Bioinformatics analyses of significant prognostic risk markers for thyroid papillary carcinoma.

Abstract

This study was aimed to identify the prognostic risk markers for thyroid papillary carcinoma (TPC) by bioinformatics. The clinical data of TPC and their microRNAs (miRNAs) and genes expression profile data were downloaded from The Cancer Genome Atlas. Elastic net-Cox's proportional regression hazards model (EN-COX) was used to identify the prognostic associated factors. The receiver operating characteristic (ROC) curve and Kaplan-Meier (KM) curve were used to screen the significant prognostic risk miRNA and genes. Then, the target genes of the obtained miRNAs were predicted followed by function prediction. Finally, the significant risk genes were performed literature mining and function analysis. Total 1046 miRNAs and 20531 genes in 484 cases samples were identified after data preprocessing. From the EN-COX model, 30 prognostic risk factors were obtained. Based on the 30 risk factors, 3 miRNAs and 11 genes were identified from the ROC and KM curves. The target genes of miRNA-342 such as B-cell CLL/lymphoma 2 (BCL2) were mainly enriched in the biological process related to cellular metabolic process and Disease Ontology terms of lymphoma. The target genes of miRNA-93 were mainly enriched in the pathway of G1 phase. Among the 11 prognostic risk genes, v-maf avian musculoaponeurotic fibrosarcoma oncogene homologue F (MAFF), SRY (sex-determining region Y)-box 4 (SOX4), and retinoic acid receptor, alpha (RARA) encoded transcription factors. Besides, RARA was enriched in four pathways. These prognostic markers such as miRNA-93, miRNA-342, RARA, MAFF, SOX4, and BCL2 may be used as targets for TPC chemoprevention.