Biomarkers in disease and health

Branca, Francesco; Hanley, A. Bryan; Pool-Zobel, B.L.; Verhagen, Hans

doi:10.1079/bjn2001339

Cited by 45 publications

(22 citation statements)

References 334 publications

(282 reference statements)

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“…Traditionally, metabolite biomarker discovery has been performed by comparing healthy to disease patients. 33 Most disease states of interest, however, are exceedingly complex and highly variable from patient to patient at different stages of progression and severity with potentially different prognoses. 34 In addition, there are a number of confounding variables that can be difficult to account for but that are known to influence metabolic profiles such as sex, age, diet, drug regimen, ethnicity, and body mass index.…”

Section: Applicationsmentioning

confidence: 99%

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

2012

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metaXCMS is a software program for the analysis of liquid chromatography/mass spectrometry-based untargeted metabolomic data that is designed to identify differences in metabolic profiles across multiple sample groups (e.g., “healthy” versus “active disease” versus “inactive disease”). By performing second-order (“meta”) analysis, the software facilitates prioritization of interesting metabolite features from large untargeted metabolomic datasets prior to the rate-limiting step of structural identification. Here we provide a detailed step-by-step protocol for going from raw mass spectrometry data to metaXCMS results visualized as Venn diagrams and exported Microsoft Excel spreadsheets. There is no upper limit to the number of sample groups or individual samples that can be compared by the software, and data from most commercial mass spectrometers is supported. The speed of the analysis depends on computational resources and data volume, but will generally be less than one day for most users. metaXCMS is freely available at http://metlin.scripps.edu/metaXCMS/.

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Section: Applicationsmentioning

confidence: 99%

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

2012

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“…The long, latent, precancerous state characteristic of colorectal cancer and the prevalence of adenomatous polyps make it well suited to mass screening and present opportunities to derail the disease before it starts or to treat it in its earliest detectable stages (4). Better understanding the complexities of colorectal carcinogenesis coupled with advancements in the measurement of early risk are needed to progress research and practice in the prevention of colorectal cancer (3, 5). …”

Section: Introductionmentioning

confidence: 99%

TGF-α Expression as a Potential Biomarker of Risk Within the Normal-appearing Colorectal Mucosa of Patients with and without Incident Sporadic Adenoma

Daniel¹,

Bostick

Flanders³

et al. 2009

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background: Transforming growth factor-A (TGF-A), a stimulatory growth factor and member of the epidermal growth factor family, is a mediator of oncogenesis and malignant progression in colorectal carcinogenesis. Limited evidence suggests its utility as a growth-related biomarker of risk for colorectal cancer. Methods: We measured expression of TGF-A in biopsies of normal-appearing colorectal mucosa using automated immunohistochemistry and quantitative image analysis in a subsample of 29 cases and 31 controls from a colonoscopy-based case-control study (n = 203) of biomarkers of risk for incident sporadic

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“…The study of nutritional factors that contribute to cancer has advanced more slowly than have nutritional studies of some other common chronic diseases such as heart disease for several reasons, most importantly the need for validated intermediate risk factors or biomarkers for cancer (Schatzkin and Gail, 2002;Branca et al, 2001). In the absence of measures such as specific lipid fractions or blood pressure that are direct determinants for heart disease development, we must rely primarily on measuring cancer outcomes, which may take decades to develop.…”

Section: Introductionmentioning

confidence: 99%

Diet and cancer prevention

2004

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Dietary effects are presumed to underlie many of the large international differences in incidence seen for most cancers. Apart from alcohol and a few micronutrients, however, the role of specific nutritional factors remains illdefined. The evidence for a role of energy balance, physical inactivity, and obesity has strengthened, while for dietary fat it has weakened. Phytochemicals such as folate, lycopene and flavonoids are still the subject of active research. As the mechanisms underlying human carcinogenesis are better understood, dietary research will focus increasingly on intermediate markers such as the insulin-like growth factors and potentially carcinogenic metabolites.

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Biomarkers in disease and health

Cited by 45 publications

References 334 publications

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

TGF-α Expression as a Potential Biomarker of Risk Within the Normal-appearing Colorectal Mucosa of Patients with and without Incident Sporadic Adenoma

Diet and cancer prevention

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