Statistical analysis and metabolic pathway analysis
XCMS software (Scripps Research Institute, La Jolla, CA, USA) was used
to extract and process the characteristic peaks of the original data
obtained by LC-MS, the extracted substances were added, and the ions
were annotated using a camera. Through principal component analysis, the
dimension of the data was reduced, and the overall distribution trend
was previewed. Orthogonal partial least squares discriminant analysis
was used for modeling the data. Based on a minimum fold change (≥2),
analysis of variance p-value (≤0.05), and variable projection importance
value calculated in the orthogonal projections to latent structures
discriminant analysis (OPLS-DA) model (≥1), the differential metabolites
were screened. Candidate markers were annotated with metabolites using
the HMDB, KEGG, and other databases. The pathways of different
metabolites were further screened by enrichment analysis and topological
analysis, and the key pathway with the highest correlation with the
metabolite differences was found.