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Zhao G, Cai Y, Wang Y, Fang Y, Wang S, Li N. Genetically predicted blood metabolites mediate the association between circulating immune cells and pancreatic cancer: A Mendelian randomization study. J Gene Med 2024; 26:e3691. [PMID: 38757222 DOI: 10.1002/jgm.3691] [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: 02/29/2024] [Revised: 04/02/2024] [Accepted: 04/13/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Pancreatic cancer is characterized by metabolic dysregulation and unique immunological profiles. Nevertheless, the comprehensive understanding of immune and metabolic dysregulation of pancreatic cancer remains unclear. In the present study, we aimed to investigate the causal relationship of circulating immune cells and pancreatic cancer and identify the blood metabolites as potential mediators. METHODS The exposure and outcome genome-wide association studies (GWAS) data used in the present study were obtained from the GWAS open-access database (https://gwas.mrcieu.ac.uk). The study used 731 circulating immune cell features, 1400 types of blood metabolites and pancreatic cancer from GWAS. We then performed bidirectional Mendelian randomization (MR) analyses to explore the causal relationships between the circulating immune cells and pancreatic cancer, and two-step MR to discover potential mediating blood metabolites in this process. All statistical analyses were performed in R software. The STROBE-MR (i.e. Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization) checklist for the reporting of MR studies was also used. RESULTS MR analysis identified seven types of circulating immune cells causally associated with pancreatic cancer. Furthermore, there was no strong evidence that genetically predicted pancreatic cancer had an effect on these seven types of circulating immune cells. Further two-step MR analysis found 10 types of blood metabolites were causally associated with pancreatic cancer and the associations between circulating CD39+CD8+ T cells and pancreatic cancer were mediated by blood orotates with proportions of 5.18% (p = 0.016). CONCLUSIONS The present study provides evidence supporting the causal relationships between various circulating immune cells, especially CD39+CD8+ T cells, and pancreatic cancer, with a potential effect mediated by blood orotates. Further research is needed on additional risk factors as potential mediators and establish a comprehensive immunity-metabolism network in pancreatic cancer.
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Affiliation(s)
- Guo Zhao
- Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuanting Cai
- Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuning Wang
- Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuan Fang
- Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuhang Wang
- Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ning Li
- Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Borgmästars E, Jacobson S, Simm M, Johansson M, Billing O, Lundin C, Nyström H, Öhlund D, Lubovac-Pilav Z, Jonsson P, Franklin O, Sund M. Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank. J Gastrointest Oncol 2024; 15:755-767. [PMID: 38756646 PMCID: PMC11094504 DOI: 10.21037/jgo-23-930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/31/2024] [Indexed: 05/18/2024] Open
Abstract
Background Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers. Methods We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score. Results Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714-0.854] compared to 0.681 (95% CI: 0.597-0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1]. Conclusions Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.
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Affiliation(s)
- Emmy Borgmästars
- Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Umeå, Sweden
| | - Sara Jacobson
- Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Umeå, Sweden
| | - Maja Simm
- Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Umeå, Sweden
- Department of Clinical Sciences/Obstetrics and Gynecology, Umeå University, Umeå, Sweden
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Ola Billing
- Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Umeå, Sweden
| | - Christina Lundin
- Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Umeå, Sweden
| | - Hanna Nyström
- Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Daniel Öhlund
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
- Department of Radiation Sciences/Oncology, Umeå University, Umeå, Sweden
| | | | - Pär Jonsson
- Department of Chemistry, Umeå University, Umeå, Sweden
| | - Oskar Franklin
- Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Umeå, Sweden
- Division of Surgical Oncology, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Malin Sund
- Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Umeå, Sweden
- Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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Yao F, Xu M, Zhu T, Yu G. A rare case of non-small cell lung cancer with progressive elevation of CA199 as its first manifestation. Asian J Surg 2024:S1015-9584(24)00573-6. [PMID: 38604846 DOI: 10.1016/j.asjsur.2024.03.146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 03/21/2024] [Indexed: 04/13/2024] Open
Affiliation(s)
- Fuqiang Yao
- Department of Thoracic Surgery, Shaoxing People's Hospital, Shaoxing, Zhejiang, China
| | - Minghao Xu
- Department of Thoracic Surgery, Shaoxing People's Hospital, Shaoxing, Zhejiang, China
| | - Ting Zhu
- Department of Thoracic Surgery, Shaoxing People's Hospital, Shaoxing, Zhejiang, China
| | - Guangmao Yu
- Department of Thoracic Surgery, Shaoxing People's Hospital, Shaoxing, Zhejiang, China.
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Pautova AK. Metabolic Profiling of Aromatic Compounds. Metabolites 2024; 14:107. [PMID: 38392999 PMCID: PMC10890443 DOI: 10.3390/metabo14020107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024] Open
Abstract
Metabolic profiling is a powerful modern tool in searching for novel biomarkers and indicators of normal or pathological processes in the body [...].
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Affiliation(s)
- Alisa K Pautova
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 25-2 Petrovka Str., 107031 Moscow, Russia
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Zhang H, Liu S, Wang Y, Huang H, Sun L, Yuan Y, Cheng L, Liu X, Ning K. Deep learning enhanced the diagnostic merit of serum glycome for multiple cancers. iScience 2024; 27:108715. [PMID: 38226168 PMCID: PMC10788220 DOI: 10.1016/j.isci.2023.108715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/24/2023] [Accepted: 12/11/2023] [Indexed: 01/17/2024] Open
Abstract
Protein glycosylation is associated with the pathogenesis of various cancers. The utilization of certain glycans in cancer diagnosis models holds promise, yet their accuracy is not always guaranteed. Here, we investigated the utility of deep learning techniques, specifically random forests combined with transfer learning, in enhancing serum glycome's discriminative power for cancer diagnosis (including ovarian cancer, non-small cell lung cancer, gastric cancer, and esophageal cancer). We started with ovarian cancer and demonstrated that transfer learning can achieve superior performance in data-disadvantaged cohorts (AUROC >0.9), outperforming the approach of PLS-DA. We identified a serum glycan-biomarker panel including 18 serum N-glycans and 4 glycan derived traits, most of which were featured with sialylation. Furthermore, we validated advantage of the transfer learning scheme across other cancer groups. These findings highlighted the superiority of transfer learning in improving the performance of glycans-based cancer diagnosis model and identifying cancer biomarkers, providing a new high-fidelity cancer diagnosis venue.
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Affiliation(s)
- Haobo Zhang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Si Liu
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, Fujian, China
| | - Yi Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hanhui Huang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Lukang Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Youyuan Yuan
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Liming Cheng
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xin Liu
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
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León-Letelier RA, Dou R, Vykoukal J, Yip-Schneider MT, Maitra A, Irajizad E, Wu R, Dennison JB, Do KA, Zhang J, Schmidt CM, Hanash S, Fahrmann JF. Contributions of the Microbiome-Derived Metabolome for Risk Assessment and Prognostication of Pancreatic Cancer. Clin Chem 2024; 70:102-115. [PMID: 38175578 DOI: 10.1093/clinchem/hvad186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/16/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Increasing evidence implicates microbiome involvement in the development and progression of pancreatic ductal adenocarcinoma (PDAC). Studies suggest that reflux of gut or oral microbiota can lead to colonization in the pancreas, resulting in dysbiosis that culminates in release of microbial toxins and metabolites that potentiate an inflammatory response and increase susceptibility to PDAC. Moreover, microbe-derived metabolites can exert direct effector functions on precursors and cancer cells, as well as other cell types, to either promote or attenuate tumor development and modulate treatment response. CONTENT The occurrence of microbial metabolites in biofluids thereby enables risk assessment and prognostication of PDAC, as well as having potential for design of interception strategies. In this review, we first highlight the relevance of the microbiome for progression of precancerous lesions in the pancreas and, using liquid chromatography-mass spectrometry, provide supporting evidence that microbe-derived metabolites manifest in pancreatic cystic fluid and are associated with malignant progression of intraductal papillary mucinous neoplasm(s). We secondly summarize the biomarker potential of microbe-derived metabolite signatures for (a) identifying individuals at high risk of developing or harboring PDAC and (b) predicting response to treatment and disease outcomes. SUMMARY The microbiome-derived metabolome holds considerable promise for risk assessment and prognostication of PDAC.
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Affiliation(s)
- Ricardo A León-Letelier
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Rongzhang Dou
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jody Vykoukal
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Michele T Yip-Schneider
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Anirban Maitra
- Department of Translational Molecular Pathology and Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ehsan Irajizad
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ranran Wu
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jennifer B Dennison
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kim-An Do
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jianjun Zhang
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, United States
| | - C Max Schmidt
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Samir Hanash
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Johannes F Fahrmann
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Qin YM, Sha J. Progress in understanding of relationship between intestinal microecology and pancreatic cancer. Shijie Huaren Xiaohua Zazhi 2023; 31:1001-1006. [DOI: 10.11569/wcjd.v31.i24.1001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 12/28/2023] Open
Abstract
In recent years, the association between the gut microbiota (GM) and pancreatic cancer (PC) has attracted extensive attention. Studies have shown that the oral, intestinal, and pancreatic microbiota of PC patients is different from that of healthy people, showing different characteristics. On this basis, the application of characteristic GM and its metabolites as biomarkers for early diagnosis and prognosis evaluation of PC holds great potential. Intestinal microecological therapy targeting the GM, such as probiotics and fecal microbiota transplantation, may affect the response to chemotherapy and immunotherapy by remodeling the tumor microenvironment, to improve the prognosis. In this paper, we review the role of the GM in PC development, early diagnosis, prognosis assessment, and treatment.
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Affiliation(s)
- Yu-Meng Qin
- Jingjiang People's Hospital, Taizhou 214500, Jiangsu Province, China
| | - Jie Sha
- Jingjiang People's Hospital, Taizhou 214500, Jiangsu Province, China
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