Table_8_New Analysis Framework Incorporating Mixed Mutual Information and Scalable Bayesian Networks for Multimodal High Dimensional Genomic and Epigenomic Cancer Data.DOCX

We propose a novel two-stage analysis strategy to discover candidate genes associated with the particular cancer outcomes in large multimodal genomic cancers databases, such as The Cancer Genome Atlas (TCGA). During the first stage, we use mixed mutual information to perform variable selection; during the second stage, we use scalable Bayesian network (BN) modeling to identify candidate genes and their interactions. Two crucial features of the proposed approach are (i) the ability to handle mixed data types (continuous and discrete, genomic, epigenomic, etc.) and (ii) a flexible boundary between the variable selection and network modeling stages — the boundary that can be adjusted in accordance with the investigators’ BN software scalability and hardware implementation. These two aspects result in high generalizability of the proposed analytical framework. We apply the above strategy to three different TCGA datasets (LGG, Brain Lower Grade Glioma; HNSC, Head and Neck Squamous Cell Carcinoma; STES, Stomach and Esophageal Carcinoma), linking multimodal molecular information (SNPs, mRNA expression, DNA methylation) to two clinical outcome variables (tumor status and patient survival). We identify 11 candidate genes, of which 6 have already been directly implicated in the cancer literature. One novel LGG prognostic factor suggested by our analysis, methylation of TMPRSS11F type II transmembrane serine protease, presents intriguing direction for the follow-up studies.本研究提出一种新颖的两阶段分析策略，用于在大型多模态基因组癌症数据库（如癌症基因组图谱（The Cancer Genome Atlas, TCGA））中挖掘与特定癌症结局相关的候选基因。第一阶段采用混合互信息进行变量筛选；第二阶段则利用可扩展贝叶斯网络（Bayesian Network, BN）建模技术，识别候选基因及其相互作用关系。本方法具备两项核心特性：其一，可处理混合数据类型（连续型与离散型数据、基因组数据、表观基因组数据等）；其二，变量筛选阶段与网络建模阶段之间的边界具备灵活性，可根据研究者所使用的贝叶斯网络软件可扩展性与硬件实现条件进行调整。上述两大特性使得所提出的分析框架具备极强的泛化能力。本研究将上述策略应用于三组不同的癌症基因组图谱数据集，分别为低级别脑胶质瘤（Brain Lower Grade Glioma, LGG）、头颈部鳞状细胞癌（Head and Neck Squamous Cell Carcinoma, HNSC）以及胃食管癌（Stomach and Esophageal Carcinoma, STES），将多模态分子信息（单核苷酸多态性（Single Nucleotide Polymorphisms, SNPs）、mRNA表达水平、DNA甲基化水平）与两项临床结局变量（肿瘤状态与患者生存情况）进行关联分析。本研究共筛选出11个候选基因，其中6个已被癌症相关文献直接证实与癌症存在关联。本分析所揭示的一项全新低级别脑胶质瘤预后因子——II型跨膜丝氨酸蛋白酶TMPRSS11F的甲基化状态，为后续研究提供了极具价值的探索方向。