Integrating innovative multiomics and machine learning strategies for prognostic biomarker discovery in hepatocellular carcinoma: guiding personalized treatment strategies with single-cell analysis.

Background: Precise treatments for hepatocellular carcinoma (HCC), a highly invasive and heterogeneous cancer, are lacking. We aimed to analyze multiomics data with machine learning and single-cell analysis to develop biomarkers for predicting HCC prognosis and guiding clinical practice. Methods: Obtaining data from The Cancer Genome Atlas dataset, we analyzed messenger RNA, long noncoding RNA, and microRNA expression profiles, genomic mutations, and DNA methylation data to construct cancer subtypes (CSs) and identify differentially expressed genes (DEGs). Using AddModuleScore, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA), we identified cancer subtype1-related genes (CS1RGs) at the single-cell and bulk transcriptome levels. To construct a consensus CS1-related signature (CS1RS), we developed a novel machine learning framework with 13 combinations and evaluated it in the external set. Multiomics analysis provided comprehensive insight into the prognostic signature. We then created a CS1RS-integrated nomogram for prognostic prediction, and we assessed immunotherapy responses and identified drugs for targeted personalized medicine. Results: We identified two CSs of HCC through multiomics consensus clustering. CS1 showed unfavorable survival outcomes. Comprehensive analyses revealed distinct molecular features that differentiated these subtypes, including gene expression patterns, chromatin remodeling, and immune infiltration. Single-cell RNA sequencing identified 2,028 DEGs between the high- and low-CS1 groups. We constructed a CS1RS using 13 machine learning approaches that demonstrated robust predictive accuracy across the training and validation sets. When we combined the CS1RS with clinical characteristics, we observed that they together offered a reliable tool for personalized prognosis prediction in HCC. Additionally, we observed significant correlations between the CS1RS and tumor microenvironment, immune infiltration, and drug sensitivity, providing insights into HCC progression and potential therapeutic targets. Conclusions: Developing a new risk stratification and prediction model (CS1RS) can help in the prognosis assessment, early risk warning of HCC patients, and can also be used to formulate individualized treatment plans. Our innovation is poised to significantly impact clinical intelligence and management approaches.