CPNE8 Promotes Gastric Cancer Metastasis by Modulating Focal Adhesion Pathway and Tumor Microenvironment

Machine learning based intratumor heterogeneity signature for predicting prognosis and immunotherapy benefit in stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a prevalent malignancy that is highly aggressive and heterogeneous. Intratumor heterogeneity (ITH) showed strong link to tumor progression and metastasis. High ITH may promote tumor evolution. An ITH-related signature (IRS) was created using as integrative technique including 10 machine learning methods based on TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets. The relevance of IRS in predicting the advantages of immunotherapy was assessed using a number of prediction scores and three immunotherapy datasets (GSE78220, IMvigor210 and GSE91061). Vitro experiments were performed to verify the biological functions of AKR1B1. The RSF + Enet (alpha = 0.1) projected model was proposed as the ideal IRS because it had the highest average C-index. The IRS demonstrated a strong performance in serving as an independent risk factor for the clinical outcome of STAD patients. It performed exceptionally well in predicting the overall survival rate of STAD patients, as seen by the TCGA cohort's AUC of 1-, 3-, and 5-year ROC curves, which were 0.689, 0.683, and 0.669, respectively. A low IRS score demonstrated a superior response to immunotherapy, as seen by a lower TIDE score, lower immune escape score, greater TMB score, higher PD1&CTLA4 immunophenoscore, higher response rate, and improved prognosis. Common chemotherapeutic and targeted treatment regimens had lower IC50 values in the group with higher IRS scores. Vitro experiment showed that AKR1B1 was upregulated in STAD and knockdown of AKR1B1 obviously suppressed tumor cell proliferation and migration. The present investigation produced the best IRS for STAD, which may be applied to prognostication, risk stratification, and therapy planning for STAD patients.

Upregulation of miRNA-450b-5p targets ACTB to affect drug resistance and prognosis of ovarian cancer via the PI3K/Akt signaling pathway

Background

Ovarian cancer (OC) is the most malignant gynecologic cancer, and chemoresistance is a major cause of treatment failure in patients with OC. The understanding of microRNA (miRNA) in cancer is limited, and the role of miRNA (miR)-450b-5p in cancer drug resistance is unknown. In this study, we aim to evaluate the role of miR-450b-5p in drug-resistant OC and its underlying mechanisms.

Methods

MiR-450b-5p expression was assessed in drug-sensitive and resistant OC cells via quantitative real-time polymerase chain reaction. Cell viability was evaluated using the Cell Counting Kit-8 assay. Progression-free survival (PFS) and overall survival (OS) curves were generated using the Kaplan-Meier method and the log-rank test. Target genes of miR-450b-5p were identified from the Cancer MIRNome database. Co-expressed genes were obtained from The Cancer Genome Atlas and Cancer Genome cBioportal for pathway enrichment and functional clustering analysis.

Results

The miRNA-450b-5p expression was significantly increased in A2780 and SKOV3 OC-resistant cells and significantly increased by 17-fold in the A2780-CBP-Lv-miR-450b-5p cells compared to A2780-CBP and A2780-CBP-Lv-NC cells. The up-regulated expression of miR-450b-5p increased the cell viability and half maximal inhibitory concentration (IC50) of A2780 platinum-resistant cells and was associated with poor OS. We obtained 33 potential target genes of miR-450b-5p and beta-actin (ACTB) might be a potential target of miR-450b-5p. Low expression of ACTB predicted poor OS and PFS. We obtained 362 common genes co-expressed with ACTB, which involved 4 critical pathways. PI3K acted as an upstream pathway of the other three pathways, which ultimately responded to drug resistance regulation in OC. The genes enriched in four pathways were cross-analyzed and 13 overlapping genes were obtained. These 13 genes were also significantly and positively co-expressed with ACTB at both protein and mRNA levels.

Conclusions

High expression of miRNA-450b-5p might affect drug resistance and prognosis in OC by targeting 13 co-expressed genes of ACTB directly through the PI3K/Akt signaling pathway. Thus, miR-450b-5p might provide a new therapeutic target for drug resistance in OC.

Mdm2 requires Sprouty4 to regulate focal adhesion formation and metastasis independent of p53

Although the E3 ligase Mdm2 and its homologue and binding partner MdmX are the major regulators of the p53 tumor suppressor protein, it is now evident that Mdm2 and MdmX have multiple functions that do not involve p53. As one example, it is known that Mdm2 can regulate cell migration, although mechanistic insight into this function is still lacking. Here we show in cells lacking p53 expression that knockdown of Mdm2 or MdmX, as well as pharmacological inhibition of the Mdm2/MdmX complex, not only reduces cell migration and invasion, but also impairs cell spreading and focal adhesion formation. In addition, Mdm2 knockdown decreases metastasis in vivo. Interestingly, Mdm2 downregulates the expression of Sprouty4, which is required for the Mdm2 mediated effects on cell migration, focal adhesion formation and metastasis. Further, our findings indicate that Mdm2 dampening of Sprouty4 is a prerequisite for maintaining RhoA levels in the cancer cells that we have studied. Taken together we describe a molecular mechanism whereby the Mdm2/MdmX complex through Sprouty4 regulates cellular processes leading to increase metastatic capability independently of p53.

Establishing and Validating an Innovative Focal Adhesion-Linked Gene Signature for Enhanced Prognostic Assessment in Endometrial Cancer

Studies have highlighted the significant role of focal adhesion signaling in cancer. Nevertheless, its specific involvement in the pathogenesis of endometrial cancer and its clinical significance remains uncertain. We analyzed TCGA-UCEC and GSE119041 datasets with corresponding clinical data to investigate focal adhesion-related gene expression and their clinical significance. A signature, "FA-riskScore," was developed using LASSO regression in the TCGA cohort and validated in the GSE dataset. The FA-riskScore was compared with four existing models in terms of their prediction performance. We employed univariate and multivariate Cox regression analyses towards FA-riskScore to assess its independent prognostic value. A prognostic evaluation nomogram based on our model and clinical indexes was established subsequently. Biological and immune differences between high- and low-risk groups were explored through functional enrichment, PPI network analysis, mutation mining, TME evaluation, and single-cell analysis. Sensitivity tests on commonly targeted drugs were performed on both groups, and Connectivity MAP identified potentially effective molecules for high-risk patients. qRT-PCR validated the expressions of FA-riskScore genes. FA-riskScore, based on FN1, RELN, PARVG, and PTEN, indicated a poorer prognosis for high-risk patients. Compared with published models, FA-riskScore achieved better and more stable performance. High-risk groups exhibited a more challenging TME and suppressive immune status. qRT-PCR showed differential expression in FN1, RELN, and PTEN. Connectivity MAP analysis suggested that BU-239, potassium-canrenoate, and tubocurarine are effective for high-risk patients. This study introduces a novel prognostic model for endometrial cancer and offers insights into focal adhesion's role in cancer pathogenesis.

Identification of immunotherapy-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognostic signature in gastric carcinoma

BACKGROUND

Recent advances in immunotherapy have elicited a considerable amount of attention as viable therapeutic options for several cancer types, the present study aimed to explore the immunotherapy-related genes (IRGs) and develop a prognostic risk signature in gastric carcinoma (GC) based on these genes.

METHODS

IRGs were identified by comparing immunotherapy responders and non-responders in GC. Then, GC patients were divided into distinct subtypes by unsupervised clustering method based on IRGs, and the differences in immune characteristics and prognostic stratification between these subtypes were analyzed. An immunotherapy-related risk score (IRRS) signature was developed and validated for risk classification and prognosis prediction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Besides, the predictive ability of the IRRS in immunotherapy response was also determined.

RESULTS

A total of 63 IRGs were identified, and 371 GC patients were stratified into two molecular subgroups with significantly different prognosis and immune characteristics. Then, an IRRS signature comprised of three IRGs (CENP8, NRP1, and SERPINE1) was constructed to predict the prognosis of GC patients in TCGA cohort. Importantly, external validation in multiple GEO cohorts further confirmed the universal applicability of the IRRS in distinct populations. Furthermore, we found that the IRRS was significantly correlated with patient's responsiveness to immunotherapy, GC patients with low IRRS are more likely to benefit from existing immunotherapy.

CONCLUSIONS

The risk score could serve as a robust prognostic biomarker, provide therapeutic benefits for immunotherapy and may be helpful for clinical decision making in GC patients.

Identification of circRNA biomarkers in osteosarcoma: An updated systematic review and meta-analysis

Circular RNAs (circRNAs) play a crucial role in cancer development and progression. This study aimed to identify potential circRNA biomarkers for osteosarcoma. Articles published from January 2010 to September 2023 were searched across eight databases to compare circRNA expression profiles in osteosarcoma and control samples (human, animal and cell lines). Meta-analysis was conducted under a random effects model. Subgroup analysis of circRNAs in different samples and tissues was performed. Diagnostic value was evaluated using receiver operator characteristic curves. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis explored functions of circRNA host genes. A circRNA-miRNA-mRNA axis depicted the regulatory mechanism in osteosarcoma. Among 1356 circRNAs with differential expression were identified across 226 original studies, only 74 were reported in at least three published sub-studies. Meta-analysis identified 58 dysregulated circRNAs (52 upregulated and 6 downregulated). Eleven circRNAs consistently showed dysregulation in tissues and cell lines, with hsa_circ_0005721 showing potential as a circulating biomarker in osteosarcoma. Sensitivity analysis demonstrated 97 % consistency. The overall area under the curve was 0.87 (95 % CI, 0.83-0.89). GO and KEGG enrichment analyses revealed host gene involvement in cancer. The circRNA-miRNA-mRNA axis revealed the regulatory axis and interactions within osteosarcoma specifically. This study demonstrates circRNAs as potential diagnostic biomarkers for osteosarcoma. Consistently reported dysregulated circRNAs are potential biomarkers in osteosarcoma pathogenesis, with hsa_circ_0005721 as a potential circulating biomarker for diagnosis and treatment.

Identification and validation of COL6A1 as a novel target for tumor electric field therapy in glioblastoma

BACKGROUND

Glioblastoma multiforme (GBM) is the most aggressive primary brain malignancy. Novel therapeutic modalities like tumor electric field therapy (TEFT) have shown promise, but underlying mechanisms remain unclear. The extracellular matrix (ECM) is implicated in GBM progression, warranting investigation into TEFT-ECM interplay.

METHODS

T98G cells were treated with TEFT (200 kHz, 2.2 V/m) for 72 h. Collagen type VI alpha 1 (COL6A1) was identified as hub gene via comprehensive bioinformatic analysis based on RNA sequencing (RNA-seq) and public glioma datasets. TEFT intervention models were established using T98G and Ln229 cell lines. Pre-TEFT and post-TEFT GBM tissues were collected for further validation. Focal adhesion pathway activity was assessed by western blot. Functional partners of COL6A1 were identified and validated by co-localization and survival analysis.

RESULTS

TEFT altered ECM-related gene expression in T98G cells, including the hub gene COL6A1. COL6A1 was upregulated in GBM and associated with poor prognosis. Muti-database GBM single-cell analysis revealed high-COL6A1 expression predominantly in malignant cell subpopulations. Differential expression and functional enrichment analyses suggested COL6A1 might be involved in ECM organization and focal adhesion. Western blot (WB), immunofluorescence (IF), and co-immunoprecipitation (Co-IP) experiments revealed that TEFT significantly inhibited expression of COL6A1, hindering its interaction with ITGA5, consequently suppressing the FAK/Paxillin/AKT pathway activity. These results suggested that TEFT might exert its antitumor effects by downregulating COL6A1 and thereby inhibiting the activity of the focal adhesion pathway.

CONCLUSION

TEFT could remodel the ECM of GBM cells by downregulating COL6A1 expression and inhibiting focal adhesion pathway. COL6A1 could interact with ITGA5 and activate the focal adhesion pathway, suggesting that it might be a potential therapeutic target mediating the antitumor effects of TEFT.

Molecular subtype construction and prognosis model for stomach adenocarcinoma characterized by metabolism-related genes

Background

Metabolic reprogramming is implicated in cancer progression. However, the impact of metabolism-associated genes in stomach adenocarcinomas (STAD) has not been thoroughly reviewed. Herein, we characterized metabolic transcription-correlated STAD subtypes and evaluated a metabolic RiskScore for evaluation survival.

Method

Genes related to metabolism were gathered from previous study and metabolic subtypes were screened using ConsensusClusterPlus in TCGA-STAD and GSE66229 dataset. The ssGSEA, MCP-Count, ESTIMATE and CIBERSORT determined the immune infiltration. A RiskScore model was established using the WGCNA and LASSO Cox regression in the TCGA-STAD queue and verified in the GSE66229 datasets. RT-qPCR was employed to measure the mRNA expressions of genes in the model.

Result

Two metabolism-related subtypes (C1 and C2) of STAD were constructed on account of the expression profiles of 113 prognostic metabolism genes with different immune outcomes and apparently distinct metabolic characteristic. The overall survival (OS) of C2 subtype was shorter than that of C1 subtype. Four metabolism-associated genes in turquoise model, which closely associated with C2 subtype, were employed to build the RiskScore (MATN3, OSBPL1A, SERPINE1, CPNE8) in TCGA-train dataset. Patients developed a poorer prognosis if they had a high RiskScore than having a low RiskScore. The promising effect of RiskScore was verified in the TCGA-test, TCGA-STAD and GSE66229 datasets. The prediction reliability of the RiskScore was validated by time-dependent receiver operating characteristic curve (ROC) and nomogram. Moreover, samples with high RiskScore had an enhanced immune status and TIDE score. Moreover, MATN3, OSBPL1A, SERPINE1 and CPNE8 mRNA levels were all elevated in SGC7901 cells. Inhibition of OSBPL1A decreased SGC7901 cells invasion numbers.

Conclusion

This work provided a new perspective into heterogeneity in metabolism and its association with immune escape in STAD. RiskScore was considered to be a strong prognostic label that could help individualize the treatment of STAD patients.

Correlation analysis of disulfidptosis-related gene signatures with clinical prognosis and immunotherapy response in sarcoma

Disulfidptosis, a newly discovered type of programmed cell death, could be a mechanism of cell death controlled by SLC7A11. This could be closely associated with tumor development and advancement. Nevertheless, the biological mechanism behind disulfidptosis-related genes (DRGs) in sarcoma (SARC) is uncertain. This study identified three valuable genes (SLC7A11, RPN1, GYS1) associated with disulfidptosis in sarcoma (SARC) and developed a prognostic model. The multiple databases and RT-qPCR data confirmed the upregulated expression of prognostic DRGs in SARC. The TCGA internal and ICGC external validation cohorts were utilized to validate the predictive model capacity. Our analysis of DRG riskscores revealed that the low-risk group exhibited a more favorable prognosis than the high-risk group. Furthermore, we observed a significant association between DRG riskscores and different clinical features, immune cell infiltration, immune therapeutic sensitivity, drug sensitivity, and RNA modification regulators. In addition, two external independent immunetherapy datasets and clinical tissue samples were collected, validating the value of the DRGs risk model in predicting immunotherapy response. Finally, the SLC7A11/hsa-miR-29c-3p/LINC00511, and RPN1/hsa-miR-143-3p/LINC00511 regulatory axes were constructed. This study provided DRG riskscore signatures to predict prognosis and response to immunotherapy in SARC, guiding personalized treatment decisions.

Roles and inhibitors of FAK in cancer: current advances and future directions

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase that exhibits high expression in various tumors and is associated with a poor prognosis. FAK activation promotes tumor growth, invasion, metastasis, and angiogenesis via both kinase-dependent and kinase-independent pathways. Moreover, FAK is crucial for sustaining the tumor microenvironment. The inhibition of FAK impedes tumorigenesis, metastasis, and drug resistance in cancer. Therefore, developing targeted inhibitors against FAK presents a promising therapeutic strategy. To date, numerous FAK inhibitors, including IN10018, defactinib, GSK2256098, conteltinib, and APG-2449, have been developed, which have demonstrated positive anti-tumor effects in preclinical studies and are undergoing clinical trials for several types of tumors. Moreover, many novel FAK inhibitors are currently in preclinical studies to advance targeted therapy for tumors with aberrantly activated FAK. The benefits of FAK degraders, especially in terms of their scaffold function, are increasingly evident, holding promising potential for future clinical exploration and breakthroughs. This review aims to clarify FAK's role in cancer, offering a comprehensive overview of the current status and future prospects of FAK-targeted therapy and combination approaches. The goal is to provide valuable insights for advancing anti-cancer treatment strategies.

A pan-cancer analysis of TNFAIP8L1 in human tumors

TNFAIP8L1, as a recently identified member in TNFAIP8 family, plays an important role in tumorigenesis. However, a pan-cancer analysis of TNFAIP8L1 in human tumors has not been conducted until now. The main purpose of study is to investigate TNFAIP8L1 during 33 different types of human tumors by using TCGA and GTEx. The pan-cancer analysis showed that TNFAIP8L1 was significantly over-expressed in 15 cancers and low-expressed in 9 cancers. There were distinct relations between TNFAIP8L1 expression and prognosis of patients with cancer. Furthermore, we also found that DNA methylation and RNA modification of TNFAIP8L1 were associated with many cancers. And then, we detected that TNFAIP8L1 level was positively associated with cancer-associated fibroblasts (CAFs) in many tumors. And, we obtained that TNFAIP8L1 expression was related with most of immune inhibitory and stimulatory genes in multiple types of tumors. We also found TNFAIP8L1 expression was correlated with most of chemokine, receptor, MHC, immunoinhibitor and immunostimulator gens in most of cancers. Moreover, we detected TNFAIP8L1 expression was associated with TMB and MSI in several tumors. Finally, TNFAIP8L1 gene had a significant positive association with 5 genes including BCL6B, DLL4, PCDH12, COL4A1 and DLL4 in the majority of tumors. GO enrichment and KEGG pathway analyses showed that TNFAIP8L1 in thepathogenesis of cancer may be related to "purine nucleoside binding," "purine ribonucleoside binding," "ECM-receptor interaction," etc. Our first pan-cancer study may provide a deep comprehending of TNFAIP8L1 in tumoeigenesis from different tumors.

Integrated analysis identities Rho GTPases related molecular map in patients with gastric carcinoma

The intricate involvement of Rho GTPases in a multitude of human malignancies and their diverse array of biological functions has garnered substantial attention within the scientific community. However, their expression pattern and potential role in gastric cancer (GC) remain unclear. In this study, we successfully identified two distinct subtypes associated with Rho GTPase-related gene (RGG) through consensus clustering analysis, which exhibited significant disparities in overall survival and the tumor microenvironment. Subsequently, an extensively validated risk model termed RGGscore was meticulously constructed to prognosticate the outcomes of GC patients. This model was further assessed and validated using an external cohort. Notably, the high RGGscore group was indicative of a poorer prognosis. Univariate and multivariate Cox regression analyses unveiled the RGGscore as an autonomous prognostic indicator for GC patients. Subsequent external validation, utilizing two cohorts of patients who underwent immunotherapy, demonstrated a significant correlation between a low RGGscore and improved response to immunotherapy. Additionally, the expression levels of three genes associated with RGGscore were examined using qRT-PCR. Taken together, a pioneering RGGscore model has been successfully established, showcasing its potential efficacy in offering valuable therapeutic guidance for GC.

Genetic diversity and breed-informative SNPs identification in domestic pig populations using coding SNPs

Background: The use of breed-informative genetic markers, specifically coding Single Nucleotide Polymorphisms (SNPs), is crucial for breed traceability, authentication of meat and dairy products, and the preservation and improvement of pig breeds. By identifying breed informative markers, we aimed to gain insights into the genetic mechanisms that influence production traits, enabling informed decisions in animal management and promoting sustainable pig production to meet the growing demand for animal products. Methods: Our dataset consists of 300 coding SNPs genotyped from three Italian commercial pig populations: Landrace, Yorkshire, and Duroc. Firstly, we analyzed the genetic diversity among the populations. Then, we applied a discriminant analysis of principal components to identify the most informative SNPs for discriminating between these populations. Lastly, we conducted a functional enrichment analysis to identify the most enriched pathways related to the genetic variation observed in the pig populations. Results: The alpha diversity indexes revealed a high genetic diversity within the three breeds. The higher proportion of observed heterozygosity than expected revealed an excess of heterozygotes in the populations that was supported by negative values of the fixation index (FIS) and deviations from the Hardy-Weinberg equilibrium. The Euclidean distance, the pairwise FST, and the pairwise Nei's GST genetic distances revealed that Yorkshire and Landrace breeds are genetically the closest, with distance values of 2.242, 0.029, and 0.033, respectively. Conversely, Landrace and Duroc breeds showed the highest genetic divergence, with distance values of 2.815, 0.048, and 0.052, respectively. We identified 28 significant SNPs that are related to phenotypic traits and these SNPs were able to differentiate between the pig breeds with high accuracy. The Functional Enrichment Analysis of the informative SNPs highlighted biological functions related to DNA packaging, chromatin integrity, and the preparation of DNA into higher-order structures. Conclusion: Our study sheds light on the genetic underpinnings of phenotypic variation among three Italian pig breeds, offering potential insights into the mechanisms driving breed differentiation. By prioritizing breed-specific coding SNPs, our approach enables a more focused analysis of specific genomic regions relevant to the research question compared to analyzing the entire genome.

Relationship Between MUC4 Variants and Metastatic Recurrence in Colorectal Cancer

Background

Recurrent metastasis after radical resection in patients of colorectal cancer (CRC) is a great challenge for the world, in which genomic alterations play a major role in tumorigenesis. MUC4 plays a significant role in recurrence and metastasis in tumor. This study is aimed at exploring the association between MUC4 variants and metastatic recurrence of CRC.

Methods

Forty-seven patients relapsing with metastasis and 37 patients remaining disease-free postoperatively were enrolled. Next-generation sequencing (NGS) detected mutations. Mutation and mRNA expression data were downloaded from TCGA and cBioPortal databases. We analyzed the relationship between MUC4 variants and clinical parameters, as well as possible molecular mechanisms.

Results

MUC4 variants rs56359992 and rs781124621 were associated with survival in patients with CRC. Rs56359992 was more common in patients with metastatic recurrence. MAPK pathway, PI3K-Akt pathway, JAK-STAT pathway, cell cycle, WNT pathway and mTOR pathway were found to correlate with MUC4 mutation by GO/KEGG analysis, as well as resting and activated mast cell related to MUC4 mutation by CIBERSORT analysis.

Conclusion

Genetic variants of MUC4 with CRC may constitute a molecular signature of metastatic recurrence. MUC4 may become a new target for the treatment of CRC recurrence.

Cancer-associated fibroblast expression of glutamine fructose-6-phosphate aminotransferase 2 (GFPT2) is a prognostic marker in gastric cancer

Glutamine fructose-6-phosphate aminotransferase 2 (GFPT2) is a rate-limiting enzyme in hexosamine biosynthesis involved in the occurrence and progress of many cancers. What role it plays in gastric cancer (GC) is still unclear. In this study, transcriptome sequencing data from the Harbin Medical University (HMU)-GC cohort and The Cancer Genome Atlas (TCGA) dataset were combined with the HMU-TCGA training cohort to analyze the biological function and clinical significance of GFPT2. The correlation of GFPT2 with immune cells and stromal cells was analyzed in the GC immune microenvironment through transcriptome sequencing data and a public single-cell sequencing database. In cell lines, GC tissues, and the tissue microarray, GFPT2 protein expression was confirmed by western blotting and immunohistochemistry. The mRNA of GFPT2 was highly expressed in the tumor (p < 0.001), and GC cells and tumors expressed high levels of GFPT2 protein. Compared to low expression, high GFPT2 mRNA expression was associated with higher levels of tumor invasion, higher pathological stages, and poor prognosis (p = 0.02) in GC patients. In a drug susceptibility analysis, GFPT2 mRNA expression was associated with multiple chemotherapeutic drug sensitivity, including docetaxel, paclitaxel, and cisplatin. Gene enrichment analysis found that GFPT2 was mainly primarily involved in the extracellular matrix receptor interaction pathway. The ESTIMATE, CIBERSORT, and ssGSEA algorithms showed that GFPT2 was associated with immune cell infiltration. In addition, GFPT2 was more likely to be expressed within cancer-associated fibroblasts (CAFs), and high levels of GFPT2 expression were highly correlated with four CAFs scores (all p < 0.05). Finally, a prognostic model to assess the risk of death in GC patients was constructed based on GFPT2 protein expression and lymph node metastasis rate. In conclusion, GFPT2 plays an essential role in the function of CAFs in GC. It can be used as a biomarker to assess GC prognosis and immune infiltration.