AUTHOR=You Ji-An , Gong Yuhan , Wu Yongzhe , Jin Libo , Chi Qingjia , Sun Da 

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

JOURNAL=Frontiers in Genetics

VOLUME=12

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.730920

DOI=10.3389/fgene.2021.730920

ISSN=1664-8021

ABSTRACT=<p><bold>Background:</bold> 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.</p><p><bold>Methods:</bold> 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.</p><p><bold>Results:</bold> 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.</p><p><bold>Conclusion:</bold> 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.</p>