Department of Breast Surgery, The Third Hospital of Jilin University, Changchun, Jilin 130033, China.
Department of Vascular Surgery, The Third Hospital of Jilin University, Changchun, Jilin 130033, China.
Comput Math Methods Med. 2021 Feb 19;2021:2649123. doi: 10.1155/2021/2649123. eCollection 2021.
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.
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.
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.
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.
转录因子（TFs）的表达模式可用于开发癌症的潜在预后生物标志物。在这项研究中，我们旨在确定和验证用于预测乳腺癌（BRCA）患者无病生存（DFS）的 TF 特征。
应用 Lasso 和 Cox 回归分析基于 TCGA 的基因表达数据集构建 TF 特征。在 TCGA 数据库中研究 TF 特征的预后价值，并在 3 个来自基因表达综合数据库（GEO）的独立数据集进一步验证其可靠性。比较 TF 特征与 4 个先前发表的基因特征的预后性能。为了研究 TF 特征与癌症标志之间的关联，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。
构建了一个具有良好生存预测性能的 11-TF 预后特征，基于 11-TF 特征的风险评分模型，将 BRCA 患者分为低风险和高风险组，分别显示出良好和不良的无病生存（DFS）。在调整其他临床病理因素后，风险评分是独立的预测指标。此外，11-TF 特征的生存预测性能优于 4 个先前发表的基因特征。此外，风险评分是癌症标志。最后，高风险评分与 M0 和 M2 巨噬细胞的高浸润有关，与静止记忆 CD4 T 细胞和 CD8 T 细胞的低浸润有关。
本研究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。
