Cuproptosis was a copper-dependent and unique kind of cell death that was separate from existing other forms of cell death. The last decade has witnessed a considerable increase in investigations of programmed cell death, and whether copper induced cell death was an independent form of cell death has long been argued until mechanism of cuproptosis has been revealed. After that, increasing number of researchers attempted to identify the relationship between cuproptosis and the process of cancer. Thus, in this review, we systematically detailed the systemic and cellular metabolic processes of copper and the copper-related tumor signaling pathways. Moreover, we not only focus on the discovery process of cuproptosis and its mechanism, but also outline the association between cuproptosis and cancers. Finally, we further highlight the possible therapeutic direction of employing copper ion ionophores with cuproptosis-inducing functions in combination with small molecule drugs for targeted therapy to treat specific cancers. 

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Cuproptosis: mechanisms and links with cancers

Simple Summary Cuproptosis is a newly discovered copper-dependent cell death. We aimed to explore the functions of cuproptosis in the tumor microenvironment and construct a cuproptosis-related prognosis signature for survival prediction and immunotherapeutic strategies. We comprehensively analyzed single-cell RNA-seq and bulk RNA-seq data from multiple colorectal cancer cohorts based on TCGA and GEO databases in the current study. The relationship between molecular clusters, clinical outcomes, and immune cell infiltration characteristics associated with cuproptosis was investigated. Considering the heterogeneity of colorectal cancer development, we then established a validated five-gene panel for predicting individual patient prognosis, drug sensitivity, tumor-immune microenvironment, and immunotherapy targets. Abstract Cuproptosis is a copper-induced form of mitochondrial cell death which is engaged in the proliferation and migration of a variety of tumors. Nevertheless, the role of cuproptosis in tumor microenvironment (TME) remodeling and antitumor therapy is still poorly understood. We characterized two diverse cuproptosis-associated molecular isoforms in CRC which exhibit distinct prognostic and TME characteristics. Subsequently, we constructed a cuproptosis-associated prognostic model containing five genes and divided the patients into a high CPS-score group and a low CPS-score group. Univariate and multivariate Cox analyses showed that the CPS score could be used as an independent prognostic factor. The nomogram, and its consequent calibration curves, indicated that this prognostic signature had good predictive power for CRC. The analysis of single-cell sequencing data showed the significant expression of HES4 and SPHK1 in various immune and stromal (including fibroblasts) cells. Further studies showed that tumor mutational burden (TMB), high microsatellite instability (MSI-H) ratio, immune checkpoint blockade (ICB), and human leukocyte antigen (HLA) gene expression all positively correlated with the CPS score, predicting a better reaction to immunotherapy in high CPS-core patients. The CPS score constructed from cuproptosis subtypes can be used as a predictive tool to evaluate the prognosis of CRC patients and their response to immunotherapy.

https://www.mdpi.com/2072-6694/15/2/387/pdf?version=1673002334

Multiomics Analysis Reveals Cuproptosis-Related Signature for Evaluating Prognosis and Immunotherapy Efficacy in Colorectal Cancer

Nonalcoholic fatty liver disease (NAFLD) is the most prevalent hepatic pathology worldwide. However, the precise molecular mechanisms for NAFLD are still not sufficiently explained. Recently, a new mode of cell death (cuproptosis) is found. However, the relationship between NAFLD and cuproptosis remains unclear. We analyzed three public datasets (GSE89632, GSE130970, and GSE135251) to identify cuproptosis-related genes stably expressed in NAFLD. Then, we performed a series of bioinformatics analyses to explore the relationship between NAFLD and cuproptosis-related genes. Finally, 6 high-fat diet- (HFD-) induced NAFLD C57BL/6J mouse models were established to carry out transcriptome analysis. The results of gene set variation analysis (GSVA) revealed that the cuproptosis pathway was abnormally activated to a certain degree (p = 0.035 in GSE89632, p = 0.016 in GSE130970, p = 0.22 in GSE135251), and the principal component analysis (PCA) of the cuproptosis-related genes showed that the NAFLD group separated from the control group, with the first two principal components accounting for 58.63%-74.88% of the variation. Among three datasets, two cuproptosis-related genes (DLD and PDHB, p < 0.01 or 0.001) were stably upregulated in NAFLD. Additionally, both DLD (AUC = 0.786–0.856) and PDHB (AUC = 0.771–0.836) had favorable diagnostic properties, and the multivariate logistics regression model further improved the diagnostic properties (AUC = 0.839–0.889). NADH, flavin adenine dinucleotide, and glycine targeted DLD, and pyruvic acid and NADH targeted PDHB in the DrugBank database. The DLD and PDHB were also associated with clinical pathology, especially with steatosis (DLD, p = 0.0013–0.025; PDHB, p = 0.002–0.0026) and NAFLD activity score (DLD, p = 0.004–0.02; PDHB, p = 0.003–0.031). What is more, DLD and PDHB were correlated with stromal score (DLD, R = 0.38, p < 0.001; PDHB, R = 0.31, p < 0.001) and immune score (DLD, R = 0.26, p < 0.001; PDHB, R = 0.27, p < 0.001) in NAFLD. Furthermore, Dld and Pdhb were also significantly upregulated in the NAFLD mouse model. In conclusion, cuproptosis pathways, especially DLD and PDHB, could be potential candidate genes for NAFLD diagnostic and therapeutic options.

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Identification of Cuproptosis-Related Genes in Nonalcoholic Fatty Liver Disease

Background Hepatocellular carcinoma is the third most deadly malignant tumor in the world with a poor prognosis. Although immunotherapy represents a promising therapeutic approach for HCC, the overall response rate of HCC patients to immunotherapy is less than 30%. Therefore, it is of great significance to explore prognostic factors and investigate the associated tumor immune microenvironment features. Methods By analyzing RNA-seq data of the TCGA-LIHC cohort, the set of cuproptosis related genes was extracted via correlation analysis as a generalization feature. Then, a random forest cox prognostic model was constructed and the cuproptosis random forest cox score was built by random forest feature filtering and univariate multivariate cox regression analysis. Subsequently, the prognosis prediction of CRFCS was evaluated via analyzing data of independent cohorts from GEO and ICGC by using KM and ROC methods. Moreover, mutation characterization, immune cell infiltration, immune evasion, and drug sensitivity of CRFCS in HCC were assessed. Results A cuproptosis random forest cox score was built based on a generalization feature of four cuproptosis related genes. Patients in the high CRFCS group exhibited a lower overall survival. Univariate multivariate Cox regression analysis validated CRFCS as an independent prognostic indicator. ROC analysis revealed that CRFCS was a good predictor of HCC (AUC =0.82). Mutation analysis manifested that microsatellite instability (MSI) was significantly increased in the high CRFCS group. Meanwhile, tumor microenvironment analysis showed that the high CRFCS group displayed much more immune cell infiltration compared with the low CRFCS group. The immune escape assessment analysis demonstrated that the high CRFCS group displayed a decreased TIDE score indicating a lower immune escape probability in the high CRFCS group compared with the low CRFCS group. Interestingly, immune checkpoints were highly expressed in the high CRFCS group. Drug sensitivity analysis revealed that HCC patients from the high CRFCS group had a lower IC50 of sorafenib than that from the low CRFCS group. Conclusions In this study, we constructed a cuproptosis random forest cox score (CRFCS) model. CRFCS was revealed to be a potential independent prognostic indicator of HCC and high CRFCS samples showed a poor prognosis. Interestingly, CRFCS were correlated with TME characteristics as well as clinical treatment efficacy. Importantly, compared with the low CRFCS group, the high CRFCS group may benefit from immunotherapy and sorafenib treatment.

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A cuproptosis random forest cox score model-based evaluation of prognosis, mutation characterization, immune infiltration, and drug sensitivity in hepatocellular carcinoma

Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear. Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features. Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore. Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination.

Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer

Liver hepatocellular carcinoma (LIHC) ranks sixth among the most common types of cancer with a high mortality rate. Cuproptosis is a newly discovered type of cell death in tumor, which is characterized by accumulation of intracellular copper leading to the aggregation of mitochondrial lipoproteins and destabilization of proteins. Thus, understanding the exact effects of cuproptosis-related genes in LIHC and determining their prognosticvalue is critical. However, the prognostic model of LIHC based on cuproptosis-related genes has not been reported.Firstly, we downloaded transcriptome data and clinical information of LIHC patients from TCGA and GEO (GSE76427), respectively. We then extracted the expression of cuproptosis-related genes and established a prognostic model by lasso cox regression analysis. Afterwards, the prediction performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic curve (ROC). Then, the prognostic model and the expression levels of the three genes were validated using the dataset from GEO. Subsequently, we divided LIHC patients into two subtypes by non-negative matrix factorization (NMF) classification and performed survival analysis. We constructed a Sankey plot linking different subtypes and prognostic models. Next, we calculate the drug sensitivity of each sample from patients in the high-risk group and low-risk group by the R package pRRophetic. Finally, we verified the function of LIPT1 in LIHC.Using lasso cox regression analysis, we developed a prognostic risk model based on three cuproptosis-related genes (GCSH, LIPT1 and CDKN2A). Both in the training and in the test sets, the overall survival (OS) of LIHC patients in the low-risk group was significantly longer than that in the high-risk group. By performing NMF cluster, we identified two molecular subtypes of LIHC (C1 and C2), with C1 subtype having significantly longer OS and PFS than C2 subtype. The ROC analysis indicated that our model had a precisely predictive capacity for patients with LIHC. The multivariate Cox regression analysis indicated that the risk score is an independent predictor. Subsequently, we identified 71 compounds with IC50 values that differed between the high-risk and low-risk groups. Finally, we determined that knockdown of LIPT1 gene expression inhibited proliferation and invasion of hepatoma cells.In this study, we developed a novel prognostic model for hepatocellular carcinoma based on cuproptosis-related genes that can effectively predict the prognosis of LIHC patients. The model may be helpful for clinicians to make clinical decisions for patients with LIHC and provide valuable insights for individualized treatment. Two distinct subtypes of LIHC were identified based on cuproptosis-related genes, with different prognosis and immune characteristics. In addition, we verified that LIPT1 may promote proliferation, invasion and migration of LIHC cells. LIPT1 might be a new potential target for therapy of LIHC. 

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System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma

Abstract Background Pancreatic adenocarcinoma (PAAD) is one of the most common malignant tumors of the digestive tract. Pyroptosis is a newly discovered programmed cell death that highly correlated with the prognosis of tumors. However, the prognostic value of pyroptosis in PAAD remains unclear. Methods A total of 178 pancreatic cancer PAAD samples and 167 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The “DESeq2” R package was used to identify differntially expressed pyroptosis-related genes between normal pancreatic samples and PAAD samples. The prognostic model was established in TCGA cohort based on univariate Cox and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses, which was validated in test set from Gene Expression Omnibus (GEO) cohort. Univariate independent prognostic analysis and multivariate independent prognostic analysis were used to determine whether the risk score can be used as an independent prognostic factor to predict the clinicopathological features of PAAD patients. A nomogram was used to predict the survival probability of PAAD patients, which could help in clinical decision-making. The R package "pRRophetic" was applied to calculate the drug sensitivity of each samples from high- and low-risk group. Tumor immune infiltration was investigated using an ESTIMATE algorithm. Finally, the pro‐tumor phenotype of GSDMC was explored in PANC-1 and CFPAC-1 cells. Result On the basis of univariate Cox and LASSO regression analyses, we constructed a risk model with identified five pyroptosis-related genes (IL18, CASP4, NLRP1, GSDMC, and NLRP2), which was validated in the test set. The PAAD samples were divided into high-risk and low-risk groups on the basis of the risk score's median. According to Kaplan Meier curve analysis, samples from high-risk groups had worse outcomes than those from low-risk groups. The time-dependent receiver operating characteristics (ROC) analysis revealed that the risk model could predict the prognosis of PAAD accurately. A nomogram accompanied by calibration curves was presented for predicting 1-, 2-, and 3-year survival in PAAD patients. More importantly, 4 small molecular compounds (A.443654, PD.173074, Epothilone. B, Lapatinib) were identified, which might be potential drugs for the treatment of PAAD patients. Finally, the depletion of GSDMC inhibits the proliferation, invasion, and migration of pancreatic adenocarcinoma cells. Conclusion In this study, we developed a pyroptosis-related prognostic model based on IL18, CASP4, NLRP1, NLRP2, and GSDMC , which may be helpful for clinicians to make clinical decisions for PAAD patients and provide valuable insights for individualized treatment. Our result suggest that GSDMC may promote the proliferation and migration of PAAD cell lines. These findings may provide new insights into the roles of pyroptosis-related genes in PAAD, and offer new therapeutic targets for the treatment of PAAD. 

/pdf/system-analysis-based-on-the-pyroptosis-related-genes-2e7lrgm3.pdf

System analysis based on the pyroptosis-related genes identifies GSDMC as a novel therapy target for pancreatic adenocarcinoma

Background: Colon cancer (COAD) is the third-largest common malignant tumor and the fourth major cause of cancer death in the world. Endoplasmic reticulum (ER) stress has a great influence on cell growth, migration, proliferation, invasion, angiogenesis, and chemoresistance of massive tumors. Although ER stress is known to play an important role in various types of cancer, the prognostic model based on ER stress-related genes (ERSRGs) in colon cancer has not been constructed yet. In this study, we established an ERSRGs prognostic risk model to assess the survival of COAD patients. Methods: The COAD gene expression profile and clinical information data of the training set were obtained from the GEO database (GSE40967) and the test set COAD gene expression profile and clinical informative data were downloaded from the TCGA database. The endoplasmic reticulum stress-related genes (ERSRGs) were obtained from Gene Set Enrichment Analysis (GSEA) website. Differentially expressed ERSRGs between normal samples and COAD samples were identified by R “limma” package. Based on the univariate, lasso, and multivariate Cox regression analysis, we developed an ERSRGs prognostic risk model to predict survival in COAD patients. Finally, we verified the function of WFS1 in COAD through in vitro experiments. Results: We built a 9-gene prognostic risk model based on the univariate, lasso, and multivariate Cox regression analysis. Kaplan-Meier survival analysis and Receiver operating characteristic (ROC) curve revealed that the prognostic risk model has good predictive performance. Subsequently, we screened 60 compounds with significant differences in the estimated half-maximal inhibitory concentration (IC50) between high-risk and low-risk groups. In addition, we found that the ERSRGs prognostic risk model was related to immune cell infiltration and the expression of immune checkpoint molecules. Finally, we determined that knockdown of the expression of WFS1 inhibits the proliferation of colon cancer cells. Conclusions: The prognostic risk model we built may help clinicians accurately predict the survival of patients with COAD. Our findings provide valuable insights into the role of ERSRGs in COAD and may provide new targets for COAD therapy.

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Liukai Ma

Papers

System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma

System analysis based on the pyroptosis-related genes identifies GSDMC as a novel therapy target for pancreatic adenocarcinoma

System analysis based on the ER stress-related genes identifies WFS1 as a novel therapy target for colon cancer