WGCNA, LASSO and SVM Algorithm Revealed RAC1 Correlated M0 Macrophage and the Risk Score to Predict the Survival of Hepatocellular Carcinoma Patients

Background: RAC1 is involved in the progression of HCC as a regulator, but its prognostic performance and the imbalance of immune cell infiltration mediated by it are still unclear. We aim to explore the prognostic and immune properties of RAC1 in HCC. Methods: We separately downloaded the data related to HCC from the Cancer Genome Atlas (TCGA) and GEO database. CIBERSORT deconvolution algorithm, weighted gene co-expression network analysis (WGCNA) and LASSO algorithm participate in identifying IRGs and the construction of prognostic signatures. Results: The study discovered that RAC1 expression was linked to the severity of HCC lesions, and that its high expression was linked to a poor prognosis. Cox analysis confirmed that RAC1 is a clinically independent prognostic marker. M0, M1 and M2 macrophages’ abundance are significantly different in HCC. We found 828 IRGs related to macrophage infiltration, and established a novel 11-gene signature with excellent prognostic performance. RAC1-based risk score and M0 macrophage has a good ability to predict overall survival. Conclusion: The immune state of irregular macrophage infiltration may be one of the precursors to carcinogenesis. The RAC1 correlated with M0 macrophage and the risk score to show a good performance to predict the survival of HCC patients.