Prognostic genes in hepatocellular carcinoma and their function: an analysis integrating high-throughput-based...

Area of Science:

• Oncology
• Genomics
• Immunology

Background:

• Identifying novel prognostic indicators is crucial for improving patient outcomes in hepatocellular carcinoma (HCC).
• Mapping the spatial composition of HCC tumors presents a significant challenge in current research.
• Integrating spatial transcriptome sequencing (ST-seq) and single-cell RNA sequencing (scRNA-seq) offers a promising approach to address these challenges.

Purpose of the Study:

• To identify novel prognostic genes in HCC by integrating ST-seq and scRNA-seq data.
• To assess the role of identified prognostic genes in HCC progression and patient outcomes.
• To develop a predictive model for HCC prognosis and potential immunotherapy response.

Main Methods:

• Utilized HCC datasets (TCGA-HCC, ICGC-HCC, GSE149614, GSE203612) for single-cell and spatial transcriptome analyses.
• Performed cell communication analysis, LASSO Cox regression for prognostic gene selection, and constructed a risk model and nomogram.
• Analyzed immune microenvironment, immunotherapy response, and developmental trajectories using pseudotime analysis.

Main Results:

• Identified hepatocytes and T/NK cells as key cell types, with hepatocytes showing stronger interactions.
• Discovered seven prognostic genes (three downregulated: ADH4, IGFBP3, LCAT; four upregulated: AKR1B10, GAGE2A, MAGEA6, UCHL1).
• Developed a predictive risk model and nomogram demonstrating good prognostic ability; UCHL1 correlated with T/NK cell abundance and high-risk status suggested reduced immunotherapy sensitivity.

Conclusions:

• Identified hepatocytes, T/NK cells, and seven prognostic genes for HCC.
• Developed a risk model and nomogram for predicting HCC patient prognosis.
• Computational predictions suggest high-risk HCC patients may respond poorly to immunotherapy, offering potential clinical tools.