West China Biomedical Big Data Center, West China School of Medicine (West China Hospital), Sichuan University, Chengdu, China.
Division of Nephrology, Kidney Research Institute, West China Hospital, Sichuan University, Chengdu, China.
Nephrology (Carlton). 2020 Oct;25(10):775-784. doi: 10.1111/nep.13655. Epub 2019 Sep 9.
Intervention for end-stage kidney disease (ESKD), which is associated with adverse prognoses and major economic burdens, is challenging due to its complex pathogenesis. The study was performed to identify biomarker genes and molecular mechanisms for ESKD by bioinformatics approach.
Using the Gene Expression Omnibus dataset GSE37171, this study identified pathways and genomic biomarkers associated with ESKD via a multi-stage knowledge discovery process, including identification of modules of genes by weighted gene co-expression network analysis, discovery of important involved pathways by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, selection of differentially expressed genes by the empirical Bayes method, and screening biomarker genes by the least absolute shrinkage and selection operator (Lasso) logistic regression. The results were validated using GSE70528, an independent testing dataset.
Three clinically important gene modules associated with ESKD, were identified by weighted gene co-expression network analysis. Within these modules, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed important biological pathways involved in ESKD, including transforming growth factor-β and Wnt signalling, RNA-splicing, autophagy and chromatin and histone modification. Furthermore, Lasso logistic regression was conducted to identify five final genes, namely, CNOT8, MST4, PPP2CB, PCSK7 and RBBP4 that are differentially expressed and associated with ESKD. The accuracy of the final model in distinguishing the ESKD cases and controls was 96.8% and 91.7% in the training and validation datasets, respectively.
Network-based variable selection approaches can identify biological pathways and biomarker genes associated with ESKD. The findings may inform more in-depth follow-up research and effective therapy.
由于终末期肾病（ESKD）的发病机制复杂，因此对其进行干预极具挑战性，这与不良预后和重大经济负担有关。本研究通过生物信息学方法来确定 ESKD 的生物标志物基因和分子机制。
本研究使用基因表达综合数据库（GEO）数据集 GSE37171，通过多阶段知识发现过程来识别与 ESKD 相关的途径和基因组生物标志物，包括通过加权基因共表达网络分析识别基因模块、基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析发现重要的参与途径、经验贝叶斯方法选择差异表达基因以及通过最小绝对收缩和选择算子（Lasso）逻辑回归筛选生物标志物基因。使用独立的测试数据集 GSE70528 对结果进行了验证。
通过加权基因共表达网络分析，确定了与 ESKD 相关的三个重要的临床基因模块。在这些模块中，GO 和 KEGG 富集分析揭示了与 ESKD 相关的重要生物学途径，包括转化生长因子-β和 Wnt 信号转导、RNA 剪接、自噬和染色质及组蛋白修饰。此外，还进行了 Lasso 逻辑回归以确定五个最终基因，即 CNOT8、MST4、PPP2CB、PCSK7 和 RBBP4，这些基因差异表达且与 ESKD 相关。最终模型在训练和验证数据集中区分 ESKD 病例和对照组的准确率分别为 96.8%和 91.7%。
基于网络的变量选择方法可识别与 ESKD 相关的生物学途径和生物标志物基因。这些发现可能为更深入的后续研究和有效的治疗提供信息。
