Single-cell sequencing and multiple machine learning algorithms to identify key T-cell differentiation gene for progression of NAFLD cirrhosis to hepatocellular carcinoma - Wang, De-hua; Ye, Li-hong; Ning, Jing-yuan; Zhang, Xiao-kuan; Lv, Ting-ting; Li, Zi-jie; Wang, Zhi-yu (Frontiers in Molecular Biosciences, 2024)
Hepatocellular carcinoma (HCC), which is closely associated with chronic inflammation, is the most common cancer and primarily involves dysregulated immune responses in the precancerous microenvironment. Currently, most studies have been limited to HCC incidence. However, the immunopathogenic mechanisms underlying precancerous lesions remain unknown. We obtained single-cell sequencing data (GSE136103) from 2 non-alcoholic fatty liver disease (NAFLD) cirrhosis samples and 5 healthy samples. Using pseudo-time analysis, we systematically identified five different T cell differentiation states. 81 machine learning algorithms were used to integrate the frameworks and establish the best T cell differentiation-related prognostic signature in a multi-cohort bulk transcriptome analysis. Among these, LDHA was considered a core gene, and the results were validated using multiple external datasets. In addition, we validated LDHA expression using immunohistochemistry and flow cytometry. This study concluded that LDHA is a crucial marker gene in T cells for the progression of NAFLD-cirrhosis-HCC. These findings may help predict tumor progression and offer new predictive and therapeutic targets for clinical applications.