Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.

中文翻译：

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使用反相蛋白质阵列数据预测肺癌中基于深度学习的Multi-Omics并行整合生存亚型

归因于肺癌的死亡率占全世界癌症死亡的很大一部分。随着死亡率的增加，准确预测预后变得至关重要。近年来，多组学分析已成为一种有用的生存预测工具。但是，与多组学分析有关的方法尚未完全建立，临床应用需要进一步改进。在这项研究中，我们开发了一种使用多组学数据准确预测肺癌患者生存率的新方法。通过无监督学习技术，首先使用《癌症基因组图谱》（TCGA）中六类的多组学数据集，检测了非小细胞肺癌中与生存相关的亚型。新的子类型，称为整合生存子类型，p = 0.003），我们确认这与组织病理学分类无关（独立性的卡方检验：p = 0.94）。接下来，尝试仅使用一个分类数据集来检测整合生存子类型。我们仅在反相蛋白质阵列（RPPA）上训练过的机器学习模型可以准确预测整合生存亚型（AUC = 0.99）。预测的亚型还可以区分高危和低危患者（对数秩检验：p = 0.012）。总的来说，这项研究探索了多组学分析的新潜力，可以准确地预测肺癌患者的预后。