Colorectal cancer (CRC) is a heterogeneous disease with distinct molecular properties. Tremendous works for CRC molecular subtyping are mainly based on gene expression profiling, which cannot capture the complementary information from other data types. Based on the classical multiomics data integration method similarity network fusion (SNF), which, however, suffers the trivial parameters setting, we developed local scaling SNF (Ls-SNF) that employs the local scaling method to construct patient affinity before network fusion. Local scaling infers the self-tuning of sample-to-sample distance and can eliminate the scaling problem. We have demonstrated the effectiveness of Ls-SNF on other cancer molecular subtyping in our previous study. In this study Ls-SNF applied in CRC molecular subtyping shows clear integrated patterns of gene expression, miRNA expression, and DNA methylation. Compared with the consensus molecular subtypes, subtypes identified by Ls-SNF achieved more significant association with clinical outcomes (p = 9.6 × 10⁻³, log-rank test). Certain mutations showed very significant enrichment in Ls-SNF subtypes, such as Class 3 were enriched for microsatellite instability (MSI) (p < 0.001), BRAF-mutant (p < 0.001), and CIMP high (p < 0.001). Ls-SNF subtypes also revealed better performance than some clinical risk factors in univariate and multivariate analyses (p = 0.002; p = 0.01).

中文翻译：

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使用局部缩放网络融合进行基于多组学的结直肠癌分子亚型分析。

结直肠癌 (CRC) 是一种具有不同分子特性的异质性疾病。CRC分子亚型的大量工作主要基于基因表达谱，无法从其他数据类型中获取互补信息。基于经典的多组学数据集成方法相似性网络融合（SNF），然而，其参数设置繁琐，我们开发了局部缩放 SNF（Ls-SNF），它在网络融合之前采用局部缩放方法构建患者亲和力。局部缩放推断样本到样本距离的自调整，可以消除缩放问题。在我们之前的研究中，我们已经证明了 Ls-SNF 对其他癌症分子亚型的有效性。在这项研究中，Ls-SNF 应用于 CRC 分子亚型分析显示出清晰的基因表达整合模式，miRNA 表达和 DNA 甲基化。与共有分子亚型相比，Ls-SNF 鉴定的亚型与临床结果的相关性更显着。p  = 9.6 × 10 ^-3，对数秩检验）。某些突变在 Ls-SNF 亚型中显示出非常显着的富集，例如 3 类富集了微卫星不稳定性 (MSI) ( p < 0.001)、BRAF 突变 ( p < 0.001) 和 CIMP 高 ( p < 0.001)。在单变量和多变量分析中，Ls-SNF 亚型也显示出比某些临床危险因素更好的性能（p  = 0.002；p  = 0.01）。