Lipidomic Signatures for Colorectal Cancer Diagnosis and Progression Using UPLC-QTOF-ESI+MS.
Claudiu RăchieriuDan Tudor EniuEmil MoisFlorin GraurCarmen SocaciuMihai Adrian SocaciuNadim Al HajjarPublished in: Biomolecules (2021)
Metabolomics coupled with bioinformatics may identify relevant biomolecules such as putative biomarkers of specific metabolic pathways related to colorectal diagnosis, classification and prognosis. This study performed an integrated metabolomic profiling of blood serum from 25 colorectal cancer (CRC) cases previously classified (Stage I to IV) compared with 16 controls (disease-free, non-CRC patients), using high-performance liquid chromatography and mass spectrometry (UPLC-QTOF-ESI+ MS). More than 400 metabolites were separated and identified, then all data were processed by the advanced Metaboanalyst 5.0 online software, using multi- and univariate analysis, including specificity/sensitivity relationships (area under the curve (AUC) values), enrichment and pathway analysis, identifying the specific pathways affected by cancer progression in the different stages. Several sub-classes of lipids including phosphatidylglycerols (phosphatidylcholines (PCs), phosphatidylethanolamines (PEs) and PAs), fatty acids and sterol esters as well as ceramides confirmed the "lipogenic phenotype" specific to CRC development, namely the upregulated lipogenesis associated with tumor progression. Both multivariate and univariate bioinformatics confirmed the relevance of some putative lipid biomarkers to be responsible for the altered metabolic pathways in colorectal cancer.
Keyphrases
- ms ms
- mass spectrometry
- high performance liquid chromatography
- fatty acid
- simultaneous determination
- liquid chromatography
- end stage renal disease
- multiple sclerosis
- ejection fraction
- tandem mass spectrometry
- chronic kidney disease
- gas chromatography
- machine learning
- deep learning
- data analysis
- metabolic syndrome
- high resolution
- electronic health record
- patient reported outcomes
- solid phase extraction
- health information
- adipose tissue
- peritoneal dialysis
- big data
- single cell
- long non coding rna
- artificial intelligence
- drug induced
- patient reported