Dataset Information

Pathological Grade-Associated Transcriptome Profiling of lncRNAs and mRNAs in Gliomas.

ABSTRACT: The aim of the present study was to explore the expression profiles of lncRNAs and mRNAs in glioma patients and to elucidate any potential relationship between lncRNAs and mRNAs in glioma. High-throughput transcriptome sequencing of mRNAs and lncRNAs from six normal tissues and 16 glioma tissues (grade II, six cases; grade III, four cases; and grade IV, six cases) was performed. Series test of cluster (STC) analysis was used to screen significant trending models associated with glioma. Gene co-expression networks were constructed for the differentially expressed lncRNAs and mRNAs, and gene-ontology (GO) and pathway-enrichment analyses were further performed. Quantitative real-time PCR was performed to validate the five most differentially expressed lncRNAs and mRNAs. After filtering the raw sequencing data, we found 578 lncRNAs and 3,216 mRNAs that were significantly dysregulated in glioma (fold change ? 2, p < 0.05). Twenty model profiles of lncRNA and 10 model profiles of mRNA were summarized, and three patterns of lncRNAs and two patterns of mRNAs were of clinical significance. Three gene co-expression networks between mRNAs and lncRNAs were built to clarify the relationship between lncRNAs and mRNAs in glioma. GO and pathway analyses indicated that the differentially expressed lncRNAs and mRNAs were enriched in several biological processes and signaling pathways associated with tumorigenesis. Both lncRNAs and mRNAs exhibited dynamic differential expression profiles that indicated their potential roles in different degrees of glioma malignancy. A series of bioinformatics analyses indicated that most of these lncRNAs and mRNAs are involved in important biological processes and pathways associated with the pathogenesis of glioma. These results provide potential directions and valuable resources for future investigations via the comprehensive integration of these lncRNAs and mRNAs.

SUBMITTER: Sun J

PROVIDER: S-EPMC7076085 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Pathological Grade-Associated Transcriptome Profiling of lncRNAs and mRNAs in Gliomas.

Sun Junlong J Jiang Rui R Song Mengruo M Yao Junzhong J Hou Shiqiang S Zhu Yunhua Y Ji Xiang X Sheng Hao H Tang Zhongyu Z Liu Qianqian Q Jia Zhongzheng Z Shi Wei W Shi Jinlong J

Frontiers in oncology 20200310

The aim of the present study was to explore the expression profiles of lncRNAs and mRNAs in glioma patients and to elucidate any potential relationship between lncRNAs and mRNAs in glioma. High-throughput transcriptome sequencing of mRNAs and lncRNAs from six normal tissues and 16 glioma tissues (grade II, six cases; grade III, four cases; and grade IV, six cases) was performed. Series test of cluster (STC) analysis was used to screen significant trending models associated with glioma. Gene co-e ...[more]

PMID: 32211318

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Project description:BackgroundEach year, over 16,000 patients die from malignant brain cancer in the US. Long noncoding RNAs (lncRNAs) have recently been shown to play critical roles in regulating neurogenesis and brain tumor progression. To better understand the role of lncRNAs in brain cancer, we performed a global analysis to identify and characterize all annotated and novel lncRNAs in both grade II and III gliomas as well as grade IV glioblastomas (glioblastoma multiforme [GBM]).Methods and findingsWe determined the expression of all lncRNAs in over 650 brain cancer and 70 normal brain tissue RNA sequencing datasets from The Cancer Genome Atlas (TCGA) and other publicly available datasets. We identified 611 induced and 677 repressed lncRNAs in glial tumors relative to normal brains. Hundreds of lncRNAs were specifically expressed in each of the three lower grade glioma (LGG) subtypes (IDH1/2 wt, IDH1/2 mut, and IDH1/2 mut 1p19q codeletion) and the four subtypes of GBMs (classical, mesenchymal, neural, and proneural). Overlap between the subtype-specific lncRNAs in GBMs and LGGs demonstrated similarities between mesenchymal GBMs and IDH1/2 wt LGGs, with 2-fold higher overlap than would be expected by random chance. Using a multivariate Cox regression survival model, we identified 584 and 282 lncRNAs that were associated with a poor and good prognosis, respectively, in GBM patients. We developed a survival algorithm for LGGs based on the expression of 64 lncRNAs that was associated with patient prognosis in a test set (hazard ratio [HR] = 2.168, 95% CI = 1.765-2.807, p < 0.001) and validation set (HR = 1.921, 95% CI = 1.333-2.767, p < 0.001) of patients from TCGA. The main limitations of this study are that further work is needed to investigate the clinical relevance of our findings, and that validation in an independent dataset is needed to determine the robustness of our survival algorithm.ConclusionsThis work identifies a panel of lncRNAs that appear to be prognostic in gliomas and provides a critical resource for future studies examining the role of lncRNAs in brain cancers.

Dataset Information

Pathological Grade-Associated Transcriptome Profiling of lncRNAs and mRNAs in Gliomas.

Publications

Pathological Grade-Associated Transcriptome Profiling of lncRNAs and mRNAs in Gliomas.

Similar Datasets

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