Identification of a three-gene signature associated with immune infiltration to distinguish follicular thyroid cancer and adenoma: an integrated bioinformatic analysis

Abstract Background Follicular thyroid cancer (FTC), accounting for about 15% of all thyroid cancers, was characterized by a more invasive nature and earlier hematogenous spread. The distinction between FTC and follicular thyroid adenoma (FTA) remained challenging for clinical scientists due to their highly similar cytological features at present, and frequently led to potentially unnecessary surgical procedures. The present study aimed to identify potential new biomarkers to improve diagnosis. Methods Three GEO datasets (GSE82208, GSE15045, and GSE29315) were downloaded to screen differentially expressed genes (DEGs) between FTC and FTA. GO and KEGG enrichment analysis were explored by ClusterProfiler package. A protein-protein interaction (PPI) network was established and hub genes were selected. Multivariate stepwise logistic regression analysis and receiver operating characteristic (ROC) analysis were used to construct and evaluate a gene signature model for differential diagnosis. Diagnostic efficacy of the model was further confirmed in an independent dataset GSE27155. CIBERSORT was used to characterize 22 infiltrating immune cell fractions of FTC and FTA samples. Results After gene integration, a total of 210 DEGs (55 upregulated and 155 downregulated) were identified, which were enriched in immune or cancer-related biological processes and pathways, such as cytokine-cytokine receptor interaction, fluid shear stress and atherosclerosis, and IL-17 signaling pathway. Seven hub genes were selected from PPI analysis. A three-gene signature (FOS, IL1B, and TOP2A) was constructed to distinguish FTC and FTA, with an area under the curve (AUC) of 0.839. Diagnostic efficacy of the signature was further conformed in an independent dataset GSE27155 with an AUC of 0.862. Moreover, the gene signature was associated with CD8 T-cell, which was significant different in FTC compared to FTA. Conclusions This study identified potential crucial genes and pathways that might be involved in the pathophysiology of FTC. Especially, a three-gene signature with diagnostic value was development. Furthermore, the landscape of immune infiltration in FTC was explored for the first time. The results would promote our understanding about the molecular mechanism of FTC development and provide potential target genes for differential diagnosis.