Tissue Metabonomic Phenotyping for Diagnosis and Prognosis of Human Colorectal Cancer.

Yuan Tian,Huiru Tang,Shan Xu,Bin Xiong,Yulan Wang,Jia Huang,Tangpeng Xu,Limin Zhang

doi:10.1038/srep20790

Abstract

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide and prognosis based on the conventional histological grading method for CRC remains poor. To better the situation, we analyzed the metabonomic signatures of 50 human CRC tissues and their adjacent non-involved tissues (ANIT) using high-resolution magic-angle spinning (HRMAS) 1H NMR spectroscopy together with the fatty acid compositions of these tissues using GC-FID/MS. We showed that tissue metabolic phenotypes not only discriminated CRC tissues from ANIT, but also distinguished low-grade tumor tissues (stages I-II) from the high-grade ones (stages III-IV) with high sensitivity and specificity in both cases. Metabonomic phenotypes of CRC tissues differed significantly from that of ANIT in energy metabolism, membrane biosynthesis and degradations, osmotic regulations together with the metabolism of proteins and nucleotides. Amongst all CRC tissues, the stage I tumors exhibited largest differentiations from ANIT. The combination of the differentiating metabolites showed outstanding collective power for differentiating cancer from ANIT and for distinguishing CRC tissues at different stages. These findings revealed details in the typical metabonomic phenotypes associated with CRC tissues nondestructively and demonstrated tissue metabonomic phenotyping as an important molecular pathology tool for diagnosis and prognosis of cancerous solid tumors.

Highlights

Colorectal cancer (CRC) is one of the most prevalent cancers, causing high cancer-related mortality in both developed and developing countries[1]
Visual inspection of the spectra of these tissues revealed that the levels of some metabolites such as lipids, amino acids, and choline were obviously different between tumor tissues and adjacent non-involved tissues (ANIT) (Fig. 1)
Principal component analysis (PCA) was conducted on the mean-centered 1H high resolution magic-angle spinning (HRMAS) nuclear magnetic resonance (NMR) data from 50 pairs of CRC tumor and ANIT samples to generate an overview of the dataset and detect possible outliers

Summary

Introduction

Colorectal cancer (CRC) is one of the most prevalent cancers, causing high cancer-related mortality in both developed and developing countries[1]. Clinical metabonomic studies based on urine[29,30], serum[31,32] and tissue[33,34,35] of CRC patients have provided some potential biomarkers for CRC detection and prognosis[36,37] Despite these advances, there are still few studies on how tumor tissue metabonomic phenotypes correlate with the CRC staging especially in a molecular pathology context. We used HRMAS NMR and gas chromatography-mass spectrometer (GC-MS) in combination with multivariate data analysis, to elucidate the metabonomic features of human CRC tissues at different stages, as well as their corresponding adjacent non-involved tissues (ANIT) The aims of this investigation are to define the tissue metabonomic characteristics associated with CRC at different stages and to explore the potentials of these molecular phenotypic profiles for diagnosis and prognosis of human colorectal cancer

Methods

Results

Conclusion