College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China.
Math Biosci Eng. 2024 Jan;21(1):736-764. doi: 10.3934/mbe.2024031. Epub 2022 Dec 19.
Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.
卵巢癌是一种具有不同临床病理和分子特征的肿瘤，绝大多数患者在诊断时已存在局部或广泛扩散。对患者进行早期诊断和预后预测有助于深入了解卵巢癌的发病机制，并改善治疗效果。卵巢癌的发生受到多种复杂机制的影响，包括基因组、转录组和蛋白质组。不同类型的组学分析有助于预测卵巢癌患者的生存率。卵巢癌的多组学数据表现出高度的异质性，而现有的多组学数据集成方法并未考虑不同组学数据之间的可变性和相互关系。在本文中，我们提出了一种深度学习模型 MDCADON，它利用多组学数据和跨模态视图关联发现网络。我们将随机森林引入 LASSO 回归，对 mRNA 表达、DNA 甲基化、miRNA 表达和拷贝数变异（CNV）进行特征选择，旨在选择与卵巢癌预后高度相关的重要特征。采用多模态深度神经网络对每个组学数据和临床数据的特征表示进行综合学习，并使用跨模态视图关联发现网络构建多组学发现张量，探索不同组学数据之间的相互关系。实验结果表明，MDCADON 在预测卵巢癌预后方面优于现有方法，这使得对患者进行生存分析并确定后续治疗计划成为可能。最后，我们对 MDCADON 识别的基因进行基因本体论（GO）术语分析和生物途径分析，揭示了卵巢癌的潜在机制，并为指导卵巢癌治疗提供了一定的支持。
