A 6 transcription factors-associated nomogram predicts the recurrence-free survival of thyroid papillary carcinoma

Abstract

Various researches demonstrated that transcription factors (TFs) played a crucial role in the progression and prognosis of cancer. However, few studies indicated that TFs were independent biomarkers for the prognosis of thyroid papillary carcinoma (TPC). Our aim was to establish and validate a novel TF signature for the prediction of TPC patients’ recurrence-free survival (RFS) from The Cancer Genome Atlas (TCGA) database to improve the prediction of survival in TPC patients. The genes expression data and corresponding clinical information for TPC were obtained from TCGA database. In total, 722 TFs and 545 TPC patients with eligible clinical information were determined to build a novel TF signature. All TFs were included in a univariate Cox regression model. Then, the least absolute shrinkage and selection operator Cox regression model was employed to identify candidate TFs relevant to TPC patients’ RFS. Finally, multivariate Cox regression was conducted via the candidate TFs for the selection of the TF signatures in the RFS assessment of TPC patients. We identified 6 TFs that were related to TPC patients’ RFS. Receiver operating characteristic analysis was performed in training, validation, and whole datasets, we verified the high capacity of the 6-TF panel for predicting TPC patients’ RFS (AUC at 1, 3, and 5 years were 0.880, 0.934, and 0.868, respectively, in training dataset; 0.760, 0.737, and 0.726, respectively, in validation dataset; and 0.777, 0.776, and 0.761, respectively, in entire dataset). The result of Kaplan–Meier analysis suggested that the TPC patients with low scores had longer RFS than the TPC patients with high score ( P = .003). A similar outcome was displayed in the validation dataset ( P = .001) and the entire dataset ( P = 2e-05). In addition, a nomogram was conducted through risk score, cancer status, C-index, receiver operating characteristic, and the calibration plots analysis implied good value and clinical utility of the nomogram. We constructed and validated a novel 6-TF signature-based nomogram for predicting the RFS of TPC patients.