Practical biosignatures and thorough understanding of regulatory processes of hypertrophic obstructive cardiomyopathy (HOCM) are still lacking. Firstly, public data from GSE36961 and GSE89714 datasets of Gene Expression Omnibus (GEO), Gene database of NCBI (National Center of Biotechnology Information) and Online Mendelian Inheritance in Man (OMIM) database were merged into a candidate gene set of HOCM. Secondly, weighted gene co-expression network analysis (WGCNA) for the candidate gene set was carried out to determine premier co-expressed genes. Thirdly, significant regulators were found out by virtue of a multi-factor regulatory network of long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), microRNAs (miRNAs) and transcription factors (TFs) with molecule interreactions from starBase v2.0 database and TRRUST v2 database. Ultimately, HOCM unsupervised clustering and “tsne” dimensionality reduction was employed to gain hub genes, whose classification performance was evaluated by a multinomial model of lasso logistic regression analysis binded with receiver operating characteristic (ROC) curve. Two HOCM remarkably-interrelated modules were from WGCNA, followed by the recognition of 32 crucial co-expressed genes. The multi-factor regulatory network disclosed 7 primary regulatory agents, containing lncRNAs (XIST, MALAT1, and H19), TFs (SPI1 and SP1) and miRNAs (hsa-miR-29b-39 and has-miR-29a-3p). Four clusters of HOCM and 4 hub genes (COMP, FMOD, AEBP1 and SULF1) significantly expressing in preceding four subtypes were obtained, while ROC curve demonstrated satisfactory performance of clustering and 4 genes. Our consequences furnish valuable resource which may bring about prospective mechanistic and therapeutic anatomization in HOCM.

中文翻译：

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肥厚型梗阻性心肌病的多因子调控网络及不同簇

仍然缺乏实用的生物特征和对肥厚型梗阻性心肌病 (HOCM) 调节过程的透彻理解。首先，将来自基因表达综合（GEO）的GSE36961和GSE89714数据集、NCBI（国家生物技术信息中心）基因数据库和在线孟德尔人类遗传（OMIM）数据库的公共数据合并为HOCM的候选基因集。其次，对候选基因组进行加权基因共表达网络分析（WGCNA）以确定首要的共表达基因。第三，通过来自starBase v2.0的分子相互作用的长非编码RNA（lncRNAs）、信使RNAs（mRNAs）、microRNAs（miRNAs）和转录因子（TFs）的多因子调控网络，发现了重要的调控因子。数据库和 TRRUST v2 数据库。最终，采用HOCM无监督聚类和“tsne”降维来获得中心基因，其分类性能通过与接受者操作特征（ROC）曲线绑定的lasso逻辑回归分析的多项模型进行评估。两个 HOCM 显着相关的模块来自 WGCNA，其次是识别 32 个关键的共表达基因。多因子调控网络公开了 7 个主要调控因子，包含 lncRNA（XIST、MALAT1 和 H19）、TF（SPI1 和 SP1）和 miRNA（hsa-miR-29b-39 和 has-miR-29a-3p）。获得了在前四个亚型中显着表达的四个HOCM簇和4个中枢基因（COMP、FMOD、AEBP1和SULF1），而ROC曲线显示出令人满意的聚类性能和4个基因。