Please use this identifier to cite or link to this item: http://lib.jncasr.ac.in:8080/jspui/handle/10572/2360
Title: A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma
Authors: Rao, Soumya Alige Mahabala
Srinivasan, Sujaya
Patric, Irene Rosita Pia
Hegde, Alangar Sathyaranjandas
Chandramouli, Bangalore Ashwathnarayanara
Arimappamagan, Arivazhagan
Santosh, Vani
Kondaiah, Paturu
Rao, M. R. S.
Somasundaram, Kumaravel
Keywords: Potential Serum Biomarkers
High-Grade Glioma
Gene-Expression
Malignant Gliomas
Secondary Glioblastoma
Microarray Analysis
Molecular Subtypes
Prognostic Value
Strong Predictor
Poor-Prognosis
Issue Date: 2014
Publisher: Public Library of Science
Citation: Rao, SAM; Srinivasan, S; Patric, IRP; Hegde, AS; Chandramouli, BA; Arimappamagan, A; Santosh, V; Kondaiah, P; Rao, MRS; Somasundaram, K, A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma. PLoS One 2014, 9 (1), e85200 http://dx.doi.org/10.1371/journal.pone.0085200
PLoS One
9
1
Abstract: Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.
Description: Open Access
URI: http://hdl.handle.net/10572/2360
ISSN: 1932-6203
Appears in Collections:Research Papers (M.R.S. Rao)

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