Department of Computer Science and Information Engineering, National Formosa University, 64, Wen-Hwa Road, Hu-wei 632, Yun-Lin, Taiwan.
Division of Hematology and Oncology, Department of Medicine, Taipei Veterans General Hospital, Faculty of Medicine, National Yang Ming University, Taipei 112, Taiwan.
IET Syst Biol. 2014 Apr;8(2):56-66. doi: 10.1049/iet-syb.2013.0035.
Lung cancer is one of the leading causes of death in both the USA and Taiwan, and it is thought that the cause of cancer could be because of the gain of function of an oncoprotein or the loss of function of a tumour suppressor protein. Consequently, these proteins are potential targets for drugs. In this study, differentially expressed genes are identified, via an expression dataset generated from lung adenocarcinoma tumour and adjacent non-tumour tissues. This study has integrated many complementary resources, that is, microarray, protein-protein interaction and protein complex. After constructing the lung cancer protein-protein interaction network (PPIN), the authors performed graph theory analysis of PPIN. Highly dense modules are identified, which are potential cancer-associated protein complexes. Up- and down-regulated communities were used as queries to perform functional enrichment analysis. Enriched biological processes and pathways are determined. These sets of up- and down-regulated genes were submitted to the Connectivity Map web resource to identify potential drugs. The authors' findings suggested that eight drugs from DrugBank and three drugs from NCBI can potentially reverse certain up- and down-regulated genes' expression. In conclusion, this study provides a systematic strategy to discover potential drugs and target genes for lung cancer.
肺癌是美国和中国台湾地区的主要死亡原因之一，人们认为癌症的原因可能是癌蛋白的功能获得或肿瘤抑制蛋白的功能丧失。因此，这些蛋白质是药物的潜在靶点。在这项研究中，通过从肺腺癌肿瘤和相邻非肿瘤组织生成的表达数据集来鉴定差异表达基因。本研究整合了许多互补资源，即微阵列、蛋白质-蛋白质相互作用和蛋白质复合物。构建肺癌蛋白质-蛋白质相互作用网络 (PPIN) 后，作者对 PPIN 进行了图论分析。鉴定出高度密集的模块，这些模块可能是与癌症相关的蛋白质复合物。上调和下调的社区被用作查询来进行功能富集分析。确定了丰富的生物学过程和途径。这些上调和下调基因集被提交给 Connectivity Map 网络资源以识别潜在的药物。作者的研究结果表明，来自 DrugBank 的 8 种药物和来自 NCBI 的 3 种药物可能潜在地逆转某些上调和下调基因的表达。总之，这项研究提供了一种系统的策略来发现肺癌的潜在药物和靶基因。
