Identification of a Recurrence Signature and Validation of Cell Infiltration Level of Thyroid Cancer Microenvironment

Frontiers in Endocrinology

卷:11

Yang Wang¹  Ying Wang¹  Jing Wu²  Kaile Wu³  Liang Zhang³  Yehai Liu³  Xiaobo Li³ 

[1] Neck Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China;

[2] Neck Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China;

[3] Department of Otorhinolaryngology, Head &

关键词: thyroid cancer;    signature;    GEO;    TCGA;    recurrence;   

DOI  :  10.3389/fendo.2020.00467

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown