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.
Highlights
Breast cancer (BC) is one of the most common malignancies and a leading cause of cancer death among women worldwide (Kwon et al, 2015; Hong et al, 2017; Zhang et al, 2017)
A linear combination of nine transcription factor (TF) (FUBP3, CLOCK, TFCP2L1, RFX1, PLAGL1, TBX2, KCNIP3, OTX1, and BACH2) was identified as an independent predictor of the survival of patients with BC. This nine-TF signature was found to have significant prognostic roles in patients with BC, indicating that the nine TFs may have underlying roles in the molecular pathogenesis, clinical progression, and prognosis of BC and may have the potential to improve the clinical prognosis of patients with BC
The downregulation of RFX1 has been shown to predict poor prognosis in patients with small hepatocellular carcinoma (Liu et al, 2018), while TFCP2/TFCP2L1/UBP1 has been found to act as a TF in cancer (Kotarba et al, 2018)
Summary
Breast cancer (BC) is one of the most common malignancies and a leading cause of cancer death among women worldwide (Kwon et al, 2015; Hong et al, 2017; Zhang et al, 2017). The 5-year survival rate of patients with metastatic BC is around 20% (Chau and Ashcroft, 2004); the identification of sensitive and specific biomarkers of BC prognosis is essential. It has become increasingly apparent over the past few decades that tumor biomarker signature is crucial for exploring effective treatments for BC. A recent study that implemented a machine learning approach revealed that microRNAs can serve as biomarkers in BC (Rehman et al, 2019)
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