A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer

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Abstract

Background

Accumulated evidence suggests that dysregulated expression of long non-coding RNAs (lncRNAs) may play a critical role in tumorigenesis and prognosis of cancer, indicating the potential utility of lncRNAs as cancer prognostic or diagnostic markers. However, the power of lncRNA signatures in predicting the survival of patients with non-small cell lung cancer (NSCLC) has not yet been investigated.

Methods

We performed an array-based transcriptional analysis of lncRNAs in large patient cohorts with NSCLC by repurposing microarray probes from the gene expression omnibus database. A risk score model was constructed based on the expression data of these eight lncRNAs in the training dataset of NSCLC patients and was subsequently validated in other two independent NSCLC datasets. The biological implications of prognostic lncRNAs were also analyzed using the functional enrichment analysis.

Results

An expression pattern of eight lncRNAs was found to be significantly associated with overall survival (OS) of NSCLC patients in the training dataset. With the eight-lncRNA signature, patients of the training dataset could be classified into high- and low-risk groups with significantly different OS (median survival 1.67 vs. 6.06 years, log-rank test p = 4.33E−09). The prognostic power of eight-lncRNA signature was further validated in other two non-overlapping independent NSCLC cohorts, demonstrating good reproducibility and robustness of this eight-lncRNA signature in predicting OS of NSCLC patients. Multivariate regression and stratified analysis suggested that the prognostic power of the eight-lncRNA signature was independent of clinical and pathological factors. Functional enrichment analyses revealed potential functional roles of the eight prognostic lncRNAs in tumorigenesis.

Conclusions

These findings indicate that the eight-lncRNA signature may be an effective independent prognostic molecular biomarker in the prediction of NSCLC patient survival.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0556-3) contains supplementary material, which is available to authorized users.

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Most cited references 29

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Time-dependent ROC curves for censored survival data and a diagnostic marker.

P Heagerty, T Lumley, M. S. Pepe (2000)

ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.

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Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas.

Hirokazu Okayama, Takashi Kohno, Yuko Ishii … (2012)

Activation of the EGFR, KRAS, and ALK oncogenes defines 3 different pathways of molecular pathogenesis in lung adenocarcinoma. However, many tumors lack activation of any pathway (triple-negative lung adenocarcinomas) posing a challenge for prognosis and treatment. Here, we report an extensive genome-wide expression profiling of 226 primary human stage I-II lung adenocarcinomas that elucidates molecular characteristics of tumors that harbor ALK mutations or that lack EGFR, KRAS, and ALK mutations, that is, triple-negative adenocarcinomas. One hundred and seventy-four genes were selected as being upregulated specifically in 79 lung adenocarcinomas without EGFR and KRAS mutations. Unsupervised clustering using a 174-gene signature, including ALK itself, classified these 2 groups of tumors into ALK-positive cases and 2 distinct groups of triple-negative cases (groups A and B). Notably, group A triple-negative cases had a worse prognosis for relapse and death, compared with cases with EGFR, KRAS, or ALK mutations or group B triple-negative cases. In ALK-positive tumors, 30 genes, including ALK and GRIN2A, were commonly overexpressed, whereas in group A triple-negative cases, 9 genes were commonly overexpressed, including a candidate diagnostic/therapeutic target DEPDC1, that were determined to be critical for predicting a worse prognosis. Our findings are important because they provide a molecular basis of ALK-positive lung adenocarcinomas and triple-negative lung adenocarcinomas and further stratify more or less aggressive subgroups of triple-negative lung ADC, possibly helping identify patients who may gain the most benefit from adjuvant chemotherapy after surgical resection. ©2011 AACR.

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Gene regulation by the act of long non-coding RNA transcription

Aleksandra Kornienko, Philipp Guenzl, Denise Barlow … (2013)

Long non-protein-coding RNAs (lncRNAs) are proposed to be the largest transcript class in the mouse and human transcriptomes. Two important questions are whether all lncRNAs are functional and how they could exert a function. Several lncRNAs have been shown to function through their product, but this is not the only possible mode of action. In this review we focus on a role for the process of lncRNA transcription, independent of the lncRNA product, in regulating protein-coding-gene activity in cis. We discuss examples where lncRNA transcription leads to gene silencing or activation, and describe strategies to determine if the lncRNA product or its transcription causes the regulatory effect.

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Author and article information

Contributors

Meng Zhou: biofomeng@hotmail.com

Maoni Guo: maoniguo@126.com

Dongfeng He: dongfengh@yeah.net

Xiaojun Wang: wangxjhrb@126.com

Yinqiu Cui: yinqiucui@126.com

Haixiu Yang: yanghaixiu@ems.hrbmu.edu.cn

Dapeng Hao: haodapeng@ems.hrbmu.edu.cn

Jie Sun: suncarajie@hotmail.com

Journal

Journal ID (nlm-ta): J Transl Med

Journal ID (iso-abbrev): J Transl Med

Title: Journal of Translational Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1479-5876

Publication date (Electronic): 17 July 2015

Publication date PMC-release: 17 July 2015

Publication date Collection: 2015

Volume: 13

Electronic Location Identifier: 231

Affiliations

[ ]College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 People’s Republic of China

[ ]Department of Interventional Radiology, The Affiliated Tumor Hospital of Harbin Medical University, Harbin, Heilongjiang 150040 People’s Republic of China

[ ]School of Life Sciences, Jilin University, Changchun, 130012 People’s Republic of China

Article

Publisher ID: 556

DOI: 10.1186/s12967-015-0556-3

PMC ID: 4504221

PubMed ID: 26183581

SO-VID: 4e49f1ce-a757-4aab-aa89-1bcf200b1ed2

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 2 March 2015

Date accepted : 1 June 2015

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ScienceOpen disciplines: Medicine

Keywords: long non-coding rna,non-small cell lung cancer,overall survival,signature

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ScienceOpen disciplines: Medicine

Keywords: long non-coding rna, non-small cell lung cancer, overall survival, signature

A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer

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Abstract

Background

Methods

Results

Conclusions

Electronic supplementary material

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Inflamed lung in vitro co-culture models

Most cited references 29

Time-dependent ROC curves for censored survival data and a diagnostic marker.

Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas.

Gene regulation by the act of long non-coding RNA transcription

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