Gene co-expression analysis and Network biology studies in Indian population reveals functional similarities between Gastric cancer and other metabolic disorders

Abstract

Objective: Gastric cancer (GC) is a multifactorial disease and known to have been associated with metabolic disorders. Gene expression profiling among various GC populations will help to strategize diagnosis and treatment. The current study employed microarray data analysis (MDA) and network biology methods to understand the significant genes in a GC Indian population and its association with other metabolic disorders. Materials and Methods: The microarray datasets of GC Indian population (Bangalore) was retrieved from Gene Expression Omnibus (GEO), normalized and analyzed using GeneSpring. With the fold change of ± 1.5, the differentially expressed genes (DEG) were identified. An interactome was built to study interactions and generate gene clusters. Statistical (centrality) parameters were applied to identify highly connected clusters followed by functional enrichment to identify significant pathways associated with the GC genes. Results and Discussion: MDA identified 7181 DEGs (3984 up regulated genes and 3197 down regulated) and the interactome yielded 16552 interactions and two sub clusters. Cluster 1 was found to be statistically fit. The functional characteristics of the significant genes in this cluster revealed their association with adrenal cortex hormone insufficiency, thyroid disorders and deficiencies in kidney water resorption. Conclusion: It is inferred from our study that, deficiency in Thyroid, Adrenal hormones and Antidiuretic hormone (ADH) functions has fair share in the prognosis and pathogenesis of GC Indian population. Henceforth, GC should not be viewed as separate entity in the series of cancers and gene expression profiling will help in improvising personalized medicine. Bangladesh Journal of Medical Science Vol. 21 No. 03 July’22 Page: 688-693