Screening of DNA Damage Repair Genes Involved in the Prognosis of Triple-Negative Breast Cancer Patients Based on Bioinformatics - Wang, Nan; Gu, Yuanting; Chi, Jiangrui; Liu, Xinwei; Xiong, Youyi; Zhong, Chaochao; Wang, Fang; Wang, Xinxing; Li, Lin (Frontiers in Genetics, 2021)
Background: Triple-negative breast cancer (TNBC) is a special subtype of breast cancer with poor prognosis. DNA damage response（DDR）is one of the hallmarks of this cancer. However, the association of DDR genes with the prognosis of TNBC is still unclear. Methods: We identified differentially expressed genes (DEGs) between normal and TNBC samples from The Cancer Genome Atlas (TCGA). DDR genes were obtained from the Molecular Signatures Database (MSigDB) through six DDR gene sets. After the expression of six differential genes were verified by qRT-PCR, we then overlapped the DEGs with DDR genes. Based on univariate and LASSO Cox regression analyses, a prognostic model was constructed to predict overall survival (OS). Kaplan–Meieranalysis and receiver operating characteristic (ROC) curve were used to assess the performance of the prognostic model. Cox regression analysis was applied to identify independent prognostic factors in TNBC. The Human Protein Atlas (HPA) was used to study the immunohistochemical data of 6 DEGs. The prognostic model was validated using an independent dataset. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by using gene set enrichment analysis (GSEA). Single-sample gene set enrichment analysis (ssGSEA) was employed to estimate immune cells related to this prognostic model. Finally, we constructed a transcriptional factor (TF) network and a competing endogenous RNA (ceRNA) regulatory network. Results: 23 differentially expressed DDR genes were detected between TNBC and normal samples. The six-gene prognostic model we developed was shown to be related to OS in TNBC using univariate and LASSO Cox regression analyses. All the 6 DEGs were identified as significantly up-regulated in the tumor samples compared to the normal samples in qRT-PCR. The GSEA analysis indicated that the genes in the high-risk group were mainly correlated with leukocyte migration, cytokine interaction, oxidative phosphorylation, autoimmune diseases, and coagulation cascade. The mutation data revealed the mutated genes were different. The gene-TF regulatory network showed that Replication Factor C subunit 4 (RFC4) occupied the dominant position. Conclusions: We identified six gene markers related to DDR, which can predict prognosis and serve as an independent biomarker for TNBC patients.