Wang Q, Shi C-J, Lv S-H
Department of General Surgery, Shanxi Provincial People's Hospital, Taiyuan, Shanxi Province, China.
Department of Endocrinology, The Second Affiliated Hospital of Mudanjiang Medical University, Mudanjiang, Heilongjiang Province, China.
Braz J Med Biol Res. 2017 Mar 30;50(5):e5981. doi: 10.1590/1414-431X20175981.
Different pathways act synergistically to participate in many biological processes. Thus, the purpose of our study was to extract dysregulated pathways to investigate the pathogenesis of colorectal cancer (CRC) based on the functional dependency among pathways. Protein-protein interaction (PPI) information and pathway data were retrieved from STRING and Reactome databases, respectively. After genes were aligned to the pathways, each pathway activity was calculated using the principal component analysis (PCA) method, and the seed pathway was discovered. Subsequently, we constructed the pathway interaction network (PIN), where each node represented a biological pathway based on gene expression profile, PPI data, as well as pathways. Dysregulated pathways were then selected from the PIN according to classification performance and seed pathway. A PIN including 11,960 interactions was constructed to identify dysregulated pathways. Interestingly, the interaction of mRNA splicing and mRNA splicing-major pathway had the highest score of 719.8167. Maximum change of the activity score between CRC and normal samples appeared in the pathway of DNA replication, which was selected as the seed pathway. Starting with this seed pathway, a pathway set containing 30 dysregulated pathways was obtained with an area under the curve score of 0.8598. The pathway of mRNA splicing, mRNA splicing-major pathway, and RNA polymerase I had the maximum genes of 107. Moreover, we found that these 30 pathways had crosstalks with each other. The results suggest that these dysregulated pathways might be used as biomarkers to diagnose CRC.
不同的信号通路协同作用参与多种生物学过程。因此，我们研究的目的是基于信号通路间的功能依赖性，提取失调的信号通路以探究结直肠癌（CRC）的发病机制。蛋白质-蛋白质相互作用（PPI）信息和信号通路数据分别从STRING和Reactome数据库中获取。在将基因与信号通路进行比对后，使用主成分分析（PCA）方法计算每条信号通路的活性，并发现种子信号通路。随后，我们构建了信号通路相互作用网络（PIN），其中每个节点基于基因表达谱、PPI数据以及信号通路代表一条生物学信号通路。然后根据分类性能和种子信号通路从PIN中选择失调的信号通路。构建了一个包含11,960个相互作用的PIN以识别失调的信号通路。有趣的是，mRNA剪接与mRNA剪接-主要信号通路之间的相互作用得分最高，为719.8167。CRC样本与正常样本之间活性得分的最大变化出现在DNA复制信号通路中，该通路被选为种子信号通路。从这个种子信号通路开始，获得了一个包含30条失调信号通路的信号通路集，曲线下面积得分为0.8598。mRNA剪接、mRNA剪接-主要信号通路和RNA聚合酶I信号通路的基因数量最多，为107个。此外，我们发现这30条信号通路之间存在相互作用。结果表明，这些失调的信号通路可能用作诊断CRC的生物标志物。
