Identification of early diagnostic biomarkers via WGCNA in gastric cancer

Abstract

Background: Gastric cancer (GC) is the world's second-leading cause of cancer-related mortality, continuing to make it a serious healthcare concern. Even though the prevalence of GC reduces, the prognosis for GC patients remains poor in terms of a lack of reliable biomarkers to diagnose early GC and predict chemosensitivity and recurrence. Methods and material: We integrated the gene expression patterns of gastric cancers from four RNAseq datasets (GSE113255, GSE142000, GSE118897, and GSE130823) from Gene Expression Omnibus (GEO) database to recognize differentially expressed genes (DEGs) between normal and GC samples. A gene co-expression network was built using weighted co-expression network analysis (WGCNA). Furthermore, RT-qPCR was performed to validate the in silico results. Results: The red modules in GSE113255, Turquoise in GSE142000, Brown in GSE118897, and the green-yellow module in GSE130823 datasets were found to be highly correlated with the anatomical site of GC. ITGAX, CCL14, ADHFE1, and HOXB13) as the hub gene are differentially expressed in tumor and non-tumor gastric tissues in this study. RT-qPCR demonstrated a high level of the expression of this gene. Conclusion: The expression levels of ITGAX, CCL14, ADHFE1, and HOXB13 in GC tumor tissues are considerably greater than in adjacent normal tissues. Systems biology approaches identified that these genes could be possible GC marker genes, providing ideas for other experimental studies in the future.

Keywords

• Gastric Cancer
• WGCNA
• Transcriptome analysis
• ITGAX
• CCL14
• ADHFE1