An insilico analysis: three upregulated microRNAs as potential diagnostic biomarkers of Papillary Thyroid Carcinoma (PTC).

Abstract

BACKGROUND: Papillary thyroid carcinoma (PTC) represents the most prevalent subtype of thyroid cancer. Accumulating evidence indicates that specific microRNAs are consistently dysregulated in PTC and may hold value as biomarker candidates. Accordingly, this study aims to prioritize consistently upregulated microRNAs associated with PTC using an integrative in silico analysis of publicly available datasets. METHODS: This study conducted a comprehensive analysis of miRNA expression patterns using datasets available through A Database of Differentially Expressed miRNAs in Human Cancers (dbDEMC). The dataset was then processed through a data mining approach with a cutoff of P-value < 0.05 and log2FC > 1.5 to identify miRNAs that were significantly and consistently upregulated in the datasets. The target genes are predicted through miRDIP, miRTarBase, miRPathDB, and GEPIA2. Gene ontology and pathway enrichment analysis were performed in ShinyGO and EnrichR. To assess the diagnostic ability of the three miRNAs, CancerMIRNome is used to identify the ROC curve analysis results of each miRNA. RESULTS: This exploratory in silico analysis identified 85 differentially expressed miRNAs in PTC, with hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-5p consistently upregulated across datasets. Functional enrichment and TCGA-based ROC analyses suggest that these miRNAs may have biological and discriminatory relevance in PTC. However, these findings are hypothesis-generating and require further experimental and clinical validation before any diagnostic application can be considered. CONCLUSION: In summary, our study identified the potential of hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-146b-5p as potential diagnostic biomarkers of PTC.