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A Fourteen Gene GBM Prognostic Signature Identifies Association of Immune Response Pathway and Mesenchymal Subtype with High Risk Group

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  • Arivazhagan Arimappamagan
  • Kumaravel Somasundaram
  • Kandavel Thennarasu
  • Sreekanthreddy Peddagangannagari
  • Harish Srinivasan
  • Bangalore C Shailaja
  • Cini Samuel
  • Irene Rosita Pia Patric
  • Sudhanshu Shukla
  • Balaram Thota
  • Krishnarao Venkatesh Prasanna
  • Paritosh Pandey
  • Anandh Balasubramaniam
  • Vani Santosh
  • Bangalore Ashwathnarayanara Chandramouli
  • Alangar Sathyaranjandas Hegde
  • Paturu Kondaiah
  • Manchanahalli R Sathyanarayana Rao

Abstract

Background: Recent research on glioblastoma (GBM) has focused on deducing gene signatures predicting prognosis. The present study evaluated the mRNA expression of selected genes and correlated with outcome to arrive at a prognostic gene signature. Methods: Patients with GBM (n = 123) were prospectively recruited, treated with a uniform protocol and followed up. Expression of 175 genes in GBM tissue was determined using qRT-PCR. A supervised principal component analysis followed by derivation of gene signature was performed. Independent validation of the signature was done using TCGA data. Gene Ontology and KEGG pathway analysis was carried out among patients from TCGA cohort. Results: A 14 gene signature was identified that predicted outcome in GBM. A weighted gene (WG) score was found to be an independent predictor of survival in multivariate analysis in the present cohort (HR = 2.507; B = 0.919; p

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  • 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.
  • Handle: RePEc:plo:pone00:0062042
    DOI: 10.1371/journal.pone.0062042
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    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. Tibshirani Robert J. & Efron Brad, 2002. "Pre-validation and inference in microarrays," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 1(1), pages 1-20, August.
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    1. 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.

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