Multiple Machine Learning Methods Identified RRAGD as Novel Biomarkers for Hepatocellular Carcinoma and Liver Cirrhosis

Background Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide, usually developing from cirrhosis. Distinguishing biomarkers between HCC and liver cirrhosis is crucial and limited. Disulfidptosis is a recently discovered form of cell death, and it has important prognostic value for various tumors. The mechanism of disulfidptosis in HCC and liver cirrhosis is still unclear Methods RNA sequencing data and single-cell sequencing data related to HCC and liver cirrhosis were applied for high dimensional weighted gene co-expression network analysis (hdWGCNA) and Weighted co-expression network analysis (WGCNA) methods. These methods were used for analysis of disulfidptosis related to HCC and liver cirrhosis. A diagnostic model was constructed based on machine learning. Moreover, in vitro assays demonstrated the influence of RRAGD on disulfidptosis of HCC cells. Results Applying machine learning methods, we found 7 disulfidptosis-related genes in HCC and liver cirrhosis, including FXN, HSPA1A, AGPAT2, CCND1, RRAGD, SUSD4 and DKK4. These disulfidptosis-related genes in HCC and liver cirrhosis may be used for diagnosis of HCC and liver cirrhosis. RRAGD was significantly up-regulated in both HepG2 and Huh7 cells. RRAGD knockdown induced disulfidptosis of HCC cells under glucose starvation and SLC7A11 overexpression. Conclusion Multiplex analysis based on DRGs correlated strongly with HCC and liver cirrhosis, providing new insights for developing clinical diagnosis tools and designing immunotherapy regimens for HCC and liver cirrhosis patients.