Transcription Factor Profiling to Predict Recurrence-Free Survival in Breast Cancer: Development and Validation of a Nomogram to Optimize Clinical Management

Abstract

Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer-related death in young women. Several prognostic and predictive transcription factor (TF) markers have been reported for BC; however, they are inconsistent due to small datasets, the heterogeneity of BC, and variation in data pre-processing approaches. This study aimed to identify an effective predictive TF signature for the prognosis of patients with BC. We analyzed the TF data of 868 patients with BC in The Cancer Genome Atlas (TCGA) database to investigate TF biomarkers relevant to recurrence-free survival (RFS). These patients were separated into training and internal validation datasets, with GSE2034 and GSE42568 used as external validation sets. A nine-TF signature was identified as crucially related to the RFS of patients with BC by univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and multivariate Cox proportional hazard analysis in the training dataset. Kaplan-Meier analysis revealed that the nine-TF signature could significantly distinguish high-and low-risk patients in both the internal validation dataset and the two external validation sets. Receiver operating characteristic (ROC) analysis further verified that the nine-TF signature showed a good performance for predicting the RFS of patients with BC. In addition, we developed a nomogram based on risk score and lymph node status, with C-index, ROC, and calibration plot analysis, suggesting that it displays good performance and clinical value. In summary, we used integrated bioinformatics approaches to identify an effective predictive nine-TF signature which may be a potential biomarker for BC prognosis.

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

“…In this study, our 11-TF signature was compared with 4 previously published prognostic signature panels: the 9-TF signature reported by Chen et al [ 28 ], the 12-lncRNA signature by Zhou et al [ 29 ], the 13-gene epigenetic signature by Bao et al [ 30 ], and the 12-gene prognostic signature by Mao et al [ 31 ]. As shown in Figure 4(b) , all patients in the low-risk group had a longer DFS compared to patients in the high-risk group (Chen et al: log-rank P = 1.24 e − 04; Bao et al: log-rank P = 1.24 e − 04; Mao et al: log-rank P = 3.4 e − 03; and Zhou et al: log-rank P = 1.52 e − 03).…”

“…In recent years, several studies have determined multigene panels that might serve as prognosis indicators in BRCA. For example, Chen et al [ 28 ] used gene expression data and clinical data from the TCGA and GEO databases to identify a 9-TF signature, which may play important prognostic roles in patients with BRCA. Sun et al [ 32 ] analyzed lncRNA expression profiles of BRCA patients from the GEO database to identify a nine-lncRNA signature which could be a valuable prognostic biomarker to predict the metastatic risk in patients with BRCA.…”

“…In recent years, several studies have determined multigene panels that might serve as prognosis indicators in BRCA. For example, Chen et al [28] Computational and Mathematical Methods in Medicine metastatic risk in patients with BRCA. In another study, the lncRNA expression profiles of BRCA patients were analyzed to construct a 12-lncRNA signature to predict recurrencefree survival [29].…”

“…Signatures. In this study, our 11-TF signature was compared with 4 previously published prognostic signature panels: the 9-TF signature reported by Chen et al [28], the 12-lncRNA signature by Zhou et al [29], the 13-gene epigenetic signature by Bao et al [30], and the 12-gene prognostic signature by Mao et al [31]. As shown in Figure 4 3.4.…”

Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges

“…Studies in primary cancers other than PCa provide tissue-specific tumorigenic changes that can point to similarities between PCa and other primary tumors, as well as provide clues as to how to derive clinically relevant TF signatures. For example, a nine TF signature derived from the breast cancer TCGA dataset closely recapitulated recurrence-free survival through univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and multivariate Cox proportional hazard analysis [ 114 ].…”