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A Ten-Microrna Expression Signature Predicts Survival in Glioblastoma

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A Ten-Microrna Expression Signature Predicts Survival in Glioblastoma A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma Sujaya Srinivasan, Irene Rosita Pia Patric, Kumaravel Somasundaram* Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, Karnataka, India Abstract Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio [HR] = 2.4; 95% CI = 1.4–3.8; p,0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR = 1.7; 95% CI = 1.1–2.8; p = 0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR = 2.0; 95% CI = 1.4–2.8; p,0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04–1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliom- agenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy. Citation: Srinivasan S, Patric IRP, Somasundaram K (2011) A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma. PLoS ONE 6(3): e17438. doi:10.1371/journal.pone.0017438 Editor: Francesco Falciani, University of Birmingham, United Kingdom Received August 25, 2010; Accepted February 3, 2011; Published March 31, 2011 Copyright: ß 2011 Srinivasan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The study is supported by a grant from Department of Biotechnology, Government of India (D.O.No. BT/01/CEIB/09/VI/01; http://dbtindia.nic.in/index. asp). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected] Introduction using 39 malignant astrocytoma and 7 normal brain samples and identified a 23-miRNA expression signatures which can discrimi- The grade IV astrocytoma, GBM, is the most common and nate anaplastic astrocytoma from glioblastoma [11]. Other studies malignant primary adult brain cancer [1]. Despite advances in investigated the target identification and functional characterization treatment modalities, the median survival is very poor. Since of specific miRNAs [8,10,12–17]. Many studies identifying miRNA postoperative radiotherapy alone did not provide great benefit to expression signatures predicting patient survival have been done in GBM patients, several attempts have been made to find suitable several cancers like lung cancer, lymphocytic leukemia; lung adjuvant chemotherapy. The present standard treatment appears to adenocarcinoma, breast and pancreas cancers [18–23]. However, be maximal safe resection of the tumor followed by irradiation and a miRNA signature that can predict the clinical outcome in GBM temozolomide adjuvant chemotherapy [2]. However, it was found patients has not been found so far. that not all patients were benefited from the addition of In this study, we have subjected the miRNA expression data temozolomide. Further analysis revealed that methylation of MGMT from a total of 222 GBM patients derived from The Cancer promoter to be the strongest predictor for outcome and benefit from Genome Atlas (TCGA) data set to Cox proportional regression temozolomide chemotherapy [2]. In addition, recent molecular and analysis to identify the miRNAs that can predict patient survival. genetic profiling studies have identified several markers and unique By using a sample-splitting approach, a 10 miRNA expression signatures as prognostic and predictive factors of GBM [3,4]. signature that can predict survival both in training and testing sets MicroRNAs (miRNAs) are endogenous non-coding small RNAs, was identified. More importantly, using multivariate analysis along which negatively regulate gene expression either by binding to the with patient age, the 10 miRNA expression signature was found to 39 UTR leading to inhibition of translation or degradation of be an independent predictor of patient survival. specific mRNA. Since miRNAs can act as Oncogenes or tumor suppressor genes, they have been linked to a variety of cancers [5]. It Results has been shown that classification of multiple cancers based on miRNA expression signatures is more accurate than mRNA based Identification of a 10 miRNA expression signature from signatures [6]. There have been a few attempts to profile miRNA training set expression either by microarray or RT-PCR in different grades of The 222 GBM samples were divided randomly into a training glioma [7–11]. Rao et al., profiled the expression of 756 miRNAs set (n = 111) or a testing set (n = 111). Table 1 gives the age and PLoS ONE | www.plosone.org 1 March 2011 | Volume 6 | Issue 3 | e17438 microRNA Signature and GBM Patient Survival Table 1. Clinical characteristics of GBM patients according to their low or high risk group in the training set, the testing set and the entire patient set. Characteristic Training set p-value* Testing set p-value* Entire patient set p-value* Low risk High risk Low risk High risk Low risk High risk (N = 69) (N = 42) (N = 68) (N = 43) (N = 137) (N = 85) Age (Mean 6 SD) 51.566.6 54.461.9 0.5 54612.7 58.3610.2 0.1 52.8614.8 56.4611.2 0.1 Gender Female (%) 26 (37.7) 13 (31) 0.5 27 (39.7) 12 (27.9) 0.2 53 (38.7) 25 (29.4) 0.2 Male (%) 43 (62.3) 29 (69) 41 (60.3) 31 (72.1) 84 (61.3) 60 (70.6) *Two-tailed p-value obtained from Mann-Whitney test. doi:10.1371/journal.pone.0017438.t001 gender distribution of the patients in both sets and the entire set. Validation of the 10 miRNA expression signature for miRNA expression data corresponding to 305 miRNAs derived survival prediction in the testing set from the training set was subjected to Cox proportional hazard To validate our finding, we calculated the risk score for the 111 regression analysis to identify miRNAs, whose expression profile patients from the testing set. Using the same cut-off value that was could be significantly correlated to patient survival. We identified a used for training set, the patients from the testing set were classified set of 10 miRNAs that were significantly correlated with patient into low risk and high risk groups and subjected to survival survival (Table 2). These 10 miRNAs were then used to create a comparison. Similar to the results obtained in the training set, signature by calculating a risk score for each patient. A risk score patients in the high risk group had shorter median survival than formula was obtained for predicting the patient survival (see patients in the low risk group (12.1 months versus 18.0 months; [materials and methods] for detail). Using the risk score formula, HR = 1.7; 95% CI = 1.1–2.8; p = 0.0207) (Figure 1 B; Table 3). the 10 miRNA expression signature risk score was calculated for As was seen in the training set, patient survival in the low risk group all patients in the training set. The patients were then ranked in was better than that in the high-risk group throughout the 5 year the training set according to their risk score. Using the 60th follow-up time (Table 3). Risk score based classification of the percentile risk score as cutoff in the training set, the patients were entire patient set also gave a similar result with the high risk group divided into high and low risk groups. Patients belonging to high having a shorter median survival than the low risk group (12.6 risk group had a shorter median survival than patients with low months versus 18.3 months, HR = 2.0; 95% CI = 1.4–2.8; risk score (12.6 months versus 19.3 months, HR = 2.4; 95% p,0.0001) (Table 3). The higher overall survival of the low risk CI = 1.4–3.8; p = 0.0006) (Figure 1 A; Table 3). Survival was group throughout the study period in training, testing and entire greater in the low risk group than in the high risk group patient sets compared to the high risk group is shown (Figure S1). throughout the follow-up. Overall survival in the training set was 41.8% at 2 years, 25.6% at 3 years, 23.6% at 4 years and 14.8% at 5 years in the low risk group, versus 9.0%, 3.0%, 0.0 and 0.0% Nature of 10 miRNA expression signature respectively in the high risk group (HR = 2.4; 95% CI = 1.4–3.8; Additional investigation of 10 miRNA set yielded several p = 0.0006) (Table 3; Figure S1).
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