Table_1_Screening of DNA Damage Repair Genes Involved in the Prognosis of Triple-Negative Breast Cancer Patients Based on Bioinformatics.xlsx

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 through six DDR gene sets. After the expression of six differential genes were verified by quantitative real-time polymerase chain reaction (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–Meier analysis and receiver operating characteristic 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 was used to study the immunohistochemical data of six DEGs. The prognostic model was validated using an independent dataset. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analysis were performed by using gene set enrichment analysis (GSEA). Single-sample gene set enrichment analysis was employed to estimate immune cells related to this prognostic model. Finally, we constructed a transcriptional factor (TF) network and a competing endogenous RNA regulatory network.
Results: Twenty-three 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 six 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 occupied the dominant position.
Conclusion: We identified six gene markers related to DDR, which can predict prognosis and serve as an independent biomarker for TNBC patients.

背景：三阴性乳腺癌（Triple-negative breast cancer, TNBC）是一类预后较差的特殊乳腺癌亚型。DNA损伤应答（DNA damage response, DDR）是该类癌症的标志性特征之一，但DDR基因与三阴性乳腺癌预后的关联目前仍不明确。
方法：我们从癌症基因组图谱（The Cancer Genome Atlas, TCGA）中筛选得到正常样本与TNBC样本间的差异表达基因（differentially expressed genes, DEGs）。DDR基因通过6个DDR基因集从分子特征数据库（Molecular Signatures Database）中获取。在通过实时定量聚合酶链反应（quantitative real-time polymerase chain reaction, qRT-PCR）验证6个差异基因的表达水平后，我们将DEGs与DDR基因取交集。基于单因素及LASSO Cox回归分析，构建了用于预测总生存期（overall survival, OS）的预后模型。采用Kaplan-Meier分析与受试者工作特征曲线（Receiver Operating Characteristic curve, ROC曲线）评估该预后模型的性能，通过Cox回归分析识别TNBC的独立预后因素。利用人类蛋白质图谱（Human Protein Atlas）探究6个DEGs的免疫组化数据。使用独立数据集对该预后模型进行验证。通过基因集富集分析（gene set enrichment analysis, GSEA）完成基因本体（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析。采用单样本基因集富集分析（single-sample gene set enrichment analysis, ssGSEA）估算与该预后模型相关的免疫细胞浸润情况。最终构建了转录因子（transcriptional factor, TF）调控网络与内源竞争RNA（competing endogenous RNA, ceRNA）调控网络。
结果：在TNBC与正常样本间共筛选得到23个差异表达DDR基因。通过单因素及LASSO Cox回归分析证实，我们构建的6基因预后模型与TNBC的OS显著相关。qRT-PCR结果显示，相较于正常样本，6个DEGs在肿瘤样本中均显著上调。GSEA分析结果表明，高风险组基因主要富集于白细胞迁移、细胞因子互作、氧化磷酸化、自身免疫性疾病及凝血级联反应通路。突变数据显示，不同风险组的突变基因存在差异。基因-TF调控网络显示，复制因子C亚基4（Replication Factor C subunit 4）占据核心主导地位。
结论：本研究筛选得到6个与DDR相关的基因标志物，其可用于预测TNBC患者的预后，并可作为TNBC患者的独立生物标志物。