Biomarkers for Breast Adenocarcinoma Using In Silico Approaches. (3rd March 2022)

• Record Type: Journal Article
• Authors: Pandi, Jhansi; Arulprakasam, Ajucarmelprecilla; Dhandapani, Ranjithkumar; Ramanathan, Saikishore; Thangavelu, Sathiamoorthi; Chinnappan, Jayaprakash; Vidhya Rajalakshmi, V.; Alghamdi, Saad; Shesha, Nashwa Talaat; Prasath, S.
• Other Names: Roy Arpita Academic Editor.
• Is Part Of: Evidence-based complementary and alternative medicine. Volume 2022(2022)
• Publication Date: 2022-03-03
• Subjects: Alternative medicine -- Periodicals; 615.505
• DOI: 10.1155/2022/7825272
• Languages: English
• ISSNs: 1741-427X

Abstract

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.