Data_Sheet_1_Identification of a Five-Gene Signature and Establishment of a Prognostic Nomogram to Predict Progression-Free Interval of Papillary Thyr.pdf (1.73 MB)

Data_Sheet_1_Identification of a Five-Gene Signature and Establishment of a Prognostic Nomogram to Predict Progression-Free Interval of Papillary Thyroid Carcinoma.pdf

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posted on 15.11.2019, 04:23 by Mengwei Wu, Hongwei Yuan, Xiaobin Li, Quan Liao, Ziwen Liu

Background: The incidence of papillary thyroid carcinoma (PTC) is high and increasing worldwide. Although prognosis is relatively good, it is important to select the minority of patients with poorer prognosis to avoid side effects associated with unnecessary over-treatment in low-risk patients; this requires accurate prognostic predictions.

Materials and Methods: Six PTC expression datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas Thyroid Cancer (TCGA–THCA) database. Through integrated analysis of these datasets, highly reliable differentially-expressed genes (DEGs) between tumor and normal tissue were identified and lasso Cox regression was applied to identify DEGs related to the progression-free interval (PFI) and to establish a prognostic gene signature. The performance of a five-gene signature was evaluated based on a Kaplan–Meier curve, receiver operating characteristic (ROC), and Harrell's concordance index (C-index). Multivariate Cox regression analysis was used to identify factors associated with PTC prognosis. Finally, a prognostic nomogram was established based on the TCGA-THCA dataset.

Results: A novel five-gene signature was established to predict the PTC PFI, which included PLP2, LYVE1, FABP4, TGFBR3, and FXYD6, and the ROC curve and C-index showed good performance in both training and validation datasets. This could classify patients into high- and low-risk groups with distinct PFIs and differentiate PTC tumors from normal tissue. Univariate Cox regression revealed that this signature was an independent prognostic factor for PTC. The established nomogram, incorporating the prognostic gene signature and clinical parameters, was able to predict the PFI with high efficiency. The gene signature-based nomogram was superior to the American Thyroid Association (ATA) risk stratification to predict PTC PFI.

Conclusions: Our study identified a five-gene signature and established a prognostic nomogram, which were reliable in predicting the PFI of PTC; this could be beneficial for individualized treatment and medical decision making.

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