Identification of novel gene signature predicting lymph node metastasis in papillary thyroid cancer via bioinformatics analysis and in vitro validation

Abstract

Background Although with a good prognosis of papillary thyroid cancer (PTC), the patients in PTC experiencing lymph node metastasis (LNM) remained higher recurrence and mortality rate. It was still essential to explore novel biomarkers or methods to predict and evaluate the situation in the stages of PTC. Method In this study, mRNA sequence datasets from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) were utilized to obtain differentially expressed genes (DEGs) between PTC tumor and normal specimens and DEGs related to lymph node metastasis were identified using Weighted Gene Co-expression Network Analysis (WGCNA) according to the clinical information. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to quest the biological functions and pathways. Furthermore, protein-protein interaction (PPI) network was constructed using STRING database and a prognosis model was established using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis based on the LNM-related DEGs. Finally, six hub genes were identified and verified in vitro experiments. Results LNM-related co-expression modules were identified using WGCNA analysis from samples of TCGA THCA and GSE60542. A novel six-gene signature model including COL8A2, MET, FN1, MPZL2, PDLIM4 and CLDN10 was established based on totally 52 DEGs from the intersection of those modules to predict the situation of lymph node metastasis in PTC. Those six hub genes were all higher expressed in PTC tumors and played potential biological functions on the development of PTC in vitro experiments, which had potential values as diagnostic and therapeutic targets.