AUTHOR=Wang Maobing , Cheng Lu , Qi Kuo , Wang Haiping , Li Xun 

TITLE=Identification of prognostic biomarkers related to epithelial-mesenchymal transition and anoikis in hepatocellular carcinoma using transcriptomics and single-cell sequencing

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1600546

DOI=10.3389/fcell.2025.1600546

ISSN=2296-634X

ABSTRACT=BackgroundEpithelial-mesenchymal transition (EMT) and anoikis are critically associated with hepatocellular carcinoma (HCC). However, the precise mechanisms underlying their roles in HCC remain unclear. This study aims to explore the involvement of EMT-related genes (EMTRGs) and anoikis-related genes (ARGs) in HCC.MethodsData from TCGA-HCC, ICGC-LIPI - JP, GSE149614, EMTRGs and ARGs were utilised in this study. It utilised single-cell RNA sequencing for cell sorting. Biomarkers were identified through analyses such as differential expression analysis and weighted gene co-expression network analysis (WGCNA). The risk model and nomogram were constructed based on biomarkers. Subsequently, the potential functions of biomarkers were explored through methods such as enrichment analysis and immune microenvironment analysis. Finally, to confirm the expression of these biomarkers in different prognostic groups, gene expression levels were quantified using real-time quantitative polymerase chain reaction (RT-qPCR).ResultsLAMA4, C7, KPNA2, STMN1, and SF3B4 were identified as biomarkers. The risk score emerged as an independent prognostic factor for patients with HCC. The nomogram showed that these five biomarkers had good predictive ability for the 1-, 3-, and 5-year survival rates of HCC patients. Drug sensitivity analysis revealed significant associations between the IC50 values of 23 drugs and risk scores. In the GSE149614 dataset, most biomarkers were predominantly expressed in stromal cells (endothelial cells and fibroblasts). In TCGA-HCC, all genes, except C7, were upregulated in the HCC samples. RT-qPCR analysis revealed statistically significant upregulation of STMN1 and SF3B4 transcripts in the HCC group, consistent with TCGA-HCC dataset.ConclusionThis study identified five EMTRGs and ARGs (LAMA4, C7, KPNA2, STMN1, and SF3B4) as biomarkers of HCC, offering new insights for further research in HCC pathogenesis.