Pathway enrichment analysis and gene set enrichment analysis (GSEA)

The quality of the sequencing data was analyzed using FastQC (version 0.11.5), and raw reads with low quality were removed using Trim Galore (version 0.4.4) prior to analysis of the data. All the trimmed reads were mapped to the reference genome (hg38, GRCh38) by STAR software (version 020201), and the mapped counts were extracted using the feature count from the Subread package (version 1.5.3). Subsequently, read count data containing 26,643 transcripts quantified with raw reads were preprocessed by filtering out genes with a zero read count across different samples, and 19,020 genes remained after filtering. The read count data was normalized by DESeq2, which can be used for downstream differential expression analysis. Differentially expressed genes (DEGs) in the AIB1-KD cells compared to the control cells were filtered by log2-fold change > 1 or < -1. A P value < 0.05 was considered to indicate a significant difference. GSEA was performed using the R package clusterProfiler ²⁴ with the normalized enrichment score calculated by creating 1,000 permutations of the ES and scaling the observed ES by the mean score of the permutations. The gene sets implemented were derived from the Cancer Hallmark Gene, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) database, as collected in the Molecular Signatures Database (MSigDB; version 6.2). GSEA was applied to the selected gene sets to determine gene enrichment in each dataset. All data processing was completed in a shell HPC command line environment.