Research Report

The Identification of New Biomarkers for Breast Cancer: a Study Based on TCGA and GEO Datasets  

Jian Wang1 , Rongjin  Shao1 , Weida Gong1 , Xu Lv1 , Jinping Li2
1 Cancer Hospital of Yixing City, Yixin, 214200;
2 Cancer Hospital of Ningxia Medical University, Yinchuan, 750004
Author    Correspondence author
International Journal of Clinical Case Reports, 2020, Vol. 10, No. 3   
Received: 06 Feb., 2020    Accepted: 29 Mar., 2020    Published: 27 May, 2020
© 2020 BioPublisher Publishing Platform
This article was first published in Genomics and Applied Biology in Chinese, and here was authorized to translate and publish the paper in English under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

The purpose of this study was to forecast and explore the possible mechanism and clinical value of genetic markers in the evolution of breast cancer with a merged database to screen the prognostic genes of breast cancer. First,we screened the overlapped differentially expressed genes (DEGs) of GSE22820 and TCGA breast caner datasets by R language. Second, subsequent protein–protein interactions network analysis recognized the hub genes and top three modules among these DEGs in Cytoscape software. Then more functional analysis including Gene Ontology and KEGG pathway analysis and gene set enrichment analysis were processed to investigate the role of these genes and potential underlying mechanisms in BC. And finally Kaplan–Meier analysis and Cox hazard ratio analysis were performed to elucidate the diagnostic and prognostic effects of these genes. Analysis of relevant data shows that the expression levels of fifteen genes were interrelated with survival prognosis, and the overall survival time of the patients with high expression of the gene was shorter than those with low expression (p <0.05). But the Cox proportion hazard ratio analysis that the 3 genes were Significance, UBE2T, ERCC6L, and RAD51 could be considered independent factors for prognosis survival(p<0.05). Gene set enrichment analysis showed that the cell cycle, basic transcription factors and oocyte meiosis were significantly enriched in UBE2T, ERCC6L and RAD51 genes. Finally, we got a Conclusion that The high expression of three genetic markers is a poor prognostic factor for breast cancer and can be used as an effective biomarker to predict metastasis and prognosis of breast cancer patients.

Keywords
Bioinformatics; Biomarker; Breast cancer; Prognostic markers

(The advance publishing of the abstract of this manuscript does not mean final published, the end result whether or not published will depend on the comments of peer reviewers and decision of our editorial board.)
International Journal of Clinical Case Reports
• Volume 10
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. Jian Wang
. Rongjin  Shao
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. Xu Lv
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