Protein expression was analyzed using Perseus software 1.6.15.0
(https://maxquant.net/perseus/). Protein quantification data were
log2-transformed and filtered to ensure that at least one group
contained a minimum of 70% valid values for each protein. The remaining
missing values were imputed using random numbers drawn from a normal
distribution (width = 0.3, downshift = 1.8). A two-tailed Welch’s t-test
was used to compare significant differences between the groups. Thep-values were corrected post hoc using the Benjamin and Hochberg
procedure for multiple comparison tests. Statistical significance was
set at p < 0.05.
For functional annotation and pathway enrichment analysis of
differentially expressed proteins, we utilized
Enricher-KG14, a knowledge graph and web server
application applying gene set libraries from
Enrichr15. Gene Ontology (GO) biological process terms
(https://geneontology.org) and Kyoto Encyclopedia of Genes and
Genomes (KEGG) 2021 human pathways
(https://www.genome.jp/kegg/pathway.html) were used for the
analysis. Statistical significance was set at p < 0.05.
Metascape16 (https://geneontology.org) was used
for cluster analysis of enriched ontologies of the top 150-rankeddifferentially expressed proteins and their protein–protein
interactions. The molecular complex detection (MCODE)
algorithm17 was applied to identify densely connected
network components.