College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Heilongjiang, China.
Institute of Opto-Electronics, Harbin Institute of Technology, Harbin, 150000, Heilongjiang, China.
BMC Pulm Med. 2021 Sep 4;21(1):280. doi: 10.1186/s12890-021-01646-9.
Identifying or prioritizing genes for chronic obstructive pulmonary disease (COPD), one type of complex disease, is particularly important for its prevention and treatment.
In this paper, a novel method was proposed to Prioritize genes using Expression information in Protein-protein interaction networks with disease risks transferred between genes (abbreviated as PEP). A weighted COPD PPI network was constructed using expression information and then COPD candidate genes were prioritized based on their corresponding disease risk scores in descending order.
Further analysis demonstrated that the PEP method was robust in prioritizing disease candidate genes, and superior to other existing prioritization methods exploiting either topological or functional information. Top-ranked COPD candidate genes and their significantly enriched functions were verified to be related to COPD. The top 200 candidate genes might be potential disease genes in the diagnosis and treatment of COPD.
The proposed method could provide new insights to the research of prioritizing candidate genes of COPD or other complex diseases with expression information from sequencing or microarray data.
识别或优先考虑慢性阻塞性肺疾病（COPD）等复杂疾病的基因对于预防和治疗尤为重要。
本文提出了一种利用具有疾病风险转移基因的蛋白质-蛋白质相互作用网络中的表达信息（简称 PEP）优先考虑基因的新方法。使用表达信息构建加权 COPD PPI 网络，然后根据疾病风险评分从高到低对 COPD 候选基因进行排序。
进一步的分析表明，PEP 方法在优先考虑疾病候选基因方面具有稳健性，优于其他利用拓扑或功能信息的现有优先排序方法。排名靠前的 COPD 候选基因及其显著富集的功能被验证与 COPD 有关。前 200 个候选基因可能是 COPD 诊断和治疗中的潜在疾病基因。
该方法可为 COPD 或其他具有测序或微阵列数据表达信息的复杂疾病候选基因的研究提供新的思路。
