Differential expression analysis at the individual level reveals a lncRNA prognostic signature for lung adenocarcinoma.

Autor: Peng F; Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China., Wang R; Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China., Zhang Y; Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China., Zhao Z; 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., Zhou W; Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China., Chang Z; Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China., Liang H; Department of Pharmacology, Harbin Medical University, Harbin, 150086, China., Zhao W; Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China., Qi L; Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China., Guo Z; Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China. guoz@ems.hrbmu.edu.cn.; Department of bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350001, China. guoz@ems.hrbmu.edu.cn.; Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, 350001, China. guoz@ems.hrbmu.edu.cn., Gu Y; Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China. guyunyan@ems.hrbmu.edu.cn.; Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, 150086, China. guyunyan@ems.hrbmu.edu.cn.
Jazyk: angličtina
Zdroj: Molecular cancer [Mol Cancer] 2017 Jun 06; Vol. 16 (1), pp. 98. Date of Electronic Publication: 2017 Jun 06.
DOI: 10.1186/s12943-017-0666-z
Abstrakt: Background: 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.
Methods: 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.
Results: 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 -6 , log-rank test), which was confirmed in two independent datasets of GSE50081 (P = 1.82 × 10 -2 , log-rank test) and GSE31210 (P = 7.43 × 10 -4 , 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.
Conclusions: LncRIndiv can successfully detect DE lncRNAs in individuals and be applied to identify prognostic signature for LUAD patients.
Databáze: MEDLINE
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