Recurrence of Papillary Thyroid Cancer: A Systematic Appraisal of Risk Factors

Background Thyroid cancer recurrence is associated with increased mortality and adverse outcomes. Recurrence risk is currently predicted using clinical tools, often restaging patients after treatment. Detailed understanding of recurrence risk at disease-onset could lead to personalised and improved patient care. Objective To perform a comprehensive bioinformatic and experimental analysis of 3 levels of genetic change (mRNA, microRNA, and somatic mutation) apparent in recurrent tumours and construct a new combinatorial prognostic risk model. Methods We analysed The Cancer Genome Atlas data (TCGA) to identify differentially expressed genes (mRNA/microRNA) in 46 recurrent versus 455 non-recurrent thyroid tumours. Two exonic mutational pipelines were used to identify somatic mutations. Functional gene analysis was performed in cell-based assays in multiple thyroid cell lines. The prognostic value of genes was evaluated with TCGA datasets. Results We identified a total of 128 new potential biomarkers associated with recurrence, including 40 mRNAs, 39 miRNAs and 59 genetic variants. Among differentially expressed genes, modulation of FN1, ITGα3 and MET had a significant impact on thyroid cancer cell migration. Similarly, ablation of miR-486 and miR-1179 significantly increased migration of TPC-1 and SW1736 cells. We further utilised genes with a validated functional role and identified a 5 gene risk score classifier as an independent predictor of thyroid cancer recurrence. Conclusions Our newly proposed risk model based on combinatorial mRNA and microRNA expression has potential clinical utility as a prognostic indicator of recurrence. These findings should facilitate earlier prediction of recurrence with implications for improving patient outcome by tailoring treatment to disease risk and increasing post-treatment surveillance.