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Identification of high risk anaplastic gliomas by a diagnostic and prognostic signature derived from mRNA expression profiling.

ABSTRACT: Anaplastic gliomas are characterized by variable clinical and genetic features, but there are few studies focusing on the substratification of anaplastic gliomas. To identify a more objective and applicable classification of anaplastic gliomas, we analyzed whole genome mRNA expression profiling of four independent datasets. Univariate Cox regression, linear risk score formula and receiver operating characteristic (ROC) curve were applied to derive a gene signature with best prognostic performance. The corresponding clinical and molecular information were further analyzed for interpretation of the different prognosis and the independence of the signature. Gene ontology (GO), Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were performed for functional annotation of the differences. We found a three-gene signature, by applying which, the anaplastic gliomas could be divided into low risk and high risk groups. The two groups showed a high concordance with grade II and grade IV gliomas, respectively. The high risk group was more aggressive and complex. The three-gene signature showed diagnostic and prognostic value in anaplastic gliomas.

SUBMITTER: Zhang CB

PROVIDER: S-EPMC4742201 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Identification of high risk anaplastic gliomas by a diagnostic and prognostic signature derived from mRNA expression profiling.

Zhang Chuan-Bao CB Zhu Ping P Yang Pei P Cai Jin-Quan JQ Wang Zhi-Liang ZL Li Qing-Bin QB Bao Zhao-Shi ZS Zhang Wei W Jiang Tao T

Oncotarget 20151101 34

Anaplastic gliomas are characterized by variable clinical and genetic features, but there are few studies focusing on the substratification of anaplastic gliomas. To identify a more objective and applicable classification of anaplastic gliomas, we analyzed whole genome mRNA expression profiling of four independent datasets. Univariate Cox regression, linear risk score formula and receiver operating characteristic (ROC) curve were applied to derive a gene signature with best prognostic performanc ...[more]

PMID: 26436699

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Project description:Background: Laryngeal squamous cell carcinoma (LSCC) is one of the most common malignancy in the respiratory tract and could reduce the quality of life seriously like dyspnea, dysphonia and dysphagia. Moreover, 5-year survival rate has decreased over the past 40 years. This study was designed to identify mRNAs that related to prognosis in LSCC to enable early detection and outcome improvement. Methods: Gene expression profiles from Gene Expression Omnibus (GEO) (GSE59102, GSE84957) and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) with the help of bioinformatics tools. Functional enrichment analyses including Gene Ontology (GO) and pathway analysis were carried out to investigate the role of those genes and underlying molecular mechanisms in LSCC. Cox's regression analyses (univariate, LASSO and multivariate in order) were utilized to identify DEGs related with patients' overall survival and a 4-mRNA-based prognostic risk score model was established. Univariate and multivariate Cox's regression analyses were then performed on LSCC data (90 patients left) to identify independent predictors of OS, including the signature and clinicopathologic variables. The prognostic value of the gene signature was further validated and the genes were analyzed by GEPIA to get pan-cancer expression profiles. Results: 444 differentially expressed mRNAs (250 up-regulated, 194 down-regulated) were identified based on the threshold of fold change > 2 and adjusted p value < 0.05. Univariate Cox's regression analysis showed that high risk score (HR: 3.056, 95% confidence interval [CI]: 0.135-0.649, p<0.001) and female (HR: 0.296, 95% CI: 2.020-4.624, p=0.002) were associated with relatively poor prognosis. Further multivariate Cox's regression analysis indicated that risk score and gender were independent prognostic factors (p<0.05). The risk score model could stratify patients into high- and low‑risk groups, which presents significantly differential overall survival (p= 8.252e-04). The AUCs of 1-, 3- and 5-year OS were 0.724, 0.783 and 0.818, respectively. Conclusions: Our study provides evidence that the four-mRNA signature could serve as a biomarker to predict prognosis in LSCC, especially in long-term.

Project description:BackgroundGliomas are the most common primary tumors of the central nervous system. The complexity and heterogeneity of the tumor makes it difficult to obtain good biomarkers for drug development. In this study, through The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), we analyze the common diagnostic and prognostic moleculer markers in Caucasian and Asian populations, which can be used as drug targets in the future.MethodsThe RNA-seq data from Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) were analyzed to identify signatures. Based on the signatures, the prognosis index (PI) of every patient was constructed to predict the prognostic risk. Also, gene ontology (GO) functional enrichment analysis and KEGG analysis were conducted to investigate the biological functions of these mRNAs. Glioma patients' data in the CGGA database were introduced to validate the effectiveness of the signatures among Chinese populations. Excluding the previously reported prognostic markers of gliomas from this study, the expression of HSPA5 and MTPN were examined by qRT-PCR and immunohistochemical assay.ResultsIn total, 20 mRNAs were finally selected to build PI for patients from TCGA, including 16 high-risk genes and four low-risk genes. For Chinese patients, the log-rank test p values of PI were both less than 0.0001 in two independent datasets. And the AUCs were 0.831 and 0.907 for 3 years of two datasets, respectively. Moreover, among these 20 mRNAs, 10 and 15 mRNAs also had a significant predictive effect via univariate COX analysis in CGGA_693 and CGGA_325, respectively. qRT-PCR and Immunohistochemistry assay indicated that HSPA5 and MTPN over-expressed in Glioma samples compared to normal samples.ConclusionThe 20-gene signature can forecast the risk of Glioma in TCGA effectively, moreover it can also predict the risks of Chinese patients through validation in the CGGA database. HSPA5 and MTPN are possible biomarkers of gliomas suitable for all populations to improve the prognosis of these patients.

Dataset Information

Identification of high risk anaplastic gliomas by a diagnostic and prognostic signature derived from mRNA expression profiling.

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Identification of high risk anaplastic gliomas by a diagnostic and prognostic signature derived from mRNA expression profiling.

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