Development of a Starvation Response-Based Model and Its Application in Prognostic Assessment of Liver Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (LIHC) is a highly prevalent and poorly prognostic malignancy worldwide, and nutrient deprivation in the tumor microenvironment activates the starvation response in tumor cells. Starvation response-related genes (SRRGs) play critical roles in maintaining energy metabolism and promoting tumor development, but their value in prognostic prediction of LIHC has not been clarified. Methods: We based on public databases to obtain transcriptome and single-cell RNA sequencing (scRNA-seq) data for LIHC and SRRG from previous studies. Key modules relevant to SRRGs were identified by weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis was conducted using clusterProfiler R package. Independent prognostic genes were screened to build a RiskScore model and its performance was further verified. The immune microenvironmental profile of patients in different risk groups was assessed using the single-sample gene set enrichment analysis (ssGSEA), MCP-Counter, ESTIMATE, and TIMER algorithms. Seurat package for single-cell profiling and validation of key gene expression based on Huh7 and transformed human liver epithelial-2 (THLE-2) cell lines. The LIHC cell migration and invasion were measured by conducting wound healing and transwell assays. Results: The key module identified by WGCNA showed the strongest correlation with SRRGs and the glycolysis-related SRRGs were mainly enriched in metabolism-correlated pathways. Two protective genes (FBXL5 and PON1) and three risk genes (TFF2, TBC1D30, and SLC2A1) were discovered as the independent prognostic genes for LIHC. Activation of cytokine-cytokine receptor interaction and IL-17 signaling pathway and higher infiltration of immune cells in high-risk group was observed. The five independent prognostic genes were mainly expressed in cancer stem cells and epithelial cells, in particular, SLC2A1 and TFF2 were significantly high-expressed in epithelial cells in the tumor group than in nontumor group. FBXL5 and PON1 were downregulated, while TFF2, TBC1D30, and SLC2A1 were upregulated in LIHC cells. Silencing SLC2A1 significantly inhibited LIHC cell migration and invasion. Conclusion: In this study, we constructed the first risk model based on SRRGs to accurately predict the prognosis of LIHC, which provides a new idea for individualized treatment and targeted intervention.