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Molecular Features of Glioma Determined and Validated Using Combined TCGA and GTEx Data Analyses

ABSTRACT: Gliomas are among the most common intracranial tumors which originated from neuroepithelial cells. Increasing evidence has revealed that long noncoding RNA (lncRNA)-microRNA (miRNA)-mRNA module regulation and tumor-infiltrating immune cells play important regulatory roles in the occurrence and progression of gliomas. However, the precise underlying molecular mechanisms remain largely unknown. Data on gliomas in The Cancer Genome Atlas lack normal control samples; to overcome this limitation, we combined 665 The Cancer Genome Atlas glioma RNA sequence datasets with 188 Genotype-Tissue Expression normal brain RNA sequences to construct an expression matrix profile after normalization. We systematically analyzed the expression of mRNAs, lncRNAs, and miRNAs between gliomas and normal brain tissues. Kaplan–Meier survival analyses were conducted to screen differentially expressed mRNAs, lncRNAs, and miRNAs. A prognostic miRNA-related competitive endogenous RNA network was constructed, and the core subnetworks were filtered using 6 miRNAs, 3 lncRNAs, and 11 mRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to investigate the biological functions of significantly dysregulated mRNAs. Co-expression network analysis was performed to analyze and screen the core genes. Furthermore, single-sample Gene Set Enrichment Analysis and immune checkpoint gene expression analysis were performed, as co-expression analysis indicated immune gene dysregulation in glioma. Finally, the expression of representative dysregulated genes was validated in U87 cells at the transcriptional level, establishing a foundation for further research. We identified 7017 mRNAs, 437 lncRNAs, and 9 miRNAs that were differentially expressed in gliomas. Kaplan–Meier survival analysis revealed 5684 mRNAs, 61 lncRNAs, and 7 miRNAs with potential as prognostic signatures in patients with glioma. The hub subnetwork of the competing endogenous RNA network between PART1-hsa-mir-25-SLC12A5/TACC2/BSN/TLN2/ZDHHC8 was screened out. Gene co-expression network, single-sample Gene Set Enrichment Analysis, and immune checkpoint expression analysis demonstrated that tumor-infiltrating immune cells are closely related to gliomas. We identified novel potential biomarkers to predict survival and therapeutic targets for patients with gliomas based on a large-scale sample. Importantly, we filtered pivotal genes that provide valuable information for further exploration of the molecular mechanisms underlying glioma tumorigenesis and progression.

SUBMITTER: Yang Z

PROVIDER: S-EPMC8516354 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAbnormal expression of long non-coding RNAs (lncRNA) play a significant role in the incidence and progression of high-grade serous ovarian cancer (HGSOC), which is a leading cause of mortality among gynecologic malignant tumor patients. In this study, our aim is to identify lncRNA-associated competing endogenous RNA (ceRNA ) axes that could define more reliable prognostic parameters of HGSOC, and to investigate the lncRNAs' potential mechanism of in lymphocyte infiltration.MethodsThe RNA-seq and miRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database; while for obtaining the differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs), we used edgeR, limma and DESeq2. After validating the RNA, miRNA and gene expressions, using integrated three RNA expression profiles (GSE18520, GSE27651, GSE54388) and miRNA profile (GSE47841) from the Gene Expression Omnibus (GEO) database, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analyses through ClusterProfiler. The prognostic value of these genes was determined with Kaplan-Meier survival analysis and Cox regression analysis. The ceRNA network was constructed using Cytoscape. The correlation between lncRNAs in ceRNA network and immune infiltrating cells was analyzed by using Tumor IMmune Estimation Resource (TIMER), and gene markers of tumor-infiltrating immune cells were identified using Spearman's correlation after removing the influence of tumor purity.ResultsA total of 33 DELs (25 upregulated and eight downregulated), 134 DEMs (76 upregulated and 58 downregulated), and 1,612 DEGs (949 upregulated and 663 downregulated) were detected that could be positively correlated with overall survival (OS) of HGSOC. With the 1,612 analyzed genes, we constructed a ceRNA network, which indicated a pre-dominant involvement of the immune-related pathways. Furthermore, our data revealed that LINC00665 influenced the infiltration level of macrophages and dendritic cells (DCs). On the other hand, FTX and LINC00665, which may play their possible roles through the ceRNA axis, demonstrated a potential to inhibit Tregs and prevent T-cell exhaustion.ConclusionWe defined several prognostic biomarkers for the incidence and progression of HGSOC and constructed a network for ceRNA axes; among which three were indicated to have a positive correlation with lymphocyte infiltration, namely: FTX-hsa-miR-150-5p-STK11, LINC00665-hsa-miR449b-5p-VAV3 and LINC00665-hsa-miR449b-5p-RRAGD.

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Project description:BackgroundAmong primary brain tumors, gliomas are associated with a poor prognosis and a median survival that varies depending on the tumor grade and subtype. As the most malignant form of glioma, glioblastoma (GBM) constitutes a significant health concern. Alteration in granulin(GRN) has been proved to be accountable for several diseases. However, the relationship between GRN and GBM remains unclear. We evaluated the role of GRN in GBM through The Cancer Genome Atlas (TCGA) database.MethodsFirst, we assessed the relationship between GRN and GBM through the GEPIA database. Next, the relationship between GRN and GBM prognosis was analyzed by logistic regression and multivariate cox methods. Using CIBERSORT and the GEPIA correlation module, we also investigated the link between GRN and immune infiltrates in cancer. Using the TCGA data, a gene set enrichment analysis (GSEA) was performed. We also employed Tumor Immune Estimation Resource (TIMER) to examine the data set of GRN expression and immune infiltration level in GBM and investigate the cumulative survival in GBM. We also validated tissues from GBM patients by Western blotting, RT-qPCR, and immunohistochemistry.ResultsIncreased GRN expression was shown to have a significant relationship to tumor grade in a univariate study utilizing logistic regression. Furthermore, multivariate analysis disclosed that GRN expression down-regulation is an independent predictive factor for a favorable outcome. GRN expression level positively correlates with the number of CD4+ T cells, neutrophils, macrophages, and dendritic cells (DCs) that infiltrate a GBM. The GSEA also found that the high GRN expression phenotype pathway was enriched for genes involved in immune response molecular mediator production, lymphocyte-mediated immunity, cytokine-mediated signaling pathway, leukocyte proliferation, cell chemotaxis, and CD4+ alpha beta T cell activation. Differentially enriched pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) include lysosome, apoptosis, primary immunodeficiency, chemokine signaling pathway, natural killer cell-mediated cytotoxicity, and B cell receptor signaling pathway. Validated result showed that GRN was upregulated in GBM tissues. These results suggested that GRN was a potential indicator for the status of GBM.ConclusionGRN is a prognostic biomarker and correlated with immune infiltrates in GBM.

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Molecular Features of Glioma Determined and Validated Using Combined TCGA and GTEx Data Analyses

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