IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0098419.html
   My bibliography  Save this article

An Integrated mRNA and microRNA Expression Signature for Glioblastoma Multiforme Prognosis

Author

Listed:
  • Jie Xiong
  • Zhitong Bing
  • Yanlin Su
  • Defeng Deng
  • Xiaoning Peng

Abstract

Although patients with Glioblastoma multiforme (GBM) have grave prognosis, significant variability in patient outcome is observed. The objective of this study is to identify a molecular signature for GBM prognosis. We subjected 355 mRNA and microRNA expression profiles to elastic net-regulated Cox regression for identification of an integrated RNA signature for GBM prognosis. A prognostic index (PI) was generated for patient stratification. Survival comparison was conducted by Kaplan-Meier method and a general multivariate Cox regression procedure was applied to evaluate the independence of the PI. The abilities and efficiencies of signatures to predict GBM patient outcome was assessed and compared by the area under the curve (AUC) of the receiver-operator characteristic (ROC). An integrated RNA prognostic signature consisted by 4 protective mRNAs, 12 risky mRNAs, and 1 risky microRNA was identified. Decreased survival was associated with being in the high-risk group (hazard ratio = 2.864, P

Suggested Citation

  • Jie Xiong & Zhitong Bing & Yanlin Su & Defeng Deng & Xiaoning Peng, 2014. "An Integrated mRNA and microRNA Expression Signature for Glioblastoma Multiforme Prognosis," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
    • Handle: RePEc:plo:pone00:0098419
    DOI: 10.1371/journal.pone.0098419
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098419
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0098419&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0098419?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Patrick J. Heagerty & Thomas Lumley & Margaret S. Pepe, 2000. "Time-Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker," Biometrics, The International Biometric Society, vol. 56(2), pages 337-344, June.
    2. Sujaya Srinivasan & Irene Rosita Pia Patric & Kumaravel Somasundaram, 2011. "A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-7, March.
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    4. Arivazhagan Arimappamagan & Kumaravel Somasundaram & Kandavel Thennarasu & Sreekanthreddy Peddagangannagari & Harish Srinivasan & Bangalore C Shailaja & Cini Samuel & Irene Rosita Pia Patric & Sudhans, 2013. "A Fourteen Gene GBM Prognostic Signature Identifies Association of Immune Response Pathway and Mesenchymal Subtype with High Risk Group," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-14, April.
    5. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
    6. Robert Tibshirani & Jacob Bien & Jerome Friedman & Trevor Hastie & Noah Simon & Jonathan Taylor & Ryan J. Tibshirani, 2012. "Strong rules for discarding predictors in lasso‐type problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 245-266, March.
    7. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zeng, Yaohui & Yang, Tianbao & Breheny, Patrick, 2021. "Hybrid safe–strong rules for efficient optimization in lasso-type problems," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    2. Liao Zhu & Robert A. Jarrow & Martin T. Wells, 2021. "Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-30, December.
    3. Yen, Tso-Jung & Yen, Yu-Min, 2016. "Structured variable selection via prior-induced hierarchical penalty functions," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 87-103.
    4. Wang, Cheng & Chen, Haozhe & Jiang, Binyan, 2024. "HiQR: An efficient algorithm for high-dimensional quadratic regression with penalties," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    5. Shikhar Uttam & Andrew M. Stern & Christopher J. Sevinsky & Samantha Furman & Filippo Pullara & Daniel Spagnolo & Luong Nguyen & Albert Gough & Fiona Ginty & D. Lansing Taylor & S. Chakra Chennubhotla, 2020. "Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    6. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
    7. Liao Zhu & Sumanta Basu & Robert A. Jarrow & Martin T. Wells, 2020. "High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 1-52, December.
    8. Wu, Tong Tong & He, Xin, 2012. "Coordinate ascent for penalized semiparametric regression on high-dimensional panel count data," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 25-33, January.
    9. Guo, Yi & Berman, Mark & Gao, Junbin, 2014. "Group subset selection for linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 39-52.
    10. Zhixuan Fu & Chirag R. Parikh & Bingqing Zhou, 2017. "Penalized variable selection in competing risks regression," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 353-376, July.
    11. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    12. Benedicte Sjo Tislevoll & Monica Hellesøy & Oda Helen Eck Fagerholt & Stein-Erik Gullaksen & Aashish Srivastava & Even Birkeland & Dimitrios Kleftogiannis & Pilar Ayuda-Durán & Laure Piechaczyk & Dagi, 2023. "Early response evaluation by single cell signaling profiling in acute myeloid leukemia," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    13. Dong Liu & Changwei Zhao & Yong He & Lei Liu & Ying Guo & Xinsheng Zhang, 2023. "Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix‐variate fMRI data," Biometrics, The International Biometric Society, vol. 79(3), pages 2246-2259, September.
    14. Capanu, Marinela & Giurcanu, Mihai & Begg, Colin B. & Gönen, Mithat, 2023. "Subsampling based variable selection for generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    15. Tomáš Plíhal, 2021. "Scheduled macroeconomic news announcements and Forex volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1379-1397, December.
    16. Matthew F Dixon, 2017. "A High Frequency Trade Execution Model for Supervised Learning," Papers 1710.03870, arXiv.org, revised Dec 2017.
    17. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    18. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    19. Murat Genç & M. Revan Özkale, 2021. "Usage of the GO estimator in high dimensional linear models," Computational Statistics, Springer, vol. 36(1), pages 217-239, March.
    20. Wei, Fengrong & Zhu, Hongxiao, 2012. "Group coordinate descent algorithms for nonconvex penalized regression," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 316-326.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0098419. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.