The role of a cuproptosis-related prognostic signature in colon cancer tumor microenvironment and immune responses

Cellular and molecular mechanisms of cell damage and cell death in ischemia-reperfusion injury in organ transplantation

Ischemia-reperfusion injury (IRI) is a critical pathological condition in which cell death plays a major contributory role, and negatively impacts post-transplant outcomes. At the cellular level, hypoxia due to ischemia disturbs cellular metabolism and decreases cellular bioenergetics through dysfunction of mitochondrial electron transport chain, causing a switch from cellular respiration to anaerobic metabolism, and subsequent cascades of events that lead to increased intracellular concentrations of Na+, H+ and Ca2+ and consequently cellular edema. Restoration of blood supply after ischemia provides oxygen to the ischemic tissue in excess of its requirement, resulting in over-production of reactive oxygen species (ROS), which overwhelms the cells' antioxidant defence system, and thereby causing oxidative damage in addition to activating pro-inflammatory pathways to cause cell death. Moderate ischemia and reperfusion may result in cell dysfunction, which may not lead to cell death due to activation of recovery systems to control ROS production and to ensure cell survival. However, prolonged and severe ischemia and reperfusion induce cell death by apoptosis, mitoptosis, necrosis, necroptosis, autophagy, mitophagy, mitochondrial permeability transition (MPT)-driven necrosis, ferroptosis, pyroptosis, cuproptosis and parthanoptosis. This review discusses cellular and molecular mechanisms of these various forms of cell death in the context of organ transplantation, and their inhibition, which holds clinical promise in the quest to prevent IRI and improve allograft quality and function for a long-term success of organ transplantation.

Cuproptosis gene-related, neural network-based prognosis prediction and drug-target prediction for KIRC

BACKGROUND

Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the problem of low specificity. In addition, cuproptosis, as a novel mode of cell death, has been used as a biomarker to predict disease in many cancers in recent years, which also provides an important basis for prognostic prediction in KIRC. For postoperative patients with KIRC, an important means of preventing disease recurrence is pharmacological treatment, and thus matching the appropriate drug to the specific patient's target is also particularly important. With the development of neural networks, their predictive performance in the field of medical big data has surpassed that of traditional methods, and this also applies to the field of prognosis prediction and drug-target prediction.

OBJECTIVE

The purpose of this study is to screen for cuproptosis genes related to the prognosis of KIRC and to establish a deep neural network (DNN) model for patient risk prediction, while also developing a personalized nomogram model for predicting patient survival. In addition, sensitivity drugs for KIRC were screened, and a graph neural network (GNN) model was established to predict the targets of the drugs, in order to discover potential drug action sites and provide new treatment ideas for KIRC.

METHODS

We used the Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC) database, and DrugBank database for our study. Differentially expressed genes (DEGs) were screened using TCGA data, and then a DNN-based risk prediction model was built and validated using ICGC data. Subsequently, the differences between high- and low-risk groups were analyzed and KIRC-sensitive drugs were screened, and finally a GNN model was trained using DrugBank data to predict the relevant targets of these drugs.

RESULTS

A prognostic model was built by screening 10 significantly different cuproptosis-related genes, the model had an AUC of 0.739 on the training set (TCGA data) and an AUC of 0.707 on the validation set (ICGC data), which demonstrated a good predictive performance. Based on the prognostic model in this paper, patients were also classified into high- and low-risk groups, and functional analyses were performed. In addition, 251 drugs were screened for sensitivity, and four drugs were ultimately found to have high sensitivity, with 5-Fluorouracil having the best inhibitory effect, and subsequently their corresponding targets were also predicted by GraphSAGE, with the most prominent targets including Cytochrome P450 2D6, UDP-glucuronosyltransferase 1A, and Proto-oncogene tyrosine-protein kinase receptor Ret. Notably, the average accuracy of GraphSAGE was 0.817 ± 0.013, which was higher than that of GAT and GTN.

CONCLUSION

Our KIRC risk prediction model, constructed using 10 cuproptosis-related genes, had good independent prognostic ability. In addition, we screened four highly sensitive drugs and predicted relevant targets for these four drugs that might treat KIRC. Finally, literature research revealed that four drug-target interactions have been demonstrated in previous studies and the remaining targets are potential sites of drug action for future research.

A novel cuproptosis-related gene prognostic signature in colon adenocarcinoma

Cuproptosis is the latest cell death type caused by enhanced mitochondrial-dependent energy metabolism. This study plans to establish a survival prognosis model for colon adenocarcinoma (COAD) patients based on cuproptosis-related genes (CRGs). We investigated the genetic alterations of CRGs in COAD based on The Cancer Genome Atlas database and validated in the GSE41328 dataset. Our results showed that LIPT1, PDHA1, GLS, and CDKN2A had significantly higher expression in COAD tissues than in normal tissues, while FDX1, DLD, and MTF1 had significantly lower expression in COAD tissues than in normal tissues (|(log2(fold change))| > 2, p < 0.05). DLD (hazard ratio (HR): 0.658; 95% confidence interval (CI): 0.445, 0.974; p = 0.037) and CDKN2A (HR: 1.785; 95% CI: 1.200, 2.654; p = 0.004) expressions were linked with overall survival throughout a log-rank test. CRG prognostic scores exhibited an area under the curve of 0.737, 0.646, and 0.633 at 1, 3, and 5 years. Patients with a high-risk factor suffered from poor prognosis (HR = 1.514; 95% CI: 1.022, 2.243; p = 0.0386). An independent validation dataset (GSE41328 (N = 20)) confirmed the above results. The CRGs' signature may be used as a prognostic predictor for COAD patients, providing unique insights into anticancer therapy.

Cuproptosis-related risk score predicts prognosis and characterizes the tumor microenvironment in colon adenocarcinoma

Introduction

Cuproptosis is a novel copper-dependent regulatory cell death (RCD), which is closely related to the occurrence and development of multiple cancers. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of colon adenocarcinoma (COAD) remains unclear.

Methods

Transcriptome, somatic mutation, somatic copy number alteration and the corresponding clinicopathological data of COAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). Difference, survival and correlation analyses were conducted to evaluate the characteristics of CRGs in COAD patients. Consensus unsupervised clustering analysis of CRGs expression profile was used to classify patients into different cuproptosis molecular and gene subtypes. TME characteristics of different molecular subtypes were investigated by using Gene set variation analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA). Next, CRG Risk scoring system was constructed by applying logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis and multivariate cox analysis. Real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) were used to exam the expression of key Risk scoring genes.

Results

Our study indicated that CRGs had relatively common genetic and transcriptional variations in COAD tissues. We identified three cuproptosis molecular subtypes and three gene subtypes based on CRGs expression profile and prognostic differentially expressed genes (DEGs) expression profile, and found that changes in multilayer CRGs were closely related to the clinical characteristics, overall survival (OS), different signaling pathways, and immune cell infiltration of TME. CRG Risk scoring system was constructed according to the expression of 7 key cuproptosis-related risk genes (GLS, NOX1, HOXC6, TNNT1, GLS, HOXC6 and PLA2G12B). RT-qPCR and IHC indicated that the expression of GLS, NOX1, HOXC6, TNNT1 and PLA2G12B were up-regulated in tumor tissues, compared with those in normal tissues, and all of GLS, HOXC6, NOX1 and PLA2G12B were closely related with patient survival. In addition, high CRG risk scores were significantly associated with high microsatellite instability (MSI-H), tumor mutation burden (TMB), cancer stem cell (CSC) indices, stromal and immune scores in TME, drug susceptibility, as well as patient survival. Finally, a highly accurate nomogram was constructed to promote the clinical application of the CRG Risk scoring system.

Discussion

Our comprehensive analysis showed that CRGs were greatly associated with TME, clinicopathological characteristics, and prognosis of patient with COAD. These findings may promote our understanding of CRGs in COAD, providing new insights for physicians to predict prognosis and develop more precise and individualized therapy strategies.

The therapeutic and prognostic role of cuproptosis-related genes in triple negative breast cancer

BACKGROUND

This study aimed to observe the potential impact of known cuproptosis-related genes (CRGs) on triple negative breast cancer (TNBC) development, as well as their associated molecular mechanisms, immune infiltration mechanisms and potential therapeutic agents.

RESULTS

Based on the Cox Proportional Hazard Model, 11 CRGs may be especially important in TNBC development and progression (considered as the Key-TNBC-CRGs). The expression of several Key-TNBC-CRGs (e.g., ATP7A, PIK3CA, LIAS, and LIPT) are associated with common mutations. The SCNA variation of 11 Key-TNBC-CRGs are related to differences immune infiltration profiles. In particular, depletion of ATP7A, ATP7B, CLS, LIAS, and SCL31A1 and while high amplification of NLRP3 and LIPT2 are correlated with decreased immune infiltration. In our Cox proportional hazards regression model, there is a significant difference in the overall survival between high-risk and low-risk groups. The HR in the high-risk group is 3.891 versus the low-risk group. And this model has a satisfactory performance in Prediction of 5-15-year survival, in particular in the 10-year survival (AUC = 0.836). Finally, we discovered some potential drugs for TNBC treatment based on the strategy of targeting 11 Key-TNBC-CRGs, such as Dasatinib combined with ABT-737, Erastin or Methotrexate, and Docetaxel/Ispinesib combination.

CONCLUSION

In conclusion, CRGs may play important roles in TNBC development, and they can impact tumor immune microenvironment and patient survival. The Key-TNBC-CRGs interact mutually and can be influenced by common BC-related mutations. Additionally, we established a 11-gene risk model with a robust performance in prediction of 5-15-year survival. As well, some new drugs are proposed potentially effective in TNBC based on the CRG strategy.

Comprehensive analysis of cuproptosis-related immune biomarker signature to enhance prognostic accuracy in gastric cancer

BACKGROUND

Gastric cancer (GC) is a malignant tumor with high prevalence and fatality. Cuproptosis is a recently identified copper-dependent programmed cell death mechanism. Multiple studies have demonstrated the profound impact of the immune microenvironment on tumor development. Hence, we decided to excavate the potential functional roles of cuproptosis-related immune genes (CRIGs) in GC and their values as biomarkers.

METHODS

Cuproptosis- and immune-related genes were curated from top published studies on cell cuproptosis and cellular immunity. Transcriptome data and clinical information were obtained from TCGA, GTEx, and GEO databases. Cox and LASSO analyses were used to establish a prognostic signature for GC. Long-term prognosis, immune infiltration, immune checkpoint, and drug response were compared between signature groups. CRIG expression in GC scRNA-seq was analyzed. Immunohistochemistry was used to evaluate CRIG and cuproptosis regulator FDX1 in GC tissues.

RESULTS

Seven CRIGs (ANOS1, CTLA4, ITGAV, CXCR4, NRP1, FABP3, and LGR6) were selected to establish a potent signature to forecast the long-term prognosis of patients. GC patients had worse prognosis and poor responses to chemotherapeutic drugs (5-Fluorouracil and paclitaxel) in the high-risk group. scRNA-seq revealed that CTLA4, ITGAV, CXCR4, and NRP1 enrichment in specific cell types regulated the progression of GC. Moreover, NRP1, CXCR4, LGR6, CTLA4, and FDX1 were elevated in GC tissues, with a positive correlation between their expression and FDX1.

CONCLUSIONS

To conclude, this study first provides insights into the functions of CRIGs in GC. Furthermore, a robust cuproptosis-related immune biomarker signature was established to forecast the long-term survival of GC patients accurately.