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> Disorders > Neoplastic Process > Oligodendroglioma

Oligodendroglioma

Oligodendroglioma is a type of brain tumor that arises from the glial cells responsible for producing the myelin sheath that insulates nerve fibers.
These tumors are typically slow-growing and occur most often in the cerebral hemispheres of adults.
Symptoms may include headaches, seizures, and cognitive impairments.
Accurate diagnosis and optimal treatment planning are crucial for managing this condition.
PubCompare.ai can enhance Oligodendroglioma research by leverageing artificial intelligence to streamline protocol compariosons, identify best practices from literature, preprints, and patents, and improve reproducibility and accuracy in this field.

Most cited protocols related to «Oligodendroglioma»

1
Comparing Methylation-Based CNS Tumor Classification
Cited 48 times
To compare our methylation-based classification of CNS tumours with described methylation classes of brain tumours by the Cancer Genome Atlas (TCGA) project, we downloaded the pre-processed methylation dataset described in Ceccarelli et al. 201618 including methylation data of 418 low grade glioma and 377 glioblastoma samples analysed by using the Illumina 450k array or 27k array platforms. To classify our samples according to the TCGA pan-glioma DNA methylation classification, we trained a Random Forest classifier on this dataset using the 1,300 CpG probe signature provided by the authors and using the default settings of the Random Forest algorithms implemented in the R package randomForest. The results of this classification for astrocytomas, oligodendrogliomas and glioblastomas are shown in Extended Data Figure 3d and are given on a case-by-case basis in Supplementary Table 2 and 4.
Capper D., Jones D.T., Sill M., Hovestadt V., Schrimpf D., Sturm D., Koelsche C., Sahm F., Chavez L., Reuss D.E., Kratz A., Wefers A.K., Huang K., Pajtler K.W., Schweizer L., Stichel D., Olar A., Engel N.W., Lindenberg K., Harter P.N., Braczynski A., Plate K.H., Dohmen H., Garvalov B.K., Coras R., Hölsken A., Hewer E., Bewerunge-Hudler M., Schick M., Fischer R., Beschorner R., Schittenhelm J., Staszewski O., Wani K., Varlet P., Pages M., Temming P., Lohmann D., Selt F., Witt H., Milde T., Witt O., Aronica E., Giangaspero F., Rushing E., Scheurlen W., Geisenberger C., Rodriguez F.J., Becker A., Preusser M., Haberler C., Bjerkvig R., Cryan J., Farrell M., Deckert M., Hench J., Frank S., Serrano J., Kannan K., Tsirigos A., Brück W., Hofer S., Brehmer S., Seiz-Rosenhagen M., Hänggi D., Hans V., Rozsnoki S., Hansford J.R., Kohlhof P., Kristensen B.W., Lechner M., Lopes B., Mawrin C., Ketter R., Kulozik A., Khatib Z., Heppner F., Koch A., Jouvet A., Keohane C., Mühleisen H., Mueller W., Pohl U., Prinz M., Benner A., Zapatka M., Gottardo N.G., Driever P.H., Kramm C.M., Müller H.L., Rutkowski S., von Hoff K., Frühwald M.C., Gnekow A., Fleischhack G., Tippelt S., Calaminus G., Monoranu C.M., Perry A., Jones C., Jacques T.S., Radlwimmer B., Gessi M., Pietsch T., Schramm J., Schackert G., Westphal M., Reifenberger G., Wesseling P., Weller M., Collins V.P., Blümcke I., Bendszus M., Debus J., Huang A., Jabado N., Northcott P.A., Paulus W., Gajjar A., Robinson G., Taylor M.D., Jaunmuktane Z., Ryzhova M., Platten M., Unterberg A., Wick W., Karajannis M.A., Mittelbronn M., Acker T., Hartmann C., Aldape K., Schüller U., Buslei R., Lichter P., Kool M., Herold-Mende C., Ellison D.W., Hasselblatt M., Snuderl M., Brandner S., Korshunov A., von Deimling A, & Pfister S.M. (2018). DNA methylation-based classification of central nervous system tumours. Nature, 555(7697), 469-474.
Publication 2018
Astrocytoma Brain Neoplasm, Malignant Central Nervous System Neoplasms DNA Methylation Genome Glioblastoma Glioblastoma Multiforme Glioma Malignant Neoplasms Methylation Neoplasms Oligodendroglioma
2
Comparing Methylation-Based CNS Tumor Classification
Cited 47 times
To compare our methylation-based classification of CNS tumours with described methylation classes of brain tumours by the Cancer Genome Atlas (TCGA) project, we downloaded the pre-processed methylation dataset described in Ceccarelli et al. 201618 including methylation data of 418 low grade glioma and 377 glioblastoma samples analysed by using the Illumina 450k array or 27k array platforms. To classify our samples according to the TCGA pan-glioma DNA methylation classification, we trained a Random Forest classifier on this dataset using the 1,300 CpG probe signature provided by the authors and using the default settings of the Random Forest algorithms implemented in the R package randomForest. The results of this classification for astrocytomas, oligodendrogliomas and glioblastomas are shown in Extended Data Figure 3d and are given on a case-by-case basis in Supplementary Table 2 and 4.
Capper D., Jones D.T., Sill M., Hovestadt V., Schrimpf D., Sturm D., Koelsche C., Sahm F., Chavez L., Reuss D.E., Kratz A., Wefers A.K., Huang K., Pajtler K.W., Schweizer L., Stichel D., Olar A., Engel N.W., Lindenberg K., Harter P.N., Braczynski A., Plate K.H., Dohmen H., Garvalov B.K., Coras R., Hölsken A., Hewer E., Bewerunge-Hudler M., Schick M., Fischer R., Beschorner R., Schittenhelm J., Staszewski O., Wani K., Varlet P., Pages M., Temming P., Lohmann D., Selt F., Witt H., Milde T., Witt O., Aronica E., Giangaspero F., Rushing E., Scheurlen W., Geisenberger C., Rodriguez F.J., Becker A., Preusser M., Haberler C., Bjerkvig R., Cryan J., Farrell M., Deckert M., Hench J., Frank S., Serrano J., Kannan K., Tsirigos A., Brück W., Hofer S., Brehmer S., Seiz-Rosenhagen M., Hänggi D., Hans V., Rozsnoki S., Hansford J.R., Kohlhof P., Kristensen B.W., Lechner M., Lopes B., Mawrin C., Ketter R., Kulozik A., Khatib Z., Heppner F., Koch A., Jouvet A., Keohane C., Mühleisen H., Mueller W., Pohl U., Prinz M., Benner A., Zapatka M., Gottardo N.G., Driever P.H., Kramm C.M., Müller H.L., Rutkowski S., von Hoff K., Frühwald M.C., Gnekow A., Fleischhack G., Tippelt S., Calaminus G., Monoranu C.M., Perry A., Jones C., Jacques T.S., Radlwimmer B., Gessi M., Pietsch T., Schramm J., Schackert G., Westphal M., Reifenberger G., Wesseling P., Weller M., Collins V.P., Blümcke I., Bendszus M., Debus J., Huang A., Jabado N., Northcott P.A., Paulus W., Gajjar A., Robinson G., Taylor M.D., Jaunmuktane Z., Ryzhova M., Platten M., Unterberg A., Wick W., Karajannis M.A., Mittelbronn M., Acker T., Hartmann C., Aldape K., Schüller U., Buslei R., Lichter P., Kool M., Herold-Mende C., Ellison D.W., Hasselblatt M., Snuderl M., Brandner S., Korshunov A., von Deimling A, & Pfister S.M. (2018). DNA methylation-based classification of central nervous system tumours. Nature, 555(7697), 469-474.
Publication 2018
Astrocytoma Brain Neoplasm, Malignant Central Nervous System Neoplasms DNA Methylation Genome Glioblastoma Glioblastoma Multiforme Glioma Malignant Neoplasms Methylation Neoplasms Oligodendroglioma
3
Adult Lower-Grade Glioma Molecular Profiling
Cited 38 times
The tumor samples we analyzed were from 293 adults with previously untreated lower-grade gliomas (WHO grades II and III), including 100 astrocytomas, 77 oligoastrocytomas, and 116 oligodendrogliomas. Pediatric lower-grade gliomas were excluded; their molecular pathogenesis is distinct from that of lower-grade gliomas in adults.20 (link),21 (link) Diagnoses were established at the contributing institutions; neuropathologists in our consortium reviewed the diagnoses and ensured the quality of the diagnoses and of the tissue for molecular profiling (see Supplementary Appendix 1, available with the full text of this article at NEJM.org, for sample inclusion criteria). Patient characteristics are described in Table 1, and in Table S1 (Supplementary Appendix 2) and Table S2 in Supplementary Appendix 1. We obtained appropriate consent from relevant institutional review boards, which coordinated the consent process at each tissue-source site; written informed consent was obtained from all participants. The patients’ ages, tumor locations, clinical histories and outcomes, tumor histologic classifications, and tumor grades were typical of adults with a diagnosis of diffuse glioma.1 ,2 (link)
Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. (2015). The New England journal of medicine, 372(26), 2481-2498.
Publication 2015
Adult Astrocytoma Diagnosis Ethics Committees, Research Glioma Mixed Oligodendroglioma-Astrocytoma Neoplasms Neoplasms by Site Neuropathologist Oligodendroglioma pathogenesis Patients Tissues
4
Molecular Characterization of Glioma Tumors
Cited 34 times
To better understand the genetic pathogenesis of gliomas and begin to identify potential glioma-specific molecular therapeutic targets, consistent molecular characterization of a large number of tumors is required.
This process was undertaken under a national prospective clinical trial that would eventually be IRB-approved both within the NCI intramural program as well as through both CTEP-sponsored adult brain tumor consortia (NABTT and NABTC protocol # 01-07). With the activation of this study, we collected matched tumor, blood and plasma from the 14 contributing institutions (National Institutes of Health, Henry Ford Hospital, Thomas Jefferson University, University of California San Francisco, H. Lee Moffitt Hospital, University of Wisconsin, University of Pittsburgh Medical Center, University of California Los Angeles, M.D. Anderson Cancer Center, Dana Farber Cancer Center, Duke University, Johns Hopkins University, Massachusetts General Hospital and Memorial Sloan Kettering Cancer Center). All tissue collected is sent to the Neuro-Oncology Branch laboratory for processing. The samples were provided as snap frozen sections of areas immediately adjacent to the region used for the histopathological diagnosis. Initial histopathological diagnosis is performed at the tissue collecting institution following the World Health Organization (WHO) standards(6 (link)). The initial diagnosis is reviewed by in-house neuropathologists to assure a measure of consistency across samples. To date, 874 complete frozen sample sets have been accrued, of those 389 are Glioblastoma Multiforme, 122 are Astrocytomas, 113 are Oligodendrogliomas, 33 are Mixed with the reminder still unclassified.
Clinical data on the patients is collected prospectively until the patient’s death through the NABTC Operations Office at M.D. Anderson Cancer Center, Houston, Texas and the NABTT Operations office at the Johns Hopkins University, Baltimore, MD. The clinical data collected is updated into the Rembrandt database on a quarterly basis.
In order to assure consistency in the collection, shipment, processing, assaying, storage, data retrieval and dissemination, we have put together a series of standard operating procedures (SOPs) that have resulted in a streamlined, high-throughput operation capable of handling large numbers of samples in a consistent, operator-independent fashion. Consistency of data over time is continuously monitored by looking for any signs of batch effect in the analyses.
Madhavan S., Zenklusen J.C., Kotliarov Y., Sahni H., Fine H.A, & Buetow K. (2009). Rembrandt: Helping Personalized Medicine Become a Reality Through Integrative Translational Research. Molecular cancer research : MCR, 7(2), 157-167.
Publication 2009
Adult Astrocytoma BLOOD Brain Neoplasms Diagnosis Freezing Frozen Sections Glioblastoma Multiforme Glioma Malignant Neoplasms Neoplasms Neuropathologist Oligodendroglioma pathogenesis Patients Plasma Surgery, Office Therapeutics Tissues
5
Molecular Profiling of Brain Tumors
Cited 24 times
DNA was extracted from samples of primary brain tumor and xenografts and from patient-matched normal blood lymphocytes obtained from the Tissue Bank at the Preston Robert Tisch Brain Tumor Center at Duke University and collaborating centers, as described previously.17 (link) All analyzed brain tumors were subjected to consensus review by two neuropathologists. Table 1 lists the types of brain tumors we analyzed. The samples from glioblastomas included 138 primary tumors and 13 secondary tumors. Of the 138 primary tumors, 15 were from patients under the age of 21 years. Secondary glioblastomas were categorized as WHO grade IV on the basis of histologic criteria but had been categorized as WHO grade II or III at least 1 year earlier. Of the 151 tumors, 63 had been analyzed in our previous genomewide mutation analysis of glioblastomas. None of the lower-grade tumors were included in that analysis.16 (link)
In addition to brain tumors, we analyzed 35 lung cancers, 57 gastric cancers, 27 ovarian cancers, 96 breast cancers, 114 colorectal cancers, 95 pancreatic cancers, and 7 prostate cancers, along with 4 samples from patients with chronic myelogenous leukemia, 7 from patients with chronic lymphocytic leukemia, 7 from patients with acute lymphoblastic leukemia, and 45 from patients with acute myelogenous leukemia. All samples were obtained in accordance with the Health Insurance Portability and Accountability Act. Acquisition of tissue specimens was approved by the institutional review board at the Duke University Health System and at each of the participating institutions.
Exon 4 of the IDH1 gene was amplified with the use of a polymerase-chain-reaction (PCR) assay and sequenced in DNA from the tumor and lymphocytes from each patient, as described previously.16 (link) In all gliomas and medulloblastomas without an R132 IDH1 mutation, exon 4 of the IDH2 gene (which contains the IDH2 residue equivalent to R132 of IDH1) was sequenced and analyzed for somatic mutations. In addition, we evaluated all astrocytomas and oligodendrogliomas of WHO grade I to grade III, all secondary glioblastomas, and 96 primary glioblastomas without R132 IDH1 mutations or R172 IDH2 mutations for alterations in the remaining coding exons of IDH1 and IDH2. All coding exons of TP53 and PTEN were also sequenced in the panel of diffuse astrocytomas, oligodendrogliomas, anaplastic oligodendrogliomas, anaplastic astrocytomas, and glioblastomas. EGFR amplification and the CDKN2A-CDKN2B deletion were analyzed with the use of quantitative real-time PCR in the same tumors.18 (link) We evaluated samples of oligodendrogliomas and anaplastic oligodendrogliomas for loss of heterozygosity at 1p and 19q, as described previously.15 (link),19 (link)
Yan H., Parsons D.W., Jin G., McLendon R., Rasheed B.A., Yuan W., Kos I., Batinic-Haberle I., Jones S., Riggins G.J., Friedman H., Friedman A., Reardon D., Herndon J., Kinzler K.W., Velculescu V.E., Vogelstein B, & Bigner D.D. (2009). IDH1 and IDH2 Mutations in Gliomas. The New England journal of medicine, 360(8), 765-773.
Publication 2009
7-chloro-8-hydroxy-1-(3'-iodophenyl)-3-methyl-2,3,4,5-tetrahydro-1H-3-benzazepine Anaplastic Oligodendroglioma Astrocytoma Astrocytoma, Anaplastic Biological Assay BLOOD Brain Neoplasms Brain Tumor, Primary CDKN2A Gene Chronic Lymphocytic Leukemia Colorectal Carcinoma Deletion Mutation Diploid Cell EGFR protein, human Ethics Committees, Research Exons Gastric Cancer Genes Glioblastoma Glioma Grade II Astrocytomas Heterografts IDH2, human Leukemia, Myelocytic, Acute Leukemias, Chronic Granulocytic Loss of Heterozygosity Lung Cancer Lymphocyte Malignant Neoplasm of Breast Medulloblastoma Mutation Neoplasms Neuropathologist Oligodendroglioma Ovarian Cancer Pancreatic Cancer Patients Polymerase Chain Reaction Precursor Cell Lymphoblastic Leukemia Lymphoma Prostate Cancer PTEN protein, human Real-Time Polymerase Chain Reaction Tissues TP53 protein, human

Most recents protocols related to «Oligodendroglioma»

1
Awake Surgery for Dominant and Non-Dominant Glioma
As an observational retrospective study, we reviewed a cohort of 80 patients who underwent awake surgery with intraoperative direct electrical mapping for dominant and nondominant hemispheres. All patients were treated at Department of Neurosurgery, Tangdu Hospital, Airforce Medical University, from January 2013 to December 2021. The inclusion criteria were (1) age ≥ 18 years, (2) newly diagnosed glioma, including astrocytoma, oligodendroglioma, anaplastic oligodendroglioma, anaplastic astrocytoma, anaplastic oligoastrocytoma, and glioblastoma, based on the WHO 2007 classification. The WHO 2016 classification was applied in 2017-2019 (31 cases), and the WHO 2021 classification of glioma was applied in 2021 (18 cases). The exclusion criteria included biopsy and incomplete MRI data calculating the tumor volume.
Demographic, clinical, and histological data were collected and analyzed from patients and neurocognitive and functional outcomes. The Institutional Review Board at Tangdu Hospital approved the study (TDLL-202210-18).
Wang Y., Guo S., Wang N., Liu J., Chen F., Zhai Y., Wang Y., Jiao Y., Zhao W., Fan C., Xue Y., Gao G., Ji P, & Wang L. (2023). The clinical and neurocognitive functional changes with awake brain mapping for gliomas invading eloquent areas: Institutional experience and the utility of The Montreal Cognitive Assessment. Frontiers in Oncology, 13, 1086118.
Publication 2023
Anaplasia Anaplastic Oligodendroglioma Astrocytoma Astrocytoma, Anaplastic Biopsy Electricity Ethics Committees, Research Glioblastoma Multiforme Glioma Mixed Oligodendroglioma-Astrocytoma Neurosurgical Procedures Oligodendroglioma Operative Surgical Procedures Patients
2
Prognostic Analysis of Glioma Subtypes
To determine the correlation between single gene expression and clinical characteristics and prognosis of glioma patients, we conducted univariate and multivariate independent prognostic analysis on gene expression profile, clinical profile, and survival profile in CGGA and GSE43378 by Cox regression method using survival R package. The clinical profile of glioma patients in CGGA and GSE43378 are shown in Table 1. We further screened out clinical characteristics relating to glioma prognosis and determined the prognostic value of the single gene, p<0.05 was considered statistically significant.

Clinical Characteristics of Patients with Glioma in CGGA and GSE43378

ParametersCGGA (N=749)GSE43378 (N=50)
Age
<=41, n (%)342(45.7)12(24.0)
>41, n (%)407(54.3)38(76.0)
Gender
Female, n (%)307(41.0)16(32.0)
Male, n (%)442(59.0)34(68.0)
Radio
No, n (%)124(16.6)–
Yes, n (%)625(83.4)–
Chemo
No, n (%)229(30.6)–
Yes, n (%)520(69.4)–
Histology
Astrocytoma (A), n (%)75(10.0)5(10.0)
Anaplastic astrocytoma (AA), n (%)75(10.0)7(14.0)
Anaplastic oligodendroglioma (AO), n (%)37(04.9)4(08.0)
Anaplastic oligoastrocytoma (AOA), n (%)128(17.1)2(04.0)
Oligodendroglioma (O), n (%)39(05.2)–
Oligoastrocytoma (OA), n (%)104(13.9)–
Glioblastoma (GBM), n (%)291(38.9)32(64.0)
PRS_type
Primary, n (%)502(67.0)–
Recurrent, n (%)222(29.7)–
Secondary, n (%)25(03.3)–
Grade
WHO II, n (%)218(29.1)5(10.0)
WHO III, n (%)240(32.0)13(26.0)
WHO IV, n (%)291(38.9)32(64.0)
IDH_mutation
Wildtype, n (%)339(45.3)–
Mutant, n (%)410(54.7)–
1p19q_codeletion
Non-codel, n (%)594(79.3)–
Codel, n (%)155(20.7)–
Survival state
Live, n (%)293(39.1)8(16.0)
Dead, n (%)456(60.9)42(84.0)
Zhang J.J., Zhang Y., Chen Q., Chen Q.N., Yang X., Zhu X.L., Hao C.Y, & Duan H.B. (2023). A Novel Prognostic Marker and Therapeutic Target Associated with Glioma Progression in a Tumor Immune Microenvironment. Journal of Inflammation Research, 16, 895-916.
Publication 2023
Anaplasia Anaplastic Oligodendroglioma Astrocytoma, Anaplastic Gene Expression Gene Expression Profiling Genes Glioblastoma Multiforme Glioma Males Mixed Oligodendroglioma-Astrocytoma Oligodendroglioma Patients Prognosis
3
Pathological Diagnosis of CNS Tumors
Pathological diagnosis of tumors within this study was based on the 2021 WHO classification of CNS tumors to the extent possible; however, many cases had insufficient information for precise classification using this system and some broader categories were also used29 (link). Available information on tumor pathologies were reviewed and samples were grouped into seven general pathological categories for further analysis by a neuropathologist (CGE). These categories included glioblastoma, IDH-wildtype (GBM, IDH-WT); astrocytoma, IDH-mutant (Astro, IDH-mt, Grade 2–4); oligodendroglioma (Oligo, IDH-mt, Grade 2–3); pleomorphic xanthoastrocytoma (PXA, Grade 2–3); and pilocytic astrocytoma (PA). A subset of tumors did not fall into one of those diagnostic categories and were grouped based on their grade: other high-grade glioma (HGG; Grade 3–4) and other low-grade glioma (LGG; Grade 1–2). Overall survival (OS) was obtained from public databases or calculated from electronic medical records as time from radiographic diagnosis to date of death. Records from 13 adult patients treated with BRAF-targeted therapy were further reviewed to determine treatment type(s), duration, response, and time to progression(s).
We ran an optimal cut-point analysis using maximally selected rank statistics to identify any age inflection points that significantly corresponded with survival in our overall cohort and found inflection points at 34 and 51 years (Supplementary Fig. 3). Consequently, for the purposes of this study, we defined age interval groups as <18, 18–34, 35–50, and >50 years of age.
Schreck K.C., Langat P., Bhave V.M., Li T., Woodward E., Pratilas C.A., Eberhart C.G, & Bi W.L. (2023). Integrated molecular and clinical analysis of BRAF-mutant glioma in adults. NPJ Precision Oncology, 7, 23.
Publication 2023
Adult Age Groups Astrocytoma BRAF protein, human Central Nervous System Neoplasms Diagnosis Disease Progression Glioblastoma Glioma Grade II Astrocytomas Malignant Glioma Neoplasms Neuropathologist Oligodendroglioma Oligonucleotides Pilocytic Astrocytoma X-Rays, Diagnostic
4
Integrated Analysis of Glioma Drivers
Overlapping top-ranking candidates from the networks were explored in the context of GBM, gliomas, and their roles in different cancers based on the literature. For the significant candidates, mutation data were obtained from the cBioPortal [34 (link)] database (https://www.cbioportal.org/, accessed on 7 September 2022) (Supplementary File S3 Table S8). Disease involvement information was obtained from the HPA (Supplementary File S3 Table S8). Survival analysis results were extracted from the tool glioVis [21 (link)] (http://gliovis.bioinfo.cnio.es/, accessed on 14 November 2022), using the GBM and lower-grade glioma (GBMLGG) and glioblastoma (GBM) datasets from The Cancer Genome Atlas (TCGA) [35 (link)]. The evidence table in Supplementary File S3 (Table S8) lists both outcomes, while survival curves in Figure 2B and Supplementary File S5 Figure S2 are based on the combined GBMLGG dataset. The GBM dataset compares normal samples with GBM, while the combined dataset contains GBM and other lower-grade gliomas (LGG)—oligodendroglioma, oligoastrocytoma, and astrocytoma, while normal samples are absent. Differential expression information was obtained from two sources: TCGA GBM dataset via glioVis (Supplementary File S3 Table S8), and the TCGA data processed and published by Rahman et al. [20 (link)]. The latter dataset was split into three groups: normal, GBM, and other gliomas. Samples labelled as IDH1 wild-type were considered GBM (n = 233), and all other types of gliomas were combined under ‘Other gliomas’ (n = 445). Normal samples (n = 5) were used as labelled, and these data were used to obtain the expression plots (Supplementary File S5 Figure S1). Significance was tested using the pairwise t-test, with Benjamini–Hochberg correction for multiple testing. Differential expression in the GBMLGG dataset mentioned in Supplementary File S3 Table S10 was as obtained from glioVis. Enrichment of candidates based on their known disease associations, contributions to specific biological processes, and involvement in different canonical pathways was obtained from Ingenuity Pathway Analysis (IPA, Qiagen GmbH) (Supplementary File S4 Tables S11 and S12).
Badkas A., De Landtsheer S, & Sauter T. (2023). Expanding the Disease Network of Glioblastoma Multiforme via Topological Analysis. International Journal of Molecular Sciences, 24(4), 3075.
Publication 2023
Astrocytoma Biological Processes Genome Glioblastoma Glioma Malignant Neoplasms Mixed Oligodendroglioma-Astrocytoma Mutation Oligodendroglioma
5
Comparative Analysis of Gene Expression
The GSE68848, GSE169158, and GSE4290 gene expression datasets were collected from the NCBI-GEO [48 (link)]. These expression datasets were selected because of the clear classification between the diseased and the control genes for the respective diseases. GSE68848 has a total of 580 samples, which were categorized as mixed, GBM (diseased), oligodendroglioma, astrocytoma, unknown (uncategorized), and non-tumor (control). Only the diseased and control samples were considered in our study. The SARS-CoV-2 viral infection versus the mock infection dataset was obtained from GSE169158, and the DEGs were identified with the Galaxy server [49 (link)]. The GSE4290 study collected 23 samples from normal patients as controls and 81 samples from patients with diseased glioblastomas. The analysis of the common DEGs between the diseased and the control samples was conducted with the GEO2R package (LIMMA, Linear Models for Microarray Data). Moreover, the screening of the DEGs was performed with a 0.05 p-value as a threshold.
Alzahrani F.A., Khan M.F, & Ahmad V. (2023). Recognition of Differentially Expressed Molecular Signatures and Pathways Associated with COVID-19 Poor Prognosis in Glioblastoma Patients. International Journal of Molecular Sciences, 24(4), 3562.
Publication 2023
Astrocytoma COVID 19 Gene Expression Gene Expression Regulation Glioblastoma Hereditary Diseases Infection Microarray Analysis Neoplasms Oligodendroglioma Patients

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More about "Oligodendroglioma"

Oligodendroglioma is a type of brain tumor that originates from the glial cells responsible for producing the myelin sheath, which insulates nerve fibers.
These tumors are typically slow-growing and occur most often in the cerebral hemispheres of adults.
Symptoms associated with oligodendroglioma may include headaches, seizures, and cognitive impairments.
Accurate diagnosis and optimal treatment planning are crucial for managing this condition.
PubCompare.ai can enhance oligodendroglioma research by leveraging artificial intelligence to streamline protocol comparisons, identify best practices from literature, preprints, and patents, and improve reproducibility and accuracy in this field.
Related terms and subtopics include glioma, astrocytoma, glioblastoma, brain cancer, central nervous system tumors, myelin sheath, glial cells, cerebral hemispheres, RIPA buffer, FBS, DMEM (Dulbecco's Modified Eagle's Medium), DMEM high glucose medium, B27 supplement, GOS-3, Penicillin/streptomycin, and human primary brain vascular fibroblasts.
Utilizing the Human Genome U133 Plus 2.0 Array can also provide valuable insights for oligodendroglioma research.
By leveraging the power of artificial intelligence and accessing the latest research from literature, preprints, and patents, PubCompare.ai can help researchers optimize their oligodendroglioma research workflows, leading to improved outcomes and breakthroughs in this important field of study.