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Li B, Shaikh F, Zamzam A, Syed MH, Abdin R, Qadura M. The Identification and Evaluation of Interleukin-7 as a Myokine Biomarker for Peripheral Artery Disease Prognosis. J Clin Med 2024; 13:3583. [PMID: 38930112 PMCID: PMC11205196 DOI: 10.3390/jcm13123583] [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: 05/15/2024] [Revised: 06/13/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Background/Objectives: Myokines have been demonstrated to be associated with cardiovascular diseases; however, they have not been studied as biomarkers for peripheral artery disease (PAD). We identified interleukin-7 (IL-7) as a prognostic biomarker for PAD from a panel of myokines and developed predictive models for 2-year major adverse limb events (MALEs) using clinical features and plasma IL-7 levels. Methods: A prognostic study was conducted with a cohort of 476 patients (312 with PAD and 164 without PAD) that were recruited prospectively. Their plasma concentrations of five circulating myokines were measured at recruitment, and the patients were followed for two years. The outcome of interest was two-year MALEs (composite of major amputation, vascular intervention, or acute limb ischemia). Cox proportional hazards analysis was performed to identify IL-7 as the only myokine that was associated with 2-year MALEs. The data were randomly divided into training (70%) and test sets (30%). A random forest model was trained using clinical characteristics (demographics, comorbidities, and medications) and plasma IL-7 levels with 10-fold cross-validation. The primary model evaluation metric was the F1 score. The prognostic model was used to classify patients into low vs. high risk of developing adverse limb events based on the Youden Index. Freedom from MALEs over 2 years was compared between the risk-stratified groups using Cox proportional hazards analysis. Results: Two-year MALEs occurred in 28 (9%) of patients with PAD. IL-7 was the only myokine that was statistically significantly correlated with two-year MALE (HR 1.56 [95% CI 1.12-1.88], p = 0.007). For the prognosis of 2-year MALEs, our model achieved an F1 score of 0.829 using plasma IL-7 levels in combination with clinical features. Patients classified as high-risk by the predictive model were significantly more likely to develop MALEs over a 2-year period (HR 1.66 [95% CI 1.22-1.98], p = 0.006). Conclusions: From a panel of myokines, IL-7 was identified as a prognostic biomarker for PAD. Using a combination of clinical characteristics and plasma IL-7 levels, we propose an accurate predictive model for 2-year MALEs in patients with PAD. Our model may support PAD risk stratification, guiding clinical decisions on additional vascular evaluation, specialist referrals, and medical/surgical management, thereby improving outcomes.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, ON M5S 1A1, Canada;
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, 30 Bond Street, Suite 7-076, Toronto, ON M5B 1W8, Canada; (F.S.); (A.Z.); (M.H.S.)
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A1, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Farah Shaikh
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, 30 Bond Street, Suite 7-076, Toronto, ON M5B 1W8, Canada; (F.S.); (A.Z.); (M.H.S.)
| | - Abdelrahman Zamzam
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, 30 Bond Street, Suite 7-076, Toronto, ON M5B 1W8, Canada; (F.S.); (A.Z.); (M.H.S.)
| | - Muzammil H. Syed
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, 30 Bond Street, Suite 7-076, Toronto, ON M5B 1W8, Canada; (F.S.); (A.Z.); (M.H.S.)
| | - Rawand Abdin
- Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada;
| | - Mohammad Qadura
- Department of Surgery, University of Toronto, Toronto, ON M5S 1A1, Canada;
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, 30 Bond Street, Suite 7-076, Toronto, ON M5B 1W8, Canada; (F.S.); (A.Z.); (M.H.S.)
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A1, Canada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON M5B 1W8, Canada
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Li B, Khan H, Shaikh F, Zamzam A, Abdin R, Qadura M. Identification and Evaluation of Blood-Based Biomarkers for Abdominal Aortic Aneurysm. J Proteome Res 2024. [PMID: 38647339 DOI: 10.1021/acs.jproteome.4c00254] [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: 04/25/2024]
Abstract
INTRODUCTION Blood-based biomarkers for abdominal aortic aneurysm (AAA) have been studied individually; however, we considered a panel of proteins to investigate AAA prognosis and its potential to improve predictive accuracy. MATERIALS AND METHODS Using a prospectively recruited cohort of patients with/without AAA (n = 452), we conducted a prognostic study to develop a model that accurately predicts AAA outcomes using clinical features and circulating biomarker levels. Serum concentrations of 9 biomarkers were measured at baseline, and the cohort was followed for 2 years. The primary outcome was major adverse aortic event (MAAE; composite of rapid AAA expansion [>0.5 cm/6 months or >1 cm/12 months], AAA intervention, or AAA rupture). Using 10-fold cross-validation, we trained a random forest model to predict 2 year MAAE using (1) clinical characteristics, (2) biomarkers, and (3) clinical characteristics and biomarkers. RESULTS Two-year MAAE occurred in 114 (25%) patients. Two proteins were significantly elevated in patients with AAA compared with those without AAA (angiopoietin-2 and aggrecan), composing the protein panel. For predicting 2 year MAAE, our random forest model achieved area under the receiver operating characteristic curve (AUROC) 0.74 using clinical features alone, and the addition of the 2-protein panel improved performance to AUROC 0.86. CONCLUSIONS Using a combination of clinical/biomarker data, we developed a model that accurately predicts 2 year MAAE.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto M5T 1P5, Canada
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto M5B 1W8, Canada
- Institute of Medical Science, University of Toronto, Toronto M5S 1A8, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto M5G 2C8, Canada
| | - Hamzah Khan
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto M5B 1W8, Canada
| | - Farah Shaikh
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto M5B 1W8, Canada
| | - Abdelrahman Zamzam
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto M5B 1W8, Canada
| | - Rawand Abdin
- Department of Medicine, McMaster University, Hamilton L8S 4L8, Canada
| | - Mohammad Qadura
- Department of Surgery, University of Toronto, Toronto M5T 1P5, Canada
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto M5B 1W8, Canada
- Institute of Medical Science, University of Toronto, Toronto M5S 1A8, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto M5B 1W8, Canada
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Li B, Shaikh F, Zamzam A, Syed MH, Abdin R, Qadura M. A machine learning algorithm for peripheral artery disease prognosis using biomarker data. iScience 2024; 27:109081. [PMID: 38361633 PMCID: PMC10867451 DOI: 10.1016/j.isci.2024.109081] [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: 08/17/2023] [Revised: 01/11/2024] [Accepted: 01/26/2024] [Indexed: 02/17/2024] Open
Abstract
Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada
| | - Farah Shaikh
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Abdelrahman Zamzam
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Muzammil H. Syed
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Rawand Abdin
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Mohammad Qadura
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
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González-Domínguez Á, González-Domínguez R. How far are we from reliable metabolomics-based biomarkers? The often-overlooked importance of addressing inter-individual variability factors. Biochim Biophys Acta Mol Basis Dis 2024; 1870:166910. [PMID: 37802155 DOI: 10.1016/j.bbadis.2023.166910] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/28/2023] [Accepted: 09/29/2023] [Indexed: 10/08/2023]
Abstract
Metabolomics has proven great potential to unravel the molecular basis of diseases. However, most attempts aimed at identifying reliable metabolomics-based biomarkers for diagnosis, prediction, and prognosis of diseases have repeatedly failed because of inconsistent results and unsatisfactory replication in independent cohorts. This review article explores the possible causes behind this reproducibility crisis, with special focus on the role that inter-individual variability factors play in modulating the susceptibility to disease development. Furthermore, we provide future perspectives on the applicability of metabolomics in biomedical research and its translatability into clinical practice.
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Affiliation(s)
- Álvaro González-Domínguez
- Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, 11009 Cádiz, Spain
| | - Raúl González-Domínguez
- Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, 11009 Cádiz, Spain.
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Lopez-Pedrera C, Oteros R, Ibáñez-Costa A, Luque-Tévar M, Muñoz-Barrera L, Barbarroja N, Chicano-Gálvez E, Marta-Enguita J, Orbe J, Velasco F, Perez-Sanchez C. The thrombus proteome in stroke reveals a key role of the innate immune system and new insights associated with its etiology, severity, and prognosis. J Thromb Haemost 2023; 21:2894-2907. [PMID: 37100394 DOI: 10.1016/j.jtha.2023.04.015] [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: 10/26/2022] [Revised: 03/30/2023] [Accepted: 04/13/2023] [Indexed: 04/28/2023]
Abstract
BACKGROUND Nowadays little is known about the molecular profile of the occluding thrombus of patients with ischemic stroke. OBJECTIVES To analyze the proteomic profile of thrombi in patients who experienced an ischemic stroke in order to gain insights into disease pathogenesis. METHODS Thrombi from an exploratory cohort of patients who experienced a stroke were obtained by thrombectomy and analyzed by sequential window acquisition of all theoretical spectra-mass spectrometry. Unsupervised k-means clustering analysis was performed to stratify patients who experienced a stroke. The proteomic profile was associated with both the neurological function (National Institute of Health Stroke Scale [NIHSS]) and the cerebral involvement (Alberta Stroke Program Early CT Score [ASPECTS]) prior to thrombectomy and the clinical status of patients at 3 months using the modified Rankin Scale. In an independent cohort of 210 patients who experienced a stroke, the potential role of neutrophils in stroke severity was interrogated. RESULTS Proteomic analysis identified 580 proteins in thrombi, which were stratified into 4 groups: hemostasis, proteasome and neurological diseases, structural proteins, and innate immune system and neutrophils. The thrombus proteome identified 3 clusters of patients with distinctive severity, prognosis, and etiology of the stroke. A protein signature clearly distinguished atherothrombotic and cardioembolic strokes. Several proteins were significantly correlated with the severity of the stroke (NIHSS and ASPECTS). Functional proteomic analysis highlighted the prominent role of neutrophils in stroke severity. This was in line with the association of neutrophil activation markers and count with NIHSS, ASPECTS, and the modified Rankin Scale score 90 days after the event. CONCLUSION The use of sequential window acquisition of all theoretical spectra-mass spectrometry in thrombi from patients who experienced an ischemic stroke has provided new insights into pathways and players involved in its etiology, severity, and prognosis. The prominent role of the innate immune system identified might pave the way for the development of new biomarkers and therapeutic approaches in this disease.
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Affiliation(s)
- Chary Lopez-Pedrera
- Rheumatology Service, Maimonides Institute of Biomedical Research of Córdoba (IMIBIC), Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain.
| | - Rafael Oteros
- Diagnostic and Therapeutic Neuroradiology Unit, Reina Sofia Hospital, Córdoba, Spain
| | - Alejandro Ibáñez-Costa
- Rheumatology Service, Maimonides Institute of Biomedical Research of Córdoba (IMIBIC), Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain; Department of Cell Biology, Immunology and Physiology, Agrifood Campus of International Excellence, University of Córdoba, ceiA3, Córdoba, Spain
| | - María Luque-Tévar
- Rheumatology Service, Maimonides Institute of Biomedical Research of Córdoba (IMIBIC), Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain
| | - Laura Muñoz-Barrera
- Rheumatology Service, Maimonides Institute of Biomedical Research of Córdoba (IMIBIC), Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain
| | - Nuria Barbarroja
- Rheumatology Service, Maimonides Institute of Biomedical Research of Córdoba (IMIBIC), Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain; Cobiomic Bioscience SL, EBT University of Córdoba/IMIBIC, Córdoba, Spain
| | - Eduardo Chicano-Gálvez
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides Biomedical Research Institute of Córdoba, Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain
| | - Juan Marta-Enguita
- Atherothrombosis-Laboratory, Cardiovascular Diseases Program, CIMA-Universidad Navarra, IdiSNA, Pamplona, Spain; Neurology Department, Hospital Universitario Navarra, Pamplona, Spain; RICORS-ICTUS, Instituto Salud Carlos III, Madrid, Spain
| | - Josune Orbe
- Atherothrombosis-Laboratory, Cardiovascular Diseases Program, CIMA-Universidad Navarra, IdiSNA, Pamplona, Spain; RICORS-ICTUS, Instituto Salud Carlos III, Madrid, Spain
| | - Francisco Velasco
- Department of Medicine, University of Córdoba, Maimonides Biomedical Research Institute of Córdoba, Córdoba, Spain
| | - Carlos Perez-Sanchez
- Rheumatology Service, Maimonides Institute of Biomedical Research of Córdoba (IMIBIC), Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain; Department of Cell Biology, Immunology and Physiology, Agrifood Campus of International Excellence, University of Córdoba, ceiA3, Córdoba, Spain; Cobiomic Bioscience SL, EBT University of Córdoba/IMIBIC, Córdoba, Spain. https://twitter.com/carlosps85
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Shields PG. Role of untargeted omics biomarkers of exposure and effect for tobacco research. ADDICTION NEUROSCIENCE 2023; 7:100098. [PMID: 37396411 PMCID: PMC10310069 DOI: 10.1016/j.addicn.2023.100098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.
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Affiliation(s)
- Peter G. Shields
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH
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Rischke S, Hahnefeld L, Burla B, Behrens F, Gurke R, Garrett TJ. Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects. J Mass Spectrom Adv Clin Lab 2023; 28:47-55. [PMID: 36872952 PMCID: PMC9982001 DOI: 10.1016/j.jmsacl.2023.02.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 02/14/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023] Open
Abstract
Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.
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Key Words
- (U)HPLC (Ultra-), High pressure liquid chromatography
- Biomarker Discovery Study
- HILIC, Hydrophilic interaction liquid chromatography
- HRMS, High resolution mass spectrometry
- LC-MS, Liquid chromatography – mass spectrometry
- LC-MS-Based Clinical Research
- Lipidomics
- MRM, Multiple reaction monitoring
- Metabolomics
- PCA, Principal component analysis
- QA, Quality assurance
- QC, Quality control
- RF, Random Forest
- RP, Reversed phase
- SVA, Support vector machine
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Affiliation(s)
- S Rischke
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - L Hahnefeld
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - B Burla
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - F Behrens
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany.,Division of Rheumatology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - R Gurke
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - T J Garrett
- Department of Pathology, Immunology and Laboratory Medicine and Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL 32611, USA
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Mortezapour M, Tapak L, Bahreini F, Najafi R, Afshar S. Identification of key genes in colorectal cancer diagnosis by co-expression analysis weighted gene co-expression network analysis. Comput Biol Med 2023; 157:106779. [PMID: 36931200 DOI: 10.1016/j.compbiomed.2023.106779] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/10/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
BACKGROUND The purpose of this study was using bioinformatics tools to identify biomarkers and molecular factors involved in the diagnosis of colorectal cancer, which are effective for the diagnosis and treatment of the disease. METHODS We determined differentially expressed genes (DEGs) related to colorectal cancer (CRC) using the data series retrieved from GEO database. Then the weighted gene co-expression network analysis (WGCNA) was conducted to explore co-expression modules related to CRC diagnosis. Next, the relationship between the integrated modules with clinical features such as the stage of CRC was evaluated. Other downstream analyses were performed on selected module genes. RESULTS In this study, after performing the WGCNA method, a module named blue module which was more significantly associated with the CRC stage was selected for further evaluation. Afterward, the Protein-protein interaction network through sting software for 154 genes of the blue module was constructed and eight hub genes were identified through the evaluation of constructed network with Cytoscape. Among these eight hub genes, upregulation of MMP9, SERPINH1, COL1A2, COL5A2, COL1A1, SPARC, and COL5A1 in CRC was validated in other microarray and TCGA data. Based on the results of the mRNA-miRNA interaction network, SERPINH1 was found as a target gene of miR-940. Finally, results of the DGIDB database indicated that Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, and Bevacizumab, could be used as a therapeutic agent for targeting the MMP9. Furthermore, Ocriplasmin and Collagenase clostridium histolyticum could target COL1A1, COL1A2, COL5A1, and COL5A2. CONCLUSION Taken together, the results of the current study indicated that seven hub genes including COL1A2, COL5A1, COL5A2, SERPINH1, MMP9, SPARC, and COL1A1 which were upregulated in CRC could be used as a diagnostic and progression biomarker of CRC. On the other hand, miR-940 which targets SERPINH1 could be used as a potential biomarker of CRC. More ever, Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, Bevacizumab, Ocriplasmin , and Collagenase clostridium histolyticum were introduced as therapeutic agents for CRC which their therapeutic potential should be evaluated experimentally.
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Affiliation(s)
- Mahdie Mortezapour
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Leili Tapak
- Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Fatemeh Bahreini
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Rezvan Najafi
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Saeid Afshar
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
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Waszczuk MA, Kuan PF, Yang X, Miao J, Kotov R, Luft BJ. Discovery and replication of blood-based proteomic signature of PTSD in 9/11 responders. Transl Psychiatry 2023; 13:8. [PMID: 36631443 PMCID: PMC9834302 DOI: 10.1038/s41398-022-02302-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 11/28/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023] Open
Abstract
Proteomics provides an opportunity to develop biomarkers for the early detection and monitoring of post-traumatic stress disorder (PTSD). However, research to date has been limited by small sample sizes and a lack of replication. This study performed Olink Proseek Multiplex Platform profiling of 81 proteins involved in neurological processes in 936 responders to the 9/11 disaster (mean age at blood draw = 55.41 years (SD = 7.93), 94.1% white, all men). Bivariate correlations and elastic net regressions were used in a discovery subsample to identify concurrent associations between PTSD symptom severity and the profiled proteins, and to create a multiprotein composite score. In hold-out subsamples, nine bivariate associations between PTSD symptoms and differentially expressed proteins were replicated: SKR3, NCAN, BCAN, MSR1, PVR, TNFRSF21, DRAXIN, CLM6, and SCARB2 (|r| = 0.08-0.17, p < 0.05). There were three replicated bivariate associations between lifetime PTSD diagnosis and differentially expressed proteins: SKR3, SIGLEC, and CPM (OR = 1.38-1.50, p < 0.05). The multiprotein composite score retained 38 proteins, including 10/11 proteins that replicated in bivariate tests. The composite score was significantly associated with PTSD symptom severity (β = 0.27, p < 0.001) and PTSD diagnosis (OR = 1.60, 95% CI: 1.17-2.19, p = 0.003) in the hold-out subsample. Overall, these findings suggest that PTSD is characterized by altered expression of several proteins implicated in neurological processes. Replicated associations with TNFRSF21, CLM6, and PVR support the neuroinflammatory signature of PTSD. The multiprotein composite score substantially increased associations with PTSD symptom severity over individual proteins. If generalizable to other populations, the current findings may inform the development of PTSD biomarkers.
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Affiliation(s)
- Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Xiaohua Yang
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Jiaju Miao
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Benjamin J Luft
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA.
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10
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Abstract
The current issue (volume 13 issue 6, 2021) is a Special Issue jointly dedicated to scientific content presented at the 20th triennial IUPAB Congress that was held in conjunction with both the 45th Annual Meeting of the Brazilian Biophysical Society (Sociedade Brasileira de Biofísica - SBBf) and the 50th Annual Meeting of the Brazilian Society for Biochemistry and Molecular Biology (Sociedade Brasileira de Bioquímica e Biologia Molecular - SBBq). In addition to describing the scientific and nonscientific content arising from the meeting this sub-editorial also provides a look back at some of the high points for Biophysical Reviews in the year 2021 before going on to describe a number of matters of interest to readers of the journal in relation to the coming year of 2022.
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Affiliation(s)
- Damien Hall
- WPI Nano Life Science Institute, Kanazawa University, Kakumamachi, Kanazawa, Ishikawa 920-1164 Japan
- Department of Applied Physics, Aalto University, FI-00076 Aalto, Finland
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