Instrumental developments in sensitivity and selectivity boost the application of liquid chromatography-mass spectrometry (LC-MS) in metabolomics. Gradual changes in the LC-MS instrumental response (i.e. intra-batch effect) are often unavoidable and they reduce the repeatability and reproducibility of the analysis, decrease the power to detect biological responses and hinder the interpretation of the information provided. Because of that, there is interest in the development of chemometric techniques for the post-acquisition correction of batch effects. In this work, the use of quality control (QC) samples and support vector regression (QC-SVRC) and a radial basis function kernel is proposed to correct intra-batch effects. The repeated analysis of a single sample is used for the assessment of both the correction accuracy and the effect of the distribution of QC samples throughout the batch. The QC-SVRC method is evaluated and compared with a recently developed method based on QC samples and robust cubic smoothing splines (QC-RSC). The results show that QC-SVRC slightly outperformed QC-RSC and allows a straightforward fitting of the SVRC parameters to the instrument performance by using the ε-insensitive loss parameter.

Gastric carcinogenesis is a multifactorial process described as a stepwise progression from non-active gastritis (NAG), chronic active gastritis (CAG), precursor lesions of gastric cancer (PLGC) and gastric adenocarcinoma. Gastric cancer (GC) 5-year survival rate is highly dependent upon stage of disease at diagnosis, which is based on endoscopy, biopsy and pathological examinations. Non-invasive GC biomarkers would facilitate its diagnosis at early stages leading to improved GC prognosis. We analyzed plasma samples collected from 80 patients diagnosed with NAG withoutH. pyloriinfection (NAG−), CAG withH. pyloriinfection (CAG+), PLGC and GC. A panel of 208 metabolites including acylcarnitines, amino acids and biogenic amines, sphingolipids, glycerophospholipids, hexoses, and tryptophan and phenylalanine metabolites were quantified using two complementary quantitative approaches: Biocrates AbsoluteIDQ®p180 kit and a LC-MS method designed for the analysis of 29 tryptophan pathway and phenylalanine metabolites. Significantly altered metabolic profiles were found in GC patients that allowing discrimination from NAG−, CAG+ and PLGC patients. Pathway analysis showed significantly altered tryptophan and nitrogen metabolic pathways (FDR P < 0.01). Three metabolites (histidine, tryprophan and phenylacetylglutamine) discriminated between non-GC and GC groups. These metabolic signatures open new possibilities to improve surveillance of PLGC patients using a minimally invasive blood analysis.

Liver biopsy is currently the only reliable method to establish nonalcoholic fatty liver disease (NAFLD) severity. However, this technique is invasive and occasionally associated with severe complications. Thus, non-invasive diagnostic markers for NAFLD are needed. Former studies have postulated 18 different serum microRNA biomarkers with altered levels in NAFLD patients. In the present study, we have re-examined the predictive value of these serum microRNAs and found that 9 of them (miR-34a, -192, -27b, -122, -22, -21, -197, -30c and -16) associated to NAFLD severity in our independent cohort. Moreover, miR-192, -27b, -22, -197 and -30c appeared specific for NAFLD, when compared with patients with drug-induced liver injury. Preliminary serum RNAseq analysis allowed identifying novel potential miRNA biomarkers for nonalcoholic steatohepatitis (NASH). The classification performance of validated miRNAs (and their ratios) for NASH was better than that reached by AST, whereas for advanced fibrosis prediction miRNAs did not perform better than the FIB-4 algorithm. Cross-validated models combining both clinical and miRNA variables showed enhanced predictivity. In conclusion, the circulating microRNAs validated demonstrate a better diagnostic potential than conventional serum markers to identify NASH patients and could complement and improve current fibrosis prediction algorithms. The research in this field is still open.