Dataset Information

¹H NMR-based metabolic profiling of human rectal cancer tissue.

ABSTRACT:

Background

Rectal cancer is one of the most prevalent tumor types. Understanding the metabolic profile of rectal cancer is important for developing therapeutic approaches and molecular diagnosis.

Methods

Here, we report a metabonomics profiling of tissue samples on a large cohort of human rectal cancer subjects (n?=?127) and normal controls (n?=?43) using 1H nuclear magnetic resonance (1H NMR) based metabonomics assay, which is a highly sensitive and non-destructive method for the biomarker identification in biological systems. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA) were applied to analyze the 1H-NMR profiling data to identify the distinguishing metabolites of rectal cancer.

Results

Excellent separation was obtained and distinguishing metabolites were observed among the different stages of rectal cancer tissues (stage I?=?35; stage II?=?37; stage III?=?37 and stage IV?=?18) and normal controls. A total of 38 differential metabolites were identified, 16 of which were closely correlated with the stage of rectal cancer. The up-regulation of 10 metabolites, including lactate, threonine, acetate, glutathione, uracil, succinate, serine, formate, lysine and tyrosine, were detected in the cancer tissues. On the other hand, 6 metabolites, including myo-inositol, taurine, phosphocreatine, creatine, betaine and dimethylglycine were decreased in cancer tissues. These modified metabolites revealed disturbance of energy, amino acids, ketone body and choline metabolism, which may be correlated with the progression of human rectal cancer.

Conclusion

Our findings firstly identify the distinguishing metabolites in different stages of rectal cancer tissues, indicating possibility of the attribution of metabolites disturbance to the progression of rectal cancer. The altered metabolites may be as potential biomarkers, which would provide a promising molecular diagnostic approach for clinical diagnosis of human rectal cancer. The role and underlying mechanism of metabolites in rectal cancer progression are worth being further investigated.

SUBMITTER: Wang H

PROVIDER: S-EPMC3819675 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Publications

¹H NMR-based metabolic profiling of human rectal cancer tissue.

Wang Huijuan H Wang Liang L Zhang Hailong H Deng Pengchi P Chen Jie J Zhou Bin B Hu Jing J Zou Jun J Lu Wenjie W Xiang Pu P Wu Tianming T Shao Xiaoni X Li Yuan Y Zhou Zongguang Z Zhao Ying-Lan YL

Molecular cancer 20131018 1

<h4>Background</h4>Rectal cancer is one of the most prevalent tumor types. Understanding the metabolic profile of rectal cancer is important for developing therapeutic approaches and molecular diagnosis.<h4>Methods</h4>Here, we report a metabonomics profiling of tissue samples on a large cohort of human rectal cancer subjects (n = 127) and normal controls (n = 43) using 1H nuclear magnetic resonance (1H NMR) based metabonomics assay, which is a highly sensitive and non-destructive method for the ...[more]

PMID: 24138801

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Project description:Pancreatic cancer is the third leading cause of cancer deaths in the United States with more than 53,000 expected to be diagnosed with the disease in 2018. The median survival time after diagnosis is four to six months. The poor survival statistics are due in part to the fact that pancreatic cancer is typically asymptomatic until it reaches advanced stages of the disease. Although surgical resection provides the best chance of survival, pancreatic cancer is rarely detected when surgery is still possible due, in part, to lack of effective biomarkers for early detection. The goal of the research reported here was to determine if it was possible to identify metabolic biomarkers for detection of pre-cancerous pancreatic intraepithelial neoplasia (PanIN) that precede pancreatic adenocarcinoma. The transgenic Ptf1a-Cre; LSL-KrasG12D mouse strain was used as a model of pancreatic cancer progression. Nuclear magnetic resonance (NMR) spectroscopy was employed to compare metabolic profiles of urine, sera, fecal extracts, and pancreatic tissue extracts collected from control and study mice aged 5, 11, and 15 months, including 47 mice with tumors. We were able to identify the following potential biomarkers: decreased 3-indoxylsulfate, benzoate and citrate in urine, decreased glucose, choline, and lactate in blood, and decreased phenylalanine and benzoate and increased acetoin in fecal extracts. Potential biomarkers were validated by p-values, PLS-DA VIP scores, and accuracies based on area under ROC curve analyses. Essentially, all of the metabolic profiling changes could be explained as being associated with the consequences of bicarbonate wasting caused by a complete substitution of the normal pancreatic acinar tissue by tissue entirely composed of PanIN. Given the nature of the mouse model used here, our results indicate that it may be possible to use NMR-based metabolic profiling to identify biomarkers for detection of precancerous PanIN that immediately precede pancreatic cancer.

Project description:BackgroundAmbiguity in malignant transformation of glioma has made prognostic diagnosis very challenging. Tumor malignant transformation is closely correlated with specific alterations of the metabolic profile. Exploration of the underlying metabolic alterations in glioma cells of different malignant degree is therefore vital to develop metabolic biomarkers for prognosis monitoring.MethodsWe conducted (1)H nuclear magnetic resonance (NMR)-based metabolic analysis on cell lines (CHG5, SHG44, U87, U118, U251) developed from gliomas of different malignant grades (WHO II and WHO IV). Several methods were applied to analyze the (1)H-NMR spectral data of polar extracts of cell lines and to identify characteristic metabolites, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), fuzzy c-means clustering (FCM) analysis and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). The expression analyses of glial fibrillary acidic protein (GFAP) and matrix metal proteinases (MMP-9) were used to assess malignant behaviors of cell lines. GeneGo pathway analysis was used to associate characteristic metabolites with malignant behavior protein markers GFAP and MMP-9.ResultsStable and distinct metabolic profiles of the five cell lines were obtained. The metabolic profiles of the low malignancy grade group (CHG5, SHG44) were clearly distinguished from those of the high malignancy grade group (U87, U118, U251). Seventeen characteristic metabolites were identified that could distinguish the metabolic profiles of the two groups, nine of which were mapped to processes related to GFAP and MMP-9. Furthermore, the results from both quantitative comparison and metabolic correlation analysis indicated that the significantly altered metabolites were primarily involved in perturbation of metabolic pathways of tricarboxylic acid (TCA) cycle anaplerotic flux, amino acid metabolism, anti-oxidant mechanism and choline metabolism, which could be correlated with the changes in the glioma cells' malignant behaviors.ConclusionsOur results reveal the metabolic heterogeneity of glioma cell lines with different degrees of malignancy. The obtained metabolic profiles and characteristic metabolites are closely associated with the malignant features of glioma cells, which may lay the basis for both determining the molecular mechanisms underlying glioma malignant transformation and exploiting non-invasive biomarkers for prognosis monitoring.

Dataset Information

¹H NMR-based metabolic profiling of human rectal cancer tissue.

Background

Methods

Results

Conclusion

Publications

¹H NMR-based metabolic profiling of human rectal cancer tissue.

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