Background: Gastric cancer is the second leading cause of cancer-related deaths worldwide. Computational studies of cancers facilitate understanding of the cancer development pathogenic process and lead researchers to discover efficient molecular biomarkers suitable for the prognosis or early detection of gastric cancer. In cancer systems, biology researchers seek to find the cellular mechanisms of cancer with a focus on the systemic perspective. It is not just a gene, a protein, a metabolite, or any other biological factor; it is how each of these factors works in a complex system with others and tries to look at the system behavior of each factor.
Methods: In this study, 411 samples of gastric cancer and 35 samples of healthy individuals’ gene expression data were collected based on search results in the TCGA database. Then we normalized and filtered the input genes. After analyzing weighted gene co-expression networks, the resulting modules became candidates for enrichment analysis and literature review.
Results: Examination of the results obtained from the reconstruction and analysis of gastric cancer weighted gene co-expression network led to the discovery of pink and blue modules. Then, the genes consisting of that module were enriched through the KEGG, EnrichR, and ToppGene databases. Some of these genes, such as DMB, CD6, CD8A, CDC45, and CDC20 are known to be involved in inflammation, cell cycle, and tissue damage in cancer, and some of these other genes are less commonly reported in scientific studies.
Conclusions: We can select these candidate genes as potential biomarkers to determine the prognosis and even early detection of the clinical treatment.