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Xu C, Hou P, Li X, Xiao M, Zhang Z, Li Z, Xu J, Liu G, Tan Y, Fang C. Comprehensive understanding of glioblastoma molecular phenotypes: classification, characteristics, and transition. Cancer Biol Med 2024; 21:j.issn.2095-3941.2023.0510. [PMID: 38712813 PMCID: PMC11131044 DOI: 10.20892/j.issn.2095-3941.2023.0510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/28/2024] [Indexed: 05/08/2024] Open
Abstract
Among central nervous system-associated malignancies, glioblastoma (GBM) is the most common and has the highest mortality rate. The high heterogeneity of GBM cell types and the complex tumor microenvironment frequently lead to tumor recurrence and sudden relapse in patients treated with temozolomide. In precision medicine, research on GBM treatment is increasingly focusing on molecular subtyping to precisely characterize the cellular and molecular heterogeneity, as well as the refractory nature of GBM toward therapy. Deep understanding of the different molecular expression patterns of GBM subtypes is critical. Researchers have recently proposed tetra fractional or tripartite methods for detecting GBM molecular subtypes. The various molecular subtypes of GBM show significant differences in gene expression patterns and biological behaviors. These subtypes also exhibit high plasticity in their regulatory pathways, oncogene expression, tumor microenvironment alterations, and differential responses to standard therapy. Herein, we summarize the current molecular typing scheme of GBM and the major molecular/genetic characteristics of each subtype. Furthermore, we review the mesenchymal transition mechanisms of GBM under various regulators.
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Affiliation(s)
- Can Xu
- School of Clinical Medicine, Hebei University, Department of Neurosurgery, Affiliated Hospital of Hebei University, Baoding 07100, China
- Hebei Key Laboratory of Precise Diagnosis and Treatment of Glioma, Baoding 071000, China
| | - Pengyu Hou
- Hebei Key Laboratory of Precise Diagnosis and Treatment of Glioma, Baoding 071000, China
- School of Basic Medical Sciences, Hebei University, Baoding 07100, China
| | - Xiang Li
- School of Basic Medical Sciences, Hebei University, Baoding 07100, China
| | - Menglin Xiao
- School of Clinical Medicine, Hebei University, Department of Neurosurgery, Affiliated Hospital of Hebei University, Baoding 07100, China
- Hebei Key Laboratory of Precise Diagnosis and Treatment of Glioma, Baoding 071000, China
| | - Ziqi Zhang
- School of Clinical Medicine, Hebei University, Department of Neurosurgery, Affiliated Hospital of Hebei University, Baoding 07100, China
- Hebei Key Laboratory of Precise Diagnosis and Treatment of Glioma, Baoding 071000, China
| | - Ziru Li
- Hebei Key Laboratory of Precise Diagnosis and Treatment of Glioma, Baoding 071000, China
- School of Basic Medical Sciences, Hebei University, Baoding 07100, China
| | - Jianglong Xu
- School of Clinical Medicine, Hebei University, Department of Neurosurgery, Affiliated Hospital of Hebei University, Baoding 07100, China
- Hebei Key Laboratory of Precise Diagnosis and Treatment of Glioma, Baoding 071000, China
| | - Guoming Liu
- School of Clinical Medicine, Hebei University, Department of Neurosurgery, Affiliated Hospital of Hebei University, Baoding 07100, China
- Hebei Key Laboratory of Precise Diagnosis and Treatment of Glioma, Baoding 071000, China
| | - Yanli Tan
- Hebei Key Laboratory of Precise Diagnosis and Treatment of Glioma, Baoding 071000, China
- School of Basic Medical Sciences, Hebei University, Baoding 07100, China
- Department of Pathology, Affiliated Hospital of Hebei University, Baoding 07100, China
| | - Chuan Fang
- School of Clinical Medicine, Hebei University, Department of Neurosurgery, Affiliated Hospital of Hebei University, Baoding 07100, China
- Hebei Key Laboratory of Precise Diagnosis and Treatment of Glioma, Baoding 071000, China
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Larionova TD, Bastola S, Aksinina TE, Anufrieva KS, Wang J, Shender VO, Andreev DE, Kovalenko TF, Arapidi GP, Shnaider PV, Kazakova AN, Latyshev YA, Tatarskiy VV, Shtil AA, Moreau P, Giraud F, Li C, Wang Y, Rubtsova MP, Dontsova OA, Condro M, Ellingson BM, Shakhparonov MI, Kornblum HI, Nakano I, Pavlyukov MS. Alternative RNA splicing modulates ribosomal composition and determines the spatial phenotype of glioblastoma cells. Nat Cell Biol 2022; 24:1541-1557. [PMID: 36192632 PMCID: PMC10026424 DOI: 10.1038/s41556-022-00994-w] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 08/15/2022] [Indexed: 02/08/2023]
Abstract
Glioblastoma (GBM) is characterized by exceptionally high intratumoral heterogeneity. However, the molecular mechanisms underlying the origin of different GBM cell populations remain unclear. Here, we found that the compositions of ribosomes of GBM cells in the tumour core and edge differ due to alternative RNA splicing. The acidic pH in the core switches before messenger RNA splicing of the ribosomal gene RPL22L1 towards the RPL22L1b isoform. This allows cells to survive acidosis, increases stemness and correlates with worse patient outcome. Mechanistically, RPL22L1b promotes RNA splicing by interacting with lncMALAT1 in the nucleus and inducing its degradation. Contrarily, in the tumour edge region, RPL22L1a interacts with ribosomes in the cytoplasm and upregulates the translation of multiple messenger RNAs including TP53. We found that the RPL22L1 isoform switch is regulated by SRSF4 and identified a compound that inhibits this process and decreases tumour growth. These findings demonstrate how distinct GBM cell populations arise during tumour growth. Targeting this mechanism may decrease GBM heterogeneity and facilitate therapy.
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Affiliation(s)
- Tatyana D Larionova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation
| | - Soniya Bastola
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Tatiana E Aksinina
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation
| | - Ksenia S Anufrieva
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russian Federation
- Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical and Biological Agency, Moscow, Russian Federation
| | - Jia Wang
- Department of Neurosurgery, Centre of Brain Science, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Victoria O Shender
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russian Federation
- Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical and Biological Agency, Moscow, Russian Federation
| | - Dmitriy E Andreev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russian Federation
| | - Tatiana F Kovalenko
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation
| | - Georgij P Arapidi
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russian Federation
- Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical and Biological Agency, Moscow, Russian Federation
| | - Polina V Shnaider
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russian Federation
- Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical and Biological Agency, Moscow, Russian Federation
| | - Anastasia N Kazakova
- Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical and Biological Agency, Moscow, Russian Federation
| | - Yaroslav A Latyshev
- N.N. Burdenko National Medical Research Center of Neurosurgery, Ministry of Health of the Russian Federation, Moscow, Russian Federation
| | - Victor V Tatarskiy
- Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russian Federation
| | - Alexander A Shtil
- Blokhin National Medical Research Center of Oncology, Moscow, Russian Federation
| | - Pascale Moreau
- Institute of Chemistry of Clermont-Ferrand, CNRS, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Francis Giraud
- Institute of Chemistry of Clermont-Ferrand, CNRS, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Chaoxi Li
- Department of Neurosurgery, School of Medicine and O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Yichan Wang
- Department of Neurosurgery, Centre of Brain Science, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Maria P Rubtsova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russian Federation
| | - Olga A Dontsova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russian Federation
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Michael Condro
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry, University of California Los Angeles, Los Angeles, CA, USA
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA
| | | | - Harley I Kornblum
- Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Ichiro Nakano
- Department of Neurosurgery, Medical Institute of Hokuto, Hokkaido, Japan.
| | - Marat S Pavlyukov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russian Federation.
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.
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Integrative Metabolomics Reveals Deep Tissue and Systemic Metabolic Remodeling in Glioblastoma. Cancers (Basel) 2021; 13:cancers13205157. [PMID: 34680306 PMCID: PMC8534284 DOI: 10.3390/cancers13205157] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/01/2021] [Accepted: 10/03/2021] [Indexed: 11/17/2022] Open
Abstract
(1) Background: Glioblastoma is the most common malignant brain tumor in adults. Its etiology remains unknown in most cases. Glioblastoma pathogenesis consists of a progressive infiltration of the white matter by tumoral cells leading to progressive neurological deficit, epilepsy, and/or intracranial hypertension. The mean survival is between 15 to 17 months. Given this aggressive prognosis, there is an urgent need for a better understanding of the underlying mechanisms of glioblastoma to unveil new diagnostic strategies and therapeutic targets through a deeper understanding of its biology. (2) Methods: To systematically address this issue, we performed targeted and untargeted metabolomics-based investigations on both tissue and plasma samples from patients with glioblastoma. (3) Results: This study revealed 176 differentially expressed lipids and metabolites, 148 in plasma and 28 in tissue samples. Main biochemical classes include phospholipids, acylcarnitines, sphingomyelins, and triacylglycerols. Functional analyses revealed deep metabolic remodeling in glioblastoma lipids and energy substrates, which unveils the major role of lipids in tumor progression by modulating its own environment. (4) Conclusions: Overall, our study demonstrates in situ and systemic metabolic rewiring in glioblastoma that could shed light on its underlying biological plasticity and progression to inform diagnosis and/or therapeutic strategies.
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Chen J, Lee H, Schmitt P, Choy CJ, Miller DM, Williams BJ, Bearer EL, Frieboes HB. Bioengineered Models to Study Microenvironmental Regulation of Glioblastoma Metabolism. J Neuropathol Exp Neurol 2021; 80:1012–1023. [PMID: 34524448 DOI: 10.1093/jnen/nlab092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Despite extensive research and aggressive therapies, glioblastoma (GBM) remains a central nervous system malignancy with poor prognosis. The varied histopathology of GBM suggests a landscape of differing microenvironments and clonal expansions, which may influence metabolism, driving tumor progression. Indeed, GBM metabolic plasticity in response to differing nutrient supply within these microenvironments has emerged as a key driver of aggressiveness. Additionally, emergent biophysical and biochemical interactions in the tumor microenvironment (TME) are offering new perspectives on GBM metabolism. Perivascular and hypoxic niches exert crucial roles in tumor maintenance and progression, facilitating metabolic relationships between stromal and tumor cells. Alterations in extracellular matrix and its biophysical characteristics, such as rigidity and topography, regulate GBM metabolism through mechanotransductive mechanisms. This review highlights insights gained from deployment of bioengineering models, including engineered cell culture and mathematical models, to study the microenvironmental regulation of GBM metabolism. Bioengineered approaches building upon histopathology measurements may uncover potential therapeutic strategies that target both TME-dependent mechanotransductive and biomolecular drivers of metabolism to tackle this challenging disease. Longer term, a concerted effort integrating in vitro and in silico models predictive of patient therapy response may offer a powerful advance toward tailoring of treatment to patient-specific GBM characteristics.
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Affiliation(s)
- Joseph Chen
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Hyunchul Lee
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Philipp Schmitt
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Caleb J Choy
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Donald M Miller
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Brian J Williams
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Elaine L Bearer
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Hermann B Frieboes
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
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Kosvyra A, Ntzioni E, Chouvarda I. Network analysis with biological data of cancer patients: A scoping review. J Biomed Inform 2021; 120:103873. [PMID: 34298154 DOI: 10.1016/j.jbi.2021.103873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 06/30/2021] [Accepted: 07/18/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND & OBJECTIVE Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases. METHODS & MATERIALS The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected. RESULTS & Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.
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Affiliation(s)
- A Kosvyra
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - E Ntzioni
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - I Chouvarda
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Mohan AA, Tomaszewski WH, Haskell-Mendoza AP, Hotchkiss KM, Singh K, Reedy JL, Fecci PE, Sampson JH, Khasraw M. Targeting Immunometabolism in Glioblastoma. Front Oncol 2021; 11:696402. [PMID: 34222022 PMCID: PMC8242259 DOI: 10.3389/fonc.2021.696402] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 05/26/2021] [Indexed: 12/11/2022] Open
Abstract
We have only recently begun to understand how cancer metabolism affects antitumor responses and immunotherapy outcomes. Certain immunometabolic targets have been actively pursued in other tumor types, however, glioblastoma research has been slow to exploit the therapeutic vulnerabilities of immunometabolism. In this review, we highlight the pathways that are most relevant to glioblastoma and focus on how these immunometabolic pathways influence tumor growth and immune suppression. We discuss hypoxia, glycolysis, tryptophan metabolism, arginine metabolism, 2-Hydroxyglutarate (2HG) metabolism, adenosine metabolism, and altered phospholipid metabolism, in order to provide an analysis and overview of the field of glioblastoma immunometabolism.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Mustafa Khasraw
- Preston Robert Tisch Brain Tumor Center at Duke, Department of Neurosurgery, Duke University Medical Center, Durham, NC, United States
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Abstract
The 2016 World Health Organization brain tumor classification is based on genomic and molecular profile of tumor tissue. These characteristics have improved understanding of the brain tumor and played an important role in treatment planning and prognostication. There is an ongoing effort to develop noninvasive imaging techniques that provide insight into tissue characteristics at the cellular and molecular levels. This article focuses on the molecular characteristics of gliomas, transcriptomic subtypes, and radiogenomic studies using semantic and radiomic features. The limitations and future directions of radiogenomics as a standalone diagnostic tool also are discussed.
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Affiliation(s)
- Chaitra Badve
- Department of Radiology, Division of Neuroradiology, University Hospitals Cleveland Medical Center, BSH 5056, 11100 Euclid Avenue, Cleveland, OH 44106, USA.
| | - Sangam Kanekar
- Department of Radiology and Neurology, Division of Neuroradiology, Penn State College of Medicine, Penn State Milton Hershey Medical Center, Mail Code H066 500, University Drive, Hershey, PA 17033, USA
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Integrated Metabolomics and Transcriptomics Using an Optimised Dual Extraction Process to Study Human Brain Cancer Cells and Tissues. Metabolites 2021; 11:metabo11040240. [PMID: 33919944 PMCID: PMC8070957 DOI: 10.3390/metabo11040240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/12/2021] [Accepted: 04/12/2021] [Indexed: 11/18/2022] Open
Abstract
The integration of untargeted metabolomics and transcriptomics from the same population of cells or tissue enhances the confidence in the identified metabolic pathways and understanding of the enzyme–metabolite relationship. Here, we optimised a simultaneous extraction method of metabolites/lipids and RNA from ependymoma cells (BXD-1425). Relative to established RNA (mirVana kit) or metabolite (sequential solvent addition and shaking) single extraction methods, four dual-extraction techniques were evaluated and compared (methanol:water:chloroform ratios): cryomill/mirVana (1:1:2); cryomill-wash/Econospin (5:1:2); rotation/phenol-chloroform (9:10:1); Sequential/mirVana (1:1:3). All methods extracted the same metabolites, yet rotation/phenol-chloroform did not extract lipids. Cryomill/mirVana and sequential/mirVana recovered the highest amounts of RNA, at 70 and 68% of that recovered with mirVana kit alone. sequential/mirVana, involving RNA extraction from the interphase of our established sequential solvent addition and shaking metabolomics-lipidomics extraction method, was the most efficient approach overall. Sequential/mirVana was applied to study a) the biological effect caused by acute serum starvation in BXD-1425 cells and b) primary ependymoma tumour tissue. We found (a) 64 differentially abundant metabolites and 28 differentially expressed metabolic genes, discovering four gene-metabolite interactions, and (b) all metabolites and 62% lipids were above the limit of detection, and RNA yield was sufficient for transcriptomics, in just 10 mg of tissue.
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9
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Kim Y, Varn FS, Park SH, Yoon BW, Park HR, Lee C, Verhaak RGW, Paek SH. Perspective of mesenchymal transformation in glioblastoma. Acta Neuropathol Commun 2021; 9:50. [PMID: 33762019 PMCID: PMC7992784 DOI: 10.1186/s40478-021-01151-4] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/06/2021] [Indexed: 12/20/2022] Open
Abstract
Despite aggressive multimodal treatment, glioblastoma (GBM), a grade IV primary brain tumor, still portends a poor prognosis with a median overall survival of 12–16 months. The complexity of GBM treatment mainly lies in the inter- and intra-tumoral heterogeneity, which largely contributes to the treatment-refractory and recurrent nature of GBM. By paving the road towards the development of personalized medicine for GBM patients, the cancer genome atlas classification scheme of GBM into distinct transcriptional subtypes has been considered an invaluable approach to overcoming this heterogeneity. Among the identified transcriptional subtypes, the mesenchymal subtype has been found associated with more aggressive, invasive, angiogenic, hypoxic, necrotic, inflammatory, and multitherapy-resistant features than other transcriptional subtypes. Accordingly, mesenchymal GBM patients were found to exhibit worse prognosis than other subtypes when patients with high transcriptional heterogeneity were excluded. Furthermore, identification of the master mesenchymal regulators and their downstream signaling pathways has not only increased our understanding of the complex regulatory transcriptional networks of mesenchymal GBM, but also has generated a list of potent inhibitors for clinical trials. Importantly, the mesenchymal transition of GBM has been found to be tightly associated with treatment-induced phenotypic changes in recurrence. Together, these findings indicate that elucidating the governing and plastic transcriptomic natures of mesenchymal GBM is critical in order to develop novel and selective therapeutic strategies that can improve both patient care and clinical outcomes. Thus, the focus of our review will be on the recent advances in the understanding of the transcriptome of mesenchymal GBM and discuss microenvironmental, metabolic, and treatment-related factors as critical components through which the mesenchymal signature may be acquired. We also take into consideration the transcriptomic plasticity of GBM to discuss the future perspectives in employing selective therapeutic strategies against mesenchymal GBM.
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Masalha W, Daka K, Woerner J, Pompe N, Weber S, Delev D, Krüger MT, Schnell O, Beck J, Heiland DH, Grauvogel J. Metabolic alterations in meningioma reflect the clinical course. BMC Cancer 2021; 21:211. [PMID: 33648471 PMCID: PMC7923818 DOI: 10.1186/s12885-021-07887-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 02/08/2021] [Indexed: 11/10/2022] Open
Abstract
Background Meningiomas are common brain tumours that are usually defined by benign clinical course. However, some meningiomas undergo a malignant transformation and recur within a short time period regardless of their World Health Organization (WHO) grade. The current study aimed to identify potential markers that can discriminate between benign and malignant meningioma courses. Methods We profiled the metabolites from 43 patients with low- and high-grade meningiomas. Tumour specimens were analyzed by nuclear magnetic resonance analysis; 270 metabolites were identified and clustered with the AutoPipe algorithm. Results We observed two distinct clusters marked by alterations in glycine/serine and choline/tryptophan metabolism. Glycine/serine cluster showed significantly lower WHO grades and proliferation rates. Also progression-free survival was significantly longer in the glycine/serine cluster. Conclusion Our findings suggest that alterations in glycine/serine metabolism are associated with lower proliferation and more recurrent tumours. Altered choline/tryptophan metabolism was associated with increases proliferation, and recurrence. Our results suggest that tumour malignancy can be reflected by metabolic alterations, which may support histological classifications to predict the clinical outcome of patients with meningiomas. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-07887-5.
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Affiliation(s)
- Waseem Masalha
- Department of Neurosurgery, University Medical Center Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany. .,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.
| | - Karam Daka
- Department of Neurosurgery, University Medical Center Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Jakob Woerner
- Institute of Physical Chemistry, Faculty of Chemistry and Pharmacy, University of Freiburg, Freiburg im Breisgau, Germany
| | - Nils Pompe
- Institute of Physical Chemistry, Faculty of Chemistry and Pharmacy, University of Freiburg, Freiburg im Breisgau, Germany
| | - Stefan Weber
- Institute of Physical Chemistry, Faculty of Chemistry and Pharmacy, University of Freiburg, Freiburg im Breisgau, Germany
| | - Daniel Delev
- Department of Neurosurgery, RWTH University, Aachen, Germany
| | - Marie T Krüger
- Department of Neurosurgery, Cantonal Hospital St.Gallen, st. gallen, Switzerland
| | - Oliver Schnell
- Department of Neurosurgery, University Medical Center Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Jürgen Beck
- Department of Neurosurgery, University Medical Center Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Dieter Henrik Heiland
- Department of Neurosurgery, University Medical Center Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Juergen Grauvogel
- Department of Neurosurgery, University Medical Center Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
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11
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Eicher T, Kinnebrew G, Patt A, Spencer K, Ying K, Ma Q, Machiraju R, Mathé EA. Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources. Metabolites 2020; 10:E202. [PMID: 32429287 PMCID: PMC7281435 DOI: 10.3390/metabo10050202] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/07/2020] [Accepted: 05/13/2020] [Indexed: 02/06/2023] Open
Abstract
As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.
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Affiliation(s)
- Tara Eicher
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
| | - Garrett Kinnebrew
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Bioinformatics Shared Resource Group, The Ohio State University, Columbus, OH 43210, USA
| | - Andrew Patt
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
| | - Kyle Spencer
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
- Nationwide Children’s Research Hospital, Columbus, OH 43210, USA
| | - Kevin Ying
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Molecular, Cellular and Developmental Biology Program, The Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
| | - Raghu Machiraju
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
| | - Ewy A. Mathé
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
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12
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Sun H, Zhou Y, Skaro MF, Wu Y, Qu Z, Mao F, Zhao S, Xu Y. Metabolic Reprogramming in Cancer Is Induced to Increase Proton Production. Cancer Res 2020; 80:1143-1155. [PMID: 31932456 DOI: 10.1158/0008-5472.can-19-3392] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 12/05/2019] [Accepted: 01/09/2020] [Indexed: 11/16/2022]
Abstract
Considerable metabolic reprogramming has been observed in a conserved manner across multiple cancer types, but their true causes remain elusive. We present an analysis of around 50 such reprogrammed metabolisms (RM) including the Warburg effect, nucleotide de novo synthesis, and sialic acid biosynthesis in cancer. Analyses of the biochemical reactions conducted by these RMs, coupled with gene expression data of their catalyzing enzymes, in 7,011 tissues of 14 cancer types, revealed that all RMs produce more H+ than their original metabolisms. These data strongly support a model that these RMs are induced or selected to neutralize a persistent intracellular alkaline stress due to chronic inflammation and local iron overload. To sustain these RMs for survival, cells must find metabolic exits for the nonproton products of these RMs in a continuous manner, some of which pose major challenges, such as nucleotides and sialic acids, because they are electrically charged. This analysis strongly suggests that continuous cell division and other cancerous behaviors are ways for the affected cells to remove such products in a timely and sustained manner. As supporting evidence, this model can offer simple and natural explanations to a range of long-standing open questions in cancer research including the cause of the Warburg effect. SIGNIFICANCE: Inhibiting acidifying metabolic reprogramming could be a novel strategy for treating cancer.
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Affiliation(s)
- Huiyan Sun
- Cancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun, China
- School of Artificial Intelligence, Jilin University, Changchun, China
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, Georgia
| | - Yi Zhou
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, Georgia
| | - Michael Francis Skaro
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, Georgia
| | - Yiran Wu
- iHuman Institute, Shanghai Tech University, Shanghai, China
| | - Zexing Qu
- Cancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun, China
- College of Chemistry, Jilin University, Changchun, China
| | - Fenglou Mao
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, Georgia
| | - Suwen Zhao
- iHuman Institute, Shanghai Tech University, Shanghai, China
| | - Ying Xu
- Cancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun, China.
- School of Artificial Intelligence, Jilin University, Changchun, China
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, Georgia
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13
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Current and Future Trends on Diagnosis and Prognosis of Glioblastoma: From Molecular Biology to Proteomics. Cells 2019; 8:cells8080863. [PMID: 31405017 PMCID: PMC6721640 DOI: 10.3390/cells8080863] [Citation(s) in RCA: 143] [Impact Index Per Article: 28.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/02/2019] [Accepted: 08/06/2019] [Indexed: 02/07/2023] Open
Abstract
Glioblastoma multiforme is the most aggressive malignant tumor of the central nervous system. Due to the absence of effective pharmacological and surgical treatments, the identification of early diagnostic and prognostic biomarkers is of key importance to improve the survival rate of patients and to develop new personalized treatments. On these bases, the aim of this review article is to summarize the current knowledge regarding the application of molecular biology and proteomics techniques for the identification of novel biomarkers through the analysis of different biological samples obtained from glioblastoma patients, including DNA, microRNAs, proteins, small molecules, circulating tumor cells, extracellular vesicles, etc. Both benefits and pitfalls of molecular biology and proteomics analyses are discussed, including the different mass spectrometry-based analytical techniques, highlighting how these investigation strategies are powerful tools to study the biology of glioblastoma, as well as to develop advanced methods for the management of this pathology.
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14
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Sun Q, Zhao W, Wang L, Guo F, Song D, Zhang Q, Zhang D, Fan Y, Wang J. Integration of metabolomic and transcriptomic profiles to identify biomarkers in serum of lung cancer. J Cell Biochem 2019; 120:11981-11989. [PMID: 30805978 DOI: 10.1002/jcb.28482] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 11/02/2018] [Accepted: 01/02/2019] [Indexed: 01/24/2023]
Abstract
We used blood serum samples collected from 31 lung cancer (LC) patients and 29 healthy volunteers in this study. Levels of serum metabolites were qualitative quantified with gas chromatography-mass spectrometry (GC-MS), and the data were analyzed by partial least-squares discrimination analysis (PLS-DA). Based on the Kyoto Encyclopedia of Genes and Genomes database, we performed pathway-based analysis utilizing metabolites presented at differential abundance between the LC serum samples and the normal healthy serum samples for systematical investigation on the metabolic alterations associated with LC pathogenesis. Finally, we analyzed the significantly enriched pathways as well as their relevant differentially expressed messenger RNAs, and drawn a correlation network plot to identify the serum metabolic biomarkers and the significantly altered metabolic pathways for LC. GC-MS analysis showed that 23 of the 169 metabolites identified were significantly different. PLS-DA model revealed that 13 of these metabolites were with variable importance > 1, and particularly five were with area under curve > 0.9. Pathway-based analysis demonstrated that five of eight enriched metabolic pathways were statistically significant with false discovery rate < 0.05. Lastly, the correlation networks between these pathways and their related genes suggested that 29 genes had correlation degree > 10, which were mainly engaged in the purine metabolism. In conclusion, we identified indole-3-lactate, erythritol, adenosine-5-phosphate, paracetamol and threitol as serum metabolic biomarkers for LC through metabolomics analysis. Besides, we identified the purine metabolism as the significantly altered metabolic pathway in LC with the help of transcriptomics analysis.
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Affiliation(s)
- Quan Sun
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Wei Zhao
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Lei Wang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Fei Guo
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Dongjian Song
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Qian Zhang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Da Zhang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yingzhong Fan
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jiaxiang Wang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
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15
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Jütten K, Mainz V, Gauggel S, Patel HJ, Binkofski F, Wiesmann M, Clusmann H, Na CH. Diffusion Tensor Imaging Reveals Microstructural Heterogeneity of Normal-Appearing White Matter and Related Cognitive Dysfunction in Glioma Patients. Front Oncol 2019; 9:536. [PMID: 31293974 PMCID: PMC6606770 DOI: 10.3389/fonc.2019.00536] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 06/03/2019] [Indexed: 12/12/2022] Open
Abstract
Immunohistochemical data based on isocitrate–dehydrogenase (IDH) mutation status have redefined glioma as a whole-brain disease, while occult tumor cell invasion along white matter fibers is inapparent in conventional magnetic resonance imaging (MRI). The functional and prognostic impact of focal glioma may however relate to the extent of white matter involvement. We used diffusion tensor imaging (DTI) to investigate microstructural characteristics of whole-brain normal-appearing white matter (NAWM) in relation to cognitive functions as potential surrogates for occult white matter involvement in glioma. Twenty patients (12 IDH-mutated) and 20 individually matched controls were preoperatively examined using DTI combined with a standardized neuropsychological examination. Tumor lesions including perifocal edema were masked, and fractional anisotropy (FA) as well as mean, radial, and axial diffusivity (MD, RD, and AD, respectively) of the remaining whole-brain NAWM were determined by using Tract-Based Spatial Statistics and histogram analyses. The relationship between extratumoral white matter integrity and cognitive performance was examined using partial correlation analyses controlling for age, education, and lesion volumes. In patients, mean FA and AD were decreased as compared to controls, which agrees with the notion of microstructural impairment of NAWM in glioma patients. Patients performed worse in all cognitive domains tested, and higher anisotropy and lower MD and RD values of NAWM were associated with better cognitive performance. In additional analyses, IDH-mutated and IDH-wildtype patients were compared. Patients with IDH-mutation showed higher FA, but lower MD, AD, and RD values as compared to IDH-wildtype patients, suggesting a better preserved microstructural integrity of NAWM, which may relate to a less infiltrative nature of IDH-mutated gliomas. Diffusion-based phenotyping and monitoring microstructural integrity of extratumoral whole-brain NAWM may aid in estimating occult white matter involvement and should be considered as a complementary biomarker in glioma.
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Affiliation(s)
- Kerstin Jütten
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany.,Faculty of Medicine, Institute of Medical Psychology and Medical Sociology, RWTH Aachen University, Aachen, Germany
| | - Verena Mainz
- Faculty of Medicine, Institute of Medical Psychology and Medical Sociology, RWTH Aachen University, Aachen, Germany
| | - Siegfried Gauggel
- Faculty of Medicine, Institute of Medical Psychology and Medical Sociology, RWTH Aachen University, Aachen, Germany
| | - Harshal Jayeshkumar Patel
- Division of Clinical Cognitive Sciences, RWTH Aachen University, Aachen, Germany.,Research Center Jülich GmbH, Institute of Neuroscience and Medicine (INM-4), Jülich, Germany
| | - Ferdinand Binkofski
- Division of Clinical Cognitive Sciences, RWTH Aachen University, Aachen, Germany.,Research Center Jülich GmbH, Institute of Neuroscience and Medicine (INM-4), Jülich, Germany.,Jülich Aachen Research Alliance, Translational Brain Medicine, Aachen, Germany
| | - Martin Wiesmann
- Department of Diagnostic and Interventional Neuroradiology, RWTH Aachen University, Aachen, Germany
| | - Hans Clusmann
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany
| | - Chuh-Hyoun Na
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany
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16
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Forest A, Ruiz M, Bouchard B, Boucher G, Gingras O, Daneault C, Frayne IR, Rhainds D, Tardif JC, Rioux JD, Rosiers CD. Comprehensive and Reproducible Untargeted Lipidomic Workflow Using LC-QTOF Validated for Human Plasma Analysis. J Proteome Res 2018; 17:3657-3670. [PMID: 30256116 PMCID: PMC6572761 DOI: 10.1021/acs.jproteome.8b00270] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The goal of this work was to develop a label-free, comprehensive, and reproducible high-resolution liquid chromatography-mass spectrometry (LC-MS)-based untargeted lipidomic workflow using a single instrument, which could be applied to biomarker discovery in both basic and clinical studies. For this, we have (i) optimized lipid extraction and elution to enhance coverage of polar and nonpolar lipids as well as resolution of their isomers, (ii) ensured MS signal reproducibility and linearity, and (iii) developed a bioinformatic pipeline to correct remaining biases. Workflow validation is reported for 48 replicates of a single human plasma sample: 1124 reproducible LC-MS signals were extracted (median signal intensity RSD = 10%), 50% of which are redundant due to adducts, dimers, in-source fragmentation, contaminations, or positive and negative ion duplicates. From the resulting 578 unique compounds, 428 lipids were identified by MS/MS, including acyl chain composition, of which 394 had RSD < 30% inside their linear intensity range, thereby enabling robust semiquantitation. MS signal intensity spanned 4 orders of magnitude, covering 16 lipid subclasses. Finally, the power of our workflow is illustrated by a proof-of-concept study in which 100 samples from healthy human subjects were analyzed and the data set was investigated using three different statistical testing strategies in order to compare their capacity in identifying the impact of sex and age on circulating lipids.
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Affiliation(s)
- Anik Forest
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | - Matthieu Ruiz
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
- Department of Medicine, Université de
Montréal, Montreal, Quebec, Canada
| | - Bertrand Bouchard
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | - Gabrielle Boucher
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | - Olivier Gingras
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | - Caroline Daneault
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | | | - David Rhainds
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | | | | | - Jean-Claude Tardif
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
- Department of Medicine, Université de
Montréal, Montreal, Quebec, Canada
| | - John D. Rioux
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
- Department of Medicine, Université de
Montréal, Montreal, Quebec, Canada
| | - Christine Des Rosiers
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
- Department of Nutrition, Université de
Montréal, Montreal, Quebec, Canada
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17
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Hu Y, Pan J, Xin Y, Mi X, Wang J, Gao Q, Luo H. Gene Expression Analysis Reveals Novel Gene Signatures Between Young and Old Adults in Human Prefrontal Cortex. Front Aging Neurosci 2018; 10:259. [PMID: 30210331 PMCID: PMC6119720 DOI: 10.3389/fnagi.2018.00259] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 08/08/2018] [Indexed: 11/13/2022] Open
Abstract
Human neurons function over an entire lifetime, yet the molecular mechanisms which perform their functions and protecting against neurodegenerative disease during aging are still elusive. Here, we conducted a systematic study on the human brain aging by using the weighted gene correlation network analysis (WGCNA) method to identify meaningful modules or representative biomarkers for human brain aging. Significantly, 19 distinct gene modules were detected based on the dataset GSE53890; among them, six modules related to the feature of brain aging were highly preserved in diverse independent datasets. Interestingly, network feature analysis confirmed that the blue modules demonstrated a remarkably correlation with human brain aging progress. Besides, the top hub genes including PPP3CB, CAMSAP1, ACTR3B, and GNG3 were identified and characterized by high connectivity, module membership, or gene significance in the blue module. Furthermore, these genes were validated in mice of different ages. Mechanically, the potential regulators of blue module were investigated. These findings highlight an important role of the blue module and its affiliated genes in the control of normal brain aging, which may lead to potential therapeutic interventions for brain aging by targeting the hub genes.
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Affiliation(s)
- Yang Hu
- Department of Pharmacology, School of Medicine, Jinan University, Guangzhou, China.,Department of Pathology and Pathophysiology, School of Medicine, Jinan University, Guangzhou, China.,Institute of Brain Sciences, Jinan University, Guangzhou, China
| | - Junping Pan
- Department of Pharmacology, School of Medicine, Jinan University, Guangzhou, China
| | - Yirong Xin
- Department of Pharmacology, School of Medicine, Jinan University, Guangzhou, China
| | - Xiangnan Mi
- Department of Pharmacology, School of Medicine, Jinan University, Guangzhou, China
| | - Jiahui Wang
- Department of Pharmacology, School of Medicine, Jinan University, Guangzhou, China
| | - Qin Gao
- Department of Pharmacology, School of Medicine, Jinan University, Guangzhou, China
| | - Huanmin Luo
- Department of Pharmacology, School of Medicine, Jinan University, Guangzhou, China.,Institute of Brain Sciences, Jinan University, Guangzhou, China
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18
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Diamandis E, Gabriel CPS, Würtemberger U, Guggenberger K, Urbach H, Staszewski O, Lassmann S, Schnell O, Grauvogel J, Mader I, Heiland DH. MR-spectroscopic imaging of glial tumors in the spotlight of the 2016 WHO classification. J Neurooncol 2018; 139:431-440. [PMID: 29704080 DOI: 10.1007/s11060-018-2881-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 03/25/2018] [Indexed: 01/29/2023]
Abstract
BACKGROUND The purpose of this study is to map spatial metabolite differences across three molecular subgroups of glial tumors, defined by the IDH1/2 mutation and 1p19q-co-deletion, using magnetic resonance spectroscopy. This work reports a new MR spectroscopy based classification algorithm by applying a radiomics analytics pipeline. MATERIALS 65 patients received anatomical and chemical shift imaging (5 × 5 × 20 mm voxel size). Tumor regions were segmented and registered to corresponding spectroscopic voxels. Spectroscopic features were computed (n = 860) in a radiomic approach and selected by a classification algorithm. Finally, a random forest machine-learning model was trained to predict the molecular subtypes. RESULTS A cluster analysis identified three robust spectroscopic clusters based on the mean silhouette widths. Molecular subgroups were significantly associated with the computed spectroscopic clusters (Fisher's Exact test p < 0.01). A machine-learning model was trained and validated by public available MRS data (n = 19). The analysis showed an accuracy rate in the Random Forest model by 93.8%. CONCLUSIONS MR spectroscopy is a robust tool for predicting the molecular subtype in gliomas and adds important diagnostic information to the preoperative diagnostic work-up of glial tumor patients. MR-spectroscopy could improve radiological diagnostics in the future and potentially influence clinical and surgical decisions to improve individual tumor treatment.
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Affiliation(s)
- Elie Diamandis
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Carl Phillip Simon Gabriel
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Urs Würtemberger
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Konstanze Guggenberger
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ori Staszewski
- Institute of Neuropathology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Silke Lassmann
- Institute for Pathology, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Oliver Schnell
- Department of Neurosurgery, Medical Center - University of Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Grauvogel
- Department of Neurosurgery, Medical Center - University of Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Irina Mader
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Clinic for Neuropediatrics and Neurorehabilitation, Epilepsy Center for Children and Adolescents, Schön Klinik, Vogtareuth, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dieter Henrik Heiland
- Department of Neurosurgery, Medical Center - University of Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany.
- Faculty of Medicine, University of Freiburg, Freiburg, Germany.
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Heiland DH, Gaebelein A, Börries M, Wörner J, Pompe N, Franco P, Heynckes S, Bartholomae M, hAilín DÓ, Carro MS, Prinz M, Weber S, Mader I, Delev D, Schnell O. Microenvironment-Derived Regulation of HIF Signaling Drives Transcriptional Heterogeneity in Glioblastoma Multiforme. Mol Cancer Res 2018; 16:655-668. [PMID: 29330292 DOI: 10.1158/1541-7786.mcr-17-0680] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 11/29/2017] [Accepted: 12/27/2017] [Indexed: 11/16/2022]
Abstract
The evolving and highly heterogeneous nature of malignant brain tumors underlies their limited response to therapy and poor prognosis. In addition to genetic alterations, highly dynamic processes, such as transcriptional and metabolic reprogramming, play an important role in the development of tumor heterogeneity. The current study reports an adaptive mechanism in which the metabolic environment of malignant glioma drives transcriptional reprogramming. Multiregional analysis of a glioblastoma patient biopsy revealed a metabolic landscape marked by varying stages of hypoxia and creatine enrichment. Creatine treatment and metabolism was further shown to promote a synergistic effect through upregulation of the glycine cleavage system and chemical regulation of prolyl-hydroxylase domain. Consequently, creatine maintained a reduction of reactive oxygen species and change of the α-ketoglutarate/succinate ratio, leading to an inhibition of HIF signaling in primary tumor cell lines. These effects shifted the transcriptional pattern toward a proneural subtype and reduced the rate of cell migration and invasion in vitroImplications: Transcriptional subclasses of glioblastoma multiforme are heterogeneously distributed within the same tumor. This study uncovered a regulatory function of the tumor microenvironment by metabolism-driven transcriptional reprogramming in infiltrating glioma cells. Mol Cancer Res; 16(4); 655-68. ©2018 AACR.
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Affiliation(s)
- Dieter Henrik Heiland
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany. .,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Annette Gaebelein
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Melanie Börries
- Institute of Molecular Medicine and Cell Research, Albert-Ludwigs-University, Freiburg im Breisgau, Germany.,German Cancer Consortium (DKTK), Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Wörner
- Institute of Physical Chemistry, Faculty of Chemistry and Pharmacy, University of Freiburg, Freiburg im Breisgau, Germany
| | - Nils Pompe
- Institute of Physical Chemistry, Faculty of Chemistry and Pharmacy, University of Freiburg, Freiburg im Breisgau, Germany
| | - Pamela Franco
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Sabrina Heynckes
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Mark Bartholomae
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Darren Ó hAilín
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany
| | - Maria Stella Carro
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Marco Prinz
- Institute of Neuropathology, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Stefan Weber
- Institute of Physical Chemistry, Faculty of Chemistry and Pharmacy, University of Freiburg, Freiburg im Breisgau, Germany
| | - Irina Mader
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Clinic for Neuropediatrics and Neurorehabilitation, Epilepsy Center for Children and Adolescents, Schön Klinik, Vogtareuth, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Daniel Delev
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Oliver Schnell
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
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