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Prognostic value of a nine-gene signature in glioma patients based on mRNA expression profiling.

ABSTRACT: INTRODUCTION:Gliomas are the most common primary brain tumors in adults and a significant cause of cancer-related mortality. A 9-gene signature was identified as a novel prognostic model reflecting survival situation obviously in gliomas. AIMS:To identify an mRNA expression signature to improve outcome prediction for patients with different glioma grades. RESULTS:We used whole-genome mRNA expression microarray data of 220 glioma samples of all grades from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn) as a discovery set and data from Rembrandt and GSE16011 for validation sets. Data from every single grade were analyzed by the Kaplan-Meier method with a two-sided log-rank test. Univariate Cox regression and linear risk score formula were applied to derive a gene signature with better prognostic performance. We found that patients who had high risk score according to the signature had poor overall survival compared with patients who had low risk score. Highly expressed genes in the high-risk group were analyzed by gene ontology (GO) and gene set variation analysis (GSVA). As a result, the reason for the divisibility of gliomas was likely due to cell life processes and adhesion. CONCLUSION:This 9-gene-signature prediction model provided a more accurate predictor of prognosis that denoted patients with high risk score have poor outcome. Moreover, these risk models based on defined molecular profiles showed the considerable prospect in personalized cancer management.

SUBMITTER: Bao ZS

PROVIDER: S-EPMC6493176 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Prognostic value of a nine-gene signature in glioma patients based on mRNA expression profiling.

Bao Zhao-Shi ZS Li Ming-Yang MY Wang Jia-Yin JY Zhang Chuan-Bao CB Wang Hong-Jun HJ Yan Wei W Liu Yan-Wei YW Zhang Wei W Chen Ling L Jiang Tao T

CNS neuroscience & therapeutics 20131127 2

<h4>Introduction</h4>Gliomas are the most common primary brain tumors in adults and a significant cause of cancer-related mortality. A 9-gene signature was identified as a novel prognostic model reflecting survival situation obviously in gliomas.<h4>Aims</h4>To identify an mRNA expression signature to improve outcome prediction for patients with different glioma grades.<h4>Results</h4>We used whole-genome mRNA expression microarray data of 220 glioma samples of all grades from the Chinese Glioma ...[more]

PMID: 24279471

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Project description:BackgroundGlioma is the most common type of primary brain tumor in adults. Patients with the most malignant form have an overall survival time of <16 months. Although considerable progress has been made in defining the adapted therapeutic strategies, measures to counteract tumor escape have not kept pace, due to the developed resistance of malignant glioma. In fact, identifying the nature and role of distinct tumor-infiltrating immune cells in glioma patients would decipher potential mechanisms behind therapy failure.MethodsWe integrated into our study glioma transcriptomic datasets from the Cancer Genome Atlas (TCGA) cohort (154 GBM and 516 LGG patients). LM22 immune signature was built using CIBERSORT. Hierarchical clustering and UMAP dimensional reduction algorithms were applied to identify clusters among glioma patients either in an unsupervised or supervised way. Furthermore, differential gene expression (DGE) has been performed to unravel the top expressed genes among the identified clusters. Besides, we used the least absolute shrinkage and selection operator (LASSO) and Cox regression algorithm to set up the most valuable prognostic factor.ResultsOur study revealed, following gene enrichment analysis, the presence of two distinct groups of patients. The first group, defined as cluster 1, was characterized by the presence of immune cells known to exert efficient antitumoral immune response and was associated with better patient survival, whereas the second group, cluster 2, which exhibited a poor survival, was enriched with cells and molecules, known to set an immunosuppressive pro-tumoral microenvironment. Interestingly, we revealed that gene expression signatures were also consistent with each immune cluster function. A strong presence of activated NK cells was revealed in cluster 1. In contrast, potent immunosuppressive components such as regulatory T cells, neutrophils, and M0/M1/M2 macrophages were detected in cluster 2, where, in addition, inhibitory immune checkpoints, such as PD-1, CTLA-4, and TIM-3, were also significantly upregulated. Finally, Cox regression analysis further corroborated that tumor-infiltrating cells from cluster 2 exerted a significant impact on patient prognosis.ConclusionOur work brings to light the tight implication of immune components on glioma patient prognosis. This would contribute to potentially developing better immune-based therapeutic approaches.

Project description:BackgroundAutophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.MethodsFirst, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariate, LASSO and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, the key genes mRNA expression were validated using quantitative real-time PCR in clinical tissue samples.ResultsA total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (p < 0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. For clinical sample validation, we found that EIF4EBP1and ERBB2 had higher level in EC than that in normal tissues while CDKN1B, DLC1 and GRID1 had lower level, which was consistent with the results predicted.ConclusionsBased on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

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Prognostic value of a nine-gene signature in glioma patients based on mRNA expression profiling.

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Prognostic value of a nine-gene signature in glioma patients based on mRNA expression profiling.

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