Identification of Potential Diagnostic and Prognostic Biomarkers for Gastric Cancer Based on Bioinformatic Analysis

Abstract

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.

Cancer-associated fibroblasts refine the classifications of gastric cancer with distinct prognosis and tumor microenvironment characteristics

“…Additionally, in the liver, GLT8D2 can positively regulate the expression of the ApoB100 protein in hepatocytes, promote the stable secretion of very low density lipoprotein and reduce the accumulation of triglycerides in hepatocytes, thus reducing the incidence of non-alcoholic fatty liver disease (NAFLD) ( 53 ). SPARC has been previously reported as a potential target in GC ( 54 , 55 ). The high expression of VCAN can be used as an independent predictor to indicate the adverse outcome of GC ( 56 ).…”

Identification and characterization of bone/cartilage-associated signatures in common fibrotic skin diseases

“…Stickler syndrome type II is caused by COL11A1 gene mutations ( Vogiatzi et al, 2018 ). Previous bioinformatics analysis has demonstrated that COL5A2, COL10A1, and COL11A1 may be used as a diagnostic or prognostic biomarker for tumors such as gastric cancer ( Niu et al, 2022 ), breast carcinoma ( Giussani et al, 2018 ), and non-small-cell lung cancer ( Andriani et al, 2018 ). According to the findings of RNA sequencing, COL5A2, COL10A1, and COL11A1 have been reported to be upregulated in keloid ( Bi et al, 2021 ; Lin P. et al, 2022 ), as in our study.…”

Identification of survival-associated biomarkers based on three datasets by bioinformatics analysis in gastric cancer

“…The other four genes (COL1A2, COL4A1, SPARC and CEP55) are related to the prognosis of GC, which mainly encode collagen, acidic matrix related protein and centrosome protein of GC cells. The previous studies have found that collagen-encoding genes COL1A1 and P4HA3 may be related to the prognosis of GC[ 35 ]. At the present study, we found that COL4A1 and COL1A2 may have some effects on extracellular matrix and its interaction with receptors, protein binding, protein digestion and absorption, and regulate PI3K Akt signaling pathway.…”