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An integrated analysis of enhancer RNAs in glioma and a validation of their prognostic values.

ABSTRACT: Glioma, a highly aggressive neuroepithelial malignant brain tumor, is associated with high disability and recurrence rates. Enhancer RNA (eRNA) plays a significant role in tumor proliferation and metastasis; however, their functions in gliomas need further evaluation. We used the computational pipeline, PreSTIGE, to predict tissue-specific enhancer-derived RNAs and the underlying regulatory genes. Using data retrieved from the TCGA and CGGA databases, a LASSO regression analysis and multiCox proportional hazards regression analyses were performed to determine the hub eRNAs associated with glioma prognosis. Quantitative reverse transcription PCR was performed on the glioma samples to evaluate the expression characteristics of the identified hub eRNAs. To construct a risk signature, we selected three eRNAs, including CRNDE, MRPS31P5, and LINC00844, for their significant prognostic values. The predictive value of the risk signature was validated using the CGGA and Rembrandt cohorts. Apart from the risk signature, the nomogram performed well at predicting OS in glioma patients. An eRNA-target gene regulatory network was established, which we evaluated using a target gene enrichment analysis. Pathway and gene ontology (GO) analyses demonstrated that the risk signature is associated with mRNA processing and spliceosome in glioma. Furthermore, we found that hub eRNAs potentially regulate the expressions of numerous splicing factors, such as MOV10 and SEC31B, and are correlated with prognosis-associated alteration splicing (AS). In conclusion, we established a risk signature that comprises three eRNAs, which can accurately be utilized as targets to predict prognosis in glioma patients.

SUBMITTER: Lin H

PROVIDER: S-EPMC8430071 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundNeuroblastoma (NB) is the most common solid tumor in children. NB treatment has made significant progress; however, given the high degree of heterogeneity, basic research findings and their clinical application to NB still face challenges. Herein, we identify novel prognostic models for NB.MethodsWe obtained RNA expression data of NB and normal nervous tissue from TARGET and GTEx databases and determined the differential expression patterns of RNA binding protein (RBP) genes between normal and cancerous tissues. Lasso regression and Cox regression analyses identified the five most important differentially expressed genes and were used to construct a new prognostic model. The function and prognostic value of these RBPs were systematically studied and the predictive accuracy verified in an independent dataset.ResultsIn total, 348 differentially expressed RBPs were identified. Of these, 166 were up-regulated and 182 down-regulated RBPs. Two hubs RBPs (CPEB3 and CTU1) were identified as prognostic-related genes and were chosen to build the prognostic risk score models. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using proportional hazards regression model. A five gene prognostic model: Risk score = (-0.60901*expCPEB3)+(0.851637*expCTU1) was built. Based on this model, the overall survival of patients in the high-risk subgroup was lower (P = 2.152e-04). The area under the curve (AUC) of the receiver-operator characteristic curve of the prognostic model was 0.720 in the TARGET cohort. There were significant differences in the survival rate of patients in the high and low-risk subgroups in the validation data set GSE85047 (P = 0.1237e-08), with the AUC 0.730. The risk model was also regarded as an independent predictor of prognosis (HR = 1.535, 95% CI = 1.368-1.722, P = 2.69E-13).ConclusionsThis study identified a potential risk model for prognosis in NB using Cox regression analysis. RNA binding proteins (CPEB3 and CTU1) can be used as molecular markers of NB.

Project description:Background: Acyl-CoA dehydrogenase short-chain (ACADS) is a crucial enzyme in the fatty acid metabolism pathway located in mitochondria. However, the expression level and prognostic value of ACADS in colorectal cancer (CRC) remain unclear. Methods: The mRNA and protein expression data of ACADS was obtained from The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Oncomine. Prognostic values of ACADS were calculated using Kaplan-Meier survival analysis. Correlations between ACADS and immune infiltration were estimated using TIMER, CIBERSORT, EPIC, quanTIseq, and xCell. The UALCAN and MEXPRESS databases were utilized for Methylation analysis. The co-expression analysis based on mRNA expression and interaction network of ACADS were performed via several online tools. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on ACADS co-expressed genes were performed using the Metascape. Results: The expression analysis demonstrated that ACADS was down-regulated in CRC tissues compared with paired normal tissue. Expression of ACADS was found to be significantly associated with clinical cancer stages and the consensus molecular subgroups (CMS) constituent ratio in CRC patients. Besides, lower ACADS expression was found to predict poor prognosis and be significantly associated with common immune checkpoint genes and MMR genes in CRC. ACADS expression levels were positively related to B cells, CD4+ T cells, CD8+ T cells, M1 macrophages, neutrophils, and Tregs, while negatively correlated with M0 macrophages, M2 macrophages. The methylation level of ACADS in normal tissues was significantly higher than that in tumor tissues, and several methylation sites were identified. The enrichment analysis suggested the co-expressed genes mainly enriched in cell mitochondrial metabolism. Conclusions: The present study provided multilevel evidences for expression of ACADS in CRC and the function of ACADS in prognostic prediction, immune infiltration, and methylation. ACADS might have the potential as the novel biomarker and therapeutic target in CRC patients.

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Project description:Lung cancer is the leading cause of cancer-related deaths worldwide. Dysregulation of RNA binding proteins (RBPs) has been found in a variety of cancers and is related to oncogenesis and progression. However, the functions of RBPs in lung squamous cell carcinoma (LUSC) remain unclear. In this study, we obtained gene expression data and corresponding clinical information for LUSC from The Cancer Genome Atlas (TCGA) database, identified aberrantly expressed RBPs between tumors and normal tissue, and conducted a series of bioinformatics analyses to explore the expression and prognostic value of these RBPs. A total of 300 aberrantly expressed RBPs were obtained, comprising 59 downregulated and 241 upregulated RBPs. Functional enrichment analysis indicated that the differentially expressed RBPs were mainly associated with mRNA metabolic processes, RNA processing, RNA modification, regulation of translation, the TGF-beta signaling pathway, and the Toll-like receptor signaling pathway. Nine RBP genes (A1CF, EIF2B5, LSM1, LSM7, MBNL2, RSRC1, TRMU, TTF2, and ZCCHC5) were identified as prognosis-associated hub genes by univariate, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier survival, and multivariate Cox regression analyses, and were used to construct the prognostic model. Further analysis demonstrated that high risk scores for patients were significantly related to poor overall survival according to the model. The area under the time-dependent receiver operator characteristic curve of the prognostic model was 0.712 at 3 years and 0.696 at 5 years. We also developed a nomogram based on nine RBP genes, with internal validation in the TCGA cohort, which showed a favorable predictive efficacy for prognosis in LUSC. Our results provide novel insights into the pathogenesis of LUSC. The nine-RBP gene signature showed predictive value for LUSC prognosis, with potential applications in clinical decision-making and individualized treatment.

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An integrated analysis of enhancer RNAs in glioma and a validation of their prognostic values.

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