Plasma metabolomics and proteomics reveal novel molecular insights and biomarker panel for cholelithiasis

Abstract

Background: Cholelithiasis is a gastrointestinal disease that is associated with the highest socioeconomic cost. A diagnosis of cholelithiasis based on clinical features is significantly limited, and direct molecular insights into cholelithiasis and the relationship between cholelithiasis and clinical biochemical parameters are unclear.

Objectives: Uncovering direct molecular insights into cholelithiasis and the relationship between cholelithiasis and clinical biochemical parameters. Identifying sensitive and specific biomarkers for this disease.

Methods: Parallel metabolomic and proteomic analyses of plasma from cholelithiasis patients (CPs) and healthy control individuals (HCs) without cholelithiasis were performed using ultrahigh-performance liquid chromatography-tandem mass spectrometry. A multimodule coexpression network analysis and integrated machine learning methods, including least absolute shrinkage and selection operator, random forest, and support vector machine, were used for bioinformatic analyses. An independent cohort and the cross-validation of the combination of two cohorts were used to evaluate the diagnostic performance of the panel.

Results: Arachidonic acid metabolism was significantly different between the CP and HC groups. Glyceraldehyde-3-phosphate dehydrogenase, actin beta, phosphoglycerate mutase 1, Enolase 1, Myeloperoxidase, and actin alpha 1 were identified as potential proteins related to cholelithiasis. The correlation between the merged modules and clinical biochemical tests was calculated. A diagnostic panel composed of four candidate biomarkers, including 3-oxotetradecanoic acid, 12-hydroxydodecanoic acid, hemoglobin subunit delta (HBD), and fibrinogen beta chain (FGB), was proposed based on three modules that were significantly associated with cholelithiasis. The classification according to the diagnostic panel detected CPs with an area under the curve (AUC) of 0.955. External validation in an independent cohort resulted in similar accuracy (AUC=0.995).

Conclusions: This study provided some direct molecular insights into cholelithiasis by showing the differences in plasma metabolic and protein profiles between CPs and HCs and presented a potential biomarker panel with two metabolites (3-oxotetradecanoic acid, 12-hydroxydodecanoic acid) and two proteins (HBD, FGB) for predicting cholelithiasis. We also explored the potential correlation of clinical biochemical parameters with combined modules. These findings may provide some reference for the diagnosis of cholelithiasis in clinical practice.

Keywords: Biochemical parameters; Biomarkers; Cholelithiasis; Machine learning; Metabolomics; Proteomics.