BACKGROUND AND OBJECTIVE Cancer, as the most challenging part in the human disease history, has always been one of the main threats to human life and health. The high mortality of cancer is largely due to the complexity of cancer and the significant differences in clinical outcomes. Therefore, it will be significant to improve accuracy of cancer survival prediction, which has become one of the main fields of cancer research. Many calculation models for cancer survival prediction have been proposed at present, but most of them generate prediction models only by using single genomic data or clinical data. Multiple genomic data and clinical data have not been integrated yet to take a comprehensive consideration of cancers and predict their survival. METHOD In order to effectively integrate multiple genomic data (including genetic expression, copy number alteration, DNA methylation and exon expression) and clinical data and apply them to predictive studies on cancer survival, similar network fusion algorithm (SNF) was proposed in this paper to integrate multiple genomic data and clinical data so as to generate sample similarity matrix, min-redundancy and max-relevance algorithm (mRMR) was used to conduct feature selection of multiple genomic data and clinical data of cancer samples and generate sample feature matrix, and finally two matrixes were used for semi-supervised training through graph convolutional network (GCN) so as to obtain a cancer survival prediction method integrating multiple genomic data and clinical data based on graph convolutional network (GCGCN). RESULT Performance indexes of GCGCN model indicate that both multiple genomic data and clinical data play significant roles in the accurate survival time prediction of cancer patients. It is compared with existing survival prediction methods, and results show that cancer survival prediction method GCGCN which integrates multiple genomic data and clinical data has obviously superior prediction effect than existing survival prediction methods. CONCLUSION All study results in this paper have verified effectiveness and superiority of GCGCN in the aspect of cancer survival prediction.

中文翻译：

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基于图卷积网络的癌症生存预测方法

背景和目的癌症一直是人类疾病史上最具挑战性的一部分，一直是对人类生命和健康的主要威胁之一。癌症的高死亡率很大程度上归因于癌症的复杂性和临床结果的显着差异。因此，提高癌症生存预测的准确性具有重要意义，已成为癌症研究的主要领域之一。当前已经提出了许多用于癌症存活预测的计算模型，但是大多数仅通过使用单个基因组数据或临床数据来生成预测模型。尚未整合多种基因组数据和临床数据来全面考虑癌症并预测其生存率。方法为了有效整合多种基因组数据（包括基因表达，结果GCGCN模型的性能指标表明，多种基因组数据和临床数据在癌症患者准确的生存时间预测中均起着重要作用。与现有的生存预测方法进行比较，结果表明，将多种基因组数据和临床数据相结合的癌症生存预测方法GCGCN具有明显优于现有生存预测方法的预测效果。结论本文所有研究结果均证实了GCGCN在癌症生存预测方面的有效性和优越性。结果表明，将多种基因组数据和临床数据相结合的癌症生存预测方法GCGCN具有明显优于现有生存预测方法的预测效果。结论本文所有研究结果均证实了GCGCN在癌症生存预测方面的有效性和优越性。结果表明，将多种基因组数据和临床数据相结合的癌症生存预测方法GCGCN具有明显优于现有生存预测方法的预测效果。结论本文所有研究结果均证实了GCGCN在癌症生存预测方面的有效性和优越性。