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Qian J, Zheng W, Fang J, Cheng S, Zhang Y, Zhuang X, Song C. Causal relationships of gut microbiota, plasma metabolites, and metabolite ratios with diffuse large B-cell lymphoma: a Mendelian randomization study. Front Microbiol 2024; 15:1356437. [PMID: 38860219 PMCID: PMC11163048 DOI: 10.3389/fmicb.2024.1356437] [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: 12/15/2023] [Accepted: 05/08/2024] [Indexed: 06/12/2024] Open
Abstract
Background Recent studies have revealed changes in microbiota constitution and metabolites associated with tumor progression, however, no causal relation between microbiota or metabolites and diffuse large B-cell lymphoma (DLBCL) has yet been reported. Methods We download a microbiota dataset from the MiBioGen study, a metabolites dataset from the Canadian Longitudinal Study on Aging (CLSA) study, and a DLBCL dataset from Integrative Epidemiology Unit Open genome-wide association study (GWAS) project. Mendelian randomization (MR) analysis was conducted using the R packages, TwoSampleMR and MR-PRESSO. Five MR methods were used: MR-Egger, inverse variance weighting (IVW), weighted median, simple mode, and weighted mode. Reverse MR analyses were also conducted to explore the causal effects of DLBCL on the microbiome, metabolites, and metabolite ratios. Pleiotropy was evaluated by MR Egger regression and MR-PRESSO global analyses, heterogeneity was assessed by Cochran's Q-test, and stability analyzed using the leave-one-out method. Results 119 microorganisms, 1,091 plasma metabolite, and 309 metabolite ratios were analyzed. According to IVW analysis, five microorganisms were associated with risk of DLBCL. The genera Terrisporobacter (OR: 3.431, p = 0.049) andgenera Oscillibacter (OR: 2.406, p = 0.029) were associated with higher risk of DLBCL. Further, 27 plasma metabolites were identified as having a significant causal relationships with DLBCL, among which citrate levels had the most significant protective causal effect against DLBCL (p = 0.006), while glycosyl-N-tricosanoyl-sphingadienine levels was related to higher risk of DLBCL (p = 0.003). In addition, we identified 19 metabolite ratios with significant causal relationships to DLBCL, of which taurine/glutamate ratio had the most significant protective causal effect (p = 0.005), while the phosphoethanolamine/choline ratio was related to higher risk of DLBCL (p = 0.009). Reverse MR analysis did not reveal any significant causal influence of DLBCL on the above microbiota, metabolites, and metabolite ratios (p > 0.05). Sensitivity analyses revealed no significant heterogeneity or pleiotropy (p > 0.05). Conclusion We present the first elucidation of the causal influence of microbiota and metabolites on DLBCL using MR methods, providing novel insights for potential targeting of specific microbiota or metabolites to prevent, assist in diagnosis, and treat DLBCL.
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Affiliation(s)
- Jingrong Qian
- Department of Clinical Laboratory, Shandong Provincial Third Hospital, Shandong University, Jinan, Shandong, China
| | - Wen Zheng
- Department of Clinical Laboratory, Shandong Provincial Third Hospital, Shandong University, Jinan, Shandong, China
| | - Jun Fang
- Department of Medical Engineering, Shandong Provincial Third Hospital, Shandong University, Jinan, Shandong, China
| | - Shiliang Cheng
- Department of Clinical Laboratory, Shandong Provincial Third Hospital, Shandong University, Jinan, Shandong, China
| | - Yanli Zhang
- Department of Clinical Laboratory, Shandong Provincial Third Hospital, Shandong University, Jinan, Shandong, China
| | - Xuewei Zhuang
- Department of Clinical Laboratory, Shandong Provincial Third Hospital, Shandong University, Jinan, Shandong, China
| | - Chao Song
- Department of Administration, Shandong Provincial Third Hospital, Shandong University, Jinan, Shandong, China
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Zhu QY. Bioinformatics analysis of the pathogenic link between Epstein-Barr virus infection, systemic lupus erythematosus and diffuse large B cell lymphoma. Sci Rep 2023; 13:6310. [PMID: 37072474 PMCID: PMC10113247 DOI: 10.1038/s41598-023-33585-2] [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] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 04/15/2023] [Indexed: 05/03/2023] Open
Abstract
Epstein-Barr virus (EBV) is a risk factor for diffuse large B-cell lymphoma (DLBCL) and systemic lupus erythematosus (SLE). While prior research has suggested a potential correlation between SLE and DLBCL, the molecular mechanisms remain unclear. The present study aimed to explore the contribution of EBV infection to the pathogenesis of DLBCL in the individuals with SLE using bioinformatics approaches. The Gene Expression Omnibus database was used to compile the gene expression profiles of EBV-infected B cells (GSE49628), SLE (GSE61635), and DLBCL (GSE32018). Altogether, 72 shared common differentially expressed genes (DEGs) were extracted and enrichment analysis of the shared genes showed that p53 signaling pathway was a common feature of the pathophysiology. Six hub genes were selected using protein-protein interaction (PPI) network analysis, including CDK1, KIF23, NEK2, TOP2A, NEIL3 and DEPDC1, which showed preferable diagnostic values for SLE and DLBCL and involved in immune cell infiltration and immune responses regulation. Finally, TF-gene and miRNA-gene regulatory networks and 10 potential drugs molecule were predicted. Our study revealed the potential molecular mechanisms by which EBV infection contribute to the susceptibility of DLBCL in SLE patients for the first time and identified future biomarkers and therapeutic targets for SLE and DLBCL.
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Affiliation(s)
- Qian-Ying Zhu
- Department of Laboratory Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518003, People's Republic of China.
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Alfaifi A, Refai MY, Alsaadi M, Bahashwan S, Malhan H, Al-Kahiry W, Dammag E, Ageel A, Mahzary A, Albiheyri R, Almehdar H, Qadri I. Metabolomics: A New Era in the Diagnosis or Prognosis of B-Cell Non-Hodgkin's Lymphoma. Diagnostics (Basel) 2023; 13:diagnostics13050861. [PMID: 36900005 PMCID: PMC10000528 DOI: 10.3390/diagnostics13050861] [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: 01/20/2023] [Revised: 02/19/2023] [Accepted: 02/22/2023] [Indexed: 03/12/2023] Open
Abstract
A wide range of histological as well as clinical properties are exhibited by B-cell non-Hodgkin's lymphomas. These properties could make the diagnostics process complicated. The diagnosis of lymphomas at an initial stage is essential because early remedial actions taken against destructive subtypes are commonly deliberated as successful and restorative. Therefore, better protective action is needed to improve the condition of those patients who are extensively affected by cancer when diagnosed for the first time. The development of new and efficient methods for early detection of cancer has become crucial nowadays. Biomarkers are urgently needed for diagnosing B-cell non-Hodgkin's lymphoma and assessing the severity of the disease and its prognosis. New possibilities are now open for diagnosing cancer with the help of metabolomics. The study of all the metabolites synthesised in the human body is called "metabolomics." A patient's phenotype is directly linked with metabolomics, which can help in providing some clinically beneficial biomarkers and is applied in the diagnostics of B-cell non-Hodgkin's lymphoma. In cancer research, it can analyse the cancerous metabolome to identify the metabolic biomarkers. This review provides an understanding of B-cell non-Hodgkin's lymphoma metabolism and its applications in medical diagnostics. A description of the workflow based on metabolomics is also provided, along with the benefits and drawbacks of various techniques. The use of predictive metabolic biomarkers for the diagnosis and prognosis of B-cell non-Hodgkin's lymphoma is also explored. Thus, we can say that abnormalities related to metabolic processes can occur in a vast range of B-cell non-Hodgkin's lymphomas. The metabolic biomarkers could only be discovered and identified as innovative therapeutic objects if we explored and researched them. In the near future, the innovations involving metabolomics could prove fruitful for predicting outcomes and bringing out novel remedial approaches.
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Affiliation(s)
- Abdullah Alfaifi
- Department of Biological Science, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Fayfa General Hospital, Ministry of Health, Jazan 83581, Saudi Arabia
| | - Mohammed Y. Refai
- Department of Biochemistry, College of Science, University of Jeddah, Jeddah 21493, Saudi Arabia
| | - Mohammed Alsaadi
- Department of Biological Science, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Hematology Research Unit, King Fahad Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Salem Bahashwan
- Hematology Research Unit, King Fahad Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Hematology, Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- King Abdulaziz University Hospital, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Hafiz Malhan
- Prince Mohammed Bin Nasser Hospital, Ministry of Health, Jazan 82943, Saudi Arabia
| | - Waiel Al-Kahiry
- Prince Mohammed Bin Nasser Hospital, Ministry of Health, Jazan 82943, Saudi Arabia
| | - Enas Dammag
- Prince Mohammed Bin Nasser Hospital, Ministry of Health, Jazan 82943, Saudi Arabia
| | - Ageel Ageel
- Prince Mohammed Bin Nasser Hospital, Ministry of Health, Jazan 82943, Saudi Arabia
| | - Amjed Mahzary
- Eradah Hospital, Ministry of Health, Jazan 82943, Saudi Arabia
| | - Raed Albiheyri
- Department of Biological Science, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Hussein Almehdar
- Department of Biological Science, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Ishtiaq Qadri
- Department of Biological Science, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Correspondence:
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Abstract
Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.
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Affiliation(s)
- Zuhal Eraslan
- Department of Dermatology, Weill Cornell Medicine, New York, NY, USA
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of University of Barcelona (IBUB), University of Barcelona, Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Ulrich L Günther
- Institute of Chemistry and Metabolomics, University of Lübeck, Lübeck, Germany.
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Fei F, Zheng M, Xu Z, Sun R, Chen X, Cao B, Li J. Plasma Metabolites Forecast Occurrence and Prognosis for Patients With Diffuse Large B-Cell Lymphoma. Front Oncol 2022; 12:894891. [PMID: 35734601 PMCID: PMC9207198 DOI: 10.3389/fonc.2022.894891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 05/09/2022] [Indexed: 11/24/2022] Open
Abstract
Background Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin’s lymphoma with considerable heterogeneity and different clinical prognosis. However, plasma metabomics used to forecast occurrence and prognosis of DLBCL are rarely addressed. Method A total of 65 volunteers including 22 healthy controls (Ctrl), 25 DLBCL patients newly diagnosed (ND), and 18 DLBCL patients achieving complete remission (CR) were enrolled. A gas chromatography mass spectrometry-based untargeted plasma metabolomics analysis was performed. Results Multivariate statistical analysis displayed distinct metabolic features among Crtl, ND, and CR groups. Surprisingly, metabolic profiles of newly diagnosed DLBCL patients undergoing different prognosis showed clear and distinctive clustering. Based on the candidate metabolic biomarkers (glucose and aspartate) and clinical indicators (lymphocyte, red blood count, and hemoglobin), a distinct diagnostic equation was established showing improved diagnostic performance with an area under curve of 0.936. The enrichment of citric acid cycle, deficiency of branched chain amino acid, methionine, and cysteine in newly diagnosed DLBCL patients was closely associated with poor prognosis. In addition, we found that malate and 2-hydroxy-2-methylbutyric acid were positively correlated with the baseline tumor metabolic parameters (metabolically active tumor volume and total lesion glycolysis), and the higher abundance of plasma malate, the poorer survival. Conclusion Our preliminary data suggested plasma metabolomics study was informative to characterize the metabolic phenotypes and forecast occurrence and prognosis of DLBCL. Malate was identified as an unfavorable metabolic biomarker for prognosis-prediction of DLBCL, which provided a new insight on risk-stratification and therapeutic targets of DLBCL. More studies to confirm these associations and investigate potential mechanisms are in the process.
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Affiliation(s)
- Fei Fei
- Phase I Clinical Trials Unit, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Meihong Zheng
- Phase I Clinical Trials Unit, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Zhenzhen Xu
- Phase I Clinical Trials Unit, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Runbin Sun
- Phase I Clinical Trials Unit, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Xin Chen
- Department of General Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Bei Cao
- Phase I Clinical Trials Unit, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Juan Li
- Phase I Clinical Trials Unit, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
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