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. 2023 Sep 8:11:1240390.
doi: 10.3389/fcell.2023.1240390. eCollection 2023.

Single-cell and genetic multi-omics analysis combined with experiments confirmed the signature and potential targets of cuproptosis in hepatocellular carcinoma

Feng Cao #  1 , Yong Qi #  2 , Wenyong Wu  2 , Xutong Li  3 , Chuang Yang  4
Affiliations

Single-cell and genetic multi-omics analysis combined with experiments confirmed the signature and potential targets of cuproptosis in hepatocellular carcinoma

Feng Cao et al. Front Cell Dev Biol. .

Abstract

Background: Cuproptosis, as a recently discovered type of programmed cell death, occupies a very important role in hepatocellular carcinoma (HCC) and provides new methods for immunotherapy; however, the functions of cuproptosis in HCC are still unclear. Methods: We first analyzed the transcriptome data and clinical information of 526 HCC patients using multiple algorithms in R language and extensively described the copy number variation, prognostic and immune infiltration characteristics of cuproptosis related genes (CRGs). Then, the hub CRG related genes associated with prognosis through LASSO and Cox regression analyses and constructed a prognostic prediction model including multiple molecular markers and clinicopathological parameters through training cohorts, then this model was verified by test cohorts. On the basis of the model, the clinicopathological indicators, immune infiltration and tumor microenvironment characteristics of HCC patients were further explored via bioinformation analysis. Then, We further explored the key gene biological function by single-cell analysis, cell viability and transwell experiments. Meantime, we also explored the molecular docking of the hub genes. Results: We have screened 5 hub genes associated with HCC prognosis and constructed a prognosis prediction scoring model. And the model results showed that patients in the high-risk group had poor prognosis and the expression levels of multiple immune markers, including PD-L1, CD276 and CTLA4, were higher than those patients in the low-risk group. We found a significant correlation between risk score and M0 macrophages and memory CD4+ T cells. And the single-cell analysis and molecular experiments showed that BEX1 were higher expressed in HCC tissues and deletion inhibited the proliferation, invasion and migration and EMT pathway of HCC cells. Finally, it was observed that BEX1 could bind to sorafenib to form a stable conformation. Conclusion: The study not only revealed the multiomics characteristics of CRGs in HCC but also constructed a new high-accuracy prognostic prediction model. Meanwhile, BEX1 were also identified as hub genes that can mediate the cuproptosis of hepatocytes as potential therapeutic targets for HCC.

Keywords: cuproptosis; hepatocellular carcinoma; immune microenvironment; molecular docking; programmed cell death; single-cell RNA-sequencing.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Genetic and transcriptional alterations of CRGs in HCC. (A) CNV of CRGs in 424 samples in TCGA. (B) Chromosomal localization of CRGs with CNV. (C) Expression of CRGs. (D) CRGs interaction network. (E–J) Kaplan-Meier survival analysis of 6 CRGs associated with HCC prognosis.
FIGURE 2
FIGURE 2
Difference analysis in distinct subtypes and functional annotations of CRGs related genes. (A) Survival curves between CRGs subtypes. (B) Clinical characteristics of CRGs subtypes. (C) GSVA enrichment analysis. (D) Differences in immune cell infiltration.
FIGURE 3
FIGURE 3
Identification of hub genes and construction of the prognostic model. (A) Survival curves between the hub genes subtypes. (B) Clinical characteristics of DEGs. (C) Expression of CRGs between DEGs subtypes. (D,E) LASSO regression analysis and partial likelihood deviance. (F) Alluvial diagram of subtype and RS distributions. (G) Differences of RS in DEGs clusters. (H,I) Expression of CRGs and immune checkpoints.
FIGURE 4
FIGURE 4
Prognostic value of the CRGs signature. (A,B) RS distribution. (C,D) Survival status. (E,F) Expression of the 5 hub genes.
FIGURE 5
FIGURE 5
Prediction model and nomogram. (A,B) Kaplan-Meier curves in the training and test cohorts. (C,D) ROC curves estimate prognosis value. (E) Nomogram for predicting the OS of HCC patients. (F) Calibration curves of the nomogram.
FIGURE 6
FIGURE 6
TME characteristics and drug susceptibility. (A)The interaction network of CRGs and hub genes. (B,C) Correlation between immune cells and RS. (D) Correlations between the immune cells and 5 hub genes. (E) Differences in the StromalScore and ImmuneScore. (F) Correlation of RS with CSCs. (G,H) Mutation of genes in distinct RS group.
FIGURE 7
FIGURE 7
Western blotting and single cell analysis of the 5 hub genes. (A,B) Western blotting in HCC cell and tumor tissue. (C–G) Cell clusters and annotates for GSE149614 of HCC patients. (H–L) Expression pattern of 5 hub genes at the single-cell level in normal and tumor cell clusters through t-SNE analysis.
FIGURE 8
FIGURE 8
BEX1 biological function and molecular docking. (A) Cell viability experiment of BEX1 knockdown. (B,C) Transwell results and cadherin proteins expression in HCC cell with sh-BEX1. (D) Molecular docking of sorafenib and the 5 hub genes.
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