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Received: 11 July 2018 Revised: 23 September 2018 Accepted: 2 October 2018 DOI: 10.1111/cas.13822
ORIGINAL ARTICLE
A group of long noncoding RNAs identified by data mining can predict the prognosis of lung adenocarcinoma Meijian Liao1,2 | Qing Liu1,2 | Bing Li1,2 | Weijie Liao1,2 | Weidong Xie2,3 | Yaou Zhang2,3 1 School of Life Sciences, Tsinghua University, Beijing, China 2 Key Laboratory in Health Science and Technology, Division of Life Science and Health, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China 3
Open FIESTA Center, Tsinghua University, Shenzhen, China Correspondence Weidong Xie and Yaou Zhang, Key Laboratory in Health Science and Technology, Division of Life Science and Health, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China. Email:
[email protected] (W.X.);
[email protected] (Y.Z.) Funding information National Natural Science Foundation of China, Grant/Award Number: 31571400; Basic Research Fund of Shenzhen, Grant/ Award Number: JCYJ20150724173156330
Long noncoding RNAs (lncRNA) are reported to be potential cancer biomarkers. This study aims to find new lncRNA biomarker relevant to lung adenocarcinoma. Gene expression profile and clinical data of lung adenocarcinoma and lung squamous cell carcinoma patients were downloaded from the UCSC Xena database. These data were analyzed to identify potential lncRNA prognostic biomarkers, and the candidate lncRNAs were analyzed and verified with association analysis, meta-analysis, survival analysis, gene ontology analysis, gene set enrichment analysis, and other statistical methods. A group of 5 lncRNAs was identified from the 1965 differentially expressed (fold-change >2) genes. Four of these 5 lncRNAs were expressed at a lower level in lung adenocarcinoma tissues and the other one at a higher level (P
