Identification of a Recurrence Signature and Validation of Cell Infiltration Level of Thyroid Cancer Microenvironment
Liang Zhang, Ying Wang, Xiaobo Li, Yang Wang, Kaile Wu, Jing Wu, Yehai Liu
Abstract
Though many patients with thyroid cancer may be indolent, there are still about 50% lymph node metastases and 20% the recurrence rates. There is still no ideal method to predict its relapse. In this study, we analyzed the gene transcriptome profiles of eight Gene Expression Omnibus (GEO), and next screened 77 commonly differential expressed genes. Next, Least Absolute Shrinkage and Selection Operator (LASSO) regression model was performed and seven genes (i.e., FN1, PKIA, TMEM47, FXYD6, SDC2, CD44, and GGCT) were then identified, which is highly associated with recurrence data from the Cancer Genome Atlas (TCGA) database. These patients were then divided into low and high-risk groups with specific risk-score formula. Univariate and multivariate Cox regression further revealed that the 7-mRNA signature plays a functional causative role independent of clinicopathological characteristics. The 7-mRNA-signature integrated nomogram showed better discrimination, and decision curve analysis demonstrated that it is clinically useful. Besides, patient with lower risk score shows a relatively lower level of activated dendritic cells (DCs), resting DCs, regulatory T cells and γδT cells, and process of DCs apoptotic. In conclusion, our present immune-related classifier could produce a potential tool for predicting early-relapse.