Upregulation of multiple key molecules is correlated with poor prognosis and immune infiltrates in hepatocellular carcinoma by bulk and single-cell RNA-seq.

Background: Recent discoveries in hepatocellular carcinoma (HCC) unveil key molecules. However, due to liver cancer's high heterogeneity, predicting patient prognosis is challenging. This study aims to construct a model for predicting HCC prognosis using multiple key genes.

Methods: TCGA provided RNA expression and clinical data, differentially analyzed by DESeq2, edgeR, and Limma. The hub gene was pinpointed via CytoHubba's degree algorithm in Cytoscape. GO and KEGG analyses illuminated potential pathways. Single-cell sequencing detailed key gene expression in diverse cell types. The LASSO regression model predicted patient prognosis.

Result: In the RNA-seq analysis using three R packages, we identified 762 differentially expressed genes, with Cytoscape revealing ten key genes showing significant prognostic value ( < 0.05). GO and KEGG analyses highlighted key biological processes and pathways. IHC confirmed higher expression in cancer tissues. Reduced immune cell infiltration was observed in HCC tissues, and immune checkpoint analysis showed a strong correlation between PD1, CTLA4, and hub genes. Single-cell sequencing indicated higher expression of key genes in immune cells than hepatocytes. Cox analysis validated the riskScore as a reliable, independent prognostic marker (HR = 4.498, 95% CI: 2.526-8.007).

Conclusions: The results from differential analysis using three R packages are robust, revealing genes closely linked to immune cell infiltration in the tumor microenvironment. Additionally, a validated prognostic model for liver cancer was established based on key genes.