Stomach Cancer

Stomach cancer (Gastric cancer, GC) is a serious malignant disorder. Understanding the mechanism of stomach cancer development, and the discovery of novel diagnostic biomarkers and therapeutics are major goals in stomach cancer research. Important pathways, like, ECM-receptor interaction, Gastric acid secretion, PI3K-Akt signaling pathway, Focal adhesion, Amoebiasis, and Collecting duct acid secretion, linked with stomach cancer are identified. The genes associated with these pathways, i.g., COL1A1, COL1A2, THBS2, FN1, SPP1, ATP4A, APOE, VCAN, TIMP1, and ATP4B, are considered as the potential targets for stomach cancer drug discovery.

We have applied omics technologies in stomach cancer research, with a special focus on the utilization of computational approaches to integrate multi-omics data.  The application of data-driven systems biology and machine learning approaches could provide a predictive understanding of stomach cancer, and pave the way for the development of novel biomarkers and rational design of cancer therapeutics.

Databases, Tools and Computational Approaches

• Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo)
• GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) online analysis software
• European Genome-phenome Archive (EGA) database (https://ega-archive.org/)
• The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga)
• DAVID database (https://david.ncifcrf.gov/) to carry out GO functional annotation.
• KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis on the differentially expressed genes (DEGs).
• Gene enrichment analysis, a powerful analytical method for interpreting gene expression data.
• Construct a protein-protein interaction (PPI) network of the DEGs using STRING (http://string-db.org).
• Centrality analysis of PPI Network includes analyzing the degree, betweenness, and eigenvector of network nodes using Cytoscape plug-in CytoNCA.
• Topological analysis, an emerging method for analyzing large-scale data using geometry and methods.
• Identify hub genes using Cytoscape for visual networks.
• Perform survival analysis of hub genes through the Kaplan-Meier plotter (KM plotter, http://kmplot.com/analysis/).

CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.

References

1. Xiao-Jing Shi, et al. Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment. Frontiers in Molecular Biosciences. 2020.
2. Lopamudra Dey, Anirban Mukhopadhyay. A systems biology approach for identifying key genes and pathways of gastric cancer using microarray data. Gene Reports. 2021.