Gene expression meta-analysis in diffuse low-grade glioma and the corresponding histological subtypes

Wang, Siqi; Jin, Feng; Fan, Wenliang; Liu, Fang; Zou, Yutong; Hu, Xuehan; Xu, Haibo; Han, Ping

doi:10.1038/s41598-017-12087-y

Cited by 22 publications

(21 citation statements)

References 60 publications

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“…Moreover, several studies were highlighted by the computational approach of the impact of mutations in IDH1 like I147S, V444A, and D375Y and in IDH2 like N439D and R140G [27][28][29] ; using the same mechanism by which we carried out the sequencing of IDH1, IDH2 and TP53 genes, we were able to identify a few different amino acid substitutions in the IDH1, IDH2, and p53 proteins, such as IDH1 R132, IDH2 R173M, and p53 R175H, R158G, and K305N. 6,30 The IDH mutations ( Figure 3A and B) occurring in the catalytic site are likely to cause a loss of catalytic activity; these mutated genes encode for D2HG which is proposed to be an oncometabolite in the cytoplasm for IDH1 and in the mitochondria for IDH2; it competitively inhibits KG-dependent enzymes, including the TET family of 5-methylcytosine hydroxylases and the JmjC family of histones lysine Cancer Informatics demethylases, resulting in cell differentiation. 27,28,31,32 Thirumal Kumar et al 27,28 reveal that the drug therapeutics for D-2hydroxyglutarate dehydrogenase are very limited; therefore, understanding the nature of molecular structure caused by these mutations will serve as platform for the development of novel targets for new drug therapy for D-2-hydroxyglutaric aciduria.…”

Section: Discussionmentioning

confidence: 92%

Computational Analysis of IDH1, IDH2, and TP53 Mutations in Low-Grade Gliomas Including Oligodendrogliomas and Astrocytomas

et al. 2020

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Introduction: The emergence of new omics approaches, such as genomic algorithms to identify tumor mutations and molecular modeling tools to predict the three-dimensional structure of proteins, has facilitated the understanding of the dynamic mechanisms involved in the pathogenesis of low-grade gliomas including oligodendrogliomas and astrocytomas. Methods: In this study, we targeted known mutations involved in low-grade gliomas, starting with the sequencing of genomic regions encompassing exon 4 of isocitrate dehydrogenase 1 ( IDH1) and isocitrate dehydrogenase 2 ( IDH2) and the four exons (5-6 and 7-8) of TP53 from 32 samples, followed by computational analysis to study the impact of these mutations on the structure and function of 3 proteins IDH1, IDH2, and p53. Results: We obtain a mutation that has an effect on the catalytic site of the protein IDH1 as R132H and on the catalytic site of the protein IDH2 as R172M. Other mutations at p53 have been identified as K305N, which is a pathogenic mutation; R175 H, which is a benign mutation; and R158G, which disrupts the structural conformation of the tumor suppressor protein. Conclusion: In low-grade gliomas, mutations in IDH1, IDH2, and TP53 may be the key to tumor progression because they have an effect on the function of the protein such as mutations R132H in IDH1 and R172M in IDH2, which change the function of the enzyme alpha-ketoglutarate, or R158G in TP53, which affects the structure of the generated protein, thus their importance in understanding gliomagenesis and for more accurate diagnosis complementary to the anatomical pathology tests.

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Section: Discussionmentioning

confidence: 92%

Computational Analysis of IDH1, IDH2, and TP53 Mutations in Low-Grade Gliomas Including Oligodendrogliomas and Astrocytomas

et al. 2020

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“…Meta-analysis of gene expression in PDAC. deGs with low but continuous expression levels in all profile datasets were defined as gained genes, and DEGs that disappeared in the meta-analysis but were expressed in individual analyses or in the meta-analysis resulting from experimental errors in the platforms were defined as lost genes (20,44,45). in total, one gained gene, unkempt family zinc finger-like (UNKL), which was weakly expressed in all datasets (eS, -0.25395), and 1,278 lost genes were identified.…”

Section: Numbers -------------------------------------mentioning

confidence: 99%

“…a meta-analysis, as a large-sample study, has an advantage in addressing this limitation due to its enhanced statistical power (19). Prior meta-analyses have been applied to study tumors to confirm deGs between tumor tissue and normal tissue in glioma (20,21), lung cancer (22), bladder cancer (23), breast cancer (24), osteosarcoma (25), liver cancer (26) and pancreatic cancer (27). In the present study, integrative meta-analysis of expression data (inMeX) was used to conduct a meta-analysis based on 11 qualified microarray datasets, with the aim to identify crucial deGs between Pdac samples and normal pancreatic samples that may serve as biomarkers for Pdac treatment and prognosis.…”

Section: Introductionmentioning

confidence: 99%

Identification of differentially expressed genes in pancreatic ductal adenocarcinoma and normal pancreatic tissues based on microarray datasets

et al. 2019

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Pancreatic ductal adenocarcinoma (Pdac) is a highly aggressive malignant tumor with rapid progression and poor prognosis. in the present study, 11 high-quality microarray datasets, comprising 334 tumor samples and 151 non-tumor samples from the Gene expression omnibus, were screened, and integrative meta-analysis of expression data was used to identify gene signatures that differentiate between Pdac and normal pancreatic tissues. Following the identification of differentially expressed genes (DEGs), two-way hierarchical clustering analysis was performed for all deGs using the gplots package in r software. Hub genes were then determined through protein-protein interaction network analysis using networkanalyst. in addition, functional annotation and pathway enrichment analyses of all deGs were conducted in the database for annotation, Visualization, and integrated discovery. The expression levels and Kaplan-Meier analysis of the top 10 upregulated and downregulated genes were verified in The Cancer Genome Atlas. A total of 1,587 DEGs, including 1,004 upregulated and 583 downregulated genes, were obtained by comparing Pdac with normal tissues. of these, hematological and neurological expressed 1, integrin subunit α2 (ITGA2) and S100 calcium-binding protein a6 (S100A6) were the top upregulated genes, and kinesin family member 1a, dymeclin and β-secretase 1 were the top downregulated genes. reverse transcription-quantitative Pcr was performed to examine the expression levels of S100A6, KRT19 and GNG7, and the results suggested that S100A6 was significantly upregulated in Pdac compared with normal pancreatic tissues. ITGA2 overexpression was significantly associated with shorter overall survival times, whereas family with sequence similarity 46 member c overexpression was strongly associated with longer overall survival times. in addition, network-based meta-analysis confirmed growth factor receptor-bound protein 2 and histone deacetylase 5 as pivotal hub genes in Pdac compared with normal tissue. in conclusion, the results of the present meta-analysis identified Pdac-related gene signatures, providing new perspectives and potential targets for Pdac diagnosis and treatment.

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“…This study showed elevated CUL3 expression levels in oligodendroglial tumor tissues and demonstrated its potential as a prognostic biomarker in this malignancy. The upregulation of CUL3 and its prognostic value have been reported in LGG in previous studies, which also revealed its correlation with tumor onset [35]. Further studies should be carried out to explore the mechanisms underlying the role of CUL3 during cancer pathogenesis.…”

Section: Discussionmentioning

confidence: 62%

Screening and Authentication of Key Genes and Prognostic Value Analysis in Oligodendroglial Tumors

Bao

Peng

2020

Preprint

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Background: Emerging evidence indicates that various functional genes with altered expression are involved in the human tumor progression. This study is aimed at identifying novel key genes that may be used for oligodendroglial tumor diagnosis, prognosis, and targeted therapy. Methods: This study included three expression proﬁles (GSE15824, GSE29796 and GSE108474) obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the diﬀerentially expressed genes (DEGs) between normal samples and oligodendroglial tumor, including oligodendroglioma and anaplastic oligodendroglioma. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identiﬁed DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identiﬁed. ONCOMINE and The Cancer Genome Atlas (TCGA) databases were used to verify the expression of the hub genes in oligodendroglial tumor tissues and the hub genes on the overall survival of oligodendroglial tumor patients. Results: A total of 128 DEGs were identiﬁed from the three expression proﬁles. These DEGs were enriched with functional processes and pathways related to oligodendroglial tumor pathogenesis. From the PPI network, five hub genes were identiﬁed. The expression of the five hub genes was all upregulated in oligodendroglial tumor tissues compared with the control tissues. Kaplan-Meier survival curves indicated that high expression of cullin 3 (CUL3), cop9 signalosome subunit 8 (COPS8), cullin associated and neddylation dissociated 1 (CAND1), F-box protein 22 (FBXO22), and leucine rich repeat containing 41 (LRRC41) predicted poor overall survival in oligodendroglial tumor patients (all log-rank P < 0.01). Conclusions: These results revealed that the DEGs may serve as candidate key genes during oligodendroglial tumor pathogenesis. The five hub genes, including CUL3, COPS8, CAND1, FBXO22, and LRRC41, may serve as promising prognostic biomarkers in oligodendroglial tumor.

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Gene expression meta-analysis in diffuse low-grade glioma and the corresponding histological subtypes

Cited by 22 publications

References 60 publications

Computational Analysis of IDH1, IDH2, and TP53 Mutations in Low-Grade Gliomas Including Oligodendrogliomas and Astrocytomas

Computational Analysis of IDH1, IDH2, and TP53 Mutations in Low-Grade Gliomas Including Oligodendrogliomas and Astrocytomas

Identification of differentially expressed genes in pancreatic ductal adenocarcinoma and normal pancreatic tissues based on microarray datasets

Screening and Authentication of Key Genes and Prognostic Value Analysis in Oligodendroglial Tumors

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