Abstract
OBJECTIVE: Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with rising incidence globally. Current prognostic markers often lack precision for individualized management. This study develops a prognostic signature based on voltage-gated ion channel (VGIC) genes and explores its relationship with the tumor metabolic microenvironment in PTC. METHODS: We analyzed transcriptomic and clinical data from GEO and TCGA, focusing on VGIC genes identified via GeneCards. Prognostic VGICs were selected using univariate Cox regression, LASSO, and multivariate Cox analysis to construct a risk-score model. Model performance was assessed via ROC curves and nomograms. Kaplan-Meier analysis compared survival between high- and low-risk groups. We further evaluated differences in immune cell infiltration and metabolic microenvironment. Key differentially expressed genes (DEGs) underwent experimental validation. RESULTS: From integrated GEO and GeneCards data, we identified 172 VGIC-related DEGs. LASSO-Cox regression refined this set to six prognostic VGIC genes: PDLIM3, FGF7, ANO5, ATP2C2, PLAG1, and STMN2. High-risk patients exhibited significantly poorer overall survival. The high- and low-risk groups also showed distinct immune profiles and metabolic microenvironments. A nomogram incorporating patient age and risk score predicted 1-, 3-, and 5-year survival with strong accuracy. RT-qPCR confirmed differential expression of the six genes in PTC cell lines: PDLIM3, FGF7, ANO5, and ATP2C2 were downregulated in PTC versus normal thyroid cells. Among them, ATP2C2 emerged as a key regulator linked to lipid metabolism gene PLA2R1. Functional assays revealed that manipulating ATP2C2 levels modulated PLA2R1 expression. CONCLUSION: Specific VGIC gene signatures provide powerful prognostic biomarkers in PTC. Beyond improving prognosis, targeting these VGICs-especially ATP2C2-offers a novel approach to reshape the tumor metabolic microenvironment and inhibit PTC progression.