Liu N, Zhang GD, Bai P, Su L, Tian H, He M. Eight hub genes as potential biomarkers for breast cancer diagnosis and prognosis: A TCGA-based study. World J Clin Oncol 2022; 13(8): 675-687 [PMID: 36160462 DOI: 10.5306/wjco.v13.i8.675]
Corresponding Author of This Article
Miao He, MS, Department of Hematology and Oncology, Chongqing Traditional Chinese Medicine Hospital, Chengdu University of Traditional Chinese Medicine, Daomenkou 40, Chongqing 400011, China. zhuytzhuzh@163.com
Research Domain of This Article
Oncology
Article-Type of This Article
Clinical and Translational Research
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Oncol. Aug 24, 2022; 13(8): 675-687 Published online Aug 24, 2022. doi: 10.5306/wjco.v13.i8.675
Eight hub genes as potential biomarkers for breast cancer diagnosis and prognosis: A TCGA-based study
Nan Liu, Guo-Duo Zhang, Ping Bai, Li Su, Hao Tian, Miao He
Nan Liu, Guo-Duo Zhang, Ping Bai, Li Su, Miao He, Department of Hematology and Oncology, Chongqing Traditional Chinese Medicine Hospital, Chengdu University of Traditional Chinese Medicine, Chongqing 400011, China
Hao Tian, Department of Breast and Thyroid Surgery, Southwest Hospital, Army Medical University, Chongqing 400038, China
Author contributions: Liu N performed the experiment and wrote the paper; Liu N, Zhang GD, and Bai P contributed to the bioinformatics analysis and figure preparation; Tian H and Su L modified the structure and language of the manuscript; He M and Tian H contributed to the conception and design of the study and the revisions of the manuscript; All authors have read and approved the final manuscript.
Institutional review board statement: Not applicable.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Miao He, MS, Department of Hematology and Oncology, Chongqing Traditional Chinese Medicine Hospital, Chengdu University of Traditional Chinese Medicine, Daomenkou 40, Chongqing 400011, China. zhuytzhuzh@163.com
Received: August 2, 2021 Peer-review started: August 2, 2021 First decision: November 6, 2021 Revised: December 23, 2021 Accepted: July 26, 2022 Article in press: July 26, 2022 Published online: August 24, 2022
ARTICLE HIGHLIGHTS
Research background
Breast cancer (BC) is the most common malignant tumor in women. In 2019, 268600 new BC patients and 41760 new BC deaths were reported, accounting for 30% of all new cancer cases and 15% of cancer-related deaths. Therefore, it is particularly important to explore more sensitive and specific biomarkers for further understanding the pathogenesis of BC and the choice of treatment strategies.
Research motivation
Exploring more valuable therapeutic targets would be helpful in treating with high efficacy.
Research objectives
This study aimed to identify novel biomarkers for BC.
Research methods
The limma package of R software and clusterProfiler package were used to analyze the differentially expressed genes (DEGs) in tumor tissues compared with the normal tissues, respectively. The protein-protein interaction network (PPI) analysis was used to investigate the hub-genes through cytohubba algorithm by the Cytoscape software. Survival analysis of the hub-genes were carried out through the Kaplan-Meier database. The expression level of these hub-genes was validated in the GEPIA database and the Human Protein Atlas database.
Research results
Upregulated genes mainly enriched in the cytokine-cytokine receptor interaction, cell cycle, and p53 signaling pathway (P < 0.01). The downregulated genes were mainly enriched in the cytokine-cytokine receptor interaction, peroxisome proliferator-activated receptor signaling pathway, and AMP-activated protein kinase signaling pathway (P < 0.01).
Research conclusions
MAD2L1, PLK1, SAA1, CCNB1, SHCBP1, KIF4A, ANLN, and ERCC6L may act as biomarkers for diagnosis and prognosis in BC patients.
Research perspectives
Proper validations must be made in future studies.
