Identification of prognostic biomarkers in papillary thyroid cancer and developing non-invasive diagnostic models through integratedbioinformatics analysis

Backgrounds: Papillary thyroid cancer (PTC) is the most frequent subtype of thyroidcarcinoma, which is mainly detected in patients bearing benign thyroid nodules (BTN). Due tothe invasiveness of accurate diagnostic tests, currently there is a need to discover applicablebiomarkers for PTC. So, in this study, we aimed to identify the genes associated with prognosisin PTC. Besides, we performed a machine learning tool in order to develop a non-invasivediagnostic approach for PTC.Materials and Methods: for the purposes of the study, the miRNA dataset GSE۱۳۰۵۱۲ wasdownloaded from the GEO database and then analyzed to identify the common differentiallyexpressed miRNAs in patients with non-metastatic PTC (nm-PTC)/metastatic PTC (m-PTC)compared with BTNs. As well, the SVM was applied to differentiate patients with PTC fromthose patients with BTN using the common DEMs. A protein-protein interaction network wasalso constructed based on the targets of the common DEMs. Thereafter, functional analysis wasperformed. Moreover, the hub genes were determined and survival analysis was then executed.Results: A total of three common miRNAs were found to be differentially expressed amongpatients with nm-PTC/m-PTC compared with BTNs. In addition, it was established that theautophagosome maturation, ciliary basal body-plasma membrane docking, antigen processing asubiquitination & proteasome degradation, and class I MHC mediated antigen processing &presentation are associated with the pathogenesis of PTC. Furthermore, it was illustrated thatRPS۶KB۱, CCNT۱, SP۱, and CHD۴ may serve as new potential biomarkers for PTC prognosis.Conclusion: RPS۶KB۱, CCNT۱, SP۱, and CHD۴ may be considered as new potentialbiomarkers used for prognostic aims in PTC. However, performing validation tests is inevitablein the future.