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Chen X, Sun J, Li Y, Jiang W, Li Z, Mao J, Zhou L, Chen S, Tan G. Proteomic and metabolomic analyses illustrate the mechanisms of expression of the O 6 -methylguanine-DNA methyltransferase gene in glioblastoma. CNS Neurosci Ther 2024; 30:e14415. [PMID: 37641495 PMCID: PMC10848106 DOI: 10.1111/cns.14415] [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: 06/12/2023] [Revised: 07/29/2023] [Accepted: 08/04/2023] [Indexed: 08/31/2023] Open
Abstract
AIM Glioblastoma (GBM) has been reported to be the most common high-grade primary malignant brain tumor in clinical practice and has a poor prognosis. O6 -methylguanine-DNA methyltransferase (MGMT) promoter methylation has been related to prolonged overall survival (OS) in GBM patients after temozolomide treatment. METHODS Proteomics and metabolomics were combined to explore the dysregulated metabolites and possible protein expression alterations in white matter (control group), MGMT promoter unmethylated GBM (GBM group) or MGMT promoter methylation positive GBM (MGMT group). RESULTS In total, 2745 upregulated and 969 downregulated proteins were identified in the GBM group compared to the control group, and 131 upregulated and 299 downregulated proteins were identified in the MGMT group compared to the GBM group. Furthermore, 131 upregulated and 299 downregulated metabolites were identified in the GBM group compared to the control group, and 187 upregulated and 147 downregulated metabolites were identified in the MGMT group compared to the GBM group. The results showed that 94 upregulated and 19 downregulated proteins and 20 upregulated and 16 downregulated metabolites in the MGMT group were associated with DNA repair. KEGG pathway enrichment analysis illustrated that the dysregulated proteins and metabolites were involved in multiple metabolic pathways, including the synthesis and degradation of ketone bodies, amino sugar and nucleotide sugar metabolism. Moreover, integrated metabolomics and proteomics analysis was performed, and six key proteins were identified in the MGMT group and GBM group. Three key pathways were recognized as potential biomarkers for recognizing MGMT promoter unmethylated GBM and MGMT promoter methylation positive GBM from GBM patient samples, with areas under the curve of 0.7895, 0.7326 and 0.7026, respectively. CONCLUSION This study provides novel mechanisms to understand methylation in GBM and identifies some biomarkers for the prognosis of two different GBM types, MGMT promoter unmethylated or methylated GBM, by using metabolomics and proteomics analyses.
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Affiliation(s)
- Xi Chen
- Department of NeurosurgeryThe First Affiliated Hospital of Xiamen UniversityXiamenChina
| | - Jinli Sun
- Department of ReproductionThe First Affiliated Hospital of Xiamen UniversityXiamenChina
| | - Yukui Li
- Department of NeurosurgeryThe First Affiliated Hospital of Xiamen UniversityXiamenChina
| | - Weichao Jiang
- Department of NeurosurgeryThe First Affiliated Hospital of Xiamen UniversityXiamenChina
| | - Zhangyu Li
- Department of NeurosurgeryThe First Affiliated Hospital of Xiamen UniversityXiamenChina
| | - Jianyao Mao
- Department of NeurosurgeryThe First Affiliated Hospital of Xiamen UniversityXiamenChina
| | - Liwei Zhou
- Department of NeurosurgeryThe First Affiliated Hospital of Xiamen UniversityXiamenChina
| | - Sifang Chen
- Department of NeurosurgeryThe First Affiliated Hospital of Xiamen UniversityXiamenChina
| | - Guowei Tan
- Department of NeurosurgeryThe First Affiliated Hospital of Xiamen UniversityXiamenChina
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Miller DM, Yadanapudi K, Rai V, Rai SN, Chen J, Frieboes HB, Masters A, McCallum A, Williams BJ. Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach. Am J Med Sci 2023; 366:185-198. [PMID: 37330006 DOI: 10.1016/j.amjms.2023.06.010] [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: 12/15/2022] [Revised: 05/01/2023] [Accepted: 06/13/2023] [Indexed: 06/19/2023]
Abstract
Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.
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Affiliation(s)
- Donald M Miller
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA.
| | - Kavitha Yadanapudi
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Veeresh Rai
- Brown Cancer Center, University of Louisville, Louisville, KY, USA
| | - Shesh N Rai
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Biostatistics and Informatics Shared Resources, University of Cincinnati Cancer Center, Cincinnati, OH, USA; Cancer Data Science Center of University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Joseph Chen
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA
| | - Hermann B Frieboes
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA; Center for Preventative Medicine, University of Louisville, Louisville, KY, USA
| | - Adrianna Masters
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Radiation Oncology, University of Louisville, Louisville, KY, USA
| | - Abigail McCallum
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Neurosurgery, University of Louisville, Louisville, KY, USA
| | - Brian J Williams
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Neurosurgery, University of Louisville, Louisville, KY, USA
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Liu Y, Xiang J, Liao Y, Peng G, Shen C. Identification of tryptophan metabolic gene-related subtypes, development of prognostic models, and characterization of tumor microenvironment infiltration in gliomas. Front Mol Neurosci 2022; 15:1037835. [PMID: 36407768 PMCID: PMC9673907 DOI: 10.3389/fnmol.2022.1037835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/13/2022] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Epigenetic regulation and immunotherapy of tumor microenvironment (TME) is a hot topic in recent years. However, the potential value of tryptophan metabolism genes in regulating TME and immunotherapy is still unclear. MATERIALS AND METHODS A comprehensive study of glioma patients was carried out based on 40 tryptophan metabolic genes. Subsequently, these prognostic tryptophan metabolic genes are systematically associated with immunological characteristics and immunotherapy. A risk score model was constructed and verified in the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) cohorts to provide guidance for prognosis prediction and immunotherapy of glioma patients. RESULTS We described the changes of tryptophan metabolism genes in 966 glioma samples from genetic and transcriptional fields and evaluated their expression patterns from two independent data sets. We identified two different molecular subtypes and found that two subtypes were associated with clinicopathological features, prognosis, TME cell infiltration, and immune checkpoint blockers (ICBs). Then, four genes (IL4I1, CYP1A1, OGDHL, and ASMT) were screened out by univariate and multivariate cox regression analysis of tryptophan metabolism genes, and a risk score model for predicting the overall survival (OS) of glioma patients was constructed. And its predictive ability is verified using the CGGA database. At the same time, we verified the expression of IL4I1, CYP1A1, OGDHL, and ASMT four genes in glioma specimens and cell lines in GES4260 and GES15824. Therefore, we constructed a nomogram to improve the clinical applicability of the risk assessment model. The high risk score group, characterized by increased TMB and immune cell infiltration, was also sensitive to temozolomide immunotherapy. Our comprehensive analysis of tryptophan metabolic genes in gliomas shows that they play a potential role in tumor immune stromal microenvironment, clinicopathological features, and prognosis. CONCLUSION Tryptophan metabolism genes play an indispensable role in the complexity, diversity, and prognosis of TME. This risk score model based on tryptophan metabolism gene is a new predictor of clinical prognosis and immunotherapy response of glioma, and guides a more appropriate immunotherapy strategy for glioma patients.
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Affiliation(s)
- Yi Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Juan Xiang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yiwei Liao
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Gang Peng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chenfu Shen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
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4
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Xu Y, Zhang H, Sun Q, Geng R, Yuan F, Liu B, Chen Q. Immunomodulatory Effects of Tryptophan Metabolism in the Glioma Tumor Microenvironment. Front Immunol 2021; 12:730289. [PMID: 34659216 PMCID: PMC8517402 DOI: 10.3389/fimmu.2021.730289] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 09/20/2021] [Indexed: 12/12/2022] Open
Abstract
Gliomas are the most common primary malignant tumor in adults’ central nervous system. While current research on glioma treatment is advancing rapidly, there is still no breakthrough in long-term treatment. Abnormalities in the immune regulatory mechanism in the tumor microenvironment are essential to tumor cell survival. The alteration of amino acid metabolism is considered a sign of tumor cells, significantly impacting tumor cells and immune regulation mechanisms in the tumor microenvironment. Despite the fact that the metabolism of tryptophan in tumors is currently discussed in the literature, we herein focused on reviewing the immune regulation of tryptophan metabolism in the tumor microenvironment of gliomas and analyzed possible immune targets. The objective is to identify potential targets for the treatment of glioma and improve the efficiency of immunotherapy.
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Affiliation(s)
- Yang Xu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Huikai Zhang
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qian Sun
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Rongxin Geng
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Fanen Yuan
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Baohui Liu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qianxue Chen
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
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5
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Abstract
Since the inception of their profession, neurosurgeons have defined themselves as physicians with a surgical practice. Throughout time, neurosurgery has always taken advantage of technological advances to provide better and safer care for patients. In the ongoing precision medicine surge that drives patient-centric healthcare, neurosurgery strives to effectively embrace the era of data-driven medicine. Neuro-oncology best illustrates this convergence between surgery and precision medicine with the advent of molecular profiling, imaging and data analytics. This convenient convergence paves the way for new preventive, diagnostic, prognostic and targeted therapeutic perspectives. The prominent advances in healthcare and big data forcefully challenge the medical community to deeply rethink current and future medical practice. This work provides a historical perspective on neurosurgery. It also discusses the impact of the conceptual shift of precision medicine on neurosurgery through the lens of neuro-oncology.
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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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Firdous S, Abid R, Nawaz Z, Bukhari F, Anwer A, Cheng LL, Sadaf S. Dysregulated Alanine as a Potential Predictive Marker of Glioma-An Insight from Untargeted HRMAS-NMR and Machine Learning Data. Metabolites 2021; 11:metabo11080507. [PMID: 34436448 PMCID: PMC8402070 DOI: 10.3390/metabo11080507] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 07/22/2021] [Accepted: 07/28/2021] [Indexed: 01/04/2023] Open
Abstract
Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.
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Affiliation(s)
- Safia Firdous
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore 54590, Pakistan; (S.F.); (R.A.)
- Riphah College of Rehabilitation and Allied Health Sciences, Riphah International University, Lahore 54770, Pakistan
| | - Rizwan Abid
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore 54590, Pakistan; (S.F.); (R.A.)
| | - Zubair Nawaz
- Department of Data Science, Punjab University College of Information Technology, University of the Punjab, Lahore 54590, Pakistan; (Z.N.); (F.B.)
| | - Faisal Bukhari
- Department of Data Science, Punjab University College of Information Technology, University of the Punjab, Lahore 54590, Pakistan; (Z.N.); (F.B.)
| | - Ammar Anwer
- Punjab Institute of Neurosciences (PINS), Lahore General Hospital, Lahore 54000, Pakistan;
| | - Leo L. Cheng
- Departments of Radiology and Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA;
| | - Saima Sadaf
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore 54590, Pakistan; (S.F.); (R.A.)
- Correspondence:
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Adenomyosis is associated with specific proton nuclear magnetic resonance ( 1H-NMR) serum metabolic profiles. Fertil Steril 2021; 116:243-254. [PMID: 33849709 DOI: 10.1016/j.fertnstert.2021.02.031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To determine whether the adenomyosis phenotype affects the proton nuclear magnetic resonance (1H-NMR)-based serum metabolic profile of patients. DESIGN Cohort study. SETTING University hospital-based research center. PATIENTS Seventy-seven patients who underwent laparoscopy for a benign gynecologic condition. INTERVENTIONS Pelvic magnetic resonance imaging and collection of a venous peripheral blood sample were performed during the preoperative workup. The women were allocated to the adenomyosis group (n = 32), or the control group (n = 45). The adenomyosis group was further subdivided into two groups: diffuse adenomyosis of the inner myometrium (n = 14) and focal adenomyosis of the outer myometrium (n = 18). Other adenomyosis phenotypes were excluded. MAIN OUTCOME MEASURES Metabolomic profiling based on 1H-NMR spectroscopy in combination with statistical approaches. RESULTS The serum metabolic profiles of the patients with adenomyosis indicated lower concentrations of 3-hydroxybutyrate, glutamate, and serine compared with controls. Conversely, the concentrations of proline, choline, citrate, 2-hydroxybutyrate, and creatinine were higher in the adenomyosis group. The focal adenomyosis of the outer myometrium and the diffuse adenomyosis phenotypes also each exhibited a specific metabolic profile. CONCLUSION Serum metabolic changes were detected in women with features of adenomyosis compared with their disease-free counterparts, and a number of specific metabolic pathways appear to be engaged according to the adenomyosis phenotype. The metabolites with altered levels are particularly involved in immune activation as well as cell proliferation and cell migration. Nevertheless, this study did find evidence of a correlation between metabolite levels and symptoms thought to be related to adenomyosis. Further studies are required to determine the clinical significance of these differences in metabolic profiles.
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Abstract
Metabolic reprogramming is an important characteristics of glioma, the most common form of malignant brain tumor. In this chapter, we aim to discuss some of the recently discovered metabolic alterations in glioma, including the dysregulated TCA cycle, amino acid, nucleotide, and lipid metabolism. We have also detailed some of the metabolomic applications in gliomas, particularly the analyses of body fluids and tissues of glioma patients. With new improvement of the technology, metabolomics will become a powerful tool to discover truly meaningful biomarkers for clinical applications in gliomas. Metabolomic studies of gliomas will also facilitate a better understanding of the molecular targets/pathways and the development of new therapeutic treatments for this devastating disease.
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Yamashita D, Bernstock JD, Elsayed G, Sadahiro H, Mohyeldin A, Chagoya G, Ilyas A, Mooney J, Estevez-Ordonez D, Yamaguchi S, Flanary VL, Hackney JR, Bhat KP, Kornblum HI, Zamboni N, Kim SH, Chiocca EA, Nakano I. Targeting glioma-initiating cells via the tyrosine metabolic pathway. J Neurosurg 2020; 134:721-732. [PMID: 32059178 DOI: 10.3171/2019.11.jns192028] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 11/19/2019] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Despite an aggressive multimodal therapeutic regimen, glioblastoma (GBM) continues to portend a grave prognosis, which is driven in part by tumor heterogeneity at both the molecular and cellular levels. Accordingly, herein the authors sought to identify metabolic differences between GBM tumor core cells and edge cells and, in so doing, elucidate novel actionable therapeutic targets centered on tumor metabolism. METHODS Comprehensive metabolic analyses were performed on 20 high-grade glioma (HGG) tissues and 30 glioma-initiating cell (GIC) sphere culture models. The results of the metabolic analyses were combined with the Ivy GBM data set. Differences in tumor metabolism between GBM tumor tissue derived from within the contrast-enhancing region (i.e., tumor core) and that from the peritumoral brain lesions (i.e., tumor edge) were sought and explored. Such changes were ultimately confirmed at the protein level via immunohistochemistry. RESULTS Metabolic heterogeneity in both HGG tumor tissues and GBM sphere culture models was identified, and analyses suggested that tyrosine metabolism may serve as a possible therapeutic target in GBM, particularly in the tumor core. Furthermore, activation of the enzyme tyrosine aminotransferase (TAT) within the tyrosine metabolic pathway influenced the noted therapeutic resistance of the GBM core. CONCLUSIONS Selective inhibition of the tyrosine metabolism pathway may prove highly beneficial as an adjuvant to multimodal GBM therapies.
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Affiliation(s)
| | - Joshua D Bernstock
- 2Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | | | - Hirokazu Sadahiro
- Departments of1Neurosurgery and.,3Department of Neurosurgery, Yamaguchi University School of Medicine, Ube, Yamaguchi, Japan
| | - Ahmed Mohyeldin
- 4Department of Neurological Surgery, The Ohio State University, Wexner Medical Center, Columbus, Ohio
| | | | | | | | | | | | | | | | - Krishna P Bhat
- 6Department of Translational Molecular Pathology and Brain Tumor Center, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Harley I Kornblum
- 7Departments of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior.,8Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior.,13Broad Stem Cell Research Center, David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Nicola Zamboni
- 9Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Sung-Hak Kim
- Departments of1Neurosurgery and.,10Department of Animal Science, College of Agriculture and Life Sciences, Chonnam National University, Gwangju; and.,11Gwangju Center, Korea Basic Science Institute, Gwangju, Republic of Korea
| | - E Antonio Chiocca
- 2Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ichiro Nakano
- Departments of1Neurosurgery and.,12O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Alabama
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11
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Liu YQ, Chai RC, Wang YZ, Wang Z, Liu X, Wu F, Jiang T. Amino acid metabolism-related gene expression-based risk signature can better predict overall survival for glioma. Cancer Sci 2018; 110:321-333. [PMID: 30431206 PMCID: PMC6317920 DOI: 10.1111/cas.13878] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 10/18/2018] [Accepted: 11/11/2018] [Indexed: 12/20/2022] Open
Abstract
Metabolic reprogramming has been proposed to be a hallmark of cancer. Aside from the glycolytic pathway, the metabolic changes of cancer cells primarily involve amino acid metabolism. However, in glioma, the characteristics of the amino acid metabolism‐related gene set have not been systematically profiled. In the present study, RNA sequencing expression data from 309 patients in the Chinese Glioma Genome Atlas database were included as a training set, while another 550 patients within The Cancer Genome Atlas database were used to validate. Consensus clustering of the 309 samples yielded two robust groups. Compared with Cluster1, Cluster2 correlated with a better clinical outcome. We then developed an amino acid metabolism‐related risk signature for glioma. Our results showed that patients in the high‐risk group had dramatically shorter overall survival than low‐risk counterparts in any subgroup, stratified by isocitrate dehydrogenase and 1p/19q status based on the 2016 World Health Organization classification guidelines. The 30‐gene signature showed better prognostic value than the traditional factors “age” and “grade” by analyzing the receiver operating characteristic curve with areas under curve of 0.966, 0.692, 0.898 and 0.975, 0.677, 0.885 for 3‐ and 5‐year survival, respectively. Moreover, univariate and multivariate analysis showed that the 30‐gene signature was an independent prognostic factor for glioma. Furthermore, Gene Ontology analysis and Gene Set Enrichment Analysis showed that tumors with a high risk score correlated with various aspects of the malignancy of glioma. In summary, we demonstrated a novel amino acid metabolism‐related risk signature for predicting prognosis for glioma.
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Affiliation(s)
- Yu-Qing Liu
- Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Beijing, China.,Chinese Glioma Genome Atlas Network (CGGA), Beijing, China
| | - Rui-Chao Chai
- Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Beijing, China.,Chinese Glioma Genome Atlas Network (CGGA), Beijing, China
| | - Yong-Zhi Wang
- Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Beijing, China.,Chinese Glioma Genome Atlas Network (CGGA), Beijing, China.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zheng Wang
- Chinese Glioma Genome Atlas Network (CGGA), Beijing, China.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Liu
- Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Beijing, China.,Chinese Glioma Genome Atlas Network (CGGA), Beijing, China
| | - Fan Wu
- Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Beijing, China.,Chinese Glioma Genome Atlas Network (CGGA), Beijing, China
| | - Tao Jiang
- Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Beijing, China.,Chinese Glioma Genome Atlas Network (CGGA), Beijing, China.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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12
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Taherkhani A, Kalantari S, Oskouie AA, Nafar M, Taghizadeh M, Tabar K. Network analysis of membranous glomerulonephritis based on metabolomics data. Mol Med Rep 2018; 18:4197-4212. [PMID: 30221719 PMCID: PMC6172390 DOI: 10.3892/mmr.2018.9477] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 06/29/2018] [Indexed: 12/14/2022] Open
Abstract
Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component‑reaction‑enzyme‑gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography‑mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold‑change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein‑protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold‑changes: Dopamine, carnosine, fumarate, nicotinamide D‑ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L‑citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine‑4‑hydroxlylase and cystathionine γ‑lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: 'Pyrimidine‑containing compound biosynthetic process', 'purine ribonucleoside metabolic process', 'nucleoside catabolic process', 'ribonucleoside metabolic process' and 'aromatic amino acid family metabolic process'. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.
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Affiliation(s)
- Amir Taherkhani
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran 1971653313, Iran
| | - Shiva Kalantari
- Chronic Kidney Disease Research Center, Shahid Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Tehran 1666663111, Iran
| | - Afsaneh Arefi Oskouie
- Department of Basic Science, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran 1971653313, Iran
| | - Mohsen Nafar
- Urology Nephrology Research Center, Shahid Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Tehran 1666663111, Iran
| | - Mohammad Taghizadeh
- Bioinformatics Department, Institute of Biochemistry and Biophysics, Tehran University, Tehran 1417614411, Iran
| | - Koorosh Tabar
- Chemistry and Chemical Engineering Research Center of Iran, Tehran 1496813151, Iran
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13
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Abstract
Imaging provides an insight into biological patho-mechanisms of diseases. However, the link between the imaging phenotype and the underlying molecular processes is often not well understood. Methods such as metabolomics and proteomics reveal detailed information about these processes. Unfortunately, they provide no spatial information and thus cannot be easily correlated with functional imaging. We have developed an image-guided milling machine and unique workflows to precisely isolate tissue samples based on imaging data. The tissue samples remain cooled during the entire procedure, preventing sample degradation. This enables us to correlate, at an unprecedented spatial precision, comprehensive imaging information with metabolomics and proteomics data, leading to a better understanding of diseases. Phenotypic heterogeneity is commonly observed in diseased tissue, specifically in tumors. Multimodal imaging technologies can reveal tissue heterogeneity noninvasively in vivo, enabling imaging-based profiling of receptors, metabolism, morphology, or function on a macroscopic scale. In contrast, in vitro multiomics, immunohistochemistry, or histology techniques accurately characterize these heterogeneities in the cellular and subcellular scales in a more comprehensive but ex vivo manner. The complementary in vivo and ex vivo information would provide an enormous potential to better characterize a disease. However, this requires spatially accurate coregistration of these data by image-driven sampling as well as fast sample-preparation methods. Here, a unique image-guided milling machine and workflow for precise extraction of tissue samples from small laboratory animals or excised organs has been developed and evaluated. The samples can be delineated on tomographic images as volumes of interest and can be extracted with a spatial accuracy better than 0.25 mm. The samples remain cooled throughout the procedure to ensure metabolic stability, a precondition for accurate in vitro analysis.
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14
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Zong L, Pi Z, Liu S, Liu Z, Song F. Metabolomics analysis of multidrug-resistant breast cancer cellsin vitrousing methyl-tert-butyl ether method. RSC Adv 2018; 8:15831-15841. [PMID: 35539507 PMCID: PMC9080077 DOI: 10.1039/c7ra12952a] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 04/21/2018] [Indexed: 11/21/2022] Open
Abstract
MTBE-based cellular lipidomics to investigate the mechanisms of multidrug resistance of breast cancer.
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Affiliation(s)
- Li Zong
- National Center of Mass Spectrometry in Changchun
- Jilin Province Key Laboratory of Chinese Medicine Chemistry and Mass Spectrometry
- Changchun Institute of Applied Chemistry
- Chinese Academy of Sciences
- Changchun 130022
| | - Zifeng Pi
- National Center of Mass Spectrometry in Changchun
- Jilin Province Key Laboratory of Chinese Medicine Chemistry and Mass Spectrometry
- Changchun Institute of Applied Chemistry
- Chinese Academy of Sciences
- Changchun 130022
| | - Shu Liu
- National Center of Mass Spectrometry in Changchun
- Jilin Province Key Laboratory of Chinese Medicine Chemistry and Mass Spectrometry
- Changchun Institute of Applied Chemistry
- Chinese Academy of Sciences
- Changchun 130022
| | - Zhiqiang Liu
- National Center of Mass Spectrometry in Changchun
- Jilin Province Key Laboratory of Chinese Medicine Chemistry and Mass Spectrometry
- Changchun Institute of Applied Chemistry
- Chinese Academy of Sciences
- Changchun 130022
| | - Fengrui Song
- National Center of Mass Spectrometry in Changchun
- Jilin Province Key Laboratory of Chinese Medicine Chemistry and Mass Spectrometry
- Changchun Institute of Applied Chemistry
- Chinese Academy of Sciences
- Changchun 130022
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15
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Pandey R, Caflisch L, Lodi A, Brenner AJ, Tiziani S. Metabolomic signature of brain cancer. Mol Carcinog 2017; 56:2355-2371. [PMID: 28618012 DOI: 10.1002/mc.22694] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Revised: 06/01/2017] [Accepted: 06/13/2017] [Indexed: 12/17/2022]
Abstract
Despite advances in surgery and adjuvant therapy, brain tumors represent one of the leading causes of cancer-related mortality and morbidity in both adults and children. Gliomas constitute about 60% of all cerebral tumors, showing varying degrees of malignancy. They are difficult to treat due to dismal prognosis and limited therapeutics. Metabolomics is the untargeted and targeted analyses of endogenous and exogenous small molecules, which charact erizes the phenotype of an individual. This emerging "omics" science provides functional readouts of cellular activity that contribute greatly to the understanding of cancer biology including brain tumor biology. Metabolites are highly informative as a direct signature of biochemical activity; therefore, metabolite profiling has become a promising approach for clinical diagnostics and prognostics. The metabolic alterations are well-recognized as one of the key hallmarks in monitoring disease progression, therapy, and revealing new molecular targets for effective therapeutic intervention. Taking advantage of the latest high-throughput analytical technologies, that is, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), metabolomics is now a promising field for precision medicine and drug discovery. In the present report, we review the application of metabolomics and in vivo metabolic profiling in the context of adult gliomas and paediatric brain tumors. Analytical platforms such as high-resolution (HR) NMR, in vivo magnetic resonance spectroscopic imaging and high- and low-resolution MS are discussed. Moreover, the relevance of metabolic studies in the development of new therapeutic strategies for treatment of gliomas are reviewed.
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Affiliation(s)
- Renu Pandey
- Department of Nutritional Sciences, The University of Texas at Austin, Austin, Texas
| | - Laura Caflisch
- Department of Hematology and Medical oncology, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Alessia Lodi
- Department of Nutritional Sciences, The University of Texas at Austin, Austin, Texas
| | - Andrew J Brenner
- Department of Hematology and Medical oncology, University of Texas Health Science Center at San Antonio, San Antonio, Texas.,Department of Cancer Therapy and Research Center, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Stefano Tiziani
- Department of Nutritional Sciences, The University of Texas at Austin, Austin, Texas.,Dell Pediatric Research Institute, The University of Texas at Austin, Austin, Texas
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16
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Fangchinoline suppresses the growth and invasion of human glioblastoma cells by inhibiting the kinase activity of Akt and Akt-mediated signaling cascades. Tumour Biol 2015; 37:2709-19. [PMID: 26408176 DOI: 10.1007/s13277-015-3990-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2015] [Accepted: 08/25/2015] [Indexed: 12/19/2022] Open
Abstract
Glioblastoma multiforme (GBM) is one of the most palindromic and malignant central nervous system neoplasms, and the current treatment is not effectual for GBM. Research of specific medicine for GBM is significant. Fangchinoline possesses a wide range of pharmacological activities and attracts more attentions due to its anti-tumor effects. In this study, two WHO grade IV human GBM cell lines (U87 MG and U118 MG) were exposed to fangchinoline, and we found that fangchinoline specifically inhibits the kinase activity of Akt and markedly suppresses the phosphorylation of Thr308 and Ser473 of Akt in human GBM cells. We also observed that fangchinoline inhibits tumor cell proliferation and invasiveness and induces apoptosis through suppressing the Akt-mediated signaling cascades, including Akt/p21, Akt/Bad, and Akt/matrix metalloproteinases (MMPs). These data demonstrated that fangchinoline exerts its anti-tumor effects in human glioblastoma cells, at least partly by inhibiting the kinase activity of Akt and suppressing Akt-mediated signaling cascades.
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17
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Fillet M, Frédérich M. The emergence of metabolomics as a key discipline in the drug discovery process. DRUG DISCOVERY TODAY. TECHNOLOGIES 2015; 13:19-24. [PMID: 26190679 DOI: 10.1016/j.ddtec.2015.01.006] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Revised: 01/21/2015] [Accepted: 01/27/2015] [Indexed: 12/15/2022]
Abstract
Metabolomics is a recent science that could be defined as the comprehensive qualitative and quantitative analysis of all small molecular weight compounds present in a cell, organ (including biofluids) or organism at a specific time point. More and more applications have been found these past years to metabolomics in the pharmaceutical field. Specifically in the drug discovery process, metabolomics open new perspectives, in new targets identification, in toxicological studies and in bioactive natural products discovery. The challenge in metabolomics is to find a technological approach allowing the reproducible identification and quantitation of as much metabolites as possible. In this context, mass spectrometry and NMR are emerging as key and complementary technologies.
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Affiliation(s)
- Marianne Fillet
- Laboratory for the Analysis of Medicines, Center for Interdisciplinary Research on Medicines (CIRM), University of Liège, Liège, Belgium
| | - Michel Frédérich
- Laboratory of Pharmacognosy, Center for Interdisciplinary Research on Medicines (CIRM), University of Liège, Liège, Belgium.
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