Unveiling Potential Biomarkers for Gastric Cancer Through Bioinformatics Approaches

Introduction Gastric cancer (GC) is a prevalent and diverse disease, ranking among the most lethal cancers globally, especially in Eastern Asia. It is therefore imperative to understand the mechanisms underlying GC progression and to identify potential biomarkers and targets for diagnosing, prognosing, and treating the disease. An analysis of bioinformatics data offers comprehensive insights into large databases, including complicated genetic information. Methods The GEO database was accessed to retrieve three gene expression profiles (GSE۷۹۹۷۳, GSE۳۳۶۵۱, and GSE۵۴۱۲۹). GSE۷۹۹۷۳ (Platform GPL۵۷۰) comprised ۱۰ GC samples and ۱۰ noncancerous samples, GSE۳۳۶۵۱ (Platform GPL۲۸۹۵) comprised ۴۰ gastric tumor tissue samples and ۱۲ normal gastric tissue samples, and GSE۵۴۱۲۹ (Platform GPL۵۷۰) comprised ۱۱۱ gastric tumor tissue samples and ۲۱ noncancerous samples from people who underwent gastroscopy for health examination. GEO۲R was used with screening criteria of adj. P < ۰.۰۵, log۲ FC (fold change) > ۱.۵, or log۲ FC <−۱.۵ to detect DEGs in gastric cancer tissues. This allowed selection of the top DEGs from each dataset and the creation of volcano maps. The overlap of DEGs from three datasets were plotted using an online tool (https://bioinfogp.cnb.csic.es/tools/venny/). A functional and pathway enrichment analysis was performed using Database for Annotation, Visualization, and Integrated Discovery (DAVID) software to determine the contribution of DEGs to GC progression in terms of the molecular function, cell composition and biological process. An enrichment analysis of KEGG pathways was performed to reveal the function of DEGs and signaling pathways within cells. The PPI network diagram for the discovered DEGs was created using the online analytic tool STRING (http://www.string-db.org/), with a confidence level of ۰.۴. The interaction network map was then created using Cytoscape software۲۳, and the major gene modules within the network map were screened using the MCODE plug-in. Results A total of ۷۵۰ differentially expressed genes (DEGs) were extracted from the GSE۷۹۹۷۳, GSE۳۳۶۵۱, and GSE۵۴۱۲۹ datasets, with ۲۵۰ DEGs identified from each dataset. Notably, there were no common DEGs shared among all three datasets. However, seven genes were identified in both GSE۷۹۹۷۳ and GSE۳۳۶۵۱, seven in GSE۵۴۱۲۹ and GSE۳۳۶۵۱, and six in GSE۷۹۹۷۳ and GSE۵۴۱۲۹, resulting in a total of ۲۰ hub genes. Additionally, eight genes exhibited consistently low expression, while ten genes demonstrated high expression across two of the datasets. Conclusion Twenty hub genes associated with gastric cancer (GC) were identified through the application of various analytical techniques. The verification of these hub genes was facilitated by functional enrichment analysis, which highlighted a clustering module with the highest score. Results suggest that six of the selected hub genes—FN۱, COL۱A۱, COL۱A۲, COL۴A۱, THY۱, and HTRA۱—may serve as valuable prognostic indicators and therapeutic targets for GC. These findings provide a theoretical foundation for further exploration of the molecular mechanisms involved in the pathogenesis of GC.

کلیدواژه ها:

Gastric Cancer (GC) ، Biomarkers ، Bioinformatics ، Differentially Expressed Genes (DEGs) ، Enrichment Analysis