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Jayakrishnan T, Mariam A, Farha N, Rotroff DM, Aucejo F, Barot SV, Conces M, Nair KG, Krishnamurthi SS, Schmit SL, Liska D, Khorana AA, Kamath SD. Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer. Sci Rep 2024; 14:4294. [PMID: 38383634 PMCID: PMC10881959 DOI: 10.1038/s41598-024-54560-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/14/2024] [Indexed: 02/23/2024] Open
Abstract
Deleterious effects of environmental exposures may contribute to the rising incidence of early-onset colorectal cancer (eoCRC). We assessed the metabolomic differences between patients with eoCRC, average-onset CRC (aoCRC), and non-CRC controls, to understand pathogenic mechanisms. Patients with stage I-IV CRC and non-CRC controls were categorized based on age ≤ 50 years (eoCRC or young non-CRC controls) or ≥ 60 years (aoCRC or older non-CRC controls). Differential metabolite abundance and metabolic pathway analyses were performed on plasma samples. Multivariate Cox proportional hazards modeling was used for survival analyses. All P values were adjusted for multiple testing (false discovery rate, FDR P < 0.15 considered significant). The study population comprised 170 patients with CRC (66 eoCRC and 104 aoCRC) and 49 non-CRC controls (34 young and 15 older). Citrate was differentially abundant in aoCRC vs. eoCRC in adjusted analysis (Odds Ratio = 21.8, FDR P = 0.04). Metabolic pathways altered in patients with aoCRC versus eoCRC included arginine biosynthesis, FDR P = 0.02; glyoxylate and dicarboxylate metabolism, FDR P = 0.005; citrate cycle, FDR P = 0.04; alanine, aspartate, and glutamate metabolism, FDR P = 0.01; glycine, serine, and threonine metabolism, FDR P = 0.14; and amino-acid t-RNA biosynthesis, FDR P = 0.01. 4-hydroxyhippuric acid was significantly associated with overall survival in all patients with CRC (Hazards ratio, HR = 0.4, 95% CI 0.3-0.7, FDR P = 0.05). We identified several unique metabolic alterations, particularly the significant differential abundance of citrate in aoCRC versus eoCRC. Arginine biosynthesis was the most enriched by the differentially altered metabolites. The findings hold promise in developing strategies for early detection and novel therapies.
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Affiliation(s)
- Thejus Jayakrishnan
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
| | - Arshiya Mariam
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, USA
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, USA
| | - Nicole Farha
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
| | - Daniel M Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, USA
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, USA
| | - Federico Aucejo
- Department of Surgery, Digestive Disease & Surgery Institute, Cleveland Clinic, Cleveland, USA
| | - Shimoli V Barot
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
| | - Madison Conces
- Case Comprehensive Cancer Center, Cleveland, USA
- Department of Hematology-Oncology, University Hospital Seidman Cancer Center, Cleveland, USA
| | - Kanika G Nair
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
| | - Smitha S Krishnamurthi
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
| | - Stephanie L Schmit
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, USA
| | - David Liska
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
- Department of Colorectal Surgery, Digestive Disease & Surgery Institute, Cleveland Clinic, Cleveland, USA
| | - Alok A Khorana
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
| | - Suneel D Kamath
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA.
- Case Comprehensive Cancer Center, Cleveland, USA.
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA.
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA.
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Wang DH, Fujita Y, Dono A, Rodriguez Armendariz AG, Shah M, Putluri N, Pichardo-Rojas PS, Patel CB, Zhu JJ, Huse JT, Parker Kerrigan BC, Lang FF, Esquenazi Y, Ballester LY. The genomic alterations in glioblastoma influence the levels of CSF metabolites. Acta Neuropathol Commun 2024; 12:13. [PMID: 38243318 PMCID: PMC10799404 DOI: 10.1186/s40478-024-01722-1] [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: 10/30/2023] [Accepted: 12/31/2023] [Indexed: 01/21/2024] Open
Abstract
Cerebrospinal fluid (CSF) analysis is underutilized in patients with glioblastoma (GBM), partly due to a lack of studies demonstrating the clinical utility of CSF biomarkers. While some studies show the utility of CSF cell-free DNA analysis, studies analyzing CSF metabolites in patients with glioblastoma are limited. Diffuse gliomas have altered cellular metabolism. For example, mutations in isocitrate dehydrogenase enzymes (e.g., IDH1 and IDH2) are common in diffuse gliomas and lead to increased levels of D-2-hydroxyglutarate in CSF. However, there is a poor understanding of changes CSF metabolites in GBM patients. In this study, we performed targeted metabolomic analysis of CSF from n = 31 patients with GBM and n = 13 individuals with non-neoplastic conditions (controls), by mass spectrometry. Hierarchical clustering and sparse partial least square-discriminant analysis (sPLS-DA) revealed differences in CSF metabolites between GBM and control CSF, including metabolites associated with fatty acid oxidation and the gut microbiome (i.e., carnitine, 2-methylbutyrylcarnitine, shikimate, aminobutanal, uridine, N-acetylputrescine, and farnesyl diphosphate). In addition, we identified differences in CSF metabolites in GBM patients based on the presence/absence of TP53 or PTEN mutations, consistent with the idea that different mutations have different effects on tumor metabolism. In summary, our results increase the understanding of CSF metabolites in patients with diffuse gliomas and highlight several metabolites that could be informative biomarkers in patients with GBM.
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Affiliation(s)
- Daniel H Wang
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W. Holcombe Blvd., Suite 910, Houston, TX, 77030, USA
| | - Yoko Fujita
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 6400 Fannin St., Suite 2800, Houston, TX, 77030, USA
| | - Antonio Dono
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 6400 Fannin St., Suite 2800, Houston, TX, 77030, USA
| | - Ana G Rodriguez Armendariz
- Escuela de Medicina y Ciencias de la Salud, Tecnológico de Monterrey, Av. Ignacio Morones Prieto 3000, Sertoma, Monterrey, N.L, 64710, Mexico
| | - Mauli Shah
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W. Holcombe Blvd., Suite 910, Houston, TX, 77030, USA
| | - Nagireddy Putluri
- Advanced Technology Core, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA
| | - Pavel S Pichardo-Rojas
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 6400 Fannin St., Suite 2800, Houston, TX, 77030, USA
| | - Chirag B Patel
- Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1002, BSRB S5.8116b, Houston, TX, 77030, USA
| | - Jay-Jiguang Zhu
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 6400 Fannin St., Suite 2800, Houston, TX, 77030, USA
- Memorial Hermann Hospital-Texas Medical Center, Houston, TX, 77030, USA
| | - Jason T Huse
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W. Holcombe Blvd., Suite 910, Houston, TX, 77030, USA
- Department of Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Brittany C Parker Kerrigan
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, 1400 Holcombe Blvd., Room FC7.2000, Unit 442, Houston, TX, 77030, USA
| | - Frederick F Lang
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, 1400 Holcombe Blvd., Room FC7.2000, Unit 442, Houston, TX, 77030, USA
| | - Yoshua Esquenazi
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, 6400 Fannin St., Suite 2800, Houston, TX, 77030, USA
- Memorial Hermann Hospital-Texas Medical Center, Houston, TX, 77030, USA
- Center for Precision Health, McGovern Medical School, The University of Texas Health Science Center at Houston, 7000 Fannin St., Suite 600, Houston, TX, 77030, USA
| | - Leomar Y Ballester
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W. Holcombe Blvd., Suite 910, Houston, TX, 77030, USA.
- Department of Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
- Neuropathology and Molecular Genetic Pathology, Department of Pathology, Department of Translational Molecular Pathology, 1515 Holcombe Blvd, Unit 85, Houston, TX, 77030, USA.
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Kuang A, Kouznetsova VL, Kesari S, Tsigelny IF. Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics. Metabolites 2023; 14:11. [PMID: 38248814 PMCID: PMC10818630 DOI: 10.3390/metabo14010011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/23/2024] Open
Abstract
The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications' accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the model's accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis.
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Affiliation(s)
- Alyssa Kuang
- Haas Business School, University of California at Berkeley, Berkeley, CA 94720, USA;
| | - Valentina L. Kouznetsova
- San Diego Supercomputer Center, University of California at San Diego, La Jolla, CA 92093, USA;
- BiAna, La Jolla, CA 92038, USA
- CureScience Institute, San Diego, CA 92121, USA
| | - Santosh Kesari
- Pacific Neuroscience Institute, Santa Monica, CA 90404, USA;
| | - Igor F. Tsigelny
- San Diego Supercomputer Center, University of California at San Diego, La Jolla, CA 92093, USA;
- BiAna, La Jolla, CA 92038, USA
- CureScience Institute, San Diego, CA 92121, USA
- Department of Neurosciences, University of California at San Diego, La Jolla, CA 92093, USA
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Aboud O, Liu Y, Dahabiyeh L, Abuaisheh A, Li F, Aboubechara JP, Riess J, Bloch O, Hodeify R, Tagkopoulos I, Fiehn O. Profile Characterization of Biogenic Amines in Glioblastoma Patients Undergoing Standard-of-Care Treatment. Biomedicines 2023; 11:2261. [PMID: 37626757 PMCID: PMC10452138 DOI: 10.3390/biomedicines11082261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/29/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
INTRODUCTION Biogenic amines play important roles throughout cellular metabolism. This study explores a role of biogenic amines in glioblastoma pathogenesis. Here, we characterize the plasma levels of biogenic amines in glioblastoma patients undergoing standard-of-care treatment. METHODS We examined 138 plasma samples from 36 patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma at multiple stages of treatment. Untargeted gas chromatography-time of flight mass spectrometry (GC-TOF MS) was used to measure metabolite levels. Machine learning approaches were then used to develop a predictive tool based on these datasets. RESULTS Surgery was associated with increased levels of 12 metabolites and decreased levels of 11 metabolites. Chemoradiation was associated with increased levels of three metabolites and decreased levels of three other metabolites. Ensemble learning models, specifically random forest (RF) and AdaBoost (AB), accurately classified treatment phases with high accuracy (RF: 0.81 ± 0.04, AB: 0.78 ± 0.05). The metabolites sorbitol and N-methylisoleucine were identified as important predictive features and confirmed via SHAP. CONCLUSION To our knowledge, this is the first study to describe plasma biogenic amine signatures throughout the treatment of patients with glioblastoma. A larger study is needed to confirm these results with hopes of developing a diagnostic algorithm.
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Affiliation(s)
- Orwa Aboud
- Department of Neurology, University of California, Davis, Sacramento, CA 95817, USA
- Department of Neurological Surgery, University of California, Davis, Sacramento, CA 95817, USA
- Comprehensive Cancer Center, University of California Davis, Sacramento, CA 95817, USA
| | - Yin Liu
- Department of Neurology, University of California, Davis, Sacramento, CA 95817, USA
- Department of Neurological Surgery, University of California, Davis, Sacramento, CA 95817, USA
- Department of Ophthalmology, University of California, Davis, Sacramento, CA 95817, USA
| | - Lina Dahabiyeh
- West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Jordan, Amman 11942, Jordan
| | - Ahmad Abuaisheh
- School of Medicine, Al Balqa Applied University, Al-Salt 19117, Jordan
| | - Fangzhou Li
- Department of Computer Science, University of California, Davis, Sacramento, CA 95616, USA
- Genome Center, University of California, Davis, Sacramento, CA 95616, USA
- USDA/NSF AI Institute for Next Generation Food Systems (AIFS), Davis, CA 95616, USA
| | | | - Jonathan Riess
- Comprehensive Cancer Center, University of California Davis, Sacramento, CA 95817, USA
- Department of Internal Medicine, Division of Hematology and Oncology, University of California, Davis, Sacramento, CA 95817, USA
| | - Orin Bloch
- Department of Neurological Surgery, University of California, Davis, Sacramento, CA 95817, USA
| | - Rawad Hodeify
- Department of Biotechnology, School of Arts and Sciences, American University of Ras Al Khaimah, Ras Al-Khaimah 10021, United Arab Emirates
| | - Ilias Tagkopoulos
- Department of Computer Science, University of California, Davis, Sacramento, CA 95616, USA
- Genome Center, University of California, Davis, Sacramento, CA 95616, USA
- USDA/NSF AI Institute for Next Generation Food Systems (AIFS), Davis, CA 95616, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA
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