The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.

中文翻译：

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具有多个监管网络的计算多组学综合方法的调查和比较评估，可在泛癌数据集中识别不同的肿瘤成分。

泛癌症类别的重要性最近被认为在癌症研究中很普遍。泛癌根据其分子病理学而不是器官对癌症进行分类。在不同癌症类型中发现的多组学数据之间的分子相似性可以在生物过程和治疗发展中发挥多种作用。因此，经常使用对各种基因组数据的综合分析来揭示新的遗传和分子机制。然而，不同领域已经提出了多种用于多组学聚类的算法。不同计算聚类方法在泛癌分析性能方面的比较仍不清楚。为了提高当前综合方法在泛癌分析中的利用率，我们首先概述了五种流行的计算综合工具：相似性网络融合、多种基因组数据类型的集成聚类 (iCluster)、通过多核学习的癌症集成 (CIMLR)、用于数据集成和疾病亚型分析的扰动聚类 (PINS) 和低秩聚类 (LRACluster)。然后，结合多组学数据中的先验相互作用来检测泛癌数据集中的显着分子模式。最后，我们对这些方法进行了比较评估，并讨论了应用这些算法的关键问题。我们发现所有五种方法都可以识别不同的肿瘤成分。泛癌样本可以按不同的比例重新分类为几组。有趣的是，每种方法都可以将肿瘤分类为不同于原始癌症类型或亚型的类别，尤其适用于卵巢浆液性囊腺癌 (OV) 和乳腺浸润性癌 (BRCA) 肿瘤。此外，五种计算方法的所有集群都显示出显着的预后价值。此外，在所有方法中都鉴定了 9 个复发性差异基因和 15 个共同途径特征。结果和讨论可以帮助社区根据泛癌分析的不同研究任务或目标选择合适的综合工具。