Introduction
Ovarian cancer (OC) is the most lethal malignant gynecological tumor. Owing to the lack of early clinical symptoms and sensitive and specific diagnostic markers, more than 70% of the patients were found to be in an advanced stage [
1]. Although most patients achieve complete remission after tumor reduction surgery and adjuvant chemotherapy, 70–80% of patients experience tumor recurrence and chemotherapy resistance [
2]. Current clinical trials evaluate combinations of targeted therapies, such as antiangiogenic agents, PARP inhibitors, and immunotherapy for patients with relapse and drug resistance. In addition to different OC subtypes showing different sensitivities and resistance to treatment, studies have shown that patients with OC have considerable molecular heterogeneity at the genomic and immunological levels, providing a more complex landscape for the response to treatment and the local tumor microenvironment (TME) in OC [
2]. Therefore, there is an urgent need for an effective classification and signature in ovarian cancer regarding targeted therapy and immunotherapy to indicate prognosis and guide clinical treatment.
Copper is an essential nutrient whose oxidation–reduction (redox) properties promote copper-dependent cell growth and proliferation (cuproplasia) and play a role in mitochondria-dependent cytotoxicity (cuproptosis) when the Cu concentration exceeds a certain threshold [
3]. Many studies have shown that in various cancers, including gynecological cancer, Cu concentration in the tumors or sera of animal models and patients with cancer is increased [
4], and studies have demonstrated that abnormal Cu accumulation may promote the possibility of malignant transformation through unknown mechanisms [
5]. Excessive copper accumulation in the body endangers life. Many studies have confirmed that excessive copper accumulation can induce “apoptosis” [
6], and copper ion carriers such as disulfiram [
7] and elesclomol [
8] have been used as cancer therapeutic drugs to induce copper death. However, the specific mechanism of excessive copper-induced cell death was not clarified until March 2022. Tsvetkov et al. showed that copper death is a process in which copper directly binds to lipoylated components of the tricarboxylic acid (TCA) cycle, resulting in the aggregation of the lipoylated protein and subsequent loss of the iron-sulfur cluster protein, leading to proteotoxic stress and ultimately cell death [
9]. They demonstrated that the mechanism of copper-induced cell death differs from all other known regulatory cell death mechanisms, including apoptosis, ferroptosis, pyroptosis, and necroptosis. Therefore, Tsvetkov et al. proposed that this previously uncharacterized cell death mechanism be termed cuproptosis [
9]. The properties of cuproptosis molecular subtypes and the potential role of cuproptosis-related genes (CRGs) in the TME in OC remain unknown. With a clear definition of cuproptosis, follow-up research based on cuproptosis-related regulatory factors in cancer will provide potential mechanisms for the occurrence and treatment of cancer and new ideas for the classification, prognosis, and prediction of treatment responsiveness of cancer.
In this study, 656 OC samples were stratified into three cuproptosis-related subtypes according to the expression levels of thirteen CRGs, and the survival and immune infiltration differences among the subtypes were explored. The patients were then divided into two gene subtypes based on the differentially expressed genes (DEGs) identified in the three cuproptosis subtypes. We further established a risk score model to predict overall survival (OS) and characterize the immune landscape of OC, which accurately predicted patient outcomes and significantly correlated with immune infiltration and the sensitivity of a variety of targeted drugs.
Discussion
Ovarian cancer is the deadliest gynecological cancer with a poor prognosis. This occult disease is challenging to diagnose early, relapses easily, and produces drug resistance. The clinical results of advanced OC are still unsatisfactory [
22]. The difference in the molecular heterogeneity of OC provides a complex landscape for predicting the prognosis of patients and their response to immunotherapy. Therefore, the construction of a molecular subtype and characterization of the corresponding immune microenvironment can play a crucial role in improving the prognosis of patients with OC. In this study, we first analyzed the variation and expression of 13 CRGs in OC and their impact on patient prognosis. We found that most CRGs were dysregulated in OC, and DLD and LIAS were independent risk factors for the prognosis of patients with OC. Dihydrolipoamide dehydrogenase (DLD) is a mitochondrial enzyme that exhibits myocardial xanthase activity. In OC, studies have shown that it can be used as a tumor-associated antigen (TAA) to activate the immune system and produce specific autoantibodies during tumor occurrence and progression [
23]. It can also be used as a new diagnostic marker of OC and produces ROS related to its redox activity [
24], which plays a role in inducing the death of tumor cells. Our results showed that its expression was significantly upregulated in OC and significantly correlated with poor patient prognosis. As a mitochondrial enzyme, lipoyl synthase (LIAS) cooperates with other factors such as DLD to catalyze the final step of lipoic acid biosynthesis [
25]. In this study, the expression of LIAS in ovarian cancer was downregulated significantly, and the prognostic analysis suggested that LIAS is a protective gene.
Based on the expression levels of these CRGs, we clustered OC samples into three different molecular subtypes. Patients with different subtypes had significantly different prognoses, among which those in cluster C had the worst prognosis. After exploring the reasons for these differences, our GSVA showed that clusters A and B were mainly enriched in some immune activation pathways, and cluster C was mainly enriched in carcinogenesis pathways. We can conclude that cuproptosis is closely related to tumors and immunity. Therefore, we further studied the correlation between the three subtypes and TME cell infiltration. Notably, in samples of Clusters A and B, the infiltration of immune cells such as activated B cells, activated CD4 + T cells, activated CD8 + T cells, dendritic cells, immune B cells, MDSCs, macrophages, natural killer cells, and regulatory T cells was significantly higher than that of Cluster C. Overall, the low infiltration levels of these immune cells in cluster C partly explain the poor prognosis of patients with OC. In addition, our patients with OC with different prognoses and immune infiltration were better distinguished by the three cuproptosis subtypes. They have a particular clinical application value, and they show that cuproptosis has a potential correlation with the formation of the immune microenvironment of OC. Pathway analysis of clinically significant DEGs suggested that these genes were related to tumors and immunity. Further studies have shown that these DEGs can be divided into two gene subtypes. These findings can help us understand the relationship among cuproptosis, TME cell infiltration, and OC.
Furthermore, we constructed a risk model based on 13 key genes related to cuproptosis. It can effectively distinguish the prognosis of patients and is related to age, tumor stage, survival status, cuproptosis cluster, and gene cluster. The higher the risk score, the worse the prognosis of patients, and it is an independent risk factor for the prognosis of patients with OC, verified in TCGA cohort. Furthermore, the higher the risk score, the lower the immune score, the more immune-related pathways are enriched in patients with low-risk scores, and the fewer immune inflammatory cells are activated. Specifically, the risk score is negatively correlated with immune cell infiltration, suggesting that patients in the high-risk group have immunosuppression, which further explains the previous cuproptosis clusters.
Some immune cells in the two risk groups were found to be different. While the high-risk group had more macrophages M0, T cells CD4 memory resting, neutrophils, and mast cells activated, the low-risk group had more T cells CD8, macrophages M1, B cell memory, and dendritic cells activated. The ssGSEA algorithm results also revealed that patients in the low-risk group have higher immune activity. A large number of studies have shown that dense T cell infiltration, particularly cytotoxic CD8 T cells, indicates a favorable prognosis [
26,
27]; this finding has also been confirmed in ovarian cancer [
28]. Macrophages play a complex role in tumor immunotherapy [
29]; in most tumors, M2 macrophages are a major subtype of macrophages that have been proven to be associated with chronic inflammation and conducive to the development of tumor growth and invasive phenotype, these cells are associated with the poor prognosis of ovarian cancer, gastric cancer, and prostate cancer [
26,
30]; in contrast, high-density M1 macrophages may be associated with acute inflammation and implies a good prognosis in patients with ovarian or gastric cancer [
26,
30]. Traditional type 1 dendritic cells are required to elicit anti-tumor T cell responses, implying that migrated cDC1 can transmit tumor antigens and cross present to CD8 + T cells [
31]. Our findings support the preceding conclusions. T-follicular helper cells are significantly correlated with high expression of PD-L1, which promotes tumor immune response [
32], and CD4 T cells play a negative role in tumor immunity [
33]. However, the precise role of other immune cells in OC, such as B cells and NK cells, and their impact on patient prognosis, remain unknown or debatable [
28].
With an in-depth study of tumor immunology and molecular biology, immunotherapy has provided a new perspective on tumor treatment. Currently, OC immunotherapy can be divided into three categories: immune modulators, including immune checkpoint inhibitors (ICIs), cancer vaccines, targeted antibodies, and adaptive cell therapy [
34]. In OC, research on ICIs targeting CTLA-4, PD-1, and PD-L1 is increasing, and clinical studies have preliminarily shown their safety and effectiveness. The effect of CTLA-4 antibody is still under study, while the effect of PD-L1 inhibitor in OC has been confirmed [
35]. Several commonly used PD-1/PD-L1 inhibitors are in clinical research on OC [
36], including combined application with antiangiogenic drugs [
37] or the targeted drug PARP inhibitor [
38]. However, in general, PD-1/PD-L1 inhibitors alone are only effective in a small number of patients with OC, and the clinical effect of a combined application is better than that of a single application, especially when combined with targeted drug PARP inhibitors. Currently, relevant research is ongoing. Therefore, immune and targeted treatments for specific types of OC through molecular typing are expected to improve the prognosis of patients effectively. In this study, cuproptosis Cluster C was in an immunosuppressive state, and the expression of PDL1 and CTLA-4 was also significantly lower than that of cluster A, suggesting that these patients with worse prognoses could not benefit from PDL1 and CTLA-4 antibody treatment. However, the expression of PARP1, PARP2, and TGFB2 in cluster C patients was significantly higher than that in the patients from the other two groups, suggesting that PARP inhibitors and gemogenovatucel-T (Vigil) are expected to be beneficial to this group of patients. In the risk model, the prognosis of high-risk patients was poor, and the expression of most immune checkpoints in the high-risk group was also downregulated, meaning these patients with poor prognoses cannot benefit from ICIs. However, the expression of PARP and PDGFR in the high-risk group was still higher than that in the low-risk group, suggesting that olaparib and pazopanib have potential and due value, which provides a useful reference for the more strategic selection of immune and targeted therapies for these patients with poor prognoses. Using the risk score, we can predict the effective chemotherapy or targeted drugs for OC, providing a good reference for the personalized treatment of patients with molecular subtypes based on cuproptosis.
Our study has a few limitations. First, all our analyses were based on data from public databases, and all the samples we used were obtained retrospectively. Thus, the results may be influenced by an inherent case selection bias. To confirm the stability of our findings, we need to conduct more extensive prospective investigations and more in vitro and in vivo experimental research. Furthermore, due to the problem of retrospective research, the data of some critical clinical variables (such as presence/absence of ascites before surgery, the levels of tumor markers, use of radiotherapy and chemotherapy, and whether the operation reached R0) cannot be used for analysis in most datasets, which may affect the immune response and prognosis evaluation of cuproptosis.
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