Yang Jihong, Li Zheng, Fan Xiaohui, Cheng Yiyu
Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
PLoS One. 2014 Apr 18;9(4):e94360. doi: 10.1371/journal.pone.0094360. eCollection 2014.
A disease molecular signature is a set of biomolecular features that are prognostic of clinical phenotypes and indicative of underlying pathology. It is of great importance to develop computational approaches for finding more relevant molecular signatures. Based upon the hypothesis that various components in a molecular signature are more likely to share similar patterns, we introduced a novel three step network based approach (TSNBA) to identify the molecular signature and key pathological regulators. Protein-protein interaction (PPI) network and ranking algorithm were integrated in the first step to find pathology related proteins with high accuracy. It was followed by the second step to further screen with co-expression patterns for better pathology enrichment. Context likelihood of relatedness (CLR) algorithm was used in the third step to infer gene regulatory networks and identify key transcription regulators. We applied this approach to study IL-1 (interleukin-1) and TNF-alpha (tumor necrosis factor-alpha) stimulated inflammation. TSNBA identified inflammatory signature with high accuracy and outperformed 5 competing methods namely fold change, degree, interconnectivity, neighborhood score and network propagation based approaches. The best molecular signature, with 80% (40/50) confirmed inflammatory genes, was used to predict inflammation related genes. As a result, 8 out of 10 predicted inflammation genes that were not included in the benchmark Entrez Gene database were validated by literature evidence. Furthermore, 23 of the 32 predicted inflammation regulators were validated by literature evidence. The rest 9 were also validated with TF (transcription factor) binding site analysis. In conclusion, we developed an efficient strategy for disease molecular signature finding and key pathological regulator identification.
疾病分子特征是一组生物分子特征，可预测临床表型并指示潜在病理。开发用于发现更相关分子特征的计算方法非常重要。基于分子特征中各种成分更可能共享相似模式的假设，我们引入了一种新颖的基于网络的三步法（TSNBA）来识别分子特征和关键病理调节因子。第一步整合了蛋白质-蛋白质相互作用（PPI）网络和排名算法，以高精度找到与病理相关的蛋白质。第二步通过共表达模式进一步筛选，以实现更好的病理富集。第三步使用相关性上下文似然度（CLR）算法推断基因调控网络并识别关键转录调节因子。我们应用此方法研究白细胞介素-1（IL-1）和肿瘤坏死因子-α（TNF-α）刺激的炎症。TSNBA高精度地识别出炎症特征，并且优于5种竞争方法，即倍数变化、度、互连性、邻域得分和基于网络传播的方法。最佳分子特征中有80%（40/50）的炎症基因得到确认，用于预测炎症相关基因。结果，基准Entrez基因数据库中未包含的10个预测炎症基因中有8个得到了文献证据的验证。此外，32个预测炎症调节因子中有23个得到了文献证据的验证。其余9个也通过转录因子（TF）结合位点分析得到了验证。总之，我们开发了一种有效的策略用于疾病分子特征发现和关键病理调节因子识别。
