Zheng Meng-Li, Zhou Nai-Kang, Huang De-Liang, Luo Cheng-Hua
Department of Thoracic Surgery, The 309th Hospital, PLA, Beijing 100091, China.
J BUON. 2017 Sep-Oct;22(5):1252-1258.
The purpose of this study was to explore the pathway cross-talks and key pathways in non-small cell lung cancer (NSCLC) to better understand the underlying pathological mechanism.
Integrated gene expression data, pathway data and protein-protein interaction (PPI) data were assessed to identify the pathway regulatory interactions in NSCLC, and constructed the background and disease pathway crosstalk networks, respectively. In this work, the attractor method was implemented to identified the differential pathways, and the rank product (RP) algorithm was used to determine the importance of pathways.
Based on 787,896 PPI interactions from STRING database and 300 human pathways from KEGG, we constructed the back pathway cross-talk network with 300 nodes and 42239 edges. Integrating with expression data of NSCLC, each pathway cross-talk endowed with a weight value, and disease pathway cross-talks were identified. By RP algorithm and topology analysis of network, we selected 5 key pathways, including Alanine, DNA replication, Fanconi anemia pathway, Cell cycle and MicroRNAs in cancer under the pre-set thresholds.
We successfully revealed the disease pathway cross-talks and explored 5 key pathways in NSCLC, which may be the underlying therapeutic targets for lung cancer.
本研究旨在探索非小细胞肺癌（NSCLC）中的信号通路串扰及关键信号通路，以更好地理解其潜在病理机制。
评估整合基因表达数据、信号通路数据和蛋白质-蛋白质相互作用（PPI）数据，以识别NSCLC中的信号通路调控相互作用，并分别构建背景和疾病信号通路串扰网络。在本研究中，采用吸引子方法识别差异信号通路，并使用秩乘积（RP）算法确定信号通路的重要性。
基于STRING数据库中的787,896个PPI相互作用和KEGG中的300条人类信号通路，构建了具有300个节点和42239条边的背景信号通路串扰网络。结合NSCLC的表达数据，每条信号通路串扰赋予一个权重值，并识别出疾病信号通路串扰。通过RP算法和网络拓扑分析，在预设阈值下选择了5条关键信号通路，包括丙氨酸、DNA复制、范可尼贫血信号通路、细胞周期和癌症中的微小RNA。
我们成功揭示了NSCLC中的疾病信号通路串扰，并探索了5条关键信号通路，它们可能是肺癌潜在的治疗靶点。
