Computational Analysis of Gastric Canceromics Data to Identify Putative Biomarkers

Abstract

Background:: Gastric cancer develops as a malignant tumor in the mucosa of the stomach, and spreads through further layers. Early-stage diagnosis of gastric cancer is highly challenging because the patients either exhibit symptoms similar to stomach infections or show no signs at all. Biomarkers are active players in the cancer process by acting as indications of aberrant alterations due to malignancy. Objective:: Though there have been significant advancements in the biomarkers and therapeutic targets, there are still insufficient data to fully eradicate the disease in its early phases. Therefore, it is crucial to identify particular biomarkers for detecting and treating stomach cancer. This review aims to provide a thorough overview of data analysis in gastric cancer. Methods:: Text mining, network analysis, machine learning (ML), deep learning (DL), and structural bioinformatics approaches have been employed in this study. Results:: We have built a huge interaction network in the current study to forecast new biomarkers for gastric cancer. The four putatively unique and potential biomarker genes have been identified via a large association network in this study. Conclusion:: The molecular basis of the illness is well understood by computational approaches, which also provide biomarkers for targeted cancer therapy. These putative biomarkers may be useful in the early detection of disease. This study also shows that in H. pylori infection in early-stage gastric cancer, the top 10 hub genes constitute an essential component of the epithelial cell signaling pathways. These genes can further contribute to the future development of effective biomarkers.