Tumor Prognostic Risk Model Related to Monocytes/Macrophages in Hepatocellular Carcinoma Based on Machine Learning and Multi-Omics

Figures

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  Fig. 1. The workflow of the present study. This study integrates machine learning with multi-omics sequencing to analyze HCC. A prognostic risk model is developed and validated, with analyses of drug sensitivity, immune therapy responses, and immune function

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  Fig. 2. Preliminary analysis of single-cell RNA sequencing samples. (A) Visualization of highly variable genes. (B) Principal component analysis (PCA) of the 10 included samples. (C) Clustering of cells from the included samples. (D) Annotation of cell types through marker genes. (E) Bubble chart showing the expression of marker genes across eight cell types. (F) Violin plots displaying the expression characteristics of marker genes in eight cell types. (G) Bar charts analyzing the average expression ratio of eight cell types across 10 single-cell samples and their individual proportions in each of the 10 samples

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  Fig. 3. Cell communication analysis. (A) Construction of a cell communication network for the 10 included tumor samples. (B) Independent display of the number of cell communication events between each cell type. (C) Independent display of the communication weights between each cell type. (D) In-depth analysis of the role of the FN1 signaling pathway in cell communication using chord diagrams and heatmaps. (E) Further analysis of the SPP1 signaling pathway in cell communication through chord diagrams and heatmaps

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  Fig. 4. Predicting transcription factor regulatory networks associated with monocytes/macrophages in scRNA-seq samples using SCENIC. (A) ZNF385A, NFKB2, and NONO (three core transcription factors) will become the central hubs controlling the LIHC state in monocytes/macrophages. (B) In-depth analysis of the transcription factor-gene regulatory network centered on ZNF385A, NFKB2, and NONO

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  Fig. 5. Construction of a single-gene prognostic risk model and validation of UQCRH overexpression in HCC using external independent datasets and Western blot. (A) Intersection of monocyte/macrophage expressed genes from scRNA-seq with TCGA-LIHC expressed genes using a Venn diagram. (B) To prevent model overfitting, the prognostic risk model is further constructed based on LASSO regression analysis. (C) Differential expression analysis in paired samples from the TCGA-LIHC database revealed overexpression of UQCRH in tumor samples. (D, E) Analysis from the HPA database shows high expression of UQCRH in HCC samples. (F-H) Survival risk curves indicate that overexpression and high-risk groups in the single-gene prognostic model are associated with poor prognosis in HCC patients. (I) ROC analysis of the single-gene prognostic model risk shows good performance in predicting patient OS with an AUC score. (J) Transcriptomic analysis from the ICGC-HCC database confirms the poor prognosis associated with high UQCRH expression in patients (p = 0.0427). (K) Transcriptomic analysis from the GEO database(GSE14520) confirms overexpression of UQCRH in HCC tumor samples (p < 2.2e-16). (L) Proteomic…

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  Fig. 6. Spatial analysis of UQCRH expression and malignant cell distribution in HCC samples. (A) UMAP clustering showing different cell types in HCC, including malignant cells, T cells, and others. (B) Dot plot of marker gene expression across cell clusters, highlighting markers for malignant cells. (C) Spatial distribution of malignant cells in HCC tissue, with redder colors indicating higher abundance. (D) Spatial expression of UQCRH in HCC sample 1, with redder colors representing higher expression levels. (E) Spatial distribution of malignant cells in HCC sample 2, similar to sample 1. (F) Spatial expression of UQCRH in HCC sample 2, showing co-localization with malignant cells

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  Fig. 7. Prognostic value of UQCRH expression and risk score in HCC. (A) In the TCGA-LIHC database, high expression of UQCRH is positively correlated with clinical indicators T and Stage (p < 0.001). (B) Nomogram analysis reveals that patient prognosis is primarily associated with Risk Score (p < 0.01). (C) Calibration curves show good agreement between the predicted values of the nomogram and actual values for 1-year, 3-year, and 5-year OS probabilities

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  Fig. 8. Functional analysis of UQCRH and its associated proteins in HCC. (A) Protein-protein interaction analysis identifies direct associations between UQCRH and proteins such as UQCRB and UQCRQ. (B, C) KEGG and GO enrichment analyses show that UQCRH and its directly associated proteins are involved in biological processes and signaling pathways such as mitochondrial electron transport, maintenance of cellular energy metabolism, and oxidative phosphorylation

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  Fig. 9. Drug sensitivity, immune therapy response, and immune function analysis in high-risk and low-risk groups of HCC patients. (A) Drug sensitivity analysis indicates that the high-risk group in the prognostic model is less sensitive to commonly used anti-cancer drugs such as sorafenib and axitinib. (B) Immune therapy response prediction within the prognostic model shows that the high-risk group exhibits less pronounced responses to immune therapy. (C) ssGSEA immune function scoring reveals that the high-risk group in the prognostic model exhibits suppressed antigen presentation and poor response to IFN-II in terms of tumor immunity