroactive ligand-receptor interactions. Figure 4 E). Furthermore, gene set enrichment analysis (GSEA) showed an association between GLMP expression and gene signatures associated with advanced cancer, immune evasion, and immune response, as well as overall cancer invasiveness. Figure 4 FI).

[0046] Data from the TCGA database were analyzed to explore the relationship between GLMP expression and immune infiltration. Results showed that high GLMP expression was associated with decreased B cell infiltration and increased macrophage infiltration in GCs (GCs). Figure 5 A). Subsequently, the analysis was extended to the background of pancreatic cancer, and it was found that GLMP expression was positively correlated with the infiltration of dendritic cells (DCs), neutrophils, and macrophages, but negatively correlated with the infiltration of B cells and plasmacytoid dendritic cells (pDCs). Figure 5 B). Further analysis of the GC dataset showed that GLMP expression was positively correlated with the infiltration of Tregs, Th1 cells, NK CD56bright cells, neutrophils, macrophages, and dendritic cells, and negatively correlated with the infiltration of T helper cells. Figure 5 C). Using the CIBERSORT algorithm, it was found that GLMP expression was consistently positively correlated with macrophage infiltration, and the strongest correlation was found with the M2 phenotype. Figure 5 D). Furthermore, more detailed correlation analysis showed that GLMP expression was significantly positively correlated with the infiltration of M0 and M2 macrophages and NK cells, while it was significantly negatively correlated with the infiltration of T follicular helper cells, Tγδ cells, and memory B cells. Figure 5 D, Figure 8 AB).

[0047] RT-qPCR analysis showed that macrophages treated with CM derived from GLMP knockout cells exhibited significantly increased expression of the M1 markers TNFα and IL12A, while the expression of the M2 markers ARG1, CD163, and MRC1 was decreased. Figure 6 BC). Meanwhile, chemotactic assays showed that GLMP knockout enhanced the recruitment of M0 macrophages but decreased the chemotaxis of M2 macrophages. Figure 6 In summary, these findings support the conclusion that GLMP expression in cancer cells promotes the polarization and infiltration of M2-like macrophages, while GLMP knockdown effectively attenuates the recruitment of M2 macrophages.

[0048] In summary, this invention provides the application of GLMP in the diagnosis, prognostic assessment, and treatment of gastric cancer, clarifies the expression pattern, clinical significance, and molecular mechanism of GLMP in gastric cancer, and thus provides a precise prognostic biomarker and potential therapeutic target for gastric cancer. This offers a new technical solution for early screening, prognostic assessment, and targeted therapy of gastric cancer, ultimately improving the diagnosis and treatment outcomes and prognosis of gastric cancer patients.

[0049] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. Application of GLMP in the preparation of diagnostic reagents for gastric cancer.

2. Application of GLMP in the preparation of gastric cancer prognostic assessment reagents.

3. Application of GLMP in the preparation of gastric cancer treatment reagents.

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