Liu Rong, Cheng Yu, Yu Jing, Lv Qiao-Li, Zhou Hong-Hao
Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P.R. China; Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P.R. China.
Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, P.R. China; Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, P.R. China.
Gene. 2015 May 25;563(1):56-62. doi: 10.1016/j.gene.2015.03.008. Epub 2015 Mar 7.
Lung cancer, a tumor with heterogeneous biology, is influenced by a complex network of gene interactions. Therefore, elucidating the relationships between genes and lung cancer is critical to attain further knowledge on tumor biology. In this study, we performed weighted gene coexpression network analysis to investigate the roles of gene networks in lung cancer regulation. Gene coexpression relationships were explored in 58 samples with tumorous and matched non-tumorous lungs, and six gene modules were identified on the basis of gene coexpression patterns. The overall expression of one module was significantly higher in the normal group than in the lung cancer group. This finding was validated across six datasets (all p values <0.01). The particular module was highly enriched for genes belonging to the biological Gene Ontology category "response to wounding" (adjusted p value = 4.28 × 10(-10)). A lung cancer-specific hub network (LCHN) consisting of 15 genes was also derived from this module. A support vector machine based on classification model robustly separated lung cancer from adjacent normal tissues in the validation datasets (accuracy ranged from 91.7% to 98.5%) by using the LCHN gene signatures as predictors. Eight genes in the LCHN are associated with lung cancer. Overall, we identified a gene module associated with lung cancer, as well as an LCHN consisting of hub genes that may be candidate biomarkers and therapeutic targets for lung cancer. This integrated analysis of lung cancer transcriptome provides an alternative strategy for identification of potential oncogenic drivers.
肺癌是一种具有异质性生物学特性的肿瘤，受复杂的基因相互作用网络影响。因此，阐明基因与肺癌之间的关系对于深入了解肿瘤生物学至关重要。在本研究中，我们进行了加权基因共表达网络分析，以研究基因网络在肺癌调控中的作用。在58例肿瘤肺组织及配对的非肿瘤肺组织样本中探索基因共表达关系，并根据基因共表达模式鉴定出六个基因模块。其中一个模块在正常组中的整体表达显著高于肺癌组。这一发现在六个数据集中得到了验证（所有p值<0.01）。该特定模块高度富集于生物学基因本体类别“创伤反应”中的基因（校正p值 = 4.28 × 10(-10)）。还从该模块中衍生出一个由15个基因组成的肺癌特异性枢纽网络（LCHN）。基于分类模型的支持向量机通过使用LCHN基因特征作为预测指标，在验证数据集中能够可靠地将肺癌与相邻正常组织区分开（准确率范围为91.7%至98.5%）。LCHN中的八个基因与肺癌相关。总体而言，我们鉴定出一个与肺癌相关的基因模块，以及一个由枢纽基因组成的LCHN，这些枢纽基因可能是肺癌的候选生物标志物和治疗靶点。这种对肺癌转录组的综合分析为识别潜在的致癌驱动因素提供了一种替代策略。
