TMEM106C, BSG, COPE, CDCA8, KPNA2, LIG1, UQCRH, and CCT5: Predictive of Survival and Immunotherapy Resistance in Hepatocellular Carcinoma.

Single-cell RNA sequencing analysis of HCC samples identified 80,997 cells allocated to eight clusters, with a higher percentage of NK cells in HCC samples compared to non-tumor samples.

• A total of 80,997 cells were identified and allocated to eight distinct clusters.
• scRNA-seq data were processed using the Seurat and Harmony packages for cell clustering and batch correction.
• NK cells showed an evidently higher percentage in HCC samples relative to other cell populations.
• Data were obtained from the Gene Expression Omnibus (GEO) database.

HCC samples exhibited higher cellular senescence scores, and patients in the high senescence score group had poor prognosis.

• Senescence scores were calculated via the AUCell package.
• A higher senescence score was observed in HCC samples compared to non-tumor samples.
• Poor prognosis was noticed in patients of the high senescence score group.
• Differentially expressed genes (DEGs) were identified using the limma package.

DEGs intersected with genes highly expressed in Population 4 of NK cells were enriched in cell cycle and cell division pathways.

• DEGs were intersected with genes highly expressed in Population 4 of NK cells.
• Enrichment analysis revealed these genes were associated with cell cycle and cell division biological processes.
• This intersection approach was used to narrow candidate prognostic genes for model construction.

An eight-gene risk model was constructed using TMEM106C, BSG, COPE, CDCA8, KPNA2, LIG1, UQCRH, and CCT5, which could stratify HCC patients into different risk groups and predict prognosis.

• Prognostic genes were selected through univariate and LASSO Cox regression using the glmnet package.
• The eight genes all showed differential expression in HCC.
• The model was validated in multiple independent cohorts beyond the TCGA training dataset.
• Transcriptomic data were obtained from The Cancer Genome Atlas (TCGA).

High-risk HCC patients showed high immune infiltration and elevated expression of immune checkpoint-relevant genes, yet poor immunotherapy response.

• Immune infiltration was assessed with single-sample gene set enrichment analysis (ssGSEA), TIMER, and MCPCounter algorithms.
• Response to immune checkpoint blockade was predicted using the tumor immune dysfunction and exclusion (TIDE) platform.
• High-risk patients demonstrated an immunosuppressive tumor microenvironment characterized by high immune infiltration alongside poor immunotherapy response.
• Expression levels of immune checkpoint-relevant genes were elevated in the high-risk group.

Knockdown of CDCA8 repressed malignant phenotypes of HCC cells in experimental validation assays.

• Experimental validation included qRT-PCR, Cell Counting Kit-8 (CCK-8), wound healing, and Transwell assays.
• Assays were conducted in HCC cell lines.
• CDCA8 knockdown repressed cell proliferation as assessed by CCK-8 assay.
• Wound healing and Transwell assays demonstrated reduced migration and invasion following CDCA8 knockdown.
• CDCA8 was identified as a promising therapeutic target warranting further investigation.