Background. The expression pattern of transcription factors (TFs) can be used to develop potential prognostic biomarkers for cancer. In this study, we aimed to identify and validate a TF signature for predicting disease-free survival (DFS) of breast cancer (BRCA) patients. Methods. Lasso and the Cox regression analyses were applied to construct a TF signature based on a gene expression dataset from TCGA. The prognosis value of the TF signature was investigated in the TCGA database, and its reliability was further validated in 3 independent datasets from Gene Expression Omnibus (GEO). The prognosis performance of the TF signature was compared with 4 previously published gene signatures. To investigate the association between the TF signature and hallmarks of cancer, Gene Set Enrichment Analysis (GSEA) was carried out. The correlations of the TF signature and the levels of immune infiltration were also investigated. Results. An 11-TF prognostic signature was constructed with good survival prediction performance for BRCA patients. By using the risk score model based on the 11-TF signature, BRCA patients were stratified into low- and high-risk groups and showed good and poor disease-free survival (DFS), respectively. The risk score was an independent prediction indicator when adjusting for other clinicopathological factors. Furthermore, the 11-TF signature had a better survival prediction performance compared to 4 previously published gene signatures. Moreover, the risk score was a cancer hallmark. Finally, a high-risk score was associated with higher infiltration of M0 and M2 macrophages and was associated with a lower infiltration of resting memory CD4⁺ T cells and CD8⁺ T cells. Conclusion. The findings in this study identified and validated a novel prognostic TF signature, which is an independent biomarker for the prediction of DFS in BRCA patients.

中文翻译：

---

鉴定可预测乳腺癌生存的转录因子特征

背景。转录因子 (TF) 的表达模式可用于开发潜在的癌症预后生物标志物。在这项研究中，我们的目的是识别和验证用于预测乳腺癌 (BRCA) 患者无病生存 (DFS) 的 TF 特征。方法。应用 Lasso 和 Cox 回归分析来构建基于 TCGA 基因表达数据集的 TF 签名。在 TCGA 数据库中研究了 TF 特征的预后价值，并在来自 Gene Expression Omnibus (GEO) 的 3 个独立数据集中进一步验证了其可靠性。将 TF 特征的预后表现与 4 个先前发表的基因特征进行了比较。为了研究 TF 特征与癌症特征之间的关联，进行了基因集富集分析 (GSEA)。还研究了 TF 特征与免疫浸润水平的相关性。结果。构建的 11-TF 预后特征对 BRCA 患者具有良好的生存预测性能。通过使用基于 11-TF 特征的风险评分模型，BRCA 患者被分为低风险组和高风险组，并分别表现出良好和较差的无病生存 (DFS)。在调整其他临床病理因素时，风险评分是一个独立的预测指标。此外，与之前发布的 4 个基因签名相比，11-TF 签名具有更好的生存预测性能。此外，风险评分是癌症的标志。^{最后，高风险评分与 M0 和 M2 巨噬细胞的较高浸润相关，并且与静息记忆 CD4 +} T 细胞和 CD8 ⁺ T 细胞的较低浸润相关。结论。本研究的结果确定并验证了一种新的预后 TF 特征，它是预测 BRCA 患者 DFS 的独立生物标志物。