Zhang Chunlong, Li Chunquan, Xu Yanjun, Feng Li, Shang Desi, Yang Xinmiao, Han Junwei, Sun Zeguo, Li Yixue, Li Xia
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Mol Biosyst. 2015 May;11(5):1271-84. doi: 10.1039/c5mb00061k.
Recent studies have focused on exploring the associations between organ development and malignant tumors; however, the clinical relevance of the development signatures was inadequately addressed in lung cancer. In this study, we explored the associations between lung development and lung cancer progression by analyzing a total of two development and seven cancer datasets. We identified representative expression patterns (continuously up- and down-regulated) from development and cancer profiles, and inverse pattern associations were observed at both the gene and functional levels. Furthermore, we dissected the biological processes dominating the associations, and found that proliferation and immunity were respectively involved in the two inverse development-cancer expression patterns. Through sub-pathway analysis of the signatures with inverse expression patterns, we finally identified a 13-gene risk signature from the cell cycle sub-pathway, and evaluated its predictive performance for lung cancer patient clinical outcome using independent cohorts. Our findings indicated that the integrative analysis of development and cancer expression patterns provided a framework for identifying effective molecular signatures for clinical utility.
近期研究聚焦于探索器官发育与恶性肿瘤之间的关联；然而，发育特征在肺癌中的临床相关性尚未得到充分探讨。在本研究中，我们通过分析总共两个发育数据集和七个癌症数据集，探究了肺发育与肺癌进展之间的关联。我们从发育和癌症图谱中识别出代表性表达模式（持续上调和下调），并在基因和功能水平上均观察到相反模式的关联。此外，我们剖析了主导这些关联的生物学过程，发现增殖和免疫分别参与了两种相反的发育 - 癌症表达模式。通过对具有相反表达模式的特征进行子通路分析，我们最终从细胞周期子通路中确定了一个13基因风险特征，并使用独立队列评估了其对肺癌患者临床结局的预测性能。我们的研究结果表明，发育和癌症表达模式的综合分析为识别具有临床应用价值的有效分子特征提供了一个框架。
