Relationships of Cuproptosis-Related Genes With Clinical Outcomes and the Tumour Immune Microenvironment in Hepatocellular Carcinoma

Cuproplasia and cuproptosis in hepatocellular carcinoma: mechanisms, relationship and potential role in tumor microenvironment and treatment

BACKGROUND

Hepatocellular carcinoma (HCC) is the main phenotype of liver cancer with a poor prognosis. Copper is vital in liver function, and HCC cells rely on it for growth and metastasis, leading to cuproplasia. Excessive copper can induce cell death, termed cuproptosis. Tumor microenvironment (TME) is pivotal in HCC, especially in immunotherapy, and copper is closely related to the TME pathogenesis. However, how these two mechanisms contribute to the TME is intriguing.

MAIN BODY

We conducted the latest progress literature on cuproplasia and cuproptosis in HCC, and summarized their specific roles in TME and treatment strategies. The mechanisms of cuproplasia and cuproptosis and their relationship and role in TME have been deeply summarized. Cuproplasia fosters TME formation, angiogenesis, and metastasis, whereas cuproptosis may alleviate mitochondrial dysfunction and hypoxic conditions in the TME. Inhibiting cuproplasia and enhancing cuproptosis in HCC are essential for achieving therapeutic efficacy in HCC.

CONCLUSION

An in-depth analysis of cuproplasia and cuproptosis mechanisms within the TME of HCC unveils their opposing nature and their impact on copper regulation. Grasping the equilibrium between these two factors is crucial for a deeper understanding of HCC mechanisms to shed light on novel directions in treating HCC.

Identification and validation of potential diagnostic signature and immune cell infiltration for HIRI based on cuproptosis-related genes through bioinformatics analysis and machine learning

BACKGROUND AND AIMS

Cuproptosis has emerged as a significant contributor in the progression of various diseases. This study aimed to assess the potential impact of cuproptosis-related genes (CRGs) on the development of hepatic ischemia and reperfusion injury (HIRI).

METHODS

The datasets related to HIRI were sourced from the Gene Expression Omnibus database. The comparative analysis of differential gene expression involving CRGs was performed between HIRI and normal liver samples. Correlation analysis, function enrichment analyses, and protein-protein interactions were employed to understand the interactions and roles of these genes. Machine learning techniques were used to identify hub genes. Additionally, differences in immune cell infiltration between HIRI patients and controls were analyzed. Quantitative real-time PCR and western blotting were used to verify the expression of the hub genes.

RESULTS

Seventy-five HIRI and 80 control samples from three databases were included in the bioinformatics analysis. Three hub CRGs (NLRP3, ATP7B and NFE2L2) were identified using three machine learning models. Diagnostic accuracy was assessed using a receiver operating characteristic (ROC) curve for the hub genes, which yielded an area under the ROC curve (AUC) of 0.832. Remarkably, in the validation datasets GSE15480 and GSE228782, the three hub genes had AUC reached 0.904. Additional analyses, including nomograms, decision curves, and calibration curves, supported their predictive power for diagnosis. Enrichment analyses indicated the involvement of these genes in multiple pathways associated with HIRI progression. Comparative assessments using CIBERSORT and gene set enrichment analysis suggested elevated expression of these hub genes in activated dendritic cells, neutrophils, activated CD4 memory T cells, and activated mast cells in HIRI samples versus controls. A ceRNA network underscored a complex regulatory interplay among genes. The genes mRNA and protein levels were also verified in HIRI-affected mouse liver tissues.

CONCLUSION

Our findings have provided a comprehensive understanding of the association between cuproptosis and HIRI, establishing a promising diagnostic pattern and identifying latent therapeutic targets for HIRI treatment. Additionally, our study offers novel insights to delve deeper into the underlying mechanisms of HIRI.

Immunomodulation of cuproptosis and ferroptosis in liver cancer

According to statistics, the incidence of liver cancer is increasing yearly, and effective treatment of liver cancer is imminent. For early liver cancer, resection surgery is currently the most effective treatment. However, resection does not treat the disease in advanced patients, so finding a method with a better prognosis is necessary. In recent years, ferroptosis and cuproptosis have been gradually defined, and related studies have proved that they show excellent results in the therapy of liver cancer. Cuproptosis is a new form of cell death, and the use of cuproptosis combined with ferroptosis to inhibit the production of hepatocellular carcinoma cells has good development prospects and is worthy of in-depth discussion by researchers. In this review, we summarize the research progress on cuproptosis combined with ferroptosis in treating liver cancer, analyze the value of cuproptosis and ferroptosis in the immune of liver cancer, and propose potential pathways in oncotherapy with the combination of cuproptosis and ferroptosis, which can provide background knowledge for subsequent related research.

Identification of cuproptosis and ferroptosis-related subgroups and development of a signature for predicting prognosis and tumor microenvironment landscape in hepatocellular carcinoma

Background

Ferroptosis and cuproptosis play a crucial role in the progression and dissemination of hepatocellular carcinoma (HCC). The primary objective of this study was to develop a unique scoring system for predicting the prognosis and immunological landscape of HCC based on ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs).

Methods

As the training cohort, we assembled a novel HCC cohort by merging gene expression data and clinical data from The Cancer Genome Atlas (TCGA) database, and Gene Expression Omnibus (GEO) database. The validation cohort consisted of 230 HCC cases taken from the International Cancer Genome Consortium (ICGC) database. Multiple genomic characteristics, such as tumor mutation burden (TMB), and copy number variations were analyzed concurrently. On the basis of the expression of CRGs and FRGs, patients were classified into cuproptosis and ferroptosis subtypes. Then, we constructed a risk model using least absolute shrinkage and selection operator (LASSO) analysis and Cox regression analysis based on ferroptosis and cuproptosis-related differentially expressed genes (DEGs). Patients were separated into two groups according to median risk score. We compared the immunophenotype, tumor microenvironment (TME), cancer stem cell index, and treatment sensitivity of two groups.

Results

Three subtypes of ferroptosis and two subtypes of cuproptosis were identified among the patients. A greater likelihood of survival (P<0.05) was expected for patients in FRGcluster B and CRGcluster B. After that, a confirmed risk signature for ferroptosis and cuproptosis was developed and tested. Patients in the low-risk group had significantly higher survival rates than those in the high-risk group, according to our study (P<0.001). There was also a strong correlation between the signature and other variables including immunophenoscore, TMB, cancer stem cell index, immunological checkpoint genes, and sensitivity to chemotherapeutics.

Conclusions

Through this comprehensive research, we identified a unique risk signature associated with HCC patients' treatment status and prognosis. Our findings highlight FRGs' and CRGs' significance in clinical practice and imply ferroptosis and cuproptosis may be therapeutic targets for HCC patients.

Ferroptosis and cuproptosis prognostic signature for prediction of prognosis, immunotherapy and drug sensitivity in hepatocellular carcinoma: development and validation based on TCGA and ICGC databases

Background

Hepatocellular carcinoma (HCC) is a common malignancy. Ferroptosis and cuproptosis promote HCC spread and proliferation. While fewer studies have combined ferroptosis and cuproptosis to construct prognostic signature of HCC. This work attempts to establish a novel scoring system for predicting HCC prognosis, immunotherapy, and medication sensitivity based on ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs).

Methods

FerrDb and previous literature were used to identify FRGs. CRGs came from original research. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases included the HCC transcriptional profile and clinical information [survival time, survival status, age, gender, Tumor Node Metastasis (TNM) stage, etc.]. Correlation, Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses were used to narrow down prognostic genes and develop an HCC risk model. Using "caret", R separated TCGA-HCC samples into a training risk set and an internal test risk set. As external validation, we used ICGC samples. We employed Kaplan-Meier analysis and receiver operating characteristic (ROC) curve to evaluate the model's clinical efficacy. CIBERSORT and TIMER measured immunocytic infiltration in high- and low-risk populations.

Results

TXNRD1 [hazard ratio (HR) =1.477, P<0.001], FTL (HR =1.373, P=0.001), GPX4 (HR =1.650, P=0.004), PRDX1 (HR =1.576, P=0.002), VDAC2 (HR =1.728, P=0.008), OTUB1 (HR =1.826, P=0.002), NRAS (HR =1.596, P=0.005), SLC38A1 (HR =1.290, P=0.002), and SLC1A5 (HR =1.306, P<0.001) were distinguished to build predictive model. In both the model cohort (P<0.001) and the validation cohort (P<0.05), low-risk patients had superior overall survival (OS). The areas under the curve (AUCs) of the ROC curves in the training cohort (1-, 3-, and 5-year AUCs: 0.751, 0.727, and 0.743), internal validation cohort (1-, 3-, and 5-year AUCs: 0.826, 0.624, and 0.589), and ICGC cohort (1-, 3-, and 5-year AUCs: 0.699, 0.702, and 0.568) were calculated. Infiltration of immune cells and immunological checkpoints were also connected with our signature. Treatments with BI.2536, Epothilone.B, Gemcitabine, Mitomycin.C, Obatoclax. Mesylate, and Sunitinib may profit high-risk patients.

Conclusions

We analyzed FRGs and CRGs profiles in HCC and established a unique risk model for treatment and prognosis. Our data highlight FRGs and CRGs in clinical practice and suggest ferroptosis and cuproptosis may be therapeutic targets for HCC patients. To validate the model's clinical efficacy, more HCC cases and prospective clinical assessments are needed.