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Wang X, Shang D, Chen J, Cheng S, Chen D, Zhang Z, Liu C, Yu J, Cao H, Li L, Li L. Serum metabolomics reveals the effectiveness of human placental mesenchymal stem cell therapy for Crohn's disease. Talanta 2024; 277:126442. [PMID: 38897006 DOI: 10.1016/j.talanta.2024.126442] [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: 02/02/2024] [Revised: 06/10/2024] [Accepted: 06/15/2024] [Indexed: 06/21/2024]
Abstract
Mesenchymal stem cell (MSC) therapy offers a promising cure for Crohn's disease (CD), however, its therapeutic effects vary significantly due to individual differences. Therefore, identifying easily detectable biomarkers is essential to assess the efficacy of MSC therapy. In this study, SAMP1/Yit mice were used as a model of CD, which develop spontaneous chronic ileitis, closely resembling the characteristics present in CD patients. Serum metabolic alterations during treatment were analyzed, through the application of differential 12C-/13C-dansylation labeling liquid chromatography-mass spectrometry. Based on the significant differences and time-varying trends of serum amine/phenol-containing metabolites abundance between the control group, the model group, and the treatment group, four serum biomarkers were ultimately screened for evaluating the efficacy of MSC treatment for CD, namely 4-hydroxyphenylpyruvate, 4-hydroxyphenylacetaldehyde, caffeate, and N-acetyltryptamine, whose abundances both increased in the serum of CD model mice and decreased after MSC treatment. These metabolic alterations were associated with tyrosine metabolism, which was validated by the dysregulation of related enzymes. The discovery of biomarkers may help to improve the targeting and effectiveness of treatment and provide innovative prospects for the clinical application of MSC for CD.
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Affiliation(s)
- Xiao Wang
- Jinan Microecological Biomedicine Shandong Laboratory, Jinan City 250117, China
| | - Dandan Shang
- Jinan Microecological Biomedicine Shandong Laboratory, Jinan City 250117, China
| | - Junyao Chen
- State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City 310003, China
| | - Sheng Cheng
- State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City 310003, China
| | - Deying Chen
- State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City 310003, China
| | - Zhehua Zhang
- State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City 310003, China
| | - Chaoxu Liu
- Department of Colorectal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City 310003, China
| | - Jiong Yu
- Jinan Microecological Biomedicine Shandong Laboratory, Jinan City 250117, China; State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City 310003, China; Zhejiang Key Laboratory for Diagnosis and Treatment of Physic-chemical and Aging-related Injuries, 79 Qingchun Rd, Hangzhou City 310003, China.
| | - Hongcui Cao
- Jinan Microecological Biomedicine Shandong Laboratory, Jinan City 250117, China; State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City 310003, China; Zhejiang Key Laboratory for Diagnosis and Treatment of Physic-chemical and Aging-related Injuries, 79 Qingchun Rd, Hangzhou City 310003, China.
| | - Liang Li
- Department of Chemistry, University of Alberta, Edmonton, Alberta, T6G 2G2, Canada
| | - Lanjuan Li
- Jinan Microecological Biomedicine Shandong Laboratory, Jinan City 250117, China; State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou City 310003, China
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2
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Fu Y, Tao J, Liu T, Liu Y, Qiu J, Su D, Wang R, Luo W, Cao Z, Weng G, Zhang T, Zhao Y. Unbiasedly decoding the tumor microenvironment with single-cell multiomics analysis in pancreatic cancer. Mol Cancer 2024; 23:140. [PMID: 38982491 PMCID: PMC11232163 DOI: 10.1186/s12943-024-02050-7] [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: 05/07/2024] [Accepted: 06/21/2024] [Indexed: 07/11/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a poor prognosis and limited therapeutic options. Research on the tumor microenvironment (TME) of PDAC has propelled the development of immunotherapeutic and targeted therapeutic strategies with a promising future. The emergence of single-cell sequencing and mass spectrometry technologies, coupled with spatial omics, has collectively revealed the heterogeneity of the TME from a multiomics perspective, outlined the development trajectories of cell lineages, and revealed important functions of previously underrated myeloid cells and tumor stroma cells. Concurrently, these findings necessitated more refined annotations of biological functions at the cell cluster or single-cell level. Precise identification of all cell clusters is urgently needed to determine whether they have been investigated adequately and to identify target cell clusters with antitumor potential, design compatible treatment strategies, and determine treatment resistance. Here, we summarize recent research on the PDAC TME at the single-cell multiomics level, with an unbiased focus on the functions and potential classification bases of every cellular component within the TME, and look forward to the prospects of integrating single-cell multiomics data and retrospectively reusing bulk sequencing data, hoping to provide new insights into the PDAC TME.
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Affiliation(s)
- Yifan Fu
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
- 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Jinxin Tao
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Tao Liu
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Yueze Liu
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Jiangdong Qiu
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Dan Su
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Ruobing Wang
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Wenhao Luo
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Zhe Cao
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Guihu Weng
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Taiping Zhang
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
- Clinical Immunology Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
| | - Yupei Zhao
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
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3
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Chen J, Ji Y, Liu Y, Cen Z, Chen Y, Zhang Y, Li X, Li X. Exhaled volatolomics profiling facilitates personalized screening for gastric cancer. Cancer Lett 2024; 590:216881. [PMID: 38614384 DOI: 10.1016/j.canlet.2024.216881] [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: 01/16/2024] [Revised: 04/02/2024] [Accepted: 04/09/2024] [Indexed: 04/15/2024]
Abstract
Gastric cancer (GC) is one of the most fatal cancers, characterized by non-specific early symptoms and difficulty in detection. However, there are no valid non-invasive screening tools available for GC. Here we establish a non-invasive method that employs exhaled volatolomics and ensemble learning to detect GC. We developed a comprehensive mass spectrometry-based procedure and determined of a wide range of volatolomics from 314 breath samples. The discovery, identification and verification research screened a biomarker panel to distinguish GC from controls. This panel has achieved 0.90 (0.87-0.94, 95%CI) accuracy, with an area under curve (AUC) of 0.92 (0.89-0.94, 95%CI) in discovery cohort and 0.88 (0.83-0.91, 95%CI) accuracy with an AUC of 0.91 (0.87-0.93, 95%CI) in replication cohort, which outperformed traditional serum markers. Single-cell sequencing and gene set enrichment analysis revealed that these exhaled markers originated from aldehyde oxidation and pyruvate metabolism. Our approach advances the design of exhaled analysis for GC detection and holds promise as a non-invasive method to the clinic.
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Affiliation(s)
- Jian Chen
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, PR China
| | - Yongyan Ji
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, PR China
| | - Yongqian Liu
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, PR China
| | - Zhengnan Cen
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, PR China
| | - Yuanwen Chen
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, PR China
| | - Yixuan Zhang
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, PR China
| | - Xiaowen Li
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, PR China.
| | - Xiang Li
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, PR China.
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Du Q, Wang X, Chen J, Wang Y, Liu W, Wang L, Liu H, Jiang L, Nie Z. Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis. Analyst 2023; 148:4318-4330. [PMID: 37547947 DOI: 10.1039/d3an01051a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine.
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Affiliation(s)
- Qiuyao Du
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiao Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junyu Chen
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiran Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wenlan Liu
- Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, China
| | - Liping Wang
- Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, China
| | - Huihui Liu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lixia Jiang
- Department of Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi Province 341000, China.
| | - Zongxiu Nie
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
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Perazzoli G, García-Valdeavero OM, Peña M, Prados J, Melguizo C, Jiménez-Luna C. Evaluating Metabolite-Based Biomarkers for Early Diagnosis of Pancreatic Cancer: A Systematic Review. Metabolites 2023; 13:872. [PMID: 37512579 PMCID: PMC10384620 DOI: 10.3390/metabo13070872] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with five-year survival rates around 10%. The only curative option remains complete surgical resection, but due to the delay in diagnosis, less than 20% of patients are eligible for surgery. Therefore, discovering diagnostic biomarkers for early detection is crucial for improving clinical outcomes. Metabolomics has become a powerful technology for biomarker discovery, and several metabolomic-based panels have been proposed for PDAC diagnosis, but these advances have not yet been translated into the clinic. Therefore, this review focused on summarizing metabolites identified for the early diagnosis of PDAC in the last five years. Bibliographic searches were performed in the PubMed, Scopus and WOS databases, using the terms "Biomarkers, Tumor", "Pancreatic Neoplasms", "Early Diagnosis", "Metabolomics" and "Lipidome" (January 2018-March 2023), and resulted in the selection of fourteen original studies that compared PDAC patients with subjects with other pancreatic diseases. These investigations showed amino acid and lipid metabolic pathways as the most commonly altered, reflecting their potential for biomarker research. Furthermore, other relevant metabolites such as glucose and lactate were detected in the pancreas tissue and body fluids from PDAC patients. Our results suggest that the use of metabolomics remains a robust approach to improve the early diagnosis of PDAC. However, these studies showed heterogeneity with respect to the metabolomics techniques used and further studies will be needed to validate the clinical utility of these biomarkers.
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Affiliation(s)
- Gloria Perazzoli
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
- Department of Anatomy and Embryology, Faculty of Medicine, University of Granada, 18071 Granada, Spain
- Instituto Biosanitario de Granada (ibs.GRANADA), 18014 Granada, Spain
| | - Olga M García-Valdeavero
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
| | - Mercedes Peña
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
- Department of Anatomy and Embryology, Faculty of Medicine, University of Granada, 18071 Granada, Spain
- Instituto Biosanitario de Granada (ibs.GRANADA), 18014 Granada, Spain
| | - Jose Prados
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
- Department of Anatomy and Embryology, Faculty of Medicine, University of Granada, 18071 Granada, Spain
- Instituto Biosanitario de Granada (ibs.GRANADA), 18014 Granada, Spain
| | - Consolación Melguizo
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
- Department of Anatomy and Embryology, Faculty of Medicine, University of Granada, 18071 Granada, Spain
- Instituto Biosanitario de Granada (ibs.GRANADA), 18014 Granada, Spain
| | - Cristina Jiménez-Luna
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
- Department of Anatomy and Embryology, Faculty of Medicine, University of Granada, 18071 Granada, Spain
- Instituto Biosanitario de Granada (ibs.GRANADA), 18014 Granada, Spain
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Wang W, Rong Z, Wang G, Hou Y, Yang F, Qiu M. Cancer metabolites: promising biomarkers for cancer liquid biopsy. Biomark Res 2023; 11:66. [PMID: 37391812 DOI: 10.1186/s40364-023-00507-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 05/27/2023] [Indexed: 07/02/2023] Open
Abstract
Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.
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Affiliation(s)
- Wenxiang Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China
| | - Zhiwei Rong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, 100191, China
| | - Guangxi Wang
- Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Yan Hou
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Clinical Research Center, Peking University, Beijing, 100191, China
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China.
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China.
| | - Mantang Qiu
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China.
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China.
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Taha A, Taha-Mehlitz S, Ortlieb N, Ochs V, Honaker MD, Rosenberg R, Lock JF, Bolli M, Cattin PC. Machine learning in pancreas surgery, what is new? literature review. Front Surg 2023; 10:1142585. [PMID: 37383385 PMCID: PMC10293756 DOI: 10.3389/fsurg.2023.1142585] [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: 01/11/2023] [Accepted: 05/19/2023] [Indexed: 06/30/2023] Open
Abstract
Background Machine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery. Methods We integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included. Results A search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022. Conclusion The integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes.
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Affiliation(s)
- Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Stephanie Taha-Mehlitz
- Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Niklas Ortlieb
- Goethe University Frankfurt, Faculty of Business and Economics, Frankfurt am Main, Germany
| | - Vincent Ochs
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Michael Drew Honaker
- Department of Surgery, East Carolina University, Brody School of Medicine, Greenville, NC, United States
| | - Robert Rosenberg
- Cantonal Hospital Basel-Landschaft, Centre for Gastrointestinal and Liver Diseases, Liestal, Switzerland
| | - Johan F. Lock
- Department of General, Visceral, Transplantation, Vascular and Pediatric Surgery, University Hospital Würzburg, Würzburg, Germany
| | - Martin Bolli
- Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Philippe C. Cattin
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
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8
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Aalkjær C, Wang T. The cardiovascular challenges in giraffes. J Muscle Res Cell Motil 2023; 44:53-60. [PMID: 35879488 DOI: 10.1007/s10974-022-09626-0] [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: 05/10/2022] [Accepted: 07/10/2022] [Indexed: 11/30/2022]
Abstract
Giraffes are the highest living animals on Earth and therefore are challenged by gravity more than any other species. In particular the cardiovascular system needs to adapt to this challenge. Giraffes have a mean blood pressure around 200 mmHg, which ensures a mean arterial pressure near the head of 100 mmHg when the giraffe is standing with the neck in a near vertical position. This immediately raises several questions. How do giraffes avoid edema in the legs where the arterial pressure is 300 mmHg or higher? How does the heart produce a pressure of 200 mmHg, and what is the energy required for this endeavor? How can the kidney tolerate a pressure of about 200 mmHg and does this mean that giraffes have a high glomerular filtration rate? What is the arterial pressure in the head of giraffes when they drink, and how is perfusion of the brain maintained when they lift their head after drinking? In this short review, we present some answers to these questions.
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Affiliation(s)
- Christian Aalkjær
- Department of Biomedicine, Aarhus University, 8000, Aarhus C, Denmark.
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Tobias Wang
- Zoophysiology, Department of Biological Sciences, Aarhus University, 8000, Aarhus C, Denmark
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9
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Zhao Z, Luo Q, Liu Y, Jiang K, Zhou L, Dai R, Wang H. Multi-level integrative analysis of the roles of lncRNAs and differential mRNAs in the progression of chronic pancreatitis to pancreatic ductal adenocarcinoma. BMC Genomics 2023; 24:101. [PMID: 36879212 PMCID: PMC9990329 DOI: 10.1186/s12864-023-09209-4] [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: 11/28/2022] [Accepted: 02/27/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors and approximately 5% of patients with chronic pancreatitis (CP) inevitably develop PDAC. This study aims explore the key gene regulation involved in the progression of CP to PDAC, with a particular emphasis on the function of lncRNAs. RESULTS A total of 103 pancreatic tissue samples collected from 11 to 92 patients with CP and PDAC, respectively, were included in this study. After normalizing and logarithmically converting the original data, differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEGs) in each dataset were selected. To determine the main functional pathways of differential mRNAs, we further annotated DEGs using gene ontology (GO) and analyzed the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. In addition, the interaction between lncRNA-miRNA-mRNA was clarified and the protein-protein interaction (PPI) network was constructed to screen for key modules and determine hub genes. Finally, quantitative real-time polymerase chain reaction (qPCR) was used to detect the changes in non-coding RNAs and key mRNAs in the pancreatic tissues of patients with CP and PDAC. In this study, 230 lncRNAs and 17,668 mRNAs were included. There were nine upregulated lncRNAs and 188 downregulated lncRNAs. Furthermore, 2334 upregulated differential mRNAs and 10,341 downregulated differential mRNAs were included in the enrichment analysis. From the KEGG enrichment analysis, cytokine-cytokine receptor interaction, calcium signaling pathway, cAMP signaling pathway, and nicotine addiction exhibited significant differences. Additionally, a total of 52 lncRNAs, 104 miRNAs, and 312 mRNAs were included in the construction of a potential lncRNA-miRNA-mRNA regulatory network. PPI network was established and two of the five central DEGs were created in this module, suggesting that lysophosphatidic acid receptor 1 (LPAR1) and regulator of calcineurin 2 (RCAN2) may play significant roles in the progression from CP to PDAC. Finally, the PCR results suggested that LINC01547/hsa-miR-4694-3p/LPAR1 and LINC00482/hsa-miR-6756-3p/RCAN2 play important roles in the carcinogenesis process of CP. CONCLUSION Two signaling axes critical in the progression of CP to PDAC were screened out. Our findings will be useful for novel insights into the molecular mechanism and potential diagnostic or therapeutic biomarkers for CP and PDAC.
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Affiliation(s)
- Zhirong Zhao
- Affiliated Hospital of Southwest Jiaotong University, The General Hospital of Western Theater Command, Chengdu, 610031, Sichuan, China.,Pancreatic injury and repair Key laboratory of Sichuan Province, The General Hospital of Western Theater Command, Chengdu, Sichuan, China
| | - Qiang Luo
- Department of Cardiology, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China
| | - Yi Liu
- School of Medicine, Jianghan University, 430056, Wuhan, Hubei, China
| | - Kexin Jiang
- Affiliated Hospital of Southwest Jiaotong University, The General Hospital of Western Theater Command, Chengdu, 610031, Sichuan, China
| | - Lichen Zhou
- Affiliated Hospital of Southwest Jiaotong University, The General Hospital of Western Theater Command, Chengdu, 610031, Sichuan, China
| | - Ruiwu Dai
- Affiliated Hospital of Southwest Jiaotong University, The General Hospital of Western Theater Command, Chengdu, 610031, Sichuan, China. .,Pancreatic injury and repair Key laboratory of Sichuan Province, The General Hospital of Western Theater Command, Chengdu, Sichuan, China.
| | - Han Wang
- Department of Cardiology, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.
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10
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Single-cell and bulk RNA sequencing identifies T cell marker genes score to predict the prognosis of pancreatic ductal adenocarcinoma. Sci Rep 2023; 13:3684. [PMID: 36878969 PMCID: PMC9988929 DOI: 10.1038/s41598-023-30972-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 03/03/2023] [Indexed: 03/08/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, with limited biomarkers identified to predict its prognosis and treatment response of immune checkpoint blockade (ICB). This study aimed to explore the predictive ability of T cell marker genes score (TMGS) to predict their overall survival (OS) and treatment response to ICB by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. Multi-omics data of PDAC were applied in this study. The uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification. The non-negative matrix factorization (NMF) algorithm was applied to molecular subtypes clustering. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was adopted for TMGS construction. The prognosis, biological characteristics, mutation profile, and immune function status between different groups were compared. Two molecular subtypes were identified via NMF: proliferative PDAC (C1) and immune PDAC (C2). Distinct prognoses and biological characteristics were observed between them. TMGS was developed based on 10 T cell marker genes (TMGs) through LASSO-Cox regression. TMGS is an independent prognostic factor of OS in PDAC. Enrichment analysis indicated that cell cycle and cell proliferation-related pathways are significantly enriched in the high-TMGS group. Besides, high-TMGS is related to more frequent KRAS, TP53, and CDKN2A germline mutations than the low-TMGS group. Furthermore, high-TMGS is significantly associated with attenuated antitumor immunity and reduced immune cell infiltration compared to the low-TMGS group. However, high TMGS is correlated to higher tumor mutation burden (TMB), a low expression level of inhibitory immune checkpoint molecules, and a low immune dysfunction score, thus having a higher ICB response rate. On the contrary, low TMGS is related to a favorable response rate to chemotherapeutic agents and targeted therapy. By combining scRNA-seq and bulk RNA-seq data, we identified a novel biomarker, TMGS, which has remarkable performance in predicting the prognosis and guiding the treatment pattern for patients with PDAC.
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11
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Liu Z, Parveen N, Rehman U, Aziz A, Sheikh A, Abourehab MAS, Guo W, Huang J, Wang Z, Kesharwani P. Unravelling the enigma of siRNA and aptamer mediated therapies against pancreatic cancer. Mol Cancer 2023; 22:8. [PMID: 36635659 PMCID: PMC9835391 DOI: 10.1186/s12943-022-01696-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/06/2022] [Indexed: 01/13/2023] Open
Abstract
Pancreatic cancer (PC) is a fatal disease that has a poor 5-year survival rate. The poor prognosis can be attributed to both troublesome detections at the initial stage, which makes the majority of the treatment options largely unsuccessful and leads to extensive metastasis, as well as to its distinct pathophysiological characteristics, such as rich desmoplastic tumours bounded by dysplastic and hypo perfused vessels restricting the mobility of therapeutic agents. Continued attempts have been made to utilise innovative measures for battling PC to increase the therapeutic effectiveness of therapies and overcome their cytotoxicity. Combined cancer targeting and gene silencing approach has shown improved outcomes in patients' survival rates and quality of life, offering a potential solution to therapeutic complications. It particularly targets various barriers to alleviate delivery problems and diminish tumour recurrence and metastasis. While aptamers, a type of single-stranded nucleic acids with strong binding affinity and specificity to target molecules, have recently surfaced as a viable PC strategy, siRNA can interfere with the expression of certain genes. By concurrently suppressing genes and boosting targeted approach, the cocktail of siRNA/Aptamer and other therapeutic drugs can circumvent the multi-drug resistance phenomena. Additionally, combination therapy with additive or synergistic effects can considerably increase the therapeutic efficacy of anti-cancer medications. This study outlines the primary difficulties in treating PC, along with recent developments in siRNA/Aptamer mediated drug delivery to solve the major hiccup of oncology field.
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Affiliation(s)
- Zhe Liu
- grid.412636.40000 0004 1757 9485Department of Pancreatic-Biliary Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Neha Parveen
- grid.411816.b0000 0004 0498 8167Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062 India
| | - Urushi Rehman
- grid.411816.b0000 0004 0498 8167Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062 India
| | - Aisha Aziz
- grid.411816.b0000 0004 0498 8167Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062 India
| | - Afsana Sheikh
- grid.411816.b0000 0004 0498 8167Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062 India
| | - Mohammed A. S. Abourehab
- grid.412832.e0000 0000 9137 6644Department of Pharmaceutics, College of Pharmacy, Umm Al-Qura University, Makkah, 21955 Saudi Arabia
| | - Wei Guo
- grid.412636.40000 0004 1757 9485Department of Pancreatic-Biliary Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Junhao Huang
- grid.412636.40000 0004 1757 9485Department of Pancreatic-Biliary Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Zhenning Wang
- grid.412636.40000 0004 1757 9485Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, 155N. Nanjing Street, Shenyang, 110001 Liaoning China ,grid.412449.e0000 0000 9678 1884Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, China Medical University, Shenyang, 110122 Liaoning China ,grid.412449.e0000 0000 9678 1884Institute of Health Sciences, China Medical University, Shenyang, 110122 Liaoning China
| | - Prashant Kesharwani
- grid.411816.b0000 0004 0498 8167Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062 India ,grid.412431.10000 0004 0444 045XCenter for Transdisciplinary Research, Department Of Pharmacology, Saveetha Dental College, Saveetha Institute of Medical and Technical Science, Chennai, India
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12
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Chen X, Shu W, Zhao L, Wan J. Advanced mass spectrometric and spectroscopic methods coupled with machine learning for in vitro diagnosis. VIEW 2022. [DOI: 10.1002/viw.20220038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- Xiaonan Chen
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
| | - Weikang Shu
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
| | - Liang Zhao
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
| | - Jingjing Wan
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
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13
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Yuan C, Zhang W, Wang J, Huang C, Shu B, Liang Q, Huang T, Wang J, Shi Q, Tang D, Wang Y. Chinese Medicine Phenomics (Chinmedphenomics): Personalized, Precise and Promising. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:383-388. [PMID: 36939806 PMCID: PMC9712866 DOI: 10.1007/s43657-022-00074-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/15/2022] [Accepted: 08/23/2022] [Indexed: 11/07/2022]
Abstract
The systematicness of phenomics and Traditional Chinese Medicine (TCM) enable these two disciplines to interlink with each other. This article discussed the similarity in theory and application between TCM and phenomics and illustrates their respective advantages in diagnosis and treatment of diseases, forming a new discipline eventually. Chinese medicine phenomics (Chinmedphenomics) is built on classic TCM, combined with phenomics technology, and the development of which needs the mega cohort with TCM syndrome and the characteristics of precision medicine as well as multi-disciplinary cooperation, which is personalized, precise and promising, providing unique scientific insights into understanding human health.
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Affiliation(s)
- Chunchun Yuan
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Institute of Spine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Key Laboratory of the Ministry of Education about Theory and Treatment of Muscles and Bones, Shanghai, 200032 China
| | - Weiqiang Zhang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Institute of Spine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Key Laboratory of the Ministry of Education about Theory and Treatment of Muscles and Bones, Shanghai, 200032 China
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200433 China
| | - Jing Wang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Institute of Spine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Key Laboratory of the Ministry of Education about Theory and Treatment of Muscles and Bones, Shanghai, 200032 China
- Academic Research Center of Shixiaoshan' Traumatology, Shanghai, 200032 China
- Famous Traditional Chinese Medicine Office, Shanghai, 200032 China
| | - Chen Huang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Institute of Spine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Key Laboratory of the Ministry of Education about Theory and Treatment of Muscles and Bones, Shanghai, 200032 China
| | - Bing Shu
- Key Laboratory of the Ministry of Education about Theory and Treatment of Muscles and Bones, Shanghai, 200032 China
- Academic Research Center of Shixiaoshan' Traumatology, Shanghai, 200032 China
- Famous Traditional Chinese Medicine Office, Shanghai, 200032 China
| | - Qianqian Liang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Institute of Spine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Key Laboratory of the Ministry of Education about Theory and Treatment of Muscles and Bones, Shanghai, 200032 China
- Academic Research Center of Shixiaoshan' Traumatology, Shanghai, 200032 China
- Famous Traditional Chinese Medicine Office, Shanghai, 200032 China
| | - Tingrui Huang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Institute of Spine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Key Laboratory of the Ministry of Education about Theory and Treatment of Muscles and Bones, Shanghai, 200032 China
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200433 China
| | - Qi Shi
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Institute of Spine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Key Laboratory of the Ministry of Education about Theory and Treatment of Muscles and Bones, Shanghai, 200032 China
- Academic Research Center of Shixiaoshan' Traumatology, Shanghai, 200032 China
- Famous Traditional Chinese Medicine Office, Shanghai, 200032 China
| | - Dezhi Tang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Institute of Spine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Key Laboratory of the Ministry of Education about Theory and Treatment of Muscles and Bones, Shanghai, 200032 China
- Academic Research Center of Shixiaoshan' Traumatology, Shanghai, 200032 China
- Famous Traditional Chinese Medicine Office, Shanghai, 200032 China
| | - Yongjun Wang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Institute of Spine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 China
- Key Laboratory of the Ministry of Education about Theory and Treatment of Muscles and Bones, Shanghai, 200032 China
- Academic Research Center of Shixiaoshan' Traumatology, Shanghai, 200032 China
- Famous Traditional Chinese Medicine Office, Shanghai, 200032 China
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14
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Zhou J, Zhong L. Applications of liquid chromatography-mass spectrometry based metabolomics in predictive and personalized medicine. Front Mol Biosci 2022; 9:1049016. [PMID: 36406271 PMCID: PMC9669074 DOI: 10.3389/fmolb.2022.1049016] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/24/2022] [Indexed: 11/05/2022] Open
Abstract
Metabolomics is a fast-developing technique used in biomedical researches focusing on pathological mechanism illustration or novel biomarker development for diseases. The ability of simultaneously quantifying thousands of metabolites in samples makes metabolomics a promising technique in predictive or personalized medicine-oriented researches and applications. Liquid chromatography-mass spectrometry is the most widely employed analytical strategy for metabolomics. In this current mini-review, we provide a brief update on the recent developments and novel applications of LC-MS based metabolomics in the predictive and personalized medicine sector, such as early diagnosis, molecular phenotyping or prognostic evaluation. COVID-19 related metabolomic studies are also summarized. We also discuss the prospects of metabolomics in precision medicine-oriented researches, as well as critical issues that need to be addressed when employing metabolomic strategy in clinical applications.
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Affiliation(s)
- Juntuo Zhou
- Beijing Boyuan Precision Medicine Co., Ltd., Beijing, China
- *Correspondence: Juntuo Zhou, ; Lijun Zhong,
| | - Lijun Zhong
- Center of Medical and Health Analysis, Peking University Health Science Center, Beijing, China
- *Correspondence: Juntuo Zhou, ; Lijun Zhong,
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15
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SHANG Z, LIU Y, YUAN YY, WANG XY, YU HY, GAO W. Early identification of STEMI patients with emergency chest pain using lipidomics combined with machine learning. J Geriatr Cardiol 2022; 19:685-695. [PMID: 36284682 PMCID: PMC9548052 DOI: 10.11909/j.issn.1671-5411.2022.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023] Open
Abstract
OBJECTIVES To analyze the differential expression of lipid spectrum between ST-segment elevated myocardial infarction (STEMI) and patients with emergency chest pain and excluded coronary artery disease (CAD), and establish the predictive model which could predict STEMI in the early stage. METHODS We conducted a single-center, nested case-control study using the emergency chest pain cohort of Peking University Third Hospital. Untargeted lipidomics were conducted while LASSO regression as well as XGBoost combined with greedy algorithm were used to select lipid molecules. RESULTS Fifty-two STEMI patients along with 52 controls were enrolled. A total of 1925 lipid molecules were detected. There were 93 lipid molecules in the positive ion mode which were differentially expressed between the STEMI and the control group, while in the negative ion mode, there were 73 differentially expressed lipid molecules. In the positive ion mode, the differentially expressed lipid subclasses were mainly diacylglycerol (DG), lysophophatidylcholine (LPC), acylcarnitine (CAR), lysophosphatidyl ethanolamine (LPE), and phosphatidylcholine (PC), while in the negative ion mode, significantly expressed lipid subclasses were mainly free fatty acid (FA), LPE, PC, phosphatidylethanolamine (PE), and phosphatidylinositol (PI). LASSO regression selected 22 lipids while XGBoost combined with greedy algorithm selected 10 lipids. PC (15: 0/18: 2), PI (19: 4), and LPI (20: 3) were the overlapping lipid molecules selected by the two feature screening methods. Logistic model established using the three lipids had excellent performance in discrimination and calibration both in the derivation set (AUC: 0.972) and an internal validation set (AUC: 0.967). In 19 STEMI patients with normal cardiac troponin, 18 patients were correctly diagnosed using lipid model. CONCLUSIONS The differentially expressed lipids were mainly DG, CAR, LPC, LPE, PC, PI, PE, and FA. Using lipid molecules selected by XGBoost combined with greedy algorithm and LASSO regression to establish model could accurately predict STEMI even in the more earlier stage.
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Affiliation(s)
- Zhi SHANG
- Department of Cardiology, Peking University Third Hospital, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Beijing, China
| | - Yang LIU
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, China
| | - Yu-Yao YUAN
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, China
| | - Xin-Yu WANG
- Department of Cardiology, Peking University Third Hospital, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Beijing, China
| | - Hai-Yi YU
- Department of Cardiology, Peking University Third Hospital, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Beijing, China
| | - Wei GAO
- Department of Cardiology, Peking University Third Hospital, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Beijing, China
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16
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Pu M, Tai Y, Yuan L, Zhang Y, Guo H, Hao Z, Chen J, Qi X, Wang G, Tao Z, Ren J. The contribution of proteasomal impairment to autophagy activation by C9orf72 poly-GA aggregates. Cell Mol Life Sci 2022; 79:501. [PMID: 36036324 DOI: 10.1007/s00018-022-04518-5] [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: 04/27/2022] [Revised: 07/18/2022] [Accepted: 08/08/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Poly-GA, a dipeptide repeat protein unconventionally translated from GGGGCC (G4C2) repeat expansions in C9orf72, is abundant in C9orf72-related amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) (C9orf72-ALS/FTD). Although the poly-GA aggregates have been identified in C9orf72-ALS/FTD neurons, the effects on UPS (ubiquitin-proteasome system) and autophagy and their exact molecular mechanisms have not been fully elucidated. RESULTS Herein, our in vivo experiments indicate that the mice expressing ploy-GA with 150 repeats instead of 30 repeats exhibit significant aggregates in cells. Mice expressing 150 repeats ploy-GA shows behavioral deficits and activates autophagy in the brain. In vitro findings suggest that the poly-GA aggregates influence proteasomal by directly binding proteasome subunit PSMD2. Subsequently, the poly-GA aggregates activate phosphorylation and ubiquitination of p62 to recruit autophagosomes. Ultimately, the poly-GA aggregates lead to compensatory activation of autophagy. In vivo studies further reveal that rapamycin (autophagy activator) treatment significantly improves the degenerative symptoms and alleviates neuronal injury in mice expressing 150 repeats poly-GA. Meanwhile, rapamycin administration to mice expressing 150 repeats poly-GA reduces neuroinflammation and aggregates in the brain. CONCLUSION In summary, we elucidate the relationship between poly-GA in the proteasome and autophagy: when poly-GA forms complexes with the proteasome, it recruits autophagosomes and affects proteasome function. Our study provides support for further promoting the comprehension of the pathogenesis of C9orf72, which may bring a hint for the exploration of rapamycin for the treatment of ALS/FTD.
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Affiliation(s)
- Mei Pu
- Center for Drug Safety Evaluation and Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yusi Tai
- Center for Drug Safety Evaluation and Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Luyang Yuan
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
| | - Yu Zhang
- Key Laboratory of Receptor Research, Department of Neuropharmacology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Huijie Guo
- Center for Drug Safety Evaluation and Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zongbing Hao
- Laboratory of Molecular Neuropathology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and Department of Pharmacology, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China
| | - Jing Chen
- Center for Drug Safety Evaluation and Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai, China
| | - Xinming Qi
- Center for Drug Safety Evaluation and Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai, China
| | - Guanghui Wang
- Laboratory of Molecular Neuropathology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and Department of Pharmacology, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China.
| | - Zhouteng Tao
- Center for Drug Safety Evaluation and Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai, China.
- State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China.
| | - Jin Ren
- Center for Drug Safety Evaluation and Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China.
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17
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Identification of ferroptosis-related signature with potential implications in prognosis and immunotherapy of renal cell carcinoma. Apoptosis 2022; 27:946-960. [PMID: 36028785 DOI: 10.1007/s10495-022-01766-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/13/2022] [Indexed: 11/02/2022]
Abstract
Developing individualized therapies for different renal cell carcinoma patients is pivotal for improving the efficacy of immunotherapy. It has been reported that ferroptosis is involved in T cell-mediated anti-tumor immunity, and that therapeutic approaches targeting tumor ferroptosis pathway in combination with immune checkpoint blockade drugs improve the efficacy of cancer immunotherapy. This study focused specifically on ferroptosis genes to identify novel biomarkers that reflect prognosis in different renal cell carcinoma subtypes. LASSO algorithm and multivariate Cox regression were initiated for identifying ferroptosis-related multigene risk signature (FRGsig) and established a FRGsig score model. We used multiple tumor microenvironment gene signatures and methods to infer tumor microenvironment status and immune cell invasion levels. Our study found that high FRGsig score was associated with poor prognosis in patients with predominant histologic subtypes of renal cell carcinoma. And high FRGsig score samples had higher levels of anti-tumor immunity cells infiltration, and there was a feedback mechanism whereby anti-tumor inflammation promoted the recruitment or differentiation of immunosuppressive cells. FRGsig was a potential biomarker for predicting the response to immune checkpoint blockade therapy in kidney clear cell carcinoma and kidney papillary cell carcinoma, and the kidney papillary cell carcinoma patients with high FRGsig was associated with better response to anti-VEGF therapy. Our findings provided further insights into assessing immunotherapy sensitivity of predominant histologic subtypes of renal cell carcinoma. FRGsig might be a potential biomarker for predicting the efficacy of angiogenic blocking drugs or immune checkpoint inhibitors in different renal cell carcinoma subtypes, enabling more precise patient selection.
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Metabolic Pathways as a Novel Landscape in Pancreatic Ductal Adenocarcinoma. Cancers (Basel) 2022; 14:cancers14153799. [PMID: 35954462 PMCID: PMC9367608 DOI: 10.3390/cancers14153799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/01/2022] [Accepted: 08/03/2022] [Indexed: 11/17/2022] Open
Abstract
Metabolism plays a fundamental role in both human physiology and pathology, including pancreatic ductal adenocarcinoma (PDAC) and other tumors. Anabolic and catabolic processes do not only have energetic implications but are tightly associated with other cellular activities, such as DNA duplication, redox reactions, and cell homeostasis. PDAC displays a marked metabolic phenotype and the observed reduction in tumor growth induced by calorie restriction with in vivo models supports the crucial role of metabolism in this cancer type. The aggressiveness of PDAC might, therefore, be reduced by interventions on bioenergetic circuits. In this review, we describe the main metabolic mechanisms involved in PDAC growth and the biological features that may favor its onset and progression within an immunometabolic context. We also discuss the need to bridge the gap between basic research and clinical practice in order to offer alternative therapeutic approaches for PDAC patients in the more immediate future.
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19
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Zuzčák M, Trnka J. Cellular metabolism in pancreatic cancer as a tool for prognosis and treatment (Review). Int J Oncol 2022; 61:93. [PMID: 35730611 PMCID: PMC9256076 DOI: 10.3892/ijo.2022.5383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 05/10/2022] [Indexed: 11/28/2022] Open
Abstract
Pancreatic cancer (PC) has one of the highest fatality rates and the currently available therapeutic options are not sufficient to improve its overall poor prognosis. In addition to insufficient effectiveness of anticancer treatments, the lack of clear early symptoms and early metastatic spread maintain the PC survival rates at a low level. Metabolic reprogramming is among the hallmarks of cancer and could be exploited for the diagnosis and treatment of PC. PC is characterized by its heterogeneity and, apart from molecular subtypes, the identification of metabolic subtypes in PC could aid in the development of more individualized therapeutic approaches and may lead to improved clinical outcomes. In addition to the deregulated utilization of glucose in aerobic glycolysis, PC cells can use a wide range of substrates, including branched‑chain amino acids, glutamine and lipids to fulfil their energy requirements, as well as biosynthetic needs. The tumor microenvironment in PC supports tumor growth, metastatic spread, treatment resistance and the suppression of the host immune response. Moreover, reciprocal interactions between cancer and stromal cells enhance their metabolic reprogramming. PC stem cells (PCSCs) with an increased resistance and distinct metabolic properties are associated with disease relapses and cancer spread, and represent another significant candidate for therapeutic targeting. The present review discusses the metabolic signatures observed in PC, a disease with a multifaceted and often transient metabolic landscape. In addition, the metabolic pathways utilized by PC cells, as well as stromal cells are discussed, providing examples of how they could present novel targets for therapeutic interventions and elaborating on how interactions between the various cell types affect their metabolism. Furthermore, the importance of PCSCs is discussed, focusing specifically on their metabolic adaptations.
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Affiliation(s)
- Michal Zuzčák
- Department of Biochemistry, Cell and Molecular Biology, Third Faculty of Medicine, Charles University, 10000 Prague, Czech Republic
- Center for Research on Nutrition, Metabolism and Diabetes, Third Faculty of Medicine, Charles University, 10000 Prague, Czech Republic
| | - Jan Trnka
- Department of Biochemistry, Cell and Molecular Biology, Third Faculty of Medicine, Charles University, 10000 Prague, Czech Republic
- Center for Research on Nutrition, Metabolism and Diabetes, Third Faculty of Medicine, Charles University, 10000 Prague, Czech Republic
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20
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Yang H, Jian L, Jin Q, Xia K, Cai-Ru W, Jun S, Chen H, Wei W, Ben-Jing S, Shi-Hong L, Shi-Wei L, Juan W, Wei Z. CCL7 playing a dominant role in recruiting early OCPs to facilitate osteolysis at metastatic site of colorectal cancer. Cell Commun Signal 2022; 20:94. [PMID: 35715847 PMCID: PMC9205124 DOI: 10.1186/s12964-022-00867-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/27/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Chemoattractant is critical to recruitment of osteoclast precursors and stimulates tumor bone metastasis. However, the role of chemoattractant in bone metastasis of colorectal cancer (CRC) is still unclear. METHODS Histochemistry analysis and TRAP staining were utilized to detect the bone resorption and activation of osteoclasts (OCs) after administration of CCL7 neutralizing antibody or CCR1 siRNA. qRT-PCR analysis and ELISA assay were performed to detect the mRNA level and protein level of chemoattractant. BrdU assay and Tunel assay were used to detect the proliferation and apoptosis of osteoclast precursors (OCPs). The migration of OCPs was detected by Transwell assay. Western blots assay was performed to examine the protein levels of pathways regulating the expression of CCL7 or CCR1. RESULTS OCPs-derived CCL7 was significantly upregulated in bone marrow after bone metastasis of CRC. Blockage of CCL7 efficiently prevented bone resorption. Administration of CCL7 promoted the migration of OCPs. Lactate promoted the expression of CCL7 through JNK pathway. In addition, CCR1 was the most important receptor of CCL7. CONCLUSION Our study indicates the essential role of CCL7-CCR1 signaling for recruitment of OCPs in early bone metastasis of CRC. Targeting CCL7 or CCR1 could restore the bone volume, which could be a potential therapeutical target. Video Abstract.
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Affiliation(s)
- He Yang
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China.,Chengdu Medical College, Rongdu Avenue No. 601, Chengdu, 610000, People's Republic of China
| | - Li Jian
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China.,Chengdu Medical College, Rongdu Avenue No. 601, Chengdu, 610000, People's Republic of China
| | - Qian Jin
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China
| | - Kang Xia
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China.
| | - Wang Cai-Ru
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China
| | - Sheng Jun
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China
| | - Huang Chen
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China
| | - Wang Wei
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China
| | - Song Ben-Jing
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China
| | - Li Shi-Hong
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China
| | - Long Shi-Wei
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China
| | - Wu Juan
- Department of Pharmacy, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China.
| | - Zheng Wei
- Department of Orthopedics, General Hospital of Western Theater Command, Rongdu Avenue No. 270, Chengdu, 610000, People's Republic of China. .,Chengdu Medical College, Rongdu Avenue No. 601, Chengdu, 610000, People's Republic of China.
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Islam MM, Goertzen A, Singh PK, Saha R. Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling. iScience 2022; 25:104483. [PMID: 35712079 PMCID: PMC9194136 DOI: 10.1016/j.isci.2022.104483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/08/2022] [Accepted: 05/23/2022] [Indexed: 11/18/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a major research focus because of its poor therapy response and dismal prognosis. PDAC cells adapt their metabolism to the surrounding environment, often relying on diverse nutrient sources. Because traditional experimental techniques appear exhaustive to find a viable therapeutic strategy, a highly curated and omics-informed PDAC genome-scale metabolic model was reconstructed using patient-specific transcriptomics data. From the model-predictions, several new metabolic functions were explored as potential therapeutic targets in addition to the known metabolic hallmarks of PDAC. Significant downregulation in the peroxisomal beta oxidation pathway, flux modulation in the carnitine shuttle system, and upregulation in the reactive oxygen species detoxification pathway reactions were observed. These unique metabolic traits of PDAC were correlated with potential drug combinations targeting genes with poor prognosis in PDAC. Overall, this study provides a better understanding of the metabolic vulnerabilities in PDAC and will lead to novel effective therapeutic strategies.
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Affiliation(s)
- Mohammad Mazharul Islam
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Andrea Goertzen
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Pankaj K. Singh
- Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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22
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Design of a Mechanobioreactor to Apply Anisotropic, Biaxial Strain to Large Thin Biomaterials for Tissue Engineered Heart Valve Applications. Ann Biomed Eng 2022; 50:1073-1089. [PMID: 35622208 DOI: 10.1007/s10439-022-02984-3] [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: 02/01/2022] [Accepted: 05/16/2022] [Indexed: 01/05/2023]
Abstract
Repair and replacement solutions for congenitally diseased heart valves capable of post-surgery growth and adaptation have remained elusive. Tissue engineered heart valves (TEHVs) offer a potential biological solution that addresses the drawbacks of existing valve replacements. Typically, TEHVs are made from thin, fibrous biomaterials that either become cell populated in vitro or in situ. Often, TEHV designs poorly mimic the anisotropic mechanical properties of healthy native valves leading to inadequate biomechanical function. Mechanical conditioning of engineered tissues with anisotropic strain application can induce extracellular matrix remodelling to alter the anisotropic mechanical properties of a construct, but implementation has been limited to small-scale set-ups. To address this limitation for TEHV applications, we designed and built a mechanobioreactor capable of modulating biaxial strain anisotropy applied to large, thin, biomaterial sheets in vitro. The bioreactor can independently control two orthogonal stretch axes to modulate applied strain anisotropy on biomaterial sheets from 13 × 13 mm2 to 70 × 40 mm2. A design of experiments was performed using experimentally validated finite element (FE) models and demonstrated that biaxial strain was applied uniformly over a larger percentage of the cell seeded area for larger sheets (13 × 13 mm2: 58% of sheet area vs. 52 × 31 mm2: 86% of sheet area). Furthermore, bioreactor prototypes demonstrated that over 70% of the cell seeding area remained uniformly strained under different prescribed protocols: equibiaxial amplitudes between 5 to 40%, cyclic frequencies between 0.1 to 2.5 Hz and anisotropic strain ratios between 0:1 (constrained uniaxial) to 2:1. Lastly, proof-of-concept experiments were conducted where we applied equibiaxial (εx = εy = 8.75%) and anisotropic (εx = 12.5%, εy = 5%) strain protocols to cell-seeded, electrospun scaffolds. Cell nuclei and F-actin aligned to the vector-sum strain direction of each prescribed protocol (nuclei alignment: equibiaxial: 43.2° ± 1.8°, anisotropic: 17.5° ± 1.7°; p < 0.001). The abilities of this bioreactor to prescribe different strain amplitude, frequency and strain anisotropy protocols to cell-seeded scaffolds will enable future studies into the effects of anisotropic loading protocols on mechanically conditioned TEHVs and other engineered planar connective tissues.
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