Identification of a Transcription Factor Signature That Can Predict Breast Cancer Survival

Abstract

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.

Identification of a Novel Gene Signature with DDR and EMT Difunctionalities for Predicting Prognosis, Immune Activity, and Drug Response in Breast Cancer

“…Subsequently, a prognostic model consisting of 6-DEDGs was constructed by univariate Cox regression and LASSO Cox regression analysis. The model had better predictive capability for BRCA's survival compared to previous prognostic genetic models (AUC = 0.5-0.6) [29,30], especially in the 5-year survival rate (AUC = 0.701). The AUC value is proportional to the model's predictive performance, and AUC greater than 0.7 means higher predictive ability [8].…”

Basement-Membrane-Related Gene Signature Predicts Prognosis in WHO Grade II/III Gliomas

“…In our study, the immune infiltration analysis showed that monocytes were associated with a good prognosis, while the opposite was true for resting memory CD4 T cells, in line with previous studies [ 39 ]. In addition, previous studies have also revealed that the M2 macrophages confer dismal prognosis in gliomas [ 40 ] and other types of tumors [ 41 ]. The mechanistic roles of macrophages in cancer progression have been summarized elsewhere [ 42 ].…”

Identification and validation of a novel prognostic signature based on transcription factors in breast cancer by bioinformatics analysis

“…TFs may serve as biomarkers to predict the prognosis of BRCA (23,24). Several TF-related signatures have been reported to predict the clinical outcomes of many cancers (19,(25)(26)(27); however, few studies have focused on TF-related signatures that can predict OS in BRCA. In our study, we established and validated a 9-TF signature risk model that could predict OS in BRCA via a comprehensive analysis of TF data.…”

“…Studies have developed a microRNA (miRNA) -based signature for BRCA (17) and an immunerelated long non-coding RNAs (lncRNAs) prognostic signature for TNBC (18), which provided the basis for predicting prognosis. Another study identified a novel prognostic TF signature for predicting disease-free survival (DFS) of BRCA patients (19). Nevertheless, only a few studies have systematically investigated the expression pattern, potential mechanism, and prognostic ability of TFs in BRCA.…”