Characterization of cuproptosis identified immune microenvironment and prognosis in acute myeloid leukemia

Elesclomol-copper synergizes with imidazole ketone erastin by promoting cuproptosis and ferroptosis in myelodysplastic syndromes

Myelodysplastic syndromes (MDS) encompass a collection of clonal hematopoietic malignancies distinguished by the depletion of peripheral blood cells. The treatment of MDS is hindered by the advanced age of patients, with a restricted repertoire of drugs currently accessible for therapeutic intervention. In this study, we found that ES-Cu strongly inhibited the viability of MDS cell lines and activated cuproptosis in a copper-dependent manner. Importantly, ferroptosis inducer IKE synergistically enhanced ES-Cu-mediated cytotoxicity both in vitro and in vivo. Of note, the combination of IKE and ES-Cu intensively impaired mitochondrial homeostasis with increased mitochondrial ROS, MMP hyperpolarized, down-regulated iron-sulfur proteins and declined oxygen consumption rate. Additionally, ES-Cu/IKE treatment could enhance the lipoylation-dependent oligomerization of the DLAT. To elucidate the specific order of events in the synergistic cell death, inhibitors of ferroptosis and cuproptosis were utilized to further characterize the basis of cell death. Cell viability assays showed that the glutathione and its precursor N-acetylcysteine could significantly rescue the cell death under either mono or combination treatment, demonstrating that GSH acts at the crossing point in the regulation network of cuproptosis and ferroptosis. Significantly, the reconstitution of xCT expression and knockdown of FDX1 cells have been found to contribute to the tolerance of mono treatment but have little recovery impact on the combined treatment. Collectively, these findings suggest that a synergistic interaction leading to the induction of multiple programmed cell death pathways could be a promising approach to enhance the effectiveness of therapy for MDS.

From mechanism to therapy: the journey of CD24 in cancer

CD24 is a glycosylphosphatidylinositol-anchored protein that is expressed in a wide range of tissues and cell types. It is involved in a variety of physiological and pathological processes, including cell adhesion, migration, differentiation, and apoptosis. Additionally, CD24 has been studied extensively in the context of cancer, where it has been found to play a role in tumor growth, invasion, and metastasis. In recent years, there has been growing interest in CD24 as a potential therapeutic target for cancer treatment. This review summarizes the current knowledge of CD24, including its structure, function, and its role in cancer. Finally, we provide insights into potential clinical application of CD24 and discuss possible approaches for the development of targeted cancer therapies.

Establishment and validation of a gene mutation-based risk model for predicting prognosis and therapy response in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is a malignant clonal proliferative disease of hematopoietic system. Despite tremendous progress in uncovering the AML genome, only a small number of mutations have been incorporated into risk stratification and used as therapeutic targets. In this research, we performed to construct a predictive prognosis risk model for AML patients according to gene mutations.

Methods

Next-generation sequencing (NGS) technology was utilized to detect gene mutation from 118 patients. mRNA expression profiles and related clinical information were mined from TCGA and GEO databases. Consensus cluster analysis was applied to obtain molecular subtypes, and differences in clinicopathological features, prognosis, and immune microenvironment of different clusters were systematically compared. According to the differentially expressed genes (DEGs) between clusters, univariate and LASSO regression analysis were applied to identify gene signatures to build a prognostic risk model. Patients were classified into high-risk (HR) and low-risk (LR) groups according to the median risk score (RS). Differences in prognosis, immune profile, and therapeutic sensitivity between two groups were analyzed. The independent predictive value of RS was assessed and a nomogram was developed.

Results

NGS detected 24 mutated genes, with higher mutation frequencies in CBL (63 %) and SETBP1 (49 %). Two clusters exhibited different immune microenvironments and survival probability (p = 0.0056) were identified. A total of 444 DEGs were screened in two clusters, and a mutation-associated risk model was constructed, including MPO, HGF, SH2B3, SETBP1, HLA-DRB1, LGALS1, and KDM5B. Patients in LR had a superior survival time compared to HR. Predictive performance of this model was confirmed and the developed nomogram further improved the applicability of the risk model with the AUCs for predicting 1-, 3-, 5-year survival rate were 0.829, 0.81 and 0.811, respectively. HR cases were more sensitive to erlotinib, CI-1040, and AZD6244.

Conclusion

These findings supplemented the understanding of gene mutations in AML, and constructed models had good application prospect to provide effective information for predicting prognosis and treatment response of AML.

Cuproptosis: unveiling a new frontier in cancer biology and therapeutics

Copper plays vital roles in numerous cellular processes and its imbalance can lead to oxidative stress and dysfunction. Recent research has unveiled a unique form of copper-induced cell death, termed cuproptosis, which differs from known cell death mechanisms. This process involves the interaction of copper with lipoylated tricarboxylic acid cycle enzymes, causing protein aggregation and cell death. Recently, a growing number of studies have explored the link between cuproptosis and cancer development. This review comprehensively examines the systemic and cellular metabolism of copper, including tumor-related signaling pathways influenced by copper. It delves into the discovery and mechanisms of cuproptosis and its connection to various cancers. Additionally, the review suggests potential cancer treatments using copper ionophores that induce cuproptosis, in combination with small molecule drugs, for precision therapy in specific cancer types.

Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking

Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The model utilized machine learning algorithms incorporating stacking. The GSE37642 dataset was used as the training data, and the GSE12417 and TCGA-LAML cohorts were used as the validation data. Stacking was used to merge the three prediction models, subsequently using a random survival forests algorithm to refit the final model using the stacking linear predictor and clinical factors. The prediction model, featuring stacking linear predictor and clinical factors, achieved AUC values of 0.840, 0.876 and 0.892 at 1, 2 and 3 years within the GSE37642 dataset. In external validation dataset, the corresponding AUCs were 0.741, 0.754 and 0.783. The predictive performance of the model in the external dataset surpasses that of the model simply incorporates all predictors. Additionally, the final model exhibited good calibration accuracy. In conclusion, our findings indicate that the novel prediction model refines the prognostic prediction for AML patients, while the stacking strategy displays potential for model integration.

Copper and cuproptosis-related genes in hepatocellular carcinoma: therapeutic biomarkers targeting tumor immune microenvironment and immune checkpoints

Background

Hepatocellular carcinoma (HCC), one of the most common cancers worldwide, exhibits high immune heterogeneity and mortality. Emerging studies suggest that copper (Cu) plays a key role in cell survival. However, the relationship between Cu and tumor development remains unclear.

Methods

We investigated the effects of Cu and cuproptosis-related genes (CRGs) in patients with HCC in the TCGA-LIHC (The Cancer Genome Atlas-Liver cancer, n = 347) and ICGC-LIRI-JP (International Cancer Genome Consortium-Liver Cancer-Riken-Japan, n = 203) datasets. Prognostic genes were identified by survival analysis, and a least absolute shrinkage and selection operator (Lasso) regression model was constructed using the prognostic genes in the two datasets. Additionally, we analyzed differentially expressed genes and signal pathway enrichment. We also evaluated the effects of CRGs on tumor immune cell infiltration and their co-expression with immune checkpoint genes (ICGs) and performed validation in different tumor immune microenvironments (TIMs). Finally, we performed validation using clinical samples and predicted the prognosis of patients with HCC using a nomogram.

Results

A total of 59 CRGs were included for analysis, and 15 genes that significantly influenced the survival of patients in the two datasets were identified. Patients were grouped by risk scores, and pathway enrichment analysis suggested that immune-related pathways were substantially enriched in both datasets. Tumor immune cell infiltration analysis and clinical validation revealed that PRNP (Prion protein), SNCA (Synuclein alpha), and COX17 (Cytochrome c oxidase copper chaperone COX17) may be closely correlated with immune cell infiltration and ICG expression. A nomogram was constructed to predict the prognosis of patients with HCC using patients' characteristics and risk scores.

Conclusion

CRGs may regulate the development of HCC by targeting the TIM and ICGs. CRGs such as PRNP, SNCA, and COX17 could be promising targets for HCC immune therapy in the future.