ArticleCancers2018
Targeted UPLC-MS Metabolic Analysis of Human Faeces Reveals Novel Low-Invasive Candidate Markers for Colorectal Cancer.
Article in Cancers, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
- Article
- Quality evaluation of metabolite annotation based on comprehensive simulation of MS/MS data from high-resolution mass spectrometry (HRMS) and similarity scoring.Analytical and bioanalytical chemistry · 2025Article
- Review
- Gut Microbial and Associated Metabolite Markers for Colorectal Cancer Diagnosis.Microorganisms · 2023Review
- Review
- A Novel Approach on the Use of Samples from Faecal Occult Blood Screening Kits for Metabolomics Analysis: Application in Colorectal Cancer Population.Metabolites · 2023Article
- A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients.Metabolites · 2022Article
- Serum untargeted lipidomics by UHPLC-ESI-HRMS aids the biomarker discovery of colorectal adenoma.BMC cancer · 2022Article
- A Prognostic Ferroptosis-Related lncRNA Model Associated With Immune Infiltration in Colon Cancer.Frontiers in genetics · 2022Article
- Article
- Serum Untargeted UHPLC-HRMS-Based Lipidomics to Discover the Potential Biomarker of Colorectal Advanced Adenoma.Cancer management and research · 2021Article
- Article
- Study of the Relationship Between Serum Amino Acid Metabolism and Lymph Node Metastasis in Patients with Colorectal Cancer.OncoTargets and therapy · 2020Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Low invasive tests with high sensitivity for colorectal cancer and advanced precancerous lesions will increase adherence rates, and improve clinical outcomes. We have performed an ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC-(TOF) MS)-based metabolomics study to identify faecal biomarkers for the detection of patients with advanced neoplasia. A cohort of 80 patients with advanced neoplasia (40 advanced adenomas and 40 colorectal cancers) and 49 healthy subjects were analysed in the study. We evaluated the faecal levels of 105 metabolites including glycerolipids, glycerophospholipids, sterol lipids and sphingolipids. We found 18 metabolites that were significantly altered in patients with advanced neoplasia compared to controls. The combinations of seven metabolites including ChoE(18:1), ChoE(18:2), ChoE(20:4), PE(16:0/18:1), SM(d18:1/23:0), SM(42:3) and TG(54:1), discriminated advanced neoplasia patients from healthy controls. These seven metabolites were employed to construct a predictive model that provides an area under the curve (AUC) median value of 0.821. The inclusion of faecal haemoglobin concentration in the metabolomics signature improved the predictive model to an AUC of 0.885. In silico gene expression analysis of tumour tissue supports our results and puts the differentially expressed metabolites into biological context, showing that glycerolipids and sphingolipids metabolism and GPI-anchor biosynthesis pathways may play a role in tumour progression.
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