Metabolomics-based methods for early disease diagnostics

Gowda, G. A. Nagana; Zhang, Shucha; Gu, Haiwei; Asiago, Vincent M.; Shanaiah, Narasimhamurthy; Raftery, Daniel

doi:10.1586/14737159.8.5.617

Cited by 597 publications

(468 citation statements)

References 137 publications

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“…It has been widely used to explore metabolic profiling in various biological samples. As mentioned above, NMR spectroscopy is one of the premier methods because it has several advantages; it is rapid, non-destructive, and non-invasive (Gowda et al, 2008). In the present study, we investigated the metabolomics of NPC patients by exploring metabolic biomarkers for the diagnosis of NPC.…”

Section: Discussionmentioning

confidence: 98%

“…NMR spectroscopy offers several advantages; it is rapid, non-destructive, non-biased, non-invasive, and requires little or no sample preparation. Thus, NMR can provide tremendous amounts of information on the identity and quantity of a large number of metabolites from specimens (Gowda et al, 2008). It will enable better understanding of the molecular mechanisms that regulate disease state and allow the clinician to monitor the response to certain therapies (Gowda et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, NMR can provide tremendous amounts of information on the identity and quantity of a large number of metabolites from specimens (Gowda et al, 2008). It will enable better understanding of the molecular mechanisms that regulate disease state and allow the clinician to monitor the response to certain therapies (Gowda et al, 2008). To date, few studies have revealed the metabolic profiles in NPC serum or explored metabolic biomarkers for the diagnosis of NPC.…”

Section: Introductionmentioning

confidence: 99%

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Nuclear magnetic resonance-based study reveals the metabolomics profile of nasopharyngeal carcinoma

et al. 2016

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ABSTRACT. Proton nuclear magnetic resonance ([1 H]-NMR) spectroscopy has been used to investigate metabolites in serum and several types of tissue. We used NMR spectroscopy to explore the differential metabolic profiles in serum from nasopharyngeal carcinoma (NPC) patients. Moreover, metabolites with potential as biomarkers for identifying NPC patients were primarily identified. Serum samples were collected from 40 enrolled participants comprising 20 healthy subjects and 20 NPC patients. Samples were analyzed using a 600-MHz NMR spectrometer. The [ 1 H]-NMR spectra were further analyzed with partial least squares-discriminant analysis for screening differential metabolites. NMR spectroscopy identified a total of eight metabolites that were present at different levels when the sera of NPC patients were compared with those of healthy individuals. Methionine, taurine (P < 0.05), and choline-like metabolites (P < 0.05) were mostly elevated in the sera of NPC patients. In contrast, the levels of lipids (P < 0.01), isoleucine (P < 0.05), unsaturated lipids (P < 0.01), trimethylamine oxidase (P < 0.05), and carbohydrates (P < 0.05) were lower in the sera of the NPC patients than in the healthy controls. We explored the differential metabolic profiles in sera from NPC patients. [1 H]-NMR spectroscopy can be used to identify specific metabolites, and is capable of distinguishing between NPC patients and healthy individuals.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nuclear magnetic resonance-based study reveals the metabolomics profile of nasopharyngeal carcinoma

et al. 2016

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“…Within the framework of systems biology, metabolomics focuses on the quantitative measurement of holistic endogenous metabolites and is increasingly used in clinical fields that focus on the pathophysiological and diagnostic study of diseases (6). Metabolic fingerprinting and metabolite biomarkers have been studied for use in the discrimination or diagnosis of carcinoma (7,8), diabetes mellitus (9) and inborn errors (10). Nontarget metabolomics approaches have also been used to search for new biomarkers and to explore the mechanism of carcinogenesis in hepatic diseases (11)(12)(13)(14)(15)(16)(17)(18)(19)(20).…”

Section: Hepatocellular Carcinoma (Hcc)mentioning

confidence: 99%

Metabolomics Study of Stepwise Hepatocarcinogenesis From the Model Rats to Patients: Potential Biomarkers Effective for Small Hepatocellular Carcinoma Diagnosis

Tan

Yin

Tang

et al. 2012

Molecular & Cellular Proteomics

138

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The aim of this study is to find the potential biomarkers from the rat hepatocellular carcinoma (HCC) disease model by using a non-target metabolomics method, and test their usefulness in early human HCC diagnosis. The serum metabolic profiling of the diethylnitrosamine-induced rat HCC model, which presents a stepwise histopathological progression that is similar to human HCC, was performed using liquid chromatography-mass spectrometry. Multivariate data analysis methods were utilized to identify the potential biomarkers. Three metabolites, taurocholic acid, lysophosphoethanolamine 16:0, and lysophosphatidylcholine 22:5, were defined as "marker metabolites," which can be used to distinguish the different stages of chemical hepatocarcinogenesis. These metabolites represented the abnormal metabolism during the progress of hepatocarcinogenesis, which could also be found in patients. To test their diagnosis potential 412 sera from 262 patients with HCC, 76 patients with cirrhosis and 74 patients with chronic hepatitis B were collected and studied, it was found that 3 marker metabolites were effective for the discrimination of small liver tumor (solitary nodules of less than 2 cm in diameter) patients, achieved a sensitivity of 80.5% and a specificity of 80.1%,which is better than those of ␣-fetoprotein (53 and 64%, respectively). Moreover, they were also effective for the discrimination of all HCCs and chronic liver disease patients, which could achieve a sensitivity of 87.5% and a specificity of 72.3%, better than those of ␣-fetoprotein (61.2 and 64%). These results indicate metabolomics method has

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“…Other areas where there is obvious potential for engineering input include: novel biosensors and pointof-care diagnostics for early detection of disease via the capture and detection of circulating biomarkers (liquid biopsies) [24][25][26][27]; the use of smart wearable or implantable electrochemical sensors to characterise tumour response to chemotherapy in situ or to monitor disease progression and/or signs of relapse [28,29]; artificial intelligence (AI), such as machine learning to help physicians make better diagnoses and guide treatment decisions while minimising costs [30]; metabolic and proteomic profiling of body fluids for the identification of tumoural areas in situ [31][32][33]; and disease-detecting or disease-fighting nanotechnologies (e.g. DNA nanobots [34], quantum dots [35]) for reporting disease status and selectively delivering drugs to tumour cells whilst minimising systemic side effects [36].…”

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confidence: 99%