Published online Aug 24, 2022. doi: 10.5306/wjco.v13.i8.675
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
Processing time: 386 Days and 8.1 Hours
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.
Exploring more valuable therapeutic targets would be helpful in treating with high efficacy.
This study aimed to identify novel biomarkers for BC.
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.
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).
MAD2L1, PLK1, SAA1, CCNB1, SHCBP1, KIF4A, ANLN, and ERCC6L may act as biomarkers for diagnosis and prognosis in BC patients.
Proper validations must be made in future studies.
