ABSTRACT
Perception of hub genes engaged in metastatic gastric cancer (mGC) promotes novel ways to diagnose and treat the illness. The goal of this investigation is to recognize the hub genes and reveal its molecular mechanism. In order to explore the potential facts for gastric cancer, the expression profiles of two different datasets were used (GSE161533 and GSE54129). The genes were confirmed to be part of the PPI network for gastric cancer pathogenesis and prognosis. In Cytoscape, the CytoHubba module was used to discover the hub genes. Responsible hub genes were identified. Data from Kaplan-Meier plotter confirmed the predictive value of these distinct genes in various stages of gastric malignancy. Upregulated and downregulated genes were identified to utilize for further analysis. Positive regulation by a host of viral process, positive regulation of granulocyte differentiation, negative regulation of histone H3-K9 methylation were found in DEGs analysis. In addition, five KEGG pathways were identified as an essential enhancer that include nucleotide excision repair; base excision repair; DNA replication; homologous recombination; and complement and coagulation cascades. POLE, BUB1B, POLD4, C3, BLM, CCT7, PRPF31, APEX1, PSMA7, and CDC45 were chosen as hub genes after combining the PPI results. Our study recommends that BUB1B, CCT7, APEX1, PSMA7, and CDC45 might be potential biomarkers for gastric cancer. These biomarkers are upregulated genes. Therefore, suppression of these genes will increase the survival rate in gastric cancer patients.
ABSTRACT
This work elucidates the idea of finding probable critical genes linked to breast adenocarcinoma. In this study, the GEO database gene expression profile data set (GSE70951) was retrieved to look for genes that were expressed variably across breast adenocarcinoma samples and healthy tissue samples. The genes were confirmed to be part of the PPI network for breast cancer pathogenesis and prognosis. In Cytoscape, the CytoHubba module was used to discover the hub genes. For correlation analysis, the predictive biomarker of these hub genes, as well as GEPIA, was used. A total of 155 (85 upregulated genes and 70 downregulated genes) were identified. By integrating the PPI and CytoHubba data, the major key/hub genes were selected from the results. The KM plotter is employed to find the prognosis of those major pivot genes, and the outcome shows worse prognosis in breast adenocarcinoma patients. Further experimental validation will show the predicted expression levels of those hub genes. The overall result of our study gives the consequences for the identification of a critical gene to ease the molecular targeting therapy for breast adenocarcinoma. It could be used as a prognostic biomarker and could lead to therapy options for breast adenocarcinoma.
