Next, combining with previous GWAS-identified genes (Table S1), we implemented three functional analyses for those TWAS-identified genes (Table (Table11 and Table S4). First, co-expression analysis was performed via weighted gene co-expression network analysis (WGCNA) 28. The visualization of interconnection network was conducted in Cytoscape (http://www.cytoscape.org/) 29 with the topological overlap measurement (TOM) quantifying interconnection among genes 30. Note that, TOM ranges between 0 and 1 indicating interconnection between two genes; greater TOM represents higher interconnection with the same set of genes 30. However, co-expression analysis results from WGCNA may be hard to interpret since copy number variant (CNV) itself is a common feature in cancer 31. In order to adjust for the variation in gene expression contributed by CNV, we also implemented an additional co-expression analysis with GRACE 32 — a recently proposed method which removes the influence of CNV before analysis. To implement the two analyses, we selected cervical cancer patients from TCGA who had both gene expression and copy number alteration, and finally kept 34 genes of 284 patients for GRACE and 48 genes of 296 patients for WGCNA. To apply GRACE, we quantile-normalized each gene expression and standardized each CNV.

TWAS-identified genes associated with cervical cancer across the relevant tissues

Note: EBV TL: EBV transformed lymphocytes; WB: whole blood; DGN: Depression Genes and Networks. R2 shows the prediction accuracy of the cis-SNPs on gene expression in a tissue of GTEx.

To explore functional feature for those genes, we further performed functional enrichment analysis, including gene ontology (GO) and KEGG pathway, with DAVID 6.8 (https://david.ncifcrf.gov/) 33. Enrichment analysis allows us to validate our findings by determining functional annotations for those identified genes. In order to detect interaction and association, we also conducted protein-protein interaction analysis in terms of the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING 11.0) database (https://string-db.org/) 34.

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