Author(s)

Jiahui Wang^1,2,3, Huaidong Xu⁴, Runze Wu^1,2,3, Dingpeng Ban^1,2,3, Erxi Xu^1,2,3, Huiying Li^1,2,3, Gongzu Wang^1,2,3, Shengchao Yang^1,2,3, Haidong Zhou^1,2,3, Hongbo Li^5*

¹Department of Sport Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.
²Biomedical Materials Engineering Research Center for Bone and Joint Degenerative Diseases, Baise, China.
³Graduate School of Youjiang Medical University for Nationalities, Baise, China.
⁴Department of Critical Care Medicine, Affiliated Hospital of Chongqing University, Chongqing, China.
⁵Tianlin County People’s Hospital, Baise, China.

Objective: To screen and analyze differentially expressed genes (DEGs) in gastric carcinoma (GC) using a bioinformatics approach. Methods: Data were retrieved from the GEO microarray public database in NCBI, and the microarray dataset GSE49051 was selected for analysis. The R language limma package was used to screen differentially expressed mRNAs (DEmRNAs), and the data were subjected to normalization processing. A Venn diagram was employed to identify the common DEGs between the two groups. The R language clusterProfiler package was used to perform Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses on these common DEGs. The STRING database was utilized for protein-protein interaction (PPI) analysis, and the results were imported into Cytoscape software to generate the PPI interaction network, identify core modules, and determine hub genes. Known GC-related genes were downloaded from the OMIM database and intersected with the core module genes identified by MCODE, yielding the APOH gene. Furthermore, the genes downloaded from the OMIM database were intersected with the screened DEGs, resulting in five genes: APOH, AHSG, APOA2, AMBP, and HP. The hub genes were then imported into the BioGPS database, which revealed no tissue-specific genes. Conclusion: The identified DEGs and associated signaling pathways contribute to understanding the molecular mechanisms underlying GC pathogenesis and may provide a basis for the early diagnosis of GC.

Gastric Cancer, Bioinformatics Analysis, Differentially Expressed Gene, Signaling Pathways

Wang, J.H., Xu, H.D., Wu, R.Z., Ban, D.P., Xu, E.X., Li, H.Y., Wang, G.Z., Yang, S.C., Zhou, H.D. and Li, H.B. (2026) Core Gene Screening and Correlation Analysis in Gastric Cancer. Journal of Biosciences and Medicines, 14, 81-89. doi: 10.4236/jbm.2026.144007.