Identification of a Prognostic 3-Gene Risk Prediction Model for Thyroid Cancer

Abstract

Objective: We aimed to screen the genes associated with thyroid cancer (THCA) prognosis, and construct a poly-gene risk prediction model for prognosis prediction and improvement. Methods: The HTSeq-Counts data of THCA were accessed from TCGA database, including 505 cancer samples and 57 normal tissue samples. “edgeR” package was utilized to perform differential analysis, and weighted gene co-expression network analysis (WGCNA) was applied to screen the differential co-expression genes associated with THCA tissue types. Univariant Cox regression analysis was further used for the selection of survival-related genes. Then, LASSO regression model was constructed to analyze the genes, and an optimal prognostic model was developed as well as evaluated by Kaplan-Meier and ROC curves. Results: Three thousand two hundred seven differentially expressed genes (DEGs) were obtained by differential analysis and 23 co-expression genes (|COR| > 0.5, P < 0.05) were gained after WGCNA analysis. In addition, eight genes significantly related to THCA survival were screened by univariant Cox regression analysis, and an optimal prognostic 3-gene risk prediction model was constructed after genes were analyzed by the LASSO regression model. Based on this model, patients were grouped into the high-risk group and low-risk group. Kaplan-Meier curve showed that patients in the low-risk group had much better survival than those in the high-risk group. Moreover, great accuracy of the 3-gene model was revealed by ROC curve and the remarkable correlation between the model and patients' prognosis was verified using the multivariant Cox regression analysis. Conclusion: The prognostic 3-gene model composed by GHR, GPR125, and ATP2C2 three genes can be used as an independent prognostic factor and has better prediction for the survival of THCA patients.