Multi-omics consensus clustering classification. (A) Calculation of optimal number of subtypes based on cluster prediction index and gap-statistics. (B) Heatmap of the consistency of the integration of the results of the 10 typing algorithms. (C-D) Significant multiomics characterization and prognostic differences between subtypes. (E-G) Validation of consistency of typing results and prognostic value in an external cohort.

Differences in molecular characterization of subtypes. (A) Differences in mutation landscapes of subtypes. (B-C) Genomic characterization from TMB and FGA of subtypes. (D-E) Functional enrichment of up and down-regulated differential genes between subtypes. (F) Concordance between subtypes and patients’ pathologic staging.

Phenotypic features of subtypes that may be associated with tumor development. (A) Landscape of subtype TME immune infiltration. (B) Differences in subtype responsiveness to different drugs. (C) Comparison of immune cell infiltration levels in subtypes of TME. (D) Comparison of hepatocellular carcinoma-related signaling pathways and biological process activity in subtypes

Building prognostic prediction models through machine learning. (A) 101 Machine Learning Algorithms to Build Screening Optimal Prognostic Models. (B) Calculating risk scores and comparing prognostic differences based on predictive modeling. (C-D) Correlation between 5 prognostic genes and prognosis. (E-F) Validating prognostic model efficacy in an external cohort external cohort.

Comparison and refinement of prognostic prediction models. (A) Comparison of predictive performance of prognostic models with some prognostic models published in the last 5 years. (B) Constructing a nomogram incorporating patient clinical characteristics. (C-D) Evaluating model performance based on calibration curve and decision curve analysis.

ScRNA-seq cell sorting and annotation. (A-C) Cellular subpopulation delineation and annotation based on cellular characterization genes. (D-E) Visualization of the proportions of each cell subgroup. (F) Differentially expressed genes by cellular subpopulations

Phenotypic characterization of cellular subpopulations. (A-B) Functional enrichment analysis of differentially expressed genes within cell subpopulation. (C) Analysis of the metabolic landscape of cell subpopulations. (D) Cellular polymeric communication network. (E) The contribution of signal strength in both incoming and outgoing signals shapes the landscape of cellular subgroups. (F-G) Analysis of signal transmission into and out of cellular subgroups.

Exploring the characteristics of two subtypes of malignant cells at the scRNA-seq level. (A) Mapping of malignant cells in the cell clustering results. (B) Enrichment analysis of differential genes between different subtypes of malignant cells. (C) Metabolic level analysis of different subtypes of malignant cells. (D) Annotation of the cellular subpopulation signaling landscape after malignant cell annotation. (E-G) Analysis of signal input-output patterns in cellular subpopulations. (H) Differential levels of the MIF signaling pathway in cellular subpopulation outputs. (I) Signaling modulation pattern of CS1 subtype malignant cells on other cells.