Optimizing miRNA-module diagnostic biomarkers of gastric carcinoma via integrated network analysis.
Optimizing miRNA-module diagnostic biomarkers of gastric carcinoma via integrated network analysis.
复制标题
通过集成网络分析优化胃癌的 miRNA 模块诊断生物标志物。
DOI：
10.1371/journal.pone.0198445
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Wang Z
中科院分区：
文献类型：
--
作者：
Zhang F;Xu W;Liu J;Liu X;Huo B;Li B;Wang Z
多种microRNA（miRNAs）已被认为是早期诊断胃癌的新生物标志物，但单一标志物策略可能忽略了它们之间的协同调控关系，导致诊断特异性低。因此，迫切需要多靶点模块化诊断生物标志物。在这项研究中，Zsummary和NetSVM为基础的方法被用来确定GC相关的枢纽miRNA和激活模块从临床miRNA共表达网络。由前20个中心miRNA组成的基于NetSVM的子网络达到了0.94和0.82的高灵敏度和特异性。Zsummary算法鉴定了可能作为GC诊断生物标志物的活化模块（miR-486、miR-451、miR-185和miR-600）。该模块的三个成员以前被认为是GC的生物标志物，其24个靶基因在与癌症直接相关的通路中显著富集。该模块的加权诊断ROC AUC为0.838，优化的模块单元（miR-451和miR-185）获得更高的值0.904，两者均高于单个miRNA。这些hub miRNAs和模块有可能成为GC早期诊断的强大生物标志物，并得到进一步验证。此外，这种模块化分析可以为癌症诊断和治疗的多靶点方法提供有价值的见解。
Several microRNAs (miRNAs) have been suggested as novel biomarkers for diagnosing gastric cancer (GC) at an early stage, but the single-marker strategy may ignore the co-regulatory relationships and lead to low diagnostic specificity. Thus, multi-target modular diagnostic biomarkers are urgently needed. In this study, a Zsummary and NetSVM-based method was used to identify GC-related hub miRNAs and activated modules from clinical miRNA co-expression networks. The NetSVM-based sub-network consisting of the top 20 hub miRNAs reached a high sensitivity and specificity of 0.94 and 0.82. The Zsummary algorithm identified an activated module (miR-486, miR-451, miR-185, and miR-600) which might serve as diagnostic biomarker of GC. Three members of this module were previously suggested as biomarkers of GC and its 24 target genes were significantly enriched in pathways directly related to cancer. The weighted diagnostic ROC AUC of this module was 0.838, and an optimized module unit (miR-451 and miR-185) obtained a higher value of 0.904, both of which were higher than that of individual miRNAs. These hub miRNAs and module have the potential to become robust biomarkers for early diagnosis of GC with further validations. Moreover, such modular analysis may offer valuable insights into multi-target approaches to cancer diagnosis and treatment.
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