Thyroid cancer (THCA) remains a prevalent endocrine malignancy, with limited molecular markers for accurate diagnosis and targeted therapy. This study aimed to identify key biomarkers and therapeutic targets for THCA using integrative bioinformatics and experimental validation. We used differential gene expression analysis, gene enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms to identify potential biomarkers. Receiver operating characteristic (ROC) curve analysis, immunohistochemical data from the Human Protein Atlas (HPA), quantitative real-time PCR (qPCR), and immune infiltration analysis were used for further validation. Additionally, ROC and multivariate Cox regression analyses were conducted to quantitatively evaluate the diagnostic and prognostic performance of candidate genes. Four candidate biomarkers (VEGFA, TYK2, NRP1, and C5AR1) were identified, but only C5AR1 showed statistical significance in ROC and prognostic analyses. Specifically, C5AR1 demonstrated a strong diagnostic value (AUC = 0.873, 95% CI: 0.822-0.924) and was significantly associated with poorer overall survival (hazard ratio HR = 2.41, 95% CI: 1.15-5.06, p = 0.021). Further qPCR and immune infiltration analyses confirmed that C5AR1 expression was associated with immune cell infiltration, potentially influencing THCA progression. This study identifies C5AR1 as a key biomarker in THCA, suggesting its role in immune-related tumor progression. These findings indicate statistical associations rather than causal mechanisms, highlighting the need for further experimental validation. The results provide a foundation for targeted immunotherapy strategies in THCA treatment.
Sun et al. (Thu,) studied this question.
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