Identification of Hub Genes and Key Modules in Stomach Adenocarcinoma Using nsNMF-Based Data Integration Technique

Stomach adenocarcinoma is one of the most common malignancies in the world. Recent advancements in high-throughput technologies and applications made it possible to analyze cancer-related diseases at the downstream level. In this article, we aim to perform a systems-level analysis of stomach adenocarcinoma through the integration of multiple biological data sources. Here, we have utilized the Nonsmooth Nonnegative Matrix Factorization (nsNMF) to integrate RNASeq gene expression data collected from The Cancer Genome Atlas (TCGA) with protein-protein interactions data. Initially, we have detected modules on both datasets and then integrated them by the nsNMF to obtain consensus modules. The consensus modules are then analyzed by the identification of hub genes using Maximal Clique Centrality (MCC) measure. Here, we have highlighted two hub modules containing hub genes with top MCC scores. We have also identified highly significant up-and down-regulated differentially expressed genes (DEGs) which may be important in playing significant roles in this tumorigenesis. Biological processes and pathways of the hub modules have also been investigated.