TY  - JOUR
T1  - Bioinformatics Analysis of Gene Modules and Key Genes for the Early Diagnosis of Gastric Cancer
A1  - Ling Xu
A1  - Jinyan Yang
A1  - Yu Zhang
A1  - Xudong Liu
A1  - Zhe Liu
A1  - Feipeng Sun
A1  - Ying Ma
A1  - Lei Wang
A1  - Feng Xing
JF  - Archive of International Journal of Cancer and Allied Science
JO  - Arch Int J Cancer Allied Sci
SN  - 3108-4834
Y1  - 2024
VL  - 4
IS  - 1
DO  - 10.51847/2gUBplgIMV
SP  - 24
EP  - 36
N2  - Gastric cancer (GC) remains one of the most prevalent and fatal cancers worldwide, with high rates of both incidence and mortality. Unfortunately, most patients are diagnosed at advanced stages, often through routine screenings, missing the best time for intervention. In this research, weighted gene co-expression network analysis (WGCNA) was used to identify key gene modules and hub genes associated with GC. Using the “limma” package in R, differentially expressed genes (DEGs) were examined from TCGA’s GC dataset, resulting in the identification of 4892 DEGs. To better understand their biological roles, Gene ontology (GO) enrichment and KEGG pathway analysis were performed, which revealed that the DEGs were strongly involved in processes such as extracellular matrix organization, DNA replication, the cell cycle, and the p53 signaling pathway. WGCNA was also applied to identify gene modules associated with clinical characteristics in both GC and normal tissue samples. Six distinct gene modules were identified, with two showing significant association with GC. Hub genes in these modules were determined through survival and expression analyses. In addition, one-way ANOVA was used to examine how these hub genes were expressed across different stages of GC compared to normal tissues. This analysis revealed 19 genes with significant differential expression and positive prognosis implications. These hub genes showed significant differences in expression levels between normal and different GC stages, highlighting their potential as biomarkers for early detection of GC and as valuable tools to aid in diagnosis at earlier stages.
UR  - https://smerpub.com/article/bioinformatics-analysis-of-gene-modules-and-key-genes-for-the-early-diagnosis-of-gastric-cancer-wmebqssr7r3ctvm
ER  -