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      Genomewide identification of a novel six‐LncRNA signature to improve prognosis prediction in resectable hepatocellular carcinoma

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          Abstract

          The current prognostic long noncoding RNA (lncRNA) signatures for hepatocellular carcinoma (HCC) are still controversial and need to be optimized by systematic bioinformatics analyses with suitable methods and appropriate patients. Therefore, we performed the study to establish a credible lncRNA signature for HCC outcome prediction and explore the related mechanisms. Based on the lncRNA profile and the clinical data of carefully selected HCC patients (n = 164) in TCGA, six of 12727 lncRNAs, MIR22HG, CTC‐297N7.9, CTD‐2139B15.2, RP11‐589N15.2, RP11‐343N15.5, and RP11‐479G22.8 were identified as the independent predictors of patients’ overall survival in HCC by sequential univariate Cox and 1000 times Cox LASSO regression with 10‐fold CV, and multivariate Cox analysis with 1000 times bootstrapping. In the Kaplan‐Meier analysis with patients trichotomized by the six‐lncRNA signature, high‐risk patients showed significantly shorter survival than mid‐ and low‐risk patients (log‐rank test P < 0.0001). According to the ROCs, the six‐lncRNA signature showed superior predictive capacity than the two existing four‐lncRNA combinations and the traditional prognostic clinicopathological parameter TNM stage. Furthermore, low MIR22HG and CTC‐297N7.9, but high CTD‐2139B15.2, RP11‐589N15.2, RP11‐343N15.5, and RP11‐479G22.8, were, respectively, demonstrated to be related with the malignant phenotypes of HCC. Functionally, the six lncRNAs were disclosed to involve in the regulation of multiple cell cycle and stress response‐related pathways via mediating transcription regulation and chromatin modification. In conclusion, our study identified a novel six‐lncRNA signature for resectable HCC prognosis prediction and indicated the underlying mechanisms of HCC progression and the potential functions of the six lncRNAs awaiting further elucidation.

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          Most cited references23

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          Survival model predictive accuracy and ROC curves.

          The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.
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            Long non-coding RNA: a new player in cancer

            Emerging evidence showed that long non-coding RNAs (lncRNAs) play important roles in a wide range of biological processes and dysregulated lncRNAs are involved in many complex human diseases, including cancer. Although a few lncRNAs’ functions in cancer have been characterized, the detailed regulatory mechanisms of majority of lncRNAs in cancer initiation and progression remain largely unknown. In this review, we summarized recent progress on the mechanisms and functions of lncRNAs in cancer, especially focusing on the oncogenic and tumor suppressive roles of the newly identified lncRNAs, and the pathways these novel molecules might be involved in. Their potentials as biomarkers for diagnosis and prognosis in cancer are also discussed in this paper.
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              Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks

              More and more evidences demonstrate that the long non-coding RNAs (lncRNAs) play many key roles in diverse biological processes. There is a critical need to annotate the functions of increasing available lncRNAs. In this article, we try to apply a global network-based strategy to tackle this issue for the first time. We develop a bi-colored network based global function predictor, long non-coding RNA global function predictor (‘lnc-GFP’), to predict probable functions for lncRNAs at large scale by integrating gene expression data and protein interaction data. The performance of lnc-GFP is evaluated on protein-coding and lncRNA genes. Cross-validation tests on protein-coding genes with known function annotations indicate that our method can achieve a precision up to 95%, with a suitable parameter setting. Among the 1713 lncRNAs in the bi-colored network, the 1625 (94.9%) lncRNAs in the maximum connected component are all functionally characterized. For the lncRNAs expressed in mouse embryo stem cells and neuronal cells, the inferred putative functions by our method highly match those in the known literature.
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                Author and article information

                Contributors
                cmuliuyunpeng@hotmail.com
                zli@cmu.edu.cn
                Journal
                Cancer Med
                Cancer Med
                10.1002/(ISSN)2045-7634
                CAM4
                Cancer Medicine
                John Wiley and Sons Inc. (Hoboken )
                2045-7634
                30 October 2018
                December 2018
                : 7
                : 12 ( doiID: 10.1002/cam4.2018.7.issue-12 )
                : 6219-6233
                Affiliations
                [ 1 ] Department of General Practice The First Hospital, China Medical University Shenyang China
                [ 2 ] Department of Psychology The First Hospital, China Medical University Shenyang China
                [ 3 ] Department of Hepatobiliary Surgery The First Hospital, China Medical University Shenyang China
                [ 4 ] Department of Medical Oncology The First Hospital, China Medical University Shenyang China
                [ 5 ] Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province the First Hospital of China Medical University Shenyang China
                Author notes
                [*] [* ] Correspondence

                Zhi Li, Department of Medical Oncology, The First Hospital, China Medical University, Shenyang, China.

                Email: zli@ 123456cmu.edu.cn and Yun‐Peng Liu, Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, the First Hospital of China Medical University, Shenyang, China. Email: cmuliuyunpeng@ 123456hotmail.com

                Author information
                http://orcid.org/0000-0002-6437-0348
                Article
                CAM41854
                10.1002/cam4.1854
                6308084
                30378276
                682f70a2-b1d8-4a8a-bbb7-921c6140c5dc
                © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 May 2018
                : 29 September 2018
                : 10 October 2018
                Page count
                Figures: 7, Tables: 5, Pages: 15, Words: 8650
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 81201801
                Award ID: 81302023
                Funded by: Basic Research Projects of Higher Education Institutions in Liaoning Province
                Award ID: LFWK201706
                Funded by: Science and Technology Plan Project of Liaoning Province
                Award ID: 2016007010
                Categories
                Original Research
                Cancer Biology
                Original Research
                Custom metadata
                2.0
                cam41854
                December 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.5.4 mode:remove_FC converted:27.12.2018

                Oncology & Radiotherapy
                biomarker,hepatocellular carcinoma,lncrna,prognosis
                Oncology & Radiotherapy
                biomarker, hepatocellular carcinoma, lncrna, prognosis

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