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. 2022 Nov 2:12:1009036.
doi: 10.3389/fonc.2022.1009036. eCollection 2022.

Analysis of cuproptosis in hepatocellular carcinoma using multi-omics reveals a comprehensive HCC landscape and the immune patterns of cuproptosis

Xinqiang Li  1   2 , Peng Jiang  1   2 , Ruixia Li  3 , Bin Wu  1   2 , Kai Zhao  1   2 , Shipeng Li  4 , Jinzhen Cai  1   2
Affiliations

Analysis of cuproptosis in hepatocellular carcinoma using multi-omics reveals a comprehensive HCC landscape and the immune patterns of cuproptosis

Xinqiang Li et al. Front Oncol. .

Abstract

Cuproptosis represents a novel copper-dependent regulated cell death, distinct from other known cell death processes. In this report, a comprehensive analysis of cuproptosis in hepatocellular carcinoma (HCC) was conducted using multi-omics including genomics, bulk RNA-seq, single cell RNA-seq and proteomics. ATP7A, PDHA1 and DLST comprised the top 3 mutation genes in The Cancer Genome Atlas (TCGA)-LIHC; 9 cuproptosis-related genes showed significant, independent prognostic values. Cuproptosis-related hepatocytes were identified and their function were evaluated in single cell assays. Based on cuproptosis-related gene expressions, two immune patterns were found, with the cuproptosis-C1 subtype identified as a cytotoxic immune pattern, while the cuproptosis-C2 subtype was identified as a regulatory immune pattern. Cuproptosis-C2 was associated with a number of pathways involving tumorigenesis. A prognosis model based on differentially expressed genes (DEGs) of cuproptosis patterns was constructed and validated. We established a cuproptosis index (CPI) and further performed an analysis of its clinical relevance. High CPI values were associated with increased levels of alpha-fetoprotein (AFP) and advanced tumor stages. Taken together, this comprehensive analysis provides important, new insights into cuproptosis mechanisms associated with human HCC.

Keywords: HCC; cell death; cuproptosis; omics; single cell RNA analysis.

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Conflict of interest statement

The reviewer WZ declared a shared parent affiliation with the authors XL, BW, KZ, JC to the handling editor at the time of review. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overview of the multi-omics analysis for cuproptosis. (A) Schematic diagram for the lipoic acid pathway involving cuproptosis. (B) Of the 364 patients with HCC, 24 (6.59%) showed gene mutations in 13 cuproptosis-related genes, primarily including missense variants, 5’ UTR variants and synonymous variants. (C) The CNV mutation was prevalent in cuproptosis-related genes. Columns represent the alteration frequency, blue dots the amplification frequency and red dots the deletion frequency. (D) Location of the CNV alteration in cuproptosis-related genes on the chromosome. (E) Protein-protein interactions of cuproptosis-related genes. (F) Metascape network visualization showing enrichment pathway terms. Cluster annotations are color coded. (G) Differences in gene expression levels for each cuproptosis-related gene between normal and tumor tissues. (H) Visualization of the gene correlation matrix. “X” represents a lack of statistical significance.
Figure 2
Figure 2
Validation of cuproptosis-related genes in HCC. (A–D) Immunohistology of DBT, DLD, FDX1 and SLC31A1 in normal and tumor tissues; qRT-PCR showed that compared with the adjacent normal tissues, FDX1, DBT, DLD and SLC31A1 were significantly expressed lower in tumor tissues; results of the KM analysis showing expressions of each cuproptosis-related gene that significantly influenced the survival of TCGA. Red line represents the high-risk group and dark line the low-risk group. (E) the expression of DLD, DBT by Western blot in 8 paired tissue samples, with differences between HCC and normal tissues.
Figure 3
Figure 3
Single cell analysis identifying cuproptosis-related hepatocytes. (A) UMAP visualization showing the hepatocellular carcinoma landscape of 73,589 cells containing 29 clusters across 28 HCC samples using dotplot. Each dot represents a single cell and hepatocyte subgroups are encircled with dotted lines. (B) UMAP visualization showing the distribution of the single cell HCC landscape between tumor and normal tissues. (C) Violin plots showing expression levels of 8 cuproptosis-related genes among 7 hepatocyte clusters, including FDX1, DLD, DLAT, PDHA1, PDHB, DBT, GCSH and SLC31A1. (D) Violin plots showing expression levels of the top 20 marker genes in cuproptosis-related hepatocytes. (E) Pseudotime analysis showing the predicted evolution of hepatocytes. UMAP visualization showing the distribution of hepatocytes and gene expression of FDX1.
Figure 4
Figure 4
Cuproptosis patterns in the TCGA-LIHC cohort and relevant biological pathways. (A) Similarity matrix of TCGA-LIHC patients derived from consensus clustering assays. (B) Principal component analysis results for the two distinct patterns in the TCGA-LIHC cohort. (C) GSVA scores of representative Hallmark pathways in cuproptosis-C1 and cuproptosis-C2 patterns as shown in the heatmap. (D) Dotplot showing GO term enrichment analysis of DEGs between patterns. (E) Of the 138 patients in cuproptosis-C1, 108 (78.26%) showed gene mutations. The right bar plot shows the mutation frequency of each gene and each column represents an individual patient. (F) Of the 215 patients in cuproptosis-C2, 169 (78. 6%) showed gene mutations.
Figure 5
Figure 5
Cuproptosis patterns as characterized by different immune profilers. (A) Enrichment levels of 24 immune-related cells in the cuproptosis-C1 and cuproptosis-C2 subtypes using ssGSEA. (B) Boxplots showing differences in cell type percent between immune patterns. (C) Histograms displaying the proportion of 22 different types of immune cells in patterns as based on CIBERSORT. (D) Boxplots showing estimated differences in immune scores. (E) Gene expression levels of 20 immune checkpoints between the two patterns.
Figure 6
Figure 6
Construction and validation of a cuproptosis prognosis model. (A) Heatmap showing gene expressions in the cuproptosis prognosis model including, VGLL4, DLG5, NCR3LG1 and GATSL2 in TCGA-LIHC. (B) Distribution of risk scores and survival status of TCGA-LIHC. (C) Results of KM analysis indicating that the prognosis model significantly influenced survival of patients in TCGA. Dark line represents the high-risk group and red line the low-risk group. (D) Heatmap showing gene expressions of the cuproptosis prognosis model in ICGC. (E) Distribution of risk scores and survival status of ICGC. (F) Results of KM analysis indicating that the prognosis model significantly influenced survival of patients in TCGA. Dark line represents the high-risk group and red line the low-risk group.
Figure 7
Figure 7
Establishment and evaluation of the cuproptosis index (CPI). (A) Results of KM analysis indicating that the CPI significantly influenced the survival of patients in TCGA. Red line represents the high-risk group and dark line the low-risk group. (B) Results of the multivariate analysis based on TCGA-LIHC. (C) Boxplots showing cuproptosis-C2 was associated with high CPI scores. (D) CPI scores failed to show a statistically significant difference between genders. (E) High CPI scores were associated with high levels of alpha fetoprotein. (F) Boxplots showing that TNM stage was associated with distinct CPI scores. (G) Advanced tumor stage was related with high CPI scores.
Figure 8
Figure 8
A total of 26 potential therapeutic drugs in HCC with differential IC50 based high- and low-CPI groups.
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