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Systematic identification of lncRNA-based prognostic biomarkers for glioblastoma.

ABSTRACT: Glioblastoma (GBM), a primary malignant tumor of the central nervous system, has a very poor prognosis. Analysis of global GBM samples has revealed a variety of long non-coding RNAs (lncRNAs) associated with prognosis; nevertheless, there remains a lack of accurate prognostic markers. Using RNA-Seq, methylation, copy number variation (CNV), mutation and clinical follow-up data for GBM patients from The Cancer Genome Atlas, we performed univariate analysis, multi-cluster analysis, differential analysis of different subtypes of lncRNA and coding genes, weighted gene co-expression network analyses, gene set enrichment analysis, Kyoto Encyclopedia of Genes and Genomes analysis, Gene Ontology analysis, and lncRNA CNV analyses. Our analyses yielded five lncRNAs closely related to survival and prognosis for GBM. To verify the predictive role of these five lncRNAs on the prognosis of GBM patients, the corresponding RNA-seq data from Chinese Glioma Genome Atlas were downloaded and analyzed, and comparable results were obtained. The role of one lncRNA LINC00152 has been observed previously; the others are novel findings. Expression of these lncRNAs could become effective predictors of survival and potential prognostic biomarkers for patients with GBM.

SUBMITTER: Li M

PROVIDER: S-EPMC6874448 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Systematic identification of lncRNA-based prognostic biomarkers for glioblastoma.

Li Mingdong M Long Shengrong S Hu Jinqu J Wang Zan Z Geng Chao C Ou Shaowu S

Aging 20191106 21

Glioblastoma (GBM), a primary malignant tumor of the central nervous system, has a very poor prognosis. Analysis of global GBM samples has revealed a variety of long non-coding RNAs (lncRNAs) associated with prognosis; nevertheless, there remains a lack of accurate prognostic markers. Using RNA-Seq, methylation, copy number variation (CNV), mutation and clinical follow-up data for GBM patients from The Cancer Genome Atlas, we performed univariate analysis, multi-cluster analysis, differential an ...[more]

PMID: 31692451

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Project description:Background:Glioblastoma multiforme (GBM) is extensively genetically and transcriptionally heterogeneous, which poses challenges for classification and management. Long noncoding RNAs (lncRNAs) play a critical role in the development and progression of GBM, especially in tumor-associated immune processes. Therefore, it is necessary to develop an immune-related lncRNAs (irlncRNAs) signature. Methods:Univariate and multivariate Cox regression analyses were utilized to construct a prognostic model. GBM-specific CeRNA and PPI network was constructed to predict lncRNAs targets and evaluate the interactions of immune mRNAs translated proteins. GO and KEGG pathway analyses were used to show the biological functions and pathways of CeRNA network-related immunity genes. Consensus Cluster Plus analysis was used for GBM gene clustering. Then, we evaluated GBM subtype-specific prognostic values, clinical characteristics, genes and pathways, immune infiltration access single cell RNA-seq data, and chemotherapeutics efficacy. The hub genes were finally validated. Results:A total of 17 prognostically related irlncRNAs were screened to build a prognostic model signature based on six key irlncRNAs. Based on GBM-specific CeRNAs and enrichment analysis, PLAU was predicted as a target of lncRNA-H19 and mainly enriched in the malignant related pathways. GBM subtype-A displayed the most favorable prognosis, high proportion of genes (IDH1, ATRX, and EGFR) mutation, chemoradiotherapy, and low risk and was characterized by low expression of four high-risk lncRNAs (H19, HOTAIRM1, AGAP2-AS1, and AC002456.1) and one mRNA KRT8. GSs with poor survival were mainly infiltrated by mesenchymal stem cells (MSCs) and astrocyte, and were more sensitive to gefitinib and roscovitine. Among GSs, three hub genes KRT8, NGFR, and TCEA3, were screened and validated to potentially play feasible oncogenic roles in GBM. Conclusion:Construction of lncRNAs risk model and identification of GBM subtypes based on 17 irlncRNAs, which suggesting that irlncRNAs had the promising potential for clinical immunotherapy of GBM.

Project description:Wilms' tumor (WT) is the most common type of childhood kidney cancer, and most cases present with favorable histology and respond well to standard treatment. However, a subset of patients with WT is diagnosed with bilateral, relapsed, and high-risk tumors which remain the leading cause of cancer-related death in children. Long noncoding RNAs (lncRNAs) and their aberrant expression have currently been attracting great attention as oncogenes or tumor suppressors during tumor initiation and progression. So far, their roles and related competitive endogenous RNA (ceRNA) network remain unelucidated in nephroblastoma pathogenesis. We comprehensively integrated lncRNA, microRNA (miRNA), and messenger RNA (mRNA) expression profiles from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database and screened out differentially expressed mRNAs (DEMs), lncRNAs (DELs), and miRNAs (DEMis) to construct a ceRNA network based on the information generated from miRcode, miRTarBase, TargetScan, and miRDB. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to analyze the functional characteristics of DEMs in the ceRNA network. The interaction between protein molecules was also analyzed by establishing a protein-protein interaction network. Finally, prognosis-related biomarkers were identified via survival analysis. Initially, 1647 DELs, 115 DEMis, and 3280 DEMs (|log FC|?>?2; FDR?<?0.01) were obtained using the R package. Next, we constructed a lncRNA-miRNA-mRNA network (ceRNA network), in which 176 DELs, 24 DEMis, and 141 DEMs were identified. Furthermore, 148 functional enrichment terms from GO were identified and 29 KEGG pathways were found to be significantly enriched. We also integrated patient clinical information to analyze the association between DERNAs and patient prognosis. We found that high expression of 8 DELs (LINC00473, AL445228.2, DENND5B-AS1, DLEU2, AC123595.1, AC135178.1, LINC00535, and LMO7-AS1) and 4 DEMs (CEP55, DEPDC1, PHF19, and TRIM36) correlated with poor survival in a patient with WT, whereas high expression of 2 DELs (MEG3 and RMST), 1 DEM (KIAA0922), and 1 DEMi (hsa-mir-200a) could possibly lead to better clinical outcomes. For the first time, the present study provided a novel insight into lncRNA-related ceRNA networks and identified potential prognostic biomarkers in Wilms' tumor.

Project description:Background Glioblastoma (GBM) is an intracranial brain tumor characterized by a high final lethality rate and recurrence rate, and limited available therapies. With the development of high-throughput sequencing technology, the genomic and transcriptomic features of GBM have been fully characterized. Therefore, our study aimed to identify its underlying genetic mechanisms, thus facilitating the development of novel therapies for GBM. Methods Based on the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, differential expression of RNAs in GBM and control group was analyzed. After constructing the long noncoding RNA (lncRNA)-miRNA-mRNA regulatory network of GBM, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) were performed to analyze related key nodes and the lncRNAs interacting with them. Further univariate Cox regression was conducted to explore independent factors, and then multivariate Cox regression was performed to construct risk prediction models. Results We first constructed the lncRNA-miRNA-mRNA regulatory network of GBM and two effective prediction models that included 2 mRNAs [transcription factor 12 (TCF12) and discoidin, CUB and LCCL domain containing 2 (DCBLD2)] and 5 lncRNAs (C10orf25, LINC00343, HOXA transcript antisense RNA, myeloid-specific 1 (HOTAIRM1), FGF12 antisense RNA 2 (FGF12-AS2) and H19). Additionally, we identified several key molecules [TCF12, integrin β3 (ITGB3), high mobility group AT-hook 2 (HMGA2), C10orf25 and LINC00336] closely associated with GBM prognosis. C10orf25/miR-218/DCBLD2 may be an important regulatory pathway in GBM. Conclusions Key molecules (TCF12, ITGB3, HMGA2, C10orf25, LINC00336 and H19) that are independent prognostic factors may be possible biomarkers to further optimize GBM prognosis. Two effective prognostic risk models that include 2 mRNAs (TCF12 and DCBLD2) and 5 lncRNAs (C10orf25, LINC00343, HOTAIRM1, FGF12-AS2 and H19) were constructed. C10orf25/miR-218/DCBLD2 may be an important regulatory pathway associated with the pathogenesis of GBM. Our findings contribute to further understanding the pathogenesis of GBM and finding possible candidate genes for prognostic and therapeutic usage with GBM.

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Systematic identification of lncRNA-based prognostic biomarkers for glioblastoma.

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Systematic identification of lncRNA-based prognostic biomarkers for glioblastoma.

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