Dataset Information

Transcriptomic Analysis of Glioma Based on IDH Status Identifies ACAA2 as a Prognostic Factor in Lower Grade Glioma.

ABSTRACT: Background:Glioma is the most common and lethal tumor in the central nervous system (CNS). More than 70% of WHO grade II/III gliomas were found to harbor isocitrate dehydrogenase (IDH) mutations which generated targetable metabolic vulnerabilities. Focusing on the metabolic vulnerabilities, some targeted therapies, such as NAMPT, have shown significant effects in preclinical and clinical trials. Methods:We explored the TCGA as well as CGGA database and analyzed the RNA-seq data of lower grade gliomas (LGG) with the method of weighted correlation network analysis (WGCNA). Differential expressed genes were screened, and coexpression relationships were grouped together by performing average linkage hierarchical clustering on the topological overlap. Clinical data were used to conduct Kaplan-Meier analysis. Results:In this study, we identified ACAA2 as a prognostic factor in IDH mutation lower grade glioma with the method of weighted correlation network analysis (WGCNA). The difference of ACAA2 gene expressions between the IDH wild-type (IDH-WT) group and the IDH mutant (IDH-MUT) group suggested that there may be different potential targeted therapies based on the fatty acid metabolic vulnerabilities, which promoted the personalized treatment for LGG patients.

SUBMITTER: Wu C

PROVIDER: S-EPMC7115055 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Transcriptomic Analysis of Glioma Based on IDH Status Identifies ACAA2 as a Prognostic Factor in Lower Grade Glioma.

Wu Chenxing C Song Hongwang H Fu Xiaojun X Li Shouwei S Jiang Tao T

BioMed research international 20200321

<h4>Background</h4>Glioma is the most common and lethal tumor in the central nervous system (CNS). More than 70% of WHO grade II/III gliomas were found to harbor isocitrate dehydrogenase (IDH) mutations which generated targetable metabolic vulnerabilities. Focusing on the metabolic vulnerabilities, some targeted therapies, such as NAMPT, have shown significant effects in preclinical and clinical trials.<h4>Methods</h4>We explored the TCGA as well as CGGA database and analyzed the RNA-seq data of ...[more]

PMID: 32280672

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Project description:BackgroundAbnormal expression of the eukaryotic initiation factor 3 (eIF3) subunits plays critical roles in tumorigenesis and progression, and also has potential prognostic value in cancers. However, the expression and clinical implications of eIF3 subunits in glioma remain unknown.MethodsExpression data of eIF3 for patients with gliomas were obtained from the Chinese Glioma Genome Atlas (CGGA) (n?=?272) and The Cancer Genome Atlas (TCGA) (n?=?595). Cox regression, the receiver operating characteristic (ROC) curves and Kaplan-Meier analysis were used to study the prognostic value. Gene oncology (GO) and gene set enrichment analysis (GSEA) were utilized for functional prediction.ResultsIn both the CGGA and TCGA datasets, the expression levels of eIF3d, eIF3e, eIF3f, eIF3h and eIF3l highly were associated with the IDH mutant status of gliomas. The expression of eIF3b, eIF3i, eIF3k and eIF3m was increased with the tumor grade, and was associated with poorer overall survival [All Hazard ratio (HR)?>?1 and P?<?0.05]. By contrast, the expression of eIF3a and eIF3l was decreased in higher grade gliomas and was associated with better overall survival (Both HR?<?1 and P?<?0.05). Importantly, the expression of eIF3i (located on chromosome 1p) and eIF3k (Located on chromosome 19q) were the two highest risk factors in both the CGGA [eIF3i HR?=?2.068 (1.425-3.000); eIF3k HR?=?1.737 (1.166-2.588)] and TCGA [eIF3i HR?=?1.841 (1.642-2.064); eIF3k HR?=?1.521 (1.340-1.726)] databases. Among eIF3i, eIF3k alone or in combination, the expression of eIF3i was the more robust in stratifying the survival of glioma in various pathological subgroups. The expression of eIF3i was an independent prognostic factor in IDH-mutant lower grade glioma (LGG) and could also predict the 1p/19q codeletion status of IDH-mutant LGG. Finally, GO and GSEA analysis showed that the elevated expression of eIF3i was significantly correlated with the biological processes of cell proliferation, mRNA processing, translation, T cell receptor signaling, NF-?B signaling and others.ConclusionsOur study reveals the expression alterations during glioma progression, and highlights the prognostic value of eIF3i in IDH-mutant LGG.

Project description:BACKGROUND AND PURPOSE:Isocitrate dehydrogenase (IDH)-mutant lower grade gliomas are classified as oligodendrogliomas or diffuse astrocytomas based on 1p/19q-codeletion status. We aimed to test and validate neuroradiologists' performances in predicting the codeletion status of IDH-mutant lower grade gliomas based on simple neuroimaging metrics. MATERIALS AND METHODS:One hundred two IDH-mutant lower grade gliomas with preoperative MR imaging and known 1p/19q status from The Cancer Genome Atlas composed a training dataset. Two neuroradiologists in consensus analyzed the training dataset for various imaging features: tumor texture, margins, cortical infiltration, T2-FLAIR mismatch, tumor cyst, T2* susceptibility, hydrocephalus, midline shift, maximum dimension, primary lobe, necrosis, enhancement, edema, and gliomatosis. Statistical analysis of the training data produced a multivariate classification model for codeletion prediction based on a subset of MR imaging features and patient age. To validate the classification model, 2 different independent neuroradiologists analyzed a separate cohort of 106 institutional IDH-mutant lower grade gliomas. RESULTS:Training dataset analysis produced a 2-step classification algorithm with 86.3% codeletion prediction accuracy, based on the following: 1) the presence of the T2-FLAIR mismatch sign, which was 100% predictive of noncodeleted lower grade gliomas, (n = 21); and 2) a logistic regression model based on texture, patient age, T2* susceptibility, primary lobe, and hydrocephalus. Independent validation of the classification algorithm rendered codeletion prediction accuracies of 81.1% and 79.2% in 2 independent readers. The metrics used in the algorithm were associated with moderate-substantial interreader agreement (κ = 0.56-0.79). CONCLUSIONS:We have validated a classification algorithm based on simple, reproducible neuroimaging metrics and patient age that demonstrates a moderate prediction accuracy of 1p/19q-codeletion status among IDH-mutant lower grade gliomas.

Project description:Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear. Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N 6-methyladenosine (m6A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA). Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m6A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs. Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m6A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.

Dataset Information

Transcriptomic Analysis of Glioma Based on IDH Status Identifies ACAA2 as a Prognostic Factor in Lower Grade Glioma.

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Transcriptomic Analysis of Glioma Based on IDH Status Identifies ACAA2 as a Prognostic Factor in Lower Grade Glioma.

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