Multi-omics analysis defines a cuproptosis-related prognostic model for ovarian cancer: Implication of WASF2 in cuproptosis resistance

Cuproptosis inhibits tumor progression and enhances cisplatin toxicity in ovarian cancer

Cuproptosis is a novel form of regulated cell death triggered by copper ion and copper ionophore. While cuproptosis has been actively explored as a potential target for cancer therapy, its role in ovarian cancer (OC) still remains unclear. In this study, we demonstrate that cuproptosis inhibits OC cell proliferation, migration, and invasion through FDX1 regulation and suppresses tumor growth in a mouse model. We also confirm that cuproptosis enhances OC sensitivity to cisplatin treatment both in vivo and in vitro. Moreover, our findings reveal that cuproptosis affects cholesterol biosynthesis in OC cells, with cholesterol playing a crucial role in its cytotoxic effect. Taken together, our results elucidate the effect of cuproptosis in OC and suggest it as a promising therapeutic strategy.

Integrated multiomics characterization reveals cuproptosis-related hub genes for predicting the prognosis and clinical efficacy of ovarian cancer

Background

As a prevalent malignancy in women, ovarian cancer (OC) presents a challenge in clinical practice because of its poor prognosis and poor therapeutic efficacy. The mechanism by which cuproptosis activity is accompanied by immune infiltration in OC remains unknown. Here, we investigated cuproptosis-related OC subtypes and relevant immune landscapes to develop a risk score (RS) model for survival prediction.

Methods

Cuproptosis-related genes (CRGs) were identified to construct molecular subtypes via an unsupervised clustering algorithm based on the expression profiles of survival-related CRGs in the GEO database. Single-cell datasets were used to estimate immune infiltration among subtypes. The RS oriented from molecular subtypes was developed via LASSO Cox regression in the TCGA OC dataset and independently validated in the GEO and TCGA datasets. Hub markers from RS were identified in tissues and cell lines. The function of the key gene from RS was identified in vitro.

Results

We investigated cuproptosis activity and immune infiltration to establish three clinical subtypes of OC based the differentially expressed genes (DEGs) from CRGs to create an RS model validated for clinical efficacy and prognosis. Six hub genes from the RS served as ongenic markers in OC tissues and cell lines. The function of GAS1 in the RS model revealed that it exerts oncogenic effects.

Conclusions

Our study provides a novel RS model including 6 hub genes associated with cuproptosis and immune infiltration to predict OC prognosis as well as clinical efficacy.

Copper homeostasis and copper-induced cell death in tumor immunity: implications for therapeutic strategies in cancer immunotherapy

Copper is an important trace element for maintaining key biological functions such as cellular respiration, nerve conduction, and antioxidant defense. Maintaining copper homeostasis is critical for human health, and its imbalance has been linked to various diseases, especially cancer. Cuproptosis, a novel mechanism of copper-induced cell death, provides new therapeutic opportunities for metal ion regulation to interact with cell fate. This review provides insights into the complex mechanisms of copper metabolism, the molecular basis of cuproptosis, and its association with cancer development. We assess the role of cuproptosis-related genes (CRGs) associated with tumorigenesis, their importance as prognostic indicators and therapeutic targets, and the impact of copper homeostasis on the tumor microenvironment (TME) and immune response. Ultimately, this review highlights the complex interplay between copper, cuproptosis, and cancer immunotherapy.

Copper homeostasis and cuproptosis in gynecological cancers

Copper (Cu) is an essential trace element involved in a variety of biological processes, such as antioxidant defense, mitochondrial respiration, and bio-compound synthesis. In recent years, a novel theory called cuproptosis has emerged to explain how Cu induces programmed cell death. Cu targets lipoylated enzymes in the tricarboxylic acid cycle and subsequently triggers the oligomerization of lipoylated dihydrolipoamide S-acetyltransferase, leading to the loss of Fe-S clusters and induction of heat shock protein 70. Gynecological malignancies including cervical cancer, ovarian cancer and uterine corpus endometrial carcinoma significantly impact women's quality of life and even pose a threat to their lives. Excessive Cu can promote cancer progression by enhancing tumor growth, proliferation, angiogenesis and metastasis through multiple signaling pathways. However, there are few studies investigating gynecological cancers in relation to cuproptosis. Therefore, this review discusses Cu homeostasis and cuproptosis while exploring the potential use of cuproptosis for prognosis prediction as well as its implications in the progression and treatment of gynecological cancers. Additionally, we explore the application of Cu ionophore therapy in treating gynecological malignancies.

Targeting cuproptosis for cancer therapy: mechanistic insights and clinical perspectives

Cuproptosis is a newly identified form of cell death induced by excessive copper (Cu) accumulation within cells. Mechanistically, cuproptosis results from Cu-induced aggregation of dihydrolipoamide S-acetyltransferase, correlated with the mitochondrial tricarboxylic acid cycle and the loss of iron-sulfur cluster proteins, ultimately resulting in proteotoxic stress and triggering cell death. Recently, cuproptosis has garnered significant interest in tumor research due to its potential as a crucial therapeutic strategy against cancer. In this review, we summarized the cellular and molecular mechanisms of cuproptosis and its relationship with other types of cell death. Additionally, we reviewed the current drugs or strategies available to induce cuproptosis in tumor cells, including Cu ionophores, small compounds, and nanomedicine. Furthermore, we targeted cell metabolism and specific regulatory genes in cancer therapy to enhance tumor sensitivity to cuproptosis. Finally, we discussed the feasibility of targeting cuproptosis to overcome tumor chemotherapy and immunotherapy resistance and suggested future research directions. This study suggested that targeting cuproptosis could open new avenues for developing tumor therapy.

Construction of a prognostic model for ovarian cancer based on a comprehensive bioinformatics analysis of cuproptosis-associated long non-coding RNA signatures

Ovarian cancer (OCa) is a common malignancy in women, and the role of cuproptosis and its related genes in OCa is unclear. Using the GSE14407 dataset, we analyzed the expression and correlation of cuproptosis-related genes (CRGs) between tumor and normal groups. From the TCGA-OV dataset, we identified 20 cuproptosis-related long non-coding RNAs (CuLncs) associated with patient survival through univariate Cox analysis. OCa patients were divided into early-stage and late-stage groups to analyze CuLncs expression. Cluster analysis classified patients into two clusters, with Cluster1 having a poorer prognosis. Significant differences in "Lymphatic Invasion" and "Cancer status" were observed between clusters. Seven CRGs showed significant expression differences, validated using the human protein atlas (HPA) databases. Immune analysis revealed a higher ImmuneScore in Cluster1. GSEA identified associated signaling pathways. LASSO regression included 11 CuLncs to construct and validate a survival prediction model, classifying patients into high-risk and low-risk groups. Correlations between riskScore, Cluster phenotype, ImmuneScore, and immune cell infiltration were explored. Cell experiments showed that knocking down AC023644.1 decreases OCa cell viability. In conclusion, we constructed an accurate prognostic model for OCa based on 11 CuLncs, providing a basis for prognosis assessment and potential immunotherapy targets.

Mechanisms associated with cuproptosis and implications for ovarian cancer

Ovarian cancer, a profoundly fatal gynecologic neoplasm, exerts a substantial economic strain on nations globally. The formidable challenge of its frequent relapse necessitates the exploration of novel cytotoxic agents, efficacious antineoplastic medications with minimal adverse effects, and strategies to surmount resistance to primary chemotherapeutic agents. These endeavors aim to supplement extant pharmacological interventions and elucidate molecular mechanisms underlying induced cytotoxicity, distinct from conventional therapeutic modalities. Recent scientific research has unveiled a novel form of cellular demise, known as copper-death, which is contingent upon the intracellular concentration of copper. Diverging from conventional mechanisms of cellular demise, copper-death exhibits a pronounced reliance on mitochondrial respiration, particularly the tricarboxylic acid (TCA) cycle. Tumor cells manifest distinctive metabolic profiles and elevated copper levels in comparison to their normal counterparts. The advent of copper-death presents alluring possibilities for targeted therapeutic interventions within the realm of cancer treatment. Hence, the primary objective of this review is to present an overview of the proteins and intricate mechanisms associated with copper-induced cell death, while providing a comprehensive summary of the knowledge acquired regarding potential therapeutic approaches for ovarian cancer. These findings will serve as valuable references to facilitate the advancement of customized therapeutic interventions for ovarian cancer.

Integrating single-cell and spatial transcriptomic analysis to unveil heterogeneity in high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) presents significant challenges due to its heterogeneity and late-stage diagnoses. Using single-cell and spatial transcriptomics to elucidate the complex landscape of HGSOC to understand its underlying mechanism. Our analysis reveals significant inter- and intra-tumoral diversity, manifested through distinct cellular subpopulations and varied microenvironmental niches. Notably, our findings highlight a widespread immunosuppressive environment, marked by complex networks of cell-cell interactions, particularly evident in areas of elevated tumor cell density within metastatic samples. We identify the exclusive presence of COL14A1+ neoplastic cells in metastatic specimens, alongside a strong correlation between CD8A+ NKT cells and poor prognosis, and elevated CHODL expression in HGSOC metastasis tissues. Furthermore, knockdown experiments targeting CHODL demonstrate its role in reducing migration and invasion abilities in HGSOC cells. A pivotal discovery of our study is the delineation of specific cellular signatures correlated with adverse outcomes, notably a subset of CHODL+ neoplastic cells characterized by a distinct metabolic phenotype with a predilection for lipid metabolism. The therapeutic targeting of this metabolic pathway with existing inhibitors appears promising in curbing tumor proliferation. These findings enhance our understanding of HGSOC heterogeneity and reveal potential therapeutic targets, promising more effective management strategies for this aggressive cancer subtype.

Targeting cuproplasia and cuproptosis in cancer

Copper, an essential trace element that exists in oxidized and reduced forms, has pivotal roles in a variety of biological processes, including redox chemistry, enzymatic reactions, mitochondrial respiration, iron metabolism, autophagy and immune modulation; maintaining copper homeostasis is crucial as both its deficiency and its excess are deleterious. Dysregulated copper metabolism has a dual role in tumorigenesis and cancer therapy. Specifically, cuproplasia describes copper-dependent cell growth and proliferation, including hyperplasia, metaplasia and neoplasia, whereas cuproptosis refers to a mitochondrial pathway of cell death triggered by excessive copper exposure and subsequent proteotoxic stress (although complex interactions between cuproptosis and other cell death mechanisms, such as ferroptosis, are likely and remain enigmatic). In this Review, we summarize advances in our understanding of copper metabolism, the molecular machineries underlying cuproplasia and cuproptosis, and their potential targeting for cancer therapy. These new findings advance the rapidly expanding field of translational cancer research focused on metal compounds.

Machine learning developed a fibroblast-related signature for predicting clinical outcome and drug sensitivity in ovarian cancer

Ovarian cancer (OC) is the leading cause of gynecological cancer death. Cancer-associated fibroblasts (CAF) is involved in wound healing and inflammatory processes, tumor occurrence and progression, and chemotherapy resistance in OC. GSE184880 dataset was used to identify CAF-related genes in OC. CAF-related signature (CRS) was constructed using integrative 10 machine learning methods with the datasets from the Cancer Genome Atlas, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082. The performance of CRS in predicting immunotherapy benefits was verified using 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210) and several immune calculating scores. The Lasso + StepCox[forward] method-based predicting model having a highest average C index of 0.69 was referred as the optimal CRS and it had a stable and powerful performance in predicting clinical outcome of OC patients, with the 1-, 3-, and 5-year area under curves were 0.699, 0.708, and 0.767 in the Cancer Genome Atlas cohort. The C index of CRS was higher than that of tumor grade, clinical stage, and many developed signatures. Low CRS score demonstrated lower tumor immune dysfunction and exclusion score, lower immune escape score, higher PD1&CTLA4 immunophenoscore, higher tumor mutation burden score, higher response rate and better prognosis in OC, suggesting a better immunotherapy response. OC patients with low CRS score had a lower half maximal inhibitory concentration value of some drugs (Gemcitabine, Tamoxifen, and Nilotinib, etc) and lower score of some cancer-related hallmarks (Notch signaling, hypoxia, and glycolysis, etc). The current study developed an optimal CRS in OC, which acted as an indicator for the prognosis, stratifying risk and guiding treatment for OC patients.