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Cai YD, Huang T, Feng KY, Hu L, Xie L. A unified 35-gene signature for both subtype classification and survival prediction in diffuse large B-cell lymphomas. PLoS One 2010; 5:e12726. [PMID: 20856936 PMCID: PMC2938343 DOI: 10.1371/journal.pone.0012726] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2010] [Accepted: 08/21/2010] [Indexed: 12/20/2022] Open
Abstract
Cancer subtype classification and survival prediction both relate directly to patients' specific treatment plans, making them fundamental medical issues. Although the two factors are interrelated learning problems, most studies tackle each separately. In this paper, expression levels of genes are used for both cancer subtype classification and survival prediction. We considered 350 diffuse large B-cell lymphoma (DLBCL) subjects, taken from four groups of patients (activated B-cell-like subtype dead, activated B-cell-like subtype alive, germinal center B-cell-like subtype dead, and germinal center B-cell-like subtype alive). As classification features, we used 11,271 gene expression levels of each subject. The features were first ranked by mRMR (Maximum Relevance Minimum Redundancy) principle and further selected by IFS (Incremental Feature Selection) procedure. Thirty-five gene signatures were selected after the IFS procedure, and the patients were divided into the above mentioned four groups. These four groups were combined in different ways for subtype prediction and survival prediction, specifically, the activated versus the germinal center and the alive versus the dead. Subtype prediction accuracy of the 35-gene signature was 98.6%. We calculated cumulative survival time of high-risk group and low-risk groups by the Kaplan-Meier method. The log-rank test p-value was 5.98e-08. Our methodology provides a way to study subtype classification and survival prediction simultaneously. Our results suggest that for some diseases, especially cancer, subtype classification may be used to predict survival, and, conversely, survival prediction features may shed light on subtype features.
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MESH Headings
- Biomarkers, Tumor/genetics
- Gene Expression Regulation, Neoplastic
- Humans
- Kaplan-Meier Estimate
- Lymphoma, Large B-Cell, Diffuse/classification
- Lymphoma, Large B-Cell, Diffuse/genetics
- Lymphoma, Large B-Cell, Diffuse/mortality
- Lymphoma, Large B-Cell, Diffuse/pathology
- Predictive Value of Tests
- Prognosis
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Affiliation(s)
- Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai, People's Republic of China
- Centre for Computational Systems Biology, Fudan University, Shanghai, People's Republic of China
- * E-mail: (YDC); (LX)
| | - Tao Huang
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
- Shanghai Center for Bioinformation Technology, Shanghai, People's Republic of China
| | - Kai-Yan Feng
- Shanghai Center for Bioinformation Technology, Shanghai, People's Republic of China
| | - Lele Hu
- Institute of Systems Biology, Shanghai University, Shanghai, People's Republic of China
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai, People's Republic of China
- * E-mail: (YDC); (LX)
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Meyer TE, Verwoert GC, Hwang SJ, Glazer NL, Smith AV, van Rooij FJA, Ehret GB, Boerwinkle E, Felix JF, Leak TS, Harris TB, Yang Q, Dehghan A, Aspelund T, Katz R, Homuth G, Kocher T, Rettig R, Ried JS, Gieger C, Prucha H, Pfeufer A, Meitinger T, Coresh J, Hofman A, Sarnak MJ, Chen YDI, Uitterlinden AG, Chakravarti A, Psaty BM, van Duijn CM, Kao WHL, Witteman JCM, Gudnason V, Siscovick DS, Fox CS, Köttgen A. Genome-wide association studies of serum magnesium, potassium, and sodium concentrations identify six Loci influencing serum magnesium levels. PLoS Genet 2010; 6. [PMID: 20700443 PMCID: PMC2916845 DOI: 10.1371/journal.pgen.1001045] [Citation(s) in RCA: 178] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2010] [Accepted: 07/01/2010] [Indexed: 02/06/2023] Open
Abstract
Magnesium, potassium, and sodium, cations commonly measured in serum, are involved in many physiological processes including energy metabolism, nerve and muscle function, signal transduction, and fluid and blood pressure regulation. To evaluate the contribution of common genetic variation to normal physiologic variation in serum concentrations of these cations, we conducted genome-wide association studies of serum magnesium, potassium, and sodium concentrations using ∼2.5 million genotyped and imputed common single nucleotide polymorphisms (SNPs) in 15,366 participants of European descent from the international CHARGE Consortium. Study-specific results were combined using fixed-effects inverse-variance weighted meta-analysis. SNPs demonstrating genome-wide significant (p<5×10−8) or suggestive associations (p<4×10−7) were evaluated for replication in an additional 8,463 subjects of European descent. The association of common variants at six genomic regions (in or near MUC1, ATP2B1, DCDC5, TRPM6, SHROOM3, and MDS1) with serum magnesium levels was genome-wide significant when meta-analyzed with the replication dataset. All initially significant SNPs from the CHARGE Consortium showed nominal association with clinically defined hypomagnesemia, two showed association with kidney function, two with bone mineral density, and one of these also associated with fasting glucose levels. Common variants in CNNM2, a magnesium transporter studied only in model systems to date, as well as in CNNM3 and CNNM4, were also associated with magnesium concentrations in this study. We observed no associations with serum sodium or potassium levels exceeding p<4×10−7. Follow-up studies of newly implicated genomic loci may provide additional insights into the regulation and homeostasis of human serum magnesium levels. Magnesium, potassium, and sodium are involved in important physiological processes. To better understand how common genetic variation may contribute to inter-individual differences in serum concentrations of these electrolytes, we evaluated single nucleotide polymorphisms (SNPs) across the genome in association with serum magnesium, potassium, and sodium levels in 15,366 participants of European descent from the CHARGE Consortium. We then verified the associations in an additional 8,463 study participants. Six different genomic regions contain variants that are reproducibly associated with serum magnesium levels, and only one of the regions had been previously known to influence serum magnesium concentrations in humans. The identified SNPs also show association with clinically defined hypomagnesemia, and some of them with traits that have been linked to serum magnesium levels, including kidney function, fasting glucose, and bone mineral density. We further provide evidence for a physiological role of magnesium transporters in humans which have previously only been studied in model systems. None of the SNPs evaluated in our study are significantly associated with serum levels of sodium or potassium. Additional studies are needed to investigate the underlying molecular mechanisms in order to help us understand the contribution of these newly identified regions to magnesium homeostasis.
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Affiliation(s)
- Tamra E. Meyer
- Human Genetics Center and Division of Epidemiology, The University of Texas Health Science Center at Houston, School of Public Health, Houston, Texas, United States of America
- National Cancer Institute, Division of Cancer Epidemiology and Genetics, Bethesda, Maryland, United States of America
| | - Germaine C. Verwoert
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - Shih-Jen Hwang
- National Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts, United States of America
| | - Nicole L. Glazer
- Cardiovascular Health Research Unit and Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Albert V. Smith
- Icelandic Heart Association, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | - Frank J. A. van Rooij
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - Georg B. Ehret
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
- Division of Cardiology, Geneva University Hospital, Geneva, Switzerland
| | - Eric Boerwinkle
- Human Genetics Center and Division of Epidemiology, The University of Texas Health Science Center at Houston, School of Public Health, Houston, Texas, United States of America
| | - Janine F. Felix
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - Tennille S. Leak
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Tamara B. Harris
- Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Baltimore, Maryland, United States of America
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - Thor Aspelund
- Icelandic Heart Association, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | - Ronit Katz
- Collaborative Health Studies Coordinating Center, University of Washington, Seattle, United States of America
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Thomas Kocher
- School of Dentistry, University of Greifswald, Greifswald, Germany
| | - Rainer Rettig
- Institute of Physiology, University of Greifswald, Greifswald, Germany
| | - Janina S. Ried
- Institute of Epidemiology, Helmholtz Zentrum München, Munich, Germany
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, Munich, Germany
| | - Hanna Prucha
- Institute of Human Genetics, Klinikum Rechts der Isar der TU München, Munich, Germany
- Clinic of Dermatology, Am Biederstein, Klinikum Rechts der Isar der TU München, Munich, Germany
| | - Arne Pfeufer
- Institute of Human Genetics, Klinikum Rechts der Isar der TU München, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, Munich, Germany
| | - Thomas Meitinger
- Institute of Human Genetics, Klinikum Rechts der Isar der TU München, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, Munich, Germany
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Albert Hofman
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - Mark J. Sarnak
- Division of Nephrology, Tufts Medical Center, Boston, Massachusetts, United States of America
| | - Yii-Der Ida Chen
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
- Department of Clinical Chemistry, Erasmus MC, Rotterdam, The Netherlands
| | - Aravinda Chakravarti
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, Washington, United States of America
- Group Health Research Institute, Group Health Cooperative, Seattle, Washington, United States of America
| | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - W. H. Linda Kao
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jacqueline C. M. Witteman
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- The Netherlands Genomics Initiative–sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, The Netherlands
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | - David S. Siscovick
- Cardiovascular Health Research Unit and Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Caroline S. Fox
- National Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts, United States of America
- Division of Endocrinology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States of America
- Division of Nephrology, University Hospital Freiburg, Freiburg, Germany
- * E-mail:
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61
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Quamme GA. Molecular identification of ancient and modern mammalian magnesium transporters. Am J Physiol Cell Physiol 2010; 298:C407-29. [DOI: 10.1152/ajpcell.00124.2009] [Citation(s) in RCA: 142] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
A large number of mammalian Mg2+ transporters have been hypothesized on the basis of physiological data, but few have been investigated at the molecular level. The recent identification of a number of novel proteins that mediate Mg2+ transport has enhanced our understanding of how Mg2+ is translocated across mammalian membranes. Some of these transporters have some similarity to those found in prokaryocytes and yeast cells. Human Mrs2, a mitochondrial Mg2+ channel, shares many of the properties of the bacterial CorA and yeast Alr1 proteins. The SLC41 family of mammalian Mg2+ transporters has a similarity with some regions of the bacterial MgtE transporters. The mammalian ancient conserved domain protein (ACDP) Mg2+ transporters are found in prokaryotes, suggesting an ancient origin. However, other newly identified mammalian transporters, including TRPM6/7, MagT, NIPA, MMgT, and HIP14 families, are not represented in prokaryotic genomes, suggesting more recent development. MagT, NIPA, MMgT, and HIP14 transporters were identified by differential gene expression using microarray analysis. These proteins, which are found in many different tissues and subcellular organelles, demonstrate a diversity of structural properties and biophysical functions. The mammalian Mg2+ transporters have no obvious amino acid similarities, indicating that there are many ways to transport Mg2+ across membranes. Most of these proteins transport a number of divalent cations across membranes. Only MagT1 and NIPA2 are selective for Mg2+. Many of the identified mammalian Mg2+ transporters are associated with a number of congenital disorders encompassing a wide range of tissues, including intestine, kidney, brain, nervous system, and skin. It is anticipated that future research will identify other novel Mg2+ transporters and reveal other diseases.
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Affiliation(s)
- Gary A. Quamme
- Vancouver Hospital, University of British Columbia, Vancouver, British Columbia, Canada
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63
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Shuen AY, Wong BYL, Wei C, Liu Z, Li M, Cole DEC. Genetic determinants of extracellular magnesium concentration: analysis of multiple candidate genes, and evidence for association with the estrogen receptor alpha (ESR1) locus. Clin Chim Acta 2009; 409:28-32. [PMID: 19695239 DOI: 10.1016/j.cca.2009.08.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2009] [Revised: 08/08/2009] [Accepted: 08/10/2009] [Indexed: 10/20/2022]
Abstract
BACKGROUND Serum magnesium concentration is a quantitative trait with substantial heritability. Although the pool of candidate genes continues to grow, only the histocompatibility locus has been associated with magnesium levels. To explore other possibilities, we targeted 6 candidate genes physiologically relevant to magnesium metabolism. METHODS We studied a large cohort (n=471) derived from a well-characterized population of healthy Caucasian women 18 to 35 years. Total serum magnesium and calcium were measured by atomic absorption spectrophotometry (aaMg & aaCa). Genomic DNA was amplified and SNPs in candidate genes (CASR, VDR, ESR1, CLDN16, EGF1, TRPM6) genotyped by routine methods. RESULTS We found a significant association between estrogen receptor alpha (ESR1) polymorphisms, PvuII and XbaI, and magnesium (r=-0.116, p=0.012 and r=-0.126, p=0.006, respectively). Stratifying by PvuII genotype (P/p alleles), the mean adjusted total magnesium (aaMg) concentration was significantly higher (p=0.01) in the pp group (0.823+/-0.005 mmol/l, n=130) than in PP homozygotes (0.805+/-0.006 mmol/l, n=70), and the mean in Pp heterozygotes was intermediate (0.810+/-0.005 mmol/l, n=180). No significant associations were observed with the other candidate genes tested. CONCLUSIONS The significant association between magnesium and ESR1 polymorphisms supports previous studies linking physiologic changes in serum magnesium to estrogen status.
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Affiliation(s)
- Andrew Y Shuen
- Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, Canada
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