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Jung S, Silva S, Dallal CM, LeBlanc E, Paris K, Shepherd J, Snetselaar LG, Van Horn L, Zhang Y, Dorgan JF. Untargeted serum metabolomic profiles and breast density in young women. Cancer Causes Control 2024; 35:323-334. [PMID: 37737303 DOI: 10.1007/s10552-023-01793-w] [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: 01/21/2023] [Accepted: 09/06/2023] [Indexed: 09/23/2023]
Abstract
PURPOSE OF THE STUDY Breast density is an established risk factor for breast cancer. However, little is known about metabolic influences on breast density phenotypes. We conducted untargeted serum metabolomics analyses to identify metabolic signatures associated with breast density phenotypes among young women. METHODS In a cross-sectional study of 173 young women aged 25-29 who participated in the Dietary Intervention Study in Children 2006 Follow-up Study, 449 metabolites were measured in fasting serum samples using ultra-high-performance liquid chromatography-tandem mass spectrometry. Multivariable-adjusted mixed-effects linear regression identified metabolites associated with magnetic resonance imaging measured breast density phenotypes: percent dense breast volume (%DBV), absolute dense breast volume (ADBV), and absolute non-dense breast volume (ANDBV). Metabolite results were corrected for multiple comparisons using a false discovery rate adjusted p-value (q). RESULTS The amino acids valine and leucine were significantly inversely associated with %DBV. For each 1 SD increase in valine and leucine, %DBV decreased by 20.9% (q = 0.02) and 18.4% (q = 0.04), respectively. ANDBV was significantly positively associated with 16 lipid and one amino acid metabolites, whereas no metabolites were associated with ADBV. Metabolite set enrichment analysis also revealed associations of distinct metabolic signatures with %DBV, ADBV, and ANDBV; branched chain amino acids had the strongest inverse association with %DBV (p = 0.002); whereas, diacylglycerols and phospholipids were positively associated with ANDBV (p ≤ 0.002), no significant associations were observed for ADBV. CONCLUSION Our results suggest an inverse association of branched chain amino acids with %DBV. Larger studies in diverse populations are needed.
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Affiliation(s)
- Seungyoun Jung
- Department of Nutritional Science and Food Management, Ewha Womans University, Seoul, South Korea
- Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, South Korea
| | - Sarah Silva
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Cher M Dallal
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA
| | - Erin LeBlanc
- Kaiser Permanente Center for Health Research, Portland, OR, USA
| | - Kenneth Paris
- Department of Pediatrics, Louisiana State University School of Medicine, New Orleans, LA, USA
| | - John Shepherd
- University of Hawaii Cancer Center, Honolulu, HI, USA
| | | | - Linda Van Horn
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yuji Zhang
- Division of Cancer Epidemiology, Department of Epidemiology and Public Health, University of Maryland School of Medicine, 660 West Redwood St., Howard Hall, Room 102E, Baltimore, MD, 21201, USA
| | - Joanne F Dorgan
- Division of Cancer Epidemiology, Department of Epidemiology and Public Health, University of Maryland School of Medicine, 660 West Redwood St., Howard Hall, Room 102E, Baltimore, MD, 21201, USA.
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2
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Xu Z, Marchionni L, Wang S. MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously. Bioinformatics 2023; 39:btad333. [PMID: 37216914 PMCID: PMC10250081 DOI: 10.1093/bioinformatics/btad333] [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: 12/30/2022] [Revised: 04/28/2023] [Accepted: 05/19/2023] [Indexed: 05/24/2023] Open
Abstract
MOTIVATION Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes and phenotypes, has accumulated growing attention. Using a "multi-omics" network constructed with a gene-gene network, a metabolite-metabolite network, and a gene-metabolite network to simultaneously prioritize candidate disease-associated metabolites and gene expressions could further utilize gene-metabolite interactions that are not used when prioritizing them separately. However, the number of metabolites is usually 100 times fewer than that of genes. Without accounting for this imbalance issue, we cannot effectively use gene-metabolite interactions when simultaneously prioritizing disease-associated metabolites and genes. RESULTS Here, we developed a Multi-omics Network Enhancement Prioritization (MultiNEP) framework with a weighting scheme to reweight contributions of different sub-networks in a multi-omics network to effectively prioritize candidate disease-associated metabolites and genes simultaneously. In simulation studies, MultiNEP outperforms competing methods that do not address network imbalances and identifies more true signal genes and metabolites simultaneously when we down-weight relative contributions of the gene-gene network and up-weight that of the metabolite-metabolite network to the gene-metabolite network. Applications to two human cancer cohorts show that MultiNEP prioritizes more cancer-related genes by effectively using both within- and between-omics interactions after handling network imbalance. AVAILABILITY AND IMPLEMENTATION The developed MultiNEP framework is implemented in an R package and available at: https://github.com/Karenxzr/MultiNep.
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Affiliation(s)
- Zhuoran Xu
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, United States
| | - Luigi Marchionni
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, United States
| | - Shuang Wang
- Department of Biostatistics, Columbia University, New York, NY 10032, United States
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3
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Xiao J, Hao Y, Wu X, Zhao X, Xu B, Xiao C, Zhang W, Zhang L, Cui H, Yang C, Yan P, Tang M, Wang Y, Chen L, Liu Y, Zou Y, Yang C, Yao Y, Li J, Jiang X, Zhang B. Nuclear magnetic resonance-determined lipoprotein profile and risk of breast cancer: a Mendelian randomization study. Breast Cancer Res Treat 2023; 200:115-126. [PMID: 37162625 DOI: 10.1007/s10549-023-06930-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 03/30/2023] [Indexed: 05/11/2023]
Abstract
PURPOSE While crudely quantified lipoproteins have been reported to affect the risk of breast cancer, the effects of subclass lipoproteins characterized by particle size, particle number, and lipidomes remain unknown. METHODS Utilizing nuclear magnetic resonance-based GWAS of 85 lipoprotein traits, we performed two-sample univariable Mendelian randomization (MR) to evaluate the causal relationship between each trait with breast cancer (Ncase/control = 133,384/113,789) and with its estrogen receptor (ER) subtypes. Then, we applied multivariable MR to investigate the independent effects considering both general and central obesity. RESULTS In univariable MR, a heterogeneous effect of subclass high-density lipoproteins (HDL) was observed, in which small HDL traits (ORs ranged from 0.89 to 0.94) were associated with a decreased risk of breast cancer while non-small HDLs traits (OR ranged from 1.04 to 1.08) were associated with an increased risk of breast cancer. Very-low-density lipoproteins (VLDL) traits and serum total triglycerides (TG) were associated with a decreased risk of breast cancer (ORs ranged from 0.88 to 0.94). Similar association patterns were found for ER + subtype. In multivariable MR, only the protective effects of small HDL, VLDL and TG on ER + subtype remained significant. CONCLUSION We identified a heterogeneous effect of subclass HDLs and a consistent protective effect of VLDL on breast cancer. Only the effects of small HDL and VLDL on ER + subtype remained robust after controlling for obesity. These findings provide new insight into the causal pathway underlying lipoproteins and breast cancer.
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Affiliation(s)
- Jinyu Xiao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yu Hao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xunying Zhao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Bin Xu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chenghan Xiao
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Lin Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yunjie Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yanqiu Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chunxia Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yuqin Yao
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Ben Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
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Vignoli A, Meoni G, Ghini V, Di Cesare F, Tenori L, Luchinat C, Turano P. NMR-Based Metabolomics to Evaluate Individual Response to Treatments. Handb Exp Pharmacol 2023; 277:209-245. [PMID: 36318327 DOI: 10.1007/164_2022_618] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using 1H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the 1H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.
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Affiliation(s)
- Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Gaia Meoni
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Veronica Ghini
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Francesca Di Cesare
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy.,Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), Sesto Fiorentino, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy.,Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), Sesto Fiorentino, Italy
| | - Paola Turano
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy. .,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy. .,Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), Sesto Fiorentino, Italy.
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5
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Bellerba F, Chatziioannou AC, Jasbi P, Robinot N, Keski-Rahkonen P, Trolat A, Vozar B, Hartman SJ, Scalbert A, Bonanni B, Johansson H, Sears DD, Gandini S. Metabolomic profiles of metformin in breast cancer survivors: a pooled analysis of plasmas from two randomized placebo-controlled trials. J Transl Med 2022; 20:629. [PMID: 36581893 PMCID: PMC9798585 DOI: 10.1186/s12967-022-03809-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 12/05/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Obesity is a major health concern for breast cancer survivors, being associated with high recurrence and reduced efficacy during cancer treatment. Metformin treatment is associated with reduced breast cancer incidence, recurrence and mortality. To better understand the underlying mechanisms through which metformin may reduce recurrence, we aimed to conduct metabolic profiling of overweight/obese breast cancer survivors before and after metformin treatment. METHODS Fasting plasma samples from 373 overweight or obese breast cancer survivors randomly assigned to metformin (n = 194) or placebo (n = 179) administration were collected at baseline, after 6 months (Reach For Health trial), and after 12 months (MetBreCS trial). Archival samples were concurrently analyzed using three complementary methods: untargeted LC-QTOF-MS metabolomics, targeted LC-MS metabolomics (AbsoluteIDQ p180, Biocrates), and gas chromatography phospholipid fatty acid assay. Multivariable linear regression models and family-wise error correction were used to identify metabolites that significantly changed after metformin treatment. RESULTS Participants (n = 352) with both baseline and study end point samples available were included in the analysis. After adjusting for confounders such as study center, age, body mass index and false discovery rate, we found that metformin treatment was significantly associated with decreased levels of citrulline, arginine, tyrosine, caffeine, paraxanthine, and theophylline, and increased levels of leucine, isoleucine, proline, 3-methyl-2-oxovalerate, 4-methyl-2-oxovalerate, alanine and indoxyl-sulphate. Long-chain unsaturated phosphatidylcholines (PC ae C36:4, PC ae C38:5, PC ae C36:5 and PC ae C38:6) were significantly decreased with the metformin treatment, as were phospholipid-derived long-chain n-6 fatty acids. The metabolomic profiles of metformin treatment suggest change in specific biochemical pathways known to impair cancer cell growth including activation of CYP1A2, alterations in fatty acid desaturase activity, and altered metabolism of specific amino acids, including impaired branched chain amino acid catabolism. CONCLUSIONS Our results in overweight breast cancer survivors identify new metabolic effects of metformin treatment that may mechanistically contribute to reduced risk of recurrence in this population and reduced obesity-related cancer risk reported in observational studies. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT01302379 and EudraCT Protocol #: 2015-001001-14.
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Affiliation(s)
- Federica Bellerba
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | | | - Paniz Jasbi
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
| | - Nivonirina Robinot
- International Agency for Research on Cancer, Nutrition and Metabolism Branch, Lyon, France
| | - Pekka Keski-Rahkonen
- International Agency for Research on Cancer, Nutrition and Metabolism Branch, Lyon, France
| | - Amarine Trolat
- International Agency for Research on Cancer, Nutrition and Metabolism Branch, Lyon, France
| | - Béatrice Vozar
- International Agency for Research on Cancer, Nutrition and Metabolism Branch, Lyon, France
| | - Sheri J Hartman
- Herbert Wertheim School of Public Health and Human Longevity Science, UC San Diego, La Jolla, CA, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA, USA
| | - Augustin Scalbert
- International Agency for Research on Cancer, Nutrition and Metabolism Branch, Lyon, France
| | - Bernardo Bonanni
- Division of Cancer Prevention and Genetics, IEO, European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141, Milan, Italy
| | - Harriet Johansson
- Division of Cancer Prevention and Genetics, IEO, European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141, Milan, Italy.
| | - Dorothy D Sears
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Moores Cancer Center, UC San Diego, La Jolla, CA, USA
- Department of Medicine, UC San Diego, La Jolla, CA, USA
| | - Sara Gandini
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
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6
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Lipoprotein and metabolite associations to breast cancer risk in the HUNT2 study. Br J Cancer 2022; 127:1515-1524. [PMID: 35927310 PMCID: PMC9553939 DOI: 10.1038/s41416-022-01924-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 07/06/2022] [Accepted: 07/14/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND The aim of this study was to gain an increased understanding of the aetiology of breast cancer, by investigating possible associations between serum lipoprotein subfractions and metabolites and the long-term risk of developing the disease. METHODS From a cohort of 65,200 participants within the Trøndelag Health Study (HUNT study), we identified all women who developed breast cancer within a 22-year follow-up period. Using nuclear magnetic resonance (NMR) spectroscopy, 28 metabolites and 89 lipoprotein subfractions were quantified from prediagnostic serum samples of future breast cancer patients and matching controls (n = 1199 case-control pairs). RESULTS Among premenopausal women (554 cases) 14 lipoprotein subfractions were associated with long-term breast cancer risk. In specific, different subfractions of VLDL particles (in particular VLDL-2, VLDL-3 and VLDL-4) were inversely associated with breast cancer. In addition, inverse associations were detected for total serum triglyceride levels and HDL-4 triglycerides. No significant association was found in postmenopausal women. CONCLUSIONS We identified several associations between lipoprotein subfractions and long-term risk of breast cancer in premenopausal women. Inverse associations between several VLDL subfractions and breast cancer risk were found, revealing an altered metabolism in the endogenous lipid pathway many years prior to a breast cancer diagnosis.
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7
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Huang JK, Lee HC. Emerging Evidence of Pathological Roles of Very-Low-Density Lipoprotein (VLDL). Int J Mol Sci 2022; 23:ijms23084300. [PMID: 35457118 PMCID: PMC9031540 DOI: 10.3390/ijms23084300] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 04/10/2022] [Accepted: 04/11/2022] [Indexed: 12/18/2022] Open
Abstract
Embraced with apolipoproteins (Apo) B and Apo E, triglyceride-enriched very-low-density lipoprotein (VLDL) is secreted by the liver into circulation, mainly during post-meal hours. Here, we present a brief review of the physiological role of VLDL and a systemic review of the emerging evidence supporting its pathological roles. VLDL promotes atherosclerosis in metabolic syndrome (MetS). VLDL isolated from subjects with MetS exhibits cytotoxicity to atrial myocytes, induces atrial myopathy, and promotes vulnerability to atrial fibrillation. VLDL levels are affected by a number of endocrinological disorders and can be increased by therapeutic supplementation with cortisol, growth hormone, progesterone, and estrogen. VLDL promotes aldosterone secretion, which contributes to hypertension. VLDL induces neuroinflammation, leading to cognitive dysfunction. VLDL levels are also correlated with chronic kidney disease, autoimmune disorders, and some dermatological diseases. The extra-hepatic secretion of VLDL derived from intestinal dysbiosis is suggested to be harmful. Emerging evidence suggests disturbed VLDL metabolism in sleep disorders and in cancer development and progression. In addition to VLDL, the VLDL receptor (VLDLR) may affect both VLDL metabolism and carcinogenesis. Overall, emerging evidence supports the pathological roles of VLDL in multi-organ diseases. To better understand the fundamental mechanisms of how VLDL promotes disease development, elucidation of the quality control of VLDL and of the regulation and signaling of VLDLR should be indispensable. With this, successful VLDL-targeted therapies can be discovered in the future.
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Affiliation(s)
- Jih-Kai Huang
- Department of General Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
| | - Hsiang-Chun Lee
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Internal Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Lipid Science and Aging Research Center, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80708, Taiwan
- Graduate Institute of Animal Vaccine Technology, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
- Correspondence: ; Tel.: +886-7-3121101 (ext. 7741)
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8
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Li L, Liu ZP. Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models. J Transl Med 2021; 19:514. [PMID: 34930307 PMCID: PMC8686664 DOI: 10.1186/s12967-021-03180-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group. METHODS Regularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, [Formula: see text]-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts. RESULTS The nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ([Formula: see text]). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group ([Formula: see text]) in both internal and external validations. CONCLUSIONS The proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.
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Affiliation(s)
- Lingyu Li
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
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