Construction and validation of risk models of prognostic genes associated with parthanatos in papillary thyroid carcinoma based on bioinformatics
Year: 2025
Type: article
Abstract: OBJECT: This study aimed to elucidate the role of parthanatos-related genes (PRGs) in papillary thyroid carcinoma (PTC) and construct a prognostic risk model to guide personalized treatment. METHODS: Using the GSE33630 dataset, differentially expressed PRGs were identified and analyzed via weighted gene co-expression network analysis (WGCNA) to pinpoint key module genes. Regression analysis selected seven prognostic genes for risk model construction. The model's performance was validated, and a nomogram was developed for survival prediction. Further analyses included clinical feature correlations, immune infiltration, drug sensitivity, gene set enrichment analysis (GSEA), and experimental validation via RT-qPCR. RESULTS: Seven prognostic genes (TSHZ3, SERGEF, AKAP12, SGPP2, ASGR1, AK1, PELI2) were identified. The risk model demonstrated robust predictive accuracy, stratifying patients into high- and low-risk groups with significant survival differences. GSEA revealed 29 enriched pathways (e.g., ribosome, focal adhesion), while immune infiltration analysis highlighted CD56 + NK cells and AK1 as key immune correlates. Drug sensitivity screening identified 111 differential therapeutics. Functional analysis indicated AKAP12 had the strongest functional similarity among prognostic genes. CONCLUSION: This study comprehensively mapped PRGs in PTC, established a validated risk model, and provided insights into immune-microenvironment interactions and therapeutic targets, advancing precision oncology for PTC.
Source: Discover Oncology
Authors: Rui Wang, Li Zhang, Shuxin Wen
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,690
OpenAlex ID: W4416928828