Data processing and statistical analysis
After the data acquisition using LTQ-Orbitrap, the raw data were
imported and processed (peak alignment, detection and identification)
using the Compound Discoverer 2.1 software system. The retention time
and m/z data for each peak were determined by the software. For plasma
metabolomics and lipidomics analyses, all data were normalized using the
peak areas of internal standards. For metabolomics and lipidomics
profiling of tumor tissue samples, all data were normalized to the total
protein concentrations. All multivariate data analyses were performed
using SIMCA version 14.1 software (Umetrics, Inc., Ume, Sweden) system
and MetaboAnalyst 4.0 (https://www.metaboanalyst.ca/). Multivariate data
analyses and partial least squares-discriminant analysis (PLS-DA) were
used to evaluate the differential metabolites between groups in the
plasma and tumor samples, and performed with Pareto scaling. The
components with a variable importance in the projection (VIP) value
exceeding 1.0 were selected as potential compounds that contributed
remarkably to the clustering and showed differences between the groups.
Student’s t -test was used to evaluate the statistical
significance of each group of metabolic changes and considered
significant when the value was less than 0.05.
Putative identification and searching was carried out based on the mass
adducts ([M+H]+, [M+Na]+,
[M-H]-, etc), mass fragment
(MS2) ions and retention time. The accuracy tolerance
window of the mass was set to 10 ppm while searching the metabolites.
The metabolites were initially searched and determined from online
databases such as METLIN (https://metlin.scripps.edu/), HMDB
(http://www.hmdb.ca/), and KEGG (http://www.genome.jp/kegg)
utilizing the detected m/z value and mass fragmentation patterns. Later,
the identified metabolites were further crosschecked with the
commercially available standards for the confirmation.