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.