Integrating single-cell RNA sequencing with spatial transcriptomics reveal the fibrosis-related genes in hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, with limited efficacy of current therapies in advanced cases. As a key risk factor for HCC, liver fibrosis may influence tumor progression and immune responses. However, fibrosis-related therapeutic targets remain poorly defined. This study aimed to identify fibrosis-related genes in HCC tumor microenvironment (TME).
Our research integrated single-cell RNA sequencing (GSE149614), spatial transcriptomics (GSE245908), and bulk RNA-seq data to identify fibrosis-related prognostic genes in HCC. The genes were selected via the Random Survival Forest algorithm. Additionally, bioinformatics analyses were conducted to explore gene expression patterns, immune infiltration, and spatial localization. Key genes were further validated through in EDU incorporation assay, Transwell migration assay, and CCK-8 proliferation assay.
Firstly, single-cell analysis identified endothelial cells as key fibrosis-associated cluster in HCC. Three fibrosis-related prognostic genes, LUC7L3, CREB1, and YIPF4, were further identified and validated to patient survival, immune infiltration, and metabolic activity. In addition, enrichment and drug sensitivity analyses linked key genes to tumor-related pathways and chemotherapy response. Spatial transcriptomics then confirmed the spatial distribution and interactions of these genes. Lastly, cellular assays showed that YIPF4 promoted proliferation and migration of HCC cells.
In this study, we identified fibrosis-related prognostic genes in HCC, including LUC7L3, CREB1, and YIPF4. The roles of these genes in TME were further explored through relevant analyses, potentially providing clinical evidence to support decision-making in HCC management.
Our research integrated single-cell RNA sequencing (GSE149614), spatial transcriptomics (GSE245908), and bulk RNA-seq data to identify fibrosis-related prognostic genes in HCC. The genes were selected via the Random Survival Forest algorithm. Additionally, bioinformatics analyses were conducted to explore gene expression patterns, immune infiltration, and spatial localization. Key genes were further validated through in EDU incorporation assay, Transwell migration assay, and CCK-8 proliferation assay.
Firstly, single-cell analysis identified endothelial cells as key fibrosis-associated cluster in HCC. Three fibrosis-related prognostic genes, LUC7L3, CREB1, and YIPF4, were further identified and validated to patient survival, immune infiltration, and metabolic activity. In addition, enrichment and drug sensitivity analyses linked key genes to tumor-related pathways and chemotherapy response. Spatial transcriptomics then confirmed the spatial distribution and interactions of these genes. Lastly, cellular assays showed that YIPF4 promoted proliferation and migration of HCC cells.
In this study, we identified fibrosis-related prognostic genes in HCC, including LUC7L3, CREB1, and YIPF4. The roles of these genes in TME were further explored through relevant analyses, potentially providing clinical evidence to support decision-making in HCC management.