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Cancer Cell International
Published in: Cancer Cell International 1/2018

Open Access 01-12-2018 | Primary Research

Integrated TCGA analysis implicates lncRNA CTB-193M12.5 as a prognostic factor in lung adenocarcinoma

Authors: Xuehai Wang, Gang Li, Qingsong Luo, Jiayong Xie, Chongzhi Gan

Published in: Cancer Cell International | Issue 1/2018

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Abstract

Background

Lung cancer is a malignant tumor with the highest incidence and mortality around the world. Recent advances in RNA sequencing technology have enabled insights into long non-coding RNAs (lncRNAs), a previously largely overlooked species in dissecting lung cancer pathology.

Methods

In this study, we used a comprehensive bioinformatics analysis strategy to identify lncRNAs closely associated with lung adenocarcinoma, using the RNA sequencing datasets collected from more than 500 lung adenocarcinoma patients and deposited at The Cancer Genome Atlas (TCGA) database.

Results

Differential expression analysis highlighted lncRNAs CTD-2510F5.4 and CTB-193M12.5, both of which were significantly upregulated in cancerous specimens. Moreover, network analyses showed highly correlated expression levels of both lncRNAs with those of differentially expressed protein-coding genes, and suggested central regulatory roles of both lncRNAs in the gene co-expression network. Importantly, expression of CTB-193M12.5 showed strong negative correlation with patient survival.

Conclusions

Our study mined existing TCGA datasets for novel factors associated with lung adenocarcinoma, and identified a largely unknown lncRNA as a potential prognostic factor. Further investigation is warranted to characterize the roles and significance of CTB-193M12.5 in lung adenocarcinoma biology.
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Metadata
Title
Integrated TCGA analysis implicates lncRNA CTB-193M12.5 as a prognostic factor in lung adenocarcinoma
Authors
Xuehai Wang
Gang Li
Qingsong Luo
Jiayong Xie
Chongzhi Gan
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Cancer Cell International / Issue 1/2018
Electronic ISSN: 1475-2867
DOI
https://doi.org/10.1186/s12935-018-0513-3

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