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pmid:35280505
Biomarkers for Breast Adenocarcinoma Using In Silico Approaches.
Jhansi Pandi · Ajucarmelprecilla Arulprakasam · Ranjithkumar Dhandapani · Saikishore Ramanathan · Sathiamoorthi Thangavelu · Jayaprakash Chinnappan · V Vidhya Rajalakshmi · Saad Alghamdi · Nashwa Talaat Shesha · S Prasath
Evidence-based complementary and alternative medicine : eCAM · 2022 doi:10.1155/2022/7825272
Paper
complete
Experiment
complete
Data
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Analysis
complete
Execution
absent
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Complete file
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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. 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