Integrated analysis of RNA-binding proteins in thyroid cancer

Abstract

Recently, the incidence of thyroid cancer (THCA) has been on the rise. RNA binding proteins (RBPs) and their abnormal expression are closely related to the emergence and pathogenesis of tumor diseases. In this study, we obtained gene expression data and corresponding clinical information from the TCGA database. A total of 162 aberrantly expressed RBPs were obtained, comprising 92 up-regulated and 70 down-regulated RBPs. Then, we performed a functional enrichment analysis and constructed a PPI network. Through univariate Cox regression analysis of key genes and found that NOLC1 (p = 0.036), RPS27L (p = 0.011), TDRD9 (p = 0.016), TDRD6 (p = 0.002), IFIT2 (p = 0.037), and IFIT3 (p = 0.02) were significantly related to the prognosis. Through the online website Kaplan-Meier plotter and multivariate Cox analysis, we identified 2 RBP-coding genes (RPS27L and IFIT3) to construct a predictive model in the entire TCGA dataset and then validate in two subsets. In-depth analysis revealed that the data gave by this model, the patient’s high-risk score is very closely related to the overall survival rate difference (p = 0.038). Further, we investigated the correlation between the model and the clinic, and the results indicated that the high-risk was in the male group (p = 0.011) and the T3-4 group (p = 0.046) was associated with a poor prognosis. On the whole, the conclusions of our research this time can make it possible to find more insights into the research on the pathogenesis of THCA, this could be beneficial for individualized treatment and medical decision making.