Cancer Metabolomics and the Human Metabolome Database

Wishart, David S.; Mandal, Rupasri; Stanislaus, Avalyn; Ramirez-Gaona, Miguel

doi:10.3390/metabo6010010

Cited by 119 publications

(103 citation statements)

References 53 publications

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“…In addition, the complete scope of metabolic networks remains unknown, and this uncertainty complicates metabolite analysis because a definitive list of relevant analytes (analogous to genes in the genome) is lacking. The Human Metabolome Database (HMDB) (1, 2) is a good illustration of this uncertainty: The HMDB has identified more than 40,000 putative human metabolites, but only 680 of the HMDB’s water-soluble compounds have been mapped to standard metabolic pathways in humans (3). The biological significance of many unmapped compounds remains unknown.…”

Section: Scope Of the Challengementioning

confidence: 99%

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Klein

et al. 2017

Annu. Rev. Biochem.

375

341

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Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites’ reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

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Section: Scope Of the Challengementioning

confidence: 99%

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Klein

et al. 2017

Annu. Rev. Biochem.

375

341

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“…We utilized data from two cohort studies embedded in the MetaboCCC Consortium, a consortium of four independent European cohorts to investigate metabolic profiles across the continuum of colorectal carcinogenesis: (1) the Heidelberg site of the international ColoCare Study (ClinicalTrials.gov Identifier: NCT02328677) and (2) the Colorectal Cancer Study of Austria (CORSA). The CORSA and ColoCare studies were selected given the availability of samples from colorectal cancer patients as well as controls.…”

Section: Study Populationsmentioning

confidence: 99%

“…Colorectal cancer is a major public health concern worldwide, with 1.4 million new cases and an estimated 700,000 deaths annually . Colorectal cancer is characterized by a distinct metabolic phenotype and changes in key metabolic pathways such as glycolysis or the tricarboxylic acid (TCA) cycle . Yet, underlying mechanisms involved in colorectal carcinogenesis are still unclear.…”

Section: Introductionmentioning

confidence: 99%

Plasma metabolites associated with colorectal cancer: A discovery‐replication strategy

Geijsen

Brezina

Keski‐Rahkonen

et al. 2019

Intl Journal of Cancer

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Colorectal cancer is known to arise from multiple tumorigenic pathways; however, the underlying mechanisms remain not completely understood. Metabolomics is becoming an increasingly popular tool in assessing biological processes. Previous metabolomics research focusing on colorectal cancer is limited by sample size and did not replicate findings in independent study populations to verify robustness of reported findings. Here, we performed a ultrahigh performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry (UHPLC‐QTOF‐MS) screening on EDTA plasma from 268 colorectal cancer patients and 353 controls using independent discovery and replication sets from two European cohorts (ColoCare Study: n = 180 patients/n = 153 controls; the Colorectal Cancer Study of Austria (CORSA) n = 88 patients/n = 200 controls), aiming to identify circulating plasma metabolites associated with colorectal cancer and to improve knowledge regarding colorectal cancer etiology. Multiple logistic regression models were used to test the association between disease state and metabolic features. Statistically significant associated features in the discovery set were taken forward and tested in the replication set to assure robustness of our findings. All models were adjusted for sex, age, BMI and smoking status and corrected for multiple testing using False Discovery Rate. Demographic and clinical data were abstracted from questionnaires and medical records.

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“…High-dimensional biology techniques allow characterization of the genome (54,55,125,126), transcriptome (55,(127)(128)(129)(130)(131)(132)(133)(134)(135)(136)(137), proteome (55,125,127,(138)(139)(140)(141)(142)(143)(144)(145)(146)(147)(148), lipidome (149)(150)(151)(152)(153)(154)(155), glucome (156,157), metabolome (6,55,128,(158)(159)(160)(161)(162)(163), and cytome (164)(165)(166)(167)(168)(169). Major trust of the new biology and medicine has been to gather the information derived from these platforms to optimize diagnosis, prognosi...…”

Section: Introductionmentioning

confidence: 99%

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

Romero

Erez

Maymon

et al. 2017

American Journal of Obstetrics and Gynecology

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Objective Pregnancy is accompanied by dramatic physiologic changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age, and determine biological processes that are perturbed in normal pregnancy. Materials and methods A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age (AGA) without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median=5,) were profiled to measure the abundance of 1,125 proteins using multiplex assays. Linear mixed effects models with polynomial splines were used to model protein abundance as a function of gestational age, and significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having: 1) more than 1.5 fold change between 8 and 40 weeks of gestation; and 2) a false discovery rate (FDR) adjusted p-value <0.1. Gene ontology enrichment analysis was employed to identify biological processes over-represented among the proteins that changed with advancing gestation. Results 1) Ten percent (112/1,125) of the profiled proteins changed in abundance as a function of gestational age; 2) of the 1,125 proteins analyzed Glypican-3, sialic acid-binding immunoglobulin-type lectins (Siglec)-6, placental growth factor (PlGF), C-C motif (CCL)-28, carbonic anhydrase 6, Prolactin (PRL), interleukin-1 receptor 4 (IL-1 R4), dual specificity mitogen-activated protein kinase 4 (MP2K4) and pregnancy-associated plasma protein-A (PAPP-A) had more than 5 fold change in abundance across gestation. These 9 proteins are known to be involved in a wide range of both physiologic and pathologic processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc.; and 3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis and leukocyte migration (FDR=10%). Conclusions The plasma proteome of normal pregnancy demonstrates dramatic changes in both magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy, development of biomarkers to differentiate normal vs. abnormal pregnancy, and determine the response to interventions.

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Cancer Metabolomics and the Human Metabolome Database

Cited by 119 publications

References 53 publications

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Plasma metabolites associated with colorectal cancer: A discovery‐replication strategy

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

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