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Nejatie A, Yee SS, Jeter A, Saragovi HU. The cancer glycocode as a family of diagnostic biomarkers, exemplified by tumor-associated gangliosides. Front Oncol 2023; 13:1261090. [PMID: 37954075 PMCID: PMC10637394 DOI: 10.3389/fonc.2023.1261090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/13/2023] [Indexed: 11/14/2023] Open
Abstract
One unexploited family of cancer biomarkers comprise glycoproteins, carbohydrates, and glycolipids (the Tumor Glycocode).A class of glycolipid cancer biomarkers, the tumor-marker gangliosides (TMGs) are presented here as potential diagnostics for detecting cancer, especially at early stages, as the biological function of TMGs makes them etiological. We propose that a quantitative matrix of the Cancer Biomarker Glycocode and artificial intelligence-driven algorithms will expand the menu of validated cancer biomarkers as a step to resolve some of the challenges in cancer diagnosis, and yield a combination that can identify a specific cancer, in a tissue-agnostic manner especially at early stages, to enable early intervention. Diagnosis is critical to reducing cancer mortality but many cancers lack efficient and effective diagnostic tests, especially for early stage disease. Ideal diagnostic biomarkers are etiological, samples are preferably obtained via non-invasive methods (e.g. liquid biopsy of blood or urine), and are quantitated using assays that yield high diagnostic sensitivity and specificity for efficient diagnosis, prognosis, or predicting response to therapy. Validated biomarkers with these features are rare. While the advent of proteomics and genomics has led to the identification of a multitude of proteins and nucleic acid sequences as cancer biomarkers, relatively few have been approved for clinical use. The use of multiplex arrays and artificial intelligence-driven algorithms offer the option of combining data of known biomarkers; however, for most, the sensitivity and the specificity are below acceptable criteria, and clinical validation has proven difficult. One strategic solution to this problem is to expand the biomarker families beyond those currently exploited. One unexploited family of cancer biomarkers comprise glycoproteins, carbohydrates, and glycolipids (the Tumor Glycocode). Here, we focus on a family of glycolipid cancer biomarkers, the tumor-marker gangliosides (TMGs). We discuss the diagnostic potential of TMGs for detecting cancer, especially at early stages. We include prior studies from the literature to summarize findings for ganglioside quantification, expression, detection, and biological function and its role in various cancers. We highlight the examples of TMGs exhibiting ideal properties of cancer diagnostic biomarkers, and the application of GD2 and GD3 for diagnosis of early stage cancers with high sensitivity and specificity. We propose that a quantitative matrix of the Cancer Biomarker Glycocode and artificial intelligence-driven algorithms will expand the menu of validated cancer biomarkers as a step to resolve some of the challenges in cancer diagnosis, and yield a combination that can identify a specific cancer, in a tissue-agnostic manner especially at early stages, to enable early intervention.
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Affiliation(s)
- Ali Nejatie
- Center for Translational Research, Lady Davis Research Institute-Jewish General Hospital, Montreal, QC, Canada
- Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Samantha S. Yee
- Department of Obstetrics and Gynecology, University of Chicago, Chicago, IL, United States
| | | | - Horacio Uri Saragovi
- Center for Translational Research, Lady Davis Research Institute-Jewish General Hospital, Montreal, QC, Canada
- Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
- Ophthalmology and Vision Science, McGill University, Montreal, QC, Canada
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2
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Acharjee A, Agarwal P, Gkoutos GV. Editorial: Integrative multi-modal, multi-omics analytics for the better understanding of metabolic diseases. Front Endocrinol (Lausanne) 2023; 14:1266557. [PMID: 37745706 PMCID: PMC10516571 DOI: 10.3389/fendo.2023.1266557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023] Open
Affiliation(s)
- Animesh Acharjee
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
- Institute of Translational Medicine, University Hospitals Birmingham National Health Service (NHS) Foundation Trust, Birmingham, United Kingdom
- Medical Research Council (MRC) Health Data Research United Kingdom (UK) (HDR), Midlands Site, Birmingham, United Kingdom
- Centre for Health Data Research , University of Birmingham, Birmingham, United Kingdom
| | - Prasoon Agarwal
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Division of Occupational and Environmental Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Georgios V. Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
- Institute of Translational Medicine, University Hospitals Birmingham National Health Service (NHS) Foundation Trust, Birmingham, United Kingdom
- Medical Research Council (MRC) Health Data Research United Kingdom (UK) (HDR), Midlands Site, Birmingham, United Kingdom
- Centre for Health Data Research , University of Birmingham, Birmingham, United Kingdom
- Cancer Research, National Institute for Health and Care Research (NIHR) Experimental Cancer Medicine Centre, Birmingham, United Kingdom
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Iacucci M, Jeffery L, Acharjee A, Grisan E, Buda A, Nardone OM, Smith SCL, Labarile N, Zardo D, Ungar B, Hunter S, Mao R, Cannatelli R, Shivaji UN, Parigi TL, Reynolds GM, Gkoutos GV, Ghosh S. Computer-Aided Imaging Analysis of Probe-Based Confocal Laser Endomicroscopy With Molecular Labeling and Gene Expression Identifies Markers of Response to Biological Therapy in IBD Patients: The Endo-Omics Study. Inflamm Bowel Dis 2023; 29:1409-1420. [PMID: 36378498 PMCID: PMC10472745 DOI: 10.1093/ibd/izac233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND We aimed to predict response to biologics in inflammatory bowel disease (IBD) using computerized image analysis of probe confocal laser endomicroscopy (pCLE) in vivo and assess the binding of fluorescent-labeled biologics ex vivo. Additionally, we investigated genes predictive of anti-tumor necrosis factor (TNF) response. METHODS Twenty-nine patients (15 with Crohn's disease [CD], 14 with ulcerative colitis [UC]) underwent colonoscopy with pCLE before and 12 to 14 weeks after starting anti-TNF or anti-integrin α4β7 therapy. Biopsies were taken for fluorescein isothiocyanate-labeled infliximab and vedolizumab staining and gene expression analysis. Computer-aided quantitative image analysis of pCLE was performed. Differentially expressed genes predictive of response were determined and validated in a public cohort. RESULTS In vivo, vessel tortuosity, crypt morphology, and fluorescein leakage predicted response in UC (area under the receiver-operating characteristic curve [AUROC], 0.93; accuracy 85%, positive predictive value [PPV] 89%; negative predictive value [NPV] 75%) and CD (AUROC, 0.79; accuracy 80%; PPV 75%; NPV 83%) patients. Ex vivo, increased binding of labeled biologic at baseline predicted response in UC (UC) (AUROC, 83%; accuracy 77%; PPV 89%; NPV 50%) but not in Crohn's disease (AUROC 58%). A total of 325 differentially expressed genes distinguished responders from nonresponders, 86 of which fell within the most enriched pathways. A panel including ACTN1, CXCL6, LAMA4, EMILIN1, CRIP2, CXCL13, and MAPKAPK2 showed good prediction of anti-TNF response (AUROC >0.7). CONCLUSIONS Higher mucosal binding of the drug target is associated with response to therapy in UC. In vivo, mucosal and microvascular changes detected by pCLE are associated with response to biologics in inflammatory bowel disease. Anti-TNF-responsive UC patients have a less inflamed and fibrotic state pretreatment. Chemotactic pathways involving CXCL6 or CXCL13 may be novel targets for therapy in nonresponders.
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Affiliation(s)
- Marietta Iacucci
- National Institute for Health Research Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Gastroenterology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Louisa Jeffery
- Gastroenterology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health Research Surgical Reconstruction, Queen Elizabeth Hospital Birmingham, Birmingham, UK
| | - Enrico Grisan
- Department of Information Engineering, University of Padova, Padova, Italy
- School of Engineering Computer Science and Informatics, London South Bank University, London, UK
| | - Andrea Buda
- Gastroenterology Unit, Department of Gastrointestinal Oncological Surgery, S. Maria del Prato Hospital, Feltre, Italy
| | - Olga M Nardone
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Samuel C L Smith
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Nunzia Labarile
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Davide Zardo
- Gastroenterology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Bella Ungar
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Stuart Hunter
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Ren Mao
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Rosanna Cannatelli
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Uday N Shivaji
- Gastroenterology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | | | - Gary M Reynolds
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Georgios V Gkoutos
- National Institute for Health Research Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Subrata Ghosh
- National Institute for Health Research Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Gastroenterology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
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Fernández-Carrión R, Sorlí JV, Asensio EM, Pascual EC, Portolés O, Alvarez-Sala A, Francès F, Ramírez-Sabio JB, Pérez-Fidalgo A, Villamil LV, Tinahones FJ, Estruch R, Ordovas JM, Coltell O, Corella D. DNA-Methylation Signatures of Tobacco Smoking in a High Cardiovascular Risk Population: Modulation by the Mediterranean Diet. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3635. [PMID: 36834337 PMCID: PMC9964856 DOI: 10.3390/ijerph20043635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
Biomarkers based on DNA methylation are relevant in the field of environmental health for precision health. Although tobacco smoking is one of the factors with a strong and consistent impact on DNA methylation, there are very few studies analyzing its methylation signature in southern European populations and none examining its modulation by the Mediterranean diet at the epigenome-wide level. We examined blood methylation smoking signatures on the EPIC 850 K array in this population (n = 414 high cardiovascular risk subjects). Epigenome-wide methylation studies (EWASs) were performed analyzing differential methylation CpG sites by smoking status (never, former, and current smokers) and the modulation by adherence to a Mediterranean diet score was explored. Gene-set enrichment analysis was performed for biological and functional interpretation. The predictive value of the top differentially methylated CpGs was analyzed using receiver operative curves. We characterized the DNA methylation signature of smoking in this Mediterranean population by identifying 46 differentially methylated CpGs at the EWAS level in the whole population. The strongest association was observed at the cg21566642 (p = 2.2 × 10-32) in the 2q37.1 region. We also detected other CpGs that have been consistently reported in prior research and discovered some novel differentially methylated CpG sites in subgroup analyses. In addition, we found distinct methylation profiles based on the adherence to the Mediterranean diet. Particularly, we obtained a significant interaction between smoking and diet modulating the cg5575921 methylation in the AHRR gene. In conclusion, we have characterized biomarkers of the methylation signature of tobacco smoking in this population, and suggest that the Mediterranean diet can increase methylation of certain hypomethylated sites.
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Affiliation(s)
- Rebeca Fernández-Carrión
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - José V. Sorlí
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Eva M. Asensio
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Eva C. Pascual
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain
| | - Olga Portolés
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Andrea Alvarez-Sala
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain
| | - Francesc Francès
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | | | - Alejandro Pérez-Fidalgo
- Department of Medical Oncology, University Clinic Hospital of Valencia, 46010 Valencia, Spain
- Biomedical Research Networking Centre on Cancer (CIBERONC), Health Institute Carlos III, 28029 Madrid, Spain
- INCLIVA Biomedical Research Institute, 46010 Valencia, Spain
| | - Laura V. Villamil
- Department of Physiology, School of Medicine, University Antonio Nariño, Bogotá 111511, Colombia
| | - Francisco J. Tinahones
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, Instituto de Investigación Biomédica de Málaga (IBIMA), University of Málaga, 29590 Málaga, Spain
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Department of Internal Medicine, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain
| | - Jose M. Ordovas
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA 02111, USA
- Nutritional Control of the Epigenome Group, Precision Nutrition and Obesity Program, IMDEA Food, UAM + CSIC, 28049 Madrid, Spain
| | - Oscar Coltell
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Department of Computer Languages and Systems, Universitat Jaume I, 12071 Castellón, Spain
| | - Dolores Corella
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain
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Bahcivanci B, Shafiha R, Gkoutos GV, Acharjee A. Associating transcriptomics data with inflammatory markers to understand tumour microenvironment in hepatocellular carcinoma. Cancer Med 2023; 12:696-711. [PMID: 35715992 PMCID: PMC9844659 DOI: 10.1002/cam4.4941] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 04/25/2022] [Accepted: 06/03/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Liver cancer is the fourth leading cause of cancer-related death globally which is estimated to reach more than 1 million deaths a year by 2030. Among liver cancer types, hepatocellular carcinoma (HCC) accounts for approximately 90% of the cases and is known to have a tumour promoting inflammation regardless of its underlying aetiology. However, current promising treatment approaches, such as immunotherapy, are partially effective for most of the patients due to the immunosuppressive nature of the tumour microenvironment (TME). Therefore, there is an urgent need to fully understand TME in HCC and discover new immune markers to eliminate resistance to immunotherapy. METHODS We analyse three microarray datasets, using unsupervised and supervised methods, in an effort to discover signature genes. First, univariate, and multivariate, feature selection methods, such as the Boruta algorithm, are applied. Subsequently, an optimisation procedure, which utilises random forest algorithm with three dataset pairs combinations, is performed. The resulting optimal gene sets are then combined and further subjected to network analysis and pathway enrichment analysis so as to obtain information related to their biological relevance. The microarray datasets were analysed via the MCP-counter, CIBERSORT, TIMER, EPIC, and quanTIseq deconvolution methods and an estimation of cell type abundances for each dataset sample were identified. The differences in the cell type abundances, between the adjacent and tumour sample groups, were then assessed using a Wilcoxon Rank Sum test (p-value < 0.05). RESULTS The optimal gene signature sets, derived from each of the data pairs combination, achieved AUC values ranging from 0.959 to 0.988 in external validation sets using Random Forest model. CLEC1B and PTTG1 genes are retrieved across each optimal set. Among the signature genes, PTTG1, AURKA, and UBE2C genes are found to be involved in the regulation of mitotic sister chromatid separation and anaphase-promoting complex (APC) dependent catabolic process (adjusted p-value < 0.001). Additionally, the application of deconvolution algorithms revealed significant changes in cell type abundances of Regulatory T (Treg) cells, M0 and M1 macrophages, and T CD8+ cells between adjacent and tumour samples. CONCLUSION We identified ECM1 gene as a potential immune-related marker acting through immune cell migration and macrophage polarisation. Our results indicate that macrophages, such as M0 macrophage and M1 macrophage cells, undergo significant changes in HCC TME. Moreover, our immune deconvolution approach revealed significant infiltration of Treg cells and M0 macrophages, and a significant decrease in T CD8+ cells and M1 macrophages in tumour samples.
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Affiliation(s)
- Basak Bahcivanci
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational BiologyUniversity of BirminghamBirminghamUK
| | - Roshan Shafiha
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational BiologyUniversity of BirminghamBirminghamUK
| | - Georgios V. Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational BiologyUniversity of BirminghamBirminghamUK
- Institute of Translational MedicineUniversity Hospitals Birmingham NHS Foundation TrustBirminghamUK
- NIHR Surgical Reconstruction and Microbiology Research CentreUniversity Hospital BirminghamBirminghamUK
- MRC Health Data Research UK (HDR UK)BirminghamUK
- NIHR Experimental Cancer Medicine CentreBirminghamUK
| | - Animesh Acharjee
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational BiologyUniversity of BirminghamBirminghamUK
- Institute of Translational MedicineUniversity Hospitals Birmingham NHS Foundation TrustBirminghamUK
- NIHR Surgical Reconstruction and Microbiology Research CentreUniversity Hospital BirminghamBirminghamUK
- MRC Health Data Research UK (HDR UK)BirminghamUK
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Acharjee A. Taurine as a biomarker for aging: A new avenue for translational research. ADVANCES IN BIOMARKER SCIENCES AND TECHNOLOGY 2023; 5:86-88. [PMID: 38435677 PMCID: PMC10901744 DOI: 10.1016/j.abst.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 10/09/2023] [Accepted: 10/13/2023] [Indexed: 03/05/2024] Open
Abstract
The physiologic and irreversible process of ageing is accompanied by a wide range of structural and functional shifts at multiple different levels. It is also suggested that variations in the blood concentrations of metabolites, hormones, and micronutrients may play a role in the ageing process. Recently, Singh et al. 1,2 investigated a study on Taurine shortage as a driver and biomarker of ageing and its impact on a healthy lifespan.2 They further proposed that functional abnormalities in numerous organs associated with age-related illnesses have been linked to early-life Taurine insufficiency. Taurine deficiency in the elderly and the possible benefits of Taurine supplements One of the reasons for decreasing Taurine concentration is the loss of endogenous synthesis, which may contribute to the decrease in Taurine levels seen in the elderly. While it was previously believed that the liver was responsible for most Taurine synthesis in humans, new research suggests that other organs or common intermediates may play a larger role. The authors experimented with and analysed a life-span examination of various organisms, for example, mice to assess the impacts of Taurine supplementation. They also analysed after the administration of oral Taurine supplementation in conjunction with other interventions using multi-omics data sets (RNA sequencing, metabolomics etc.) across different species.
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Affiliation(s)
- Animesh Acharjee
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, B15 2TT, Birmingham, UK
- Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, B15 2TH, Birmingham, UK
- MRC Health Data Research UK (HDR), Midlands Site, Birmingham, UK
- Centre for Health Data Research, University of Birmingham, B15 2TT, UK
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Zhang C, Quan Y, Bai Y, Yang L, Yang Y. The effect and apoptosis mechanism of 6-methoxyflavone in HeLa cells. Biomarkers 2022; 27:470-482. [PMID: 35400257 DOI: 10.1080/1354750x.2022.2062448] [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: 12/24/2022]
Abstract
INTRODUCTION Tumor cell apoptosis is a crucial indicator for judging the antiproliferative effects of anti-cancer drugs. The detection of optical and macromolecular biomarkers is the most common method for assessing the level of apoptosis. We aimed to explore the anti-tumor mechanisms of 6-methoxyflavone. MATERIAL AND METHODS Three optical methods, including the percentage of apoptotic cells, cell morphology, and subcellular ultrastructure changes, were obtained using flow cytometry, inverted fluorescence microscopy, and transmission electron microscope imaging. The mRNA or protein expression of macromolecular biomarkers related to common apoptotic pathways was determined via polymerase chain reactions or western blot assays. The functional role of the core gene biomarker was investigated through overexpression, knockdown, and phosphorylation inhibitor (GSK2656157). RESULTS Transcriptome sequencing and the optical biomarkers assays demonstrated that 6-methoxyflavone could induce apoptosis in HeLa cells. The expression of macromolecular biomarkers indicated that 6-methoxyflavone induced apoptosis through the PERK/EIF2α/ATF4/CHOP pathway. Phosphorylated PERK was identified as the core biomarker of this pathway. Both overexpression and GSK2656157 significantly altered the expression level of phosphorylated PERK in 6-methoxyflavone-treated HeLa cells. DISCUSSION AND CONCLUSION Macromolecular biomarkers such as phosphorylated PERK and phosphorylated EIF2α are of great significance for assessing the therapeutic effects of 6-methoxyflavone.
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Affiliation(s)
- Chaihong Zhang
- The First Clinical Medical College, Lanzhou University, Lanzhou, China.,Key Laboratory of Gynecological Oncology of Gansu Province, Lanzhou, China
| | - Yuchong Quan
- College of Basic Medicine, Dalian Medical University, Dalian, China
| | - Yingying Bai
- The First Clinical Medical College, Lanzhou University, Lanzhou, China.,Key Laboratory of Gynecological Oncology of Gansu Province, Lanzhou, China
| | - Lijuan Yang
- The First Clinical Medical College, Lanzhou University, Lanzhou, China.,Key Laboratory of Gynecological Oncology of Gansu Province, Lanzhou, China
| | - Yongxiu Yang
- The First Clinical Medical College, Lanzhou University, Lanzhou, China.,Key Laboratory of Gynecological Oncology of Gansu Province, Lanzhou, China.,Department of Obstetrics and Gynecology, First Hospital of Lanzhou University, Lanzhou, China
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Koppad S, Basava A, Nash K, Gkoutos GV, Acharjee A. Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes. BIOLOGY 2022; 11:biology11030365. [PMID: 35336739 PMCID: PMC8944988 DOI: 10.3390/biology11030365] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/16/2022] [Accepted: 02/23/2022] [Indexed: 01/27/2023]
Abstract
Simple Summary We developed a predictive approach using different machine learning methods to identify a number of genes that can potentially serve as novel diagnostic colon cancer biomarkers. Abstract Background: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC’s mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets. Methods: We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs. Results: We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes. Conclusions: We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC.
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Affiliation(s)
- Saraswati Koppad
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Mangalore 575025, India; (S.K.); (A.B.)
| | - Annappa Basava
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Mangalore 575025, India; (S.K.); (A.B.)
| | - Katrina Nash
- College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Georgios V. Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK;
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham B15 2WB, UK
- MRC Health Data Research UK (HDR UK), Midlands Site, Birmingham B15 2TT, UK
- NIHR Experimental Cancer Medicine Centre, Birmingham B15 2TT, UK
- NIHR Biomedical Research Centre, University Hospital Birmingham, Birmingham B15 2TT, UK
| | - Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK;
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham B15 2WB, UK
- Correspondence: ; Tel.: +44-07403642022
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Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers. Biomedicines 2021; 9:biomedicines9111636. [PMID: 34829865 PMCID: PMC8615894 DOI: 10.3390/biomedicines9111636] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 10/29/2021] [Accepted: 11/04/2021] [Indexed: 02/06/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease that presents a great challenge for treatment and prevention.. This study aims to implement a machine learning approach that employs such datasets to identify potential biomarker targets. We developed a pipeline to identify potential biomarkers for NAFLD that includes five major processes, namely, a pre-processing step, a feature selection and a generation of a random forest model and, finally, a downstream feature analysis and a provision of a potential biological interpretation. The pre-processing step includes data normalising and variable extraction accompanied by appropriate annotations. A feature selection based on a differential gene expression analysis is then conducted to identify significant features and then employ them to generate a random forest model whose performance is assessed based on a receiver operating characteristic curve. Next, the features are subjected to a downstream analysis, such as univariate analysis, a pathway enrichment analysis, a network analysis and a generation of correlation plots, boxplots and heatmaps. Once the results are obtained, the biological interpretation and the literature validation is conducted over the identified features and results. We applied this pipeline to transcriptomics and lipidomic datasets and concluded that the C4BPA gene could play a role in the development of NAFLD. The activation of the complement pathway, due to the downregulation of the C4BPA gene, leads to an increase in triglyceride content, which might further render the lipid metabolism. This approach identified the C4BPA gene, an inhibitor of the complement pathway, as a potential biomarker for the development of NAFLD.
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