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Comprehensive Landscape of Ovarian Cancer Immune Microenvironment Based on Integrated Multi-Omics Analysis.

ABSTRACT: Epithelial ovarian cancer has a low response rate to immunotherapy and a complex immune microenvironment that regulates its treatment outcomes. Understanding the immune microenvironment and its molecular basis is of great clinical significance in the effort to improve immunotherapy response and outcomes. To determine the characteristics of the immune microenvironment in ovarian cancer, we stratified ovarian cancer patients into three immune subtypes (C1, C2, and C3) using immune-related genes based on gene expression data from The Cancer Genome Atlas and found that these three subtypes had significant differences in immune characteristics and prognosis. Methylation and copy number variant analysis showed that the immune checkpoint genes that influenced immune response were significantly hypermethylated and highly deleted in the immunosuppressive C3 subtype, suggesting that epigenetic therapy may be able to reverse the efficacy of immunotherapy. In addition, the mutation frequencies of BRCA2 and CDK12 were significantly higher in the C2 subtype than in the other two subtypes, suggesting that mutation of DNA repair-related genes significantly affects the prognosis of ovarian cancer patients. Our study further elucidated the molecular characteristics of the immune microenvironment of ovarian cancer, which providing an effective hierarchical method for the immunotherapy of ovarian cancer patients, and has clinical relevance to the design of new immunotherapies and a reasonable combination strategies.

SUBMITTER: Shen J

PROVIDER: S-EPMC8247482 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of cancer data. However, high dimensional multi-omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi-omics analysis difficult. DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. However, there are few VAE-based integrated multi-omics analyses, and they are limited to pancancer. In this work, we did an integrated multi-omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD-VAE). First, we designed and developed a DL architecture for VAE and MMD-VAE. Then we used the architecture for mono-omics, integrated di-omics and tri-omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD-VAE and VAE-based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2-95.5% and 87.1-95.7%. Also, survival analysis results show that VAE and MMD-VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated multi-omics analyses perform better or similar compared to their mono-omics counterparts, and (iii) MMD-VAE performs better than VAE in most omics dataset.

Project description:BackgroundCuproptosis, i.e., copper-induced programmed cell death, has potential implications in cancer therapy. However, the impact of the cuproptosis-related gene (CRG) dihydrolipoyl dehydrogenase (DLD) on breast cancer (BC) prognosis remains underexplored.MethodsWe employed real-time quantitative PCR and multiplexed immunostaining techniques to quantify DLD expression in both BC and the adjacent non-cancerous tissues. Immunofluorescence analysis was employed to assess the influence of DLD on immune cells and immunological checkpoints in the BC microenvironment. DLD knockdown experiments were conducted in BC cell lines MDA-MB-468 and SK-BR-3, with knockdown efficiency validated via western blot. Subsequently, we performed the cell counting kit-8 (CCK-8) assay, clone formation assay, Transwell migration assay, and invasion assay. To construct a prognostic model, we employed a Lasso-Cox regression analysis of immune-related genes associated with DLD. Additionally, we established a competing endogenous RNA network based on CRGs to evaluate potential regulatory pathways.ResultsCompared to the adjacent tissues, BC tissues exhibited markedly elevated DLD expression levels. In vitro experiments demonstrated that DLD knockdown effectively inhibited BC cell migration, invasion, and proliferation. DLD exhibited positive correlations with CD68+ macrophages and PD-L1 in the tumor, as well as with macrophages and CD4+ T cells in the stroma. Tumor regions with high DLD expression were enriched in PD-L1 and macrophages, while stromal regions with high DLD expression contained CD4+ T cells and macrophages. The AUC values for 1-, 3-, and 5-year overall survival in TCGA-BRCA training set were 0.67, 0.66, and 0.66, respectively. A nomogram with a C-index of 0.715 indicated that risk score, tumor stage, and age could serve as independent prognostic factors for BC.ConclusionOur findings underscore the significant predictive significance of DLD in BC and its influence on the tumor microenvironment. DLD represents a promising diagnostic and prognostic marker for BC, offering novel avenues for the identification of therapeutic targets and the enhancement of immunotherapy in BC.

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Comprehensive Landscape of Ovarian Cancer Immune Microenvironment Based on Integrated Multi-Omics Analysis.

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