Peng Fuduan, Wang Ruiping, Zhang Yuanyuan, Zhao Zhangxiang, Zhou Wenbin, Chang Zhiqiang, Liang Haihai, Zhao Wenyuan, Qi Lishuang, Guo Zheng, Gu Yunyan
Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, 150086, China.
Mol Cancer. 2017 Jun 6;16(1):98. doi: 10.1186/s12943-017-0666-z.
Deregulations of long non-coding RNAs (lncRNAs) have been implicated in cancer initiation and progression. Current methods can only capture differential expression of lncRNAs at the population level and ignore the heterogeneous expression of lncRNAs in individual patients.
We propose a method (LncRIndiv) to identify differentially expressed (DE) lncRNAs in individual cancer patients by exploiting the disrupted ordering of expression levels of lncRNAs in each disease sample in comparison with stable normal ordering. LncRIndiv was applied to lncRNA expression profiles of lung adenocarcinoma (LUAD). Based on the expression profile of LUAD individual-level DE lncRNAs, we used a forward selection procedure to identify prognostic signature for stage I-II LUAD patients without adjuvant therapy.
In both simulated data and real pair-wise cancer and normal sample data, LncRIndiv method showed good performance. Based on the individual-level DE lncRNAs, we developed a robust prognostic signature consisting of two lncRNA (C1orf132 and TMPO-AS1) for stage I-II LUAD patients without adjuvant therapy (P = 3.06 × 10, log-rank test), which was confirmed in two independent datasets of GSE50081 (P = 1.82 × 10, log-rank test) and GSE31210 (P = 7.43 × 10, log-rank test) after adjusting other clinical factors such as smoking status and stages. Pathway analysis showed that TMPO-AS1 and C1orf132 could affect the prognosis of LUAD patients through regulating cell cycle and cell adhesion.
LncRIndiv can successfully detect DE lncRNAs in individuals and be applied to identify prognostic signature for LUAD patients.
长链非编码RNA(lncRNA)的失调与癌症的发生和发展有关。目前的方法只能在群体水平上捕获lncRNA的差异表达,而忽略了个体患者中lncRNA的异质性表达。
我们提出了一种方法(LncRIndiv),通过利用每个疾病样本中lncRNA表达水平的紊乱顺序与稳定的正常顺序相比,来识别个体癌症患者中差异表达的(DE)lncRNA。LncRIndiv应用于肺腺癌(LUAD)的lncRNA表达谱。基于LUAD个体水平DE lncRNA的表达谱,我们使用向前选择程序来识别I-II期未接受辅助治疗的LUAD患者的预后特征。
在模拟数据和真实的成对癌症与正常样本数据中,LncRIndiv方法均表现出良好的性能。基于个体水平的DE lncRNA,我们为I-II期未接受辅助治疗的LUAD患者开发了一个由两个lncRNA(C1orf132和TMPO-AS1)组成的稳健预后特征(P = 3.06×10,对数秩检验),在调整吸烟状态和分期等其他临床因素后,该特征在GSE50081(P = 1.82×10,对数秩检验)和GSE31210(P = 7.43×10,对数秩检验)的两个独立数据集中得到了证实。通路分析表明,TMPO-AS1和C1orf132可通过调节细胞周期和细胞黏附来影响LUAD患者的预后。
LncRIndiv可以成功检测个体中的DE lncRNA,并应用于识别LUAD患者的预后特征。