Background: Gastric cancer (GC) is one of the most prevalent cancers all over the world. The molecular mechanisms of GC remain unclear and not well understood. GC cases are majorly diagnosed at the late stage, resulting in a poor prognosis. Advances in molecular biology techniques allow us to get a better understanding of precise molecular mechanisms and enable us to identify the key genes in the carcinogenesis and progression of GC.Methods: The present study used datasets from the GEO database to screen differentially expressed genes (DEGs) between GC and normal gastric tissues. GO and KEGG enrichments were utilized to analyze the function of DEGs. The STRING database and Cytoscape software were applied to generate protein–protein network and find hub genes. The expression levels of hub genes were evaluated using data from the TCGA database. Survival analysis was conducted to evaluate the prognostic value of hub genes. The GEPIA database was involved to correlate key gene expressions with the pathological stage. Also, ROC curves were constructed to assess the diagnostic value of key genes.Results: A total of 607 DEGs were identified using three GEO datasets. GO analysis showed that the DEGs were mainly enriched in extracellular structure and matrix organization, collagen fibril organization, extracellular matrix (ECM), and integrin binding. KEGG enrichment was mainly enriched in protein digestion and absorption, ECM-receptor interaction, and focal adhesion. Fifteen genes were identified as hub genes, one of which was excluded for no significant expression between tumor and normal tissues. COL1A1, COL5A2, P4HA3, and SPARC showed high values in prognosis and diagnosis of GC.Conclusion: We suggest COL1A1, COL5A2, P4HA3, and SPARC as biomarkers for the diagnosis and prognosis of GC.

Background: Gastric cancer (GC) has a high mortality rate and is particularly prevalent in China. The extracellular matrix protein, prolyl 4-hydroxylase subunit alpha 3 (P4HA3), has been implicated in various cancers. We aimed to assess the diagnostic and prognostic value of P4HA3 in GC and investigate its correlation with immune cell infiltration.Methods: The present study used microarray data from the Cancer Genome Atlas (TCGA) to analyze the association of P4HA3 expression with clinicopathological features. Data from the Gene Expression Omnibus (GEO) were used for validation. Receiver operating characteristic (ROC) and Kaplan–Meier curves were constructed to determine the diagnostic and prognostic value of P4HA3 in GC. Univariate and multivariate regression analyses were performed to assess the impact of P4HA3 on overall survival (OS) rates. A protein–protein interaction (PPI) network was generated and functional enrichment evaluated. Single-sample gene set enrichment analysis (ssGSEA) was conducted to correlate P4HA3 expression with immune cell infiltration. The correlation between P4HA3 and immune check point genes was studied.Results: P4HA3 was over-expressed in GC, along with 15 other types of cancer, including breast invasive carcinoma and cholangiocarcinoma. P4HA3 showed high diagnostic and prognostic value in GC and was an independent prognostic factor. P4HA3, TNM (tumor, node, metastases) stage, pathological stage and age all correlated with OS rates. Genes related to P4HA3 were enriched in the lumen of the endoplasmic reticulum and included procollagen-proline 3-dioxygenase activity. P4HA3 expression correlated with numbers of macrophages, natural killer (NK) cells, immature dendritic cells (iDC), mast cells, eosinophils, effective memory T cells (Tem), T-helper 1 (Th1) cells and dendritic cells (DC). P4HA3 was positively correlated with hepatitis A virus cellular receptor 2 (HAVCR2) and programmed cell death 1 ligand 2 (PDCD1LG2).Conclusion: P4HA3 is a potential independent biomarker for prognosis of GC and may be an immunotherapy target in the treatment of GC.