Statistical Analysis
The compound Finder Software (Thermo Science, USA) was used for compound
component extraction and data preprocessing in samples, including
analysis such as baseline filtering, peak identification and
integration, retention time correction, peak alignment, and mass
fragment attribution. The edited data matrix was imported into Simca-P
software (version 11.0) for multivariate statistical analysis after
Centralization and Pareto scaling. The dissimilarities in the metabolome
dataset among the groups were analyzed using principal component
analysis (PCA) and partial least squares discrimination analysis
(PLS-DA). The VVC and Control groups were looked for differentially
expressed metabolites according to the variable importance in projection
(VIP) value (>1) of the PLS-DA model, statistical
significance was observed at P value < 0:05. Receiver
Operating Characteristics (ROC) analysis was used to identify the
metabolites that distinguished the patients in the VVC group from
control group and the strength of the discriminators were measured with
the Area Under the Curve (AUC) values. AUC values above 0.8 were
considered as good and above 0.9 were considered as excellent
discriminators. Furthermore, we used the human metabolome database
(HMDB, https://hmdb.ca) and our internal standard metabolite library to
identify and analyze the metabolites. Additionally, the Kyoto
Encyclopedia of Genes and Genomes (KEGG) online database and the
ingenuity pathway analysis (IPA) server were applied to understand
networks of the metabolic pathways between differential metabolites.