Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma

作者：Qingyan Kou, Zhichao Wu, Wenbin Zhao, Zhenyuan Liu, Shengxian Qiao, Qiang Mu, Xu Zhang · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-09010-1 · 被引用次数：4 · 研究领域：Ferroptosis and cancer prognosis、RNA modifications and cancer、Cancer-related molecular mechanisms research

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death globally, characterized by high morbidity and poor prognosis. The complex molecular and immune landscape of HCC makes accurate patient stratification and personalized treatment essential. In this study, we utilized large-scale gene expression data from TCGA and GSE54236, alongside eQTL GWAS data, to identify key genes that influence HCC prognosis. Machine learning analysis was performed on the genes identified through Mendelian randomization (MR) and survival association analysis, using 101 algorithms to construct a robust prognostic model. A novel riskScore model was developed by integrating genetic, clinical, and immune cell infiltration data. The prognostic performance of model was validated through survival analysis, and its association with chemotherapy and immunotherapy sensitivity. The impact of key genes on the proliferation and invasion capabilities of HCC cells was assessed through Western blot (WB), EdU, and invasion assays. A total of 27 candidate genes associated with HCC survival were identified, with 16 genes categorized as high-risk. The riskScore model demonstrated excellent performance in stratifying patients into high-risk and low-risk groups, with C-index exceeding 0.7 for both TCGA and GSE54236 datasets. High-risk patients exhibited poorer prognosis and higher immune cell infiltration, particularly T cells and neutrophils. The model also predicted drug sensitivity, with high-risk pa...