Research on biliary atresia and epigenetic factors from the perspective of transcriptomics: identification of key genes and experimental validation

Abstract

Background: Biliary atresia (BA) is a severe pediatric liver disease. However, the role of epigenetic factors in its pathogenesis remains poorly understood. This study aimed to identify key genes associated with BA and epigenetic factors, as well as to explore potential therapeutic drugs, thereby offering new insights into the treatment of this condition.

Methods: Transcriptomic datasets (training set GSE122340 and validation set GSE46960) were analyzed. The training set was used to identify differentially expressed genes (DEGs) between BA and normal samples. Candidate genes were selected by intersecting the DEGs with epigenetic factor-related genes. A protein-protein interaction (PPI) network was constructed, and key genes displaying consistent expression patterns across both datasets were identified. Localization, correlation, and Gene Set Enrichment Analysis (GSEA) of these key genes were performed. A molecular regulatory network was constructed, and drug predictions, along with molecular docking simulations, were conducted for the key genes. Experimental validation of the bioinformatics findings was carried out.

Results: A total of 3,462 DEGs were identified, from which 62 candidate genes were selected. Five key genes (AURKA, BUB1, CDK1, RAD51, TOP2A) were highlighted, all of which exhibited strong positive correlations and were linked to essential pathways, including the cell cycle. Thirteen potential drugs were identified, with three pairs showing strong binding affinities. RT-qPCR validation confirmed that, except for CDK1, AURKA, BUB1, RAD51, and TOP2A exhibited consistent trends with the bioinformatics analysis, and were significantly upregulated in the BA group.

Conclusion: This study successfully identified key genes (AURKA, BUB1, CDK1, RAD51, TOP2A) and potential therapeutic drugs for BA, providing critical insights into its pathogenesis and offering potential avenues for novel treatment strategies.

Keywords: GEO; biliary atresia; bioinformatics; epigenetic factors; molecular docking.

Figure 1

Screening and enrichment analysis of candidate genes. (A) Volcano plot displaying the DEGs between the BA group and the NM group. Red plots represent upregulated genes, while blue plots indicate downregulated genes. (B) Heatmap showing the DEGs between the BA group and the NM group, where red represents high expression and green represents low expression. (C) Venn diagram illustrating the 62 candidate genes identified. (D) GO enrichment analysis of candidate genes. In the bubble chart, bubble size represents the number of genes, and color reflects the P-value. (E) Top 10 biological functions identified by GO. (F,G) KEGG pathway analysis of candidate genes. Top 10 pathways enriched by KEGG. The top 10 pathways enriched by KEGG. DEGs, Differentially expressed genes; BA, biliary atresia; NM, normal; GO, gene ontology; KEGG; kyoto encyclopedia of genes and genomes.

Figure 2

Screening of key genes. (A) PPI network of candidate genes. Each label represents a protein, and each line indicates an interaction between proteins. (B–D) Top 9 genes with the highest scores identified by three different algorithms: MCC, MNC, and Degree. (E) Identification of 8 core genes. (F,G) Key genes are significantly upregulated in both the BA group of the training set and the validation set. BA, biliary atresia; NM, normal; PPI, protein-protein interaction; MCC, maximal clique centrality; MNC, maximum neighborhood component. ns, not significant, *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 3

Localization and tissue specificity of key genes (AURKA, BUB1, CDK1, RAD51, and TOP2A). (A) Chromosomal localization of the key genes. (B) Subcellular localization of the key genes. (C) BUB1 exhibits tissue specificity in human lymphocytes (721 B lymphoblasts).

Figure 4

Correlation analysis among key genes (AURKA, BUB1, CDK1, RAD51, and TOP2A). (A) Friends analysis indicating that CDK1 exhibits the strongest correlation with the other key genes at the functional level. (B) Correlation analysis revealing that BUB1 and TOP2A have the highest correlation, with a coefficient of 0.92. (C) Interaction network of key genes and their predicted associated genes. The inner circle consists of the five key genes, while the outer circle contains 20 other genes related to the functions of the key genes.

Figure 5

GSEA of the key genes. Both the Cell Cycle and DNA replication pathways were significantly positively correlated with the five key genes. (A) AURKA. (B) BUB1. (C) CDK1. The P53 Signaling Pathway showed a significant positive correlation with CDK1. (D) RAD51. Primary Immunodeficiency exhibited a significant positive correlation with RAD51. (E) TOP2A. The P53 Signaling Pathway demonstrated a significant positive correlation with TOP2A. GSEA, gene set enrichment analysis.

Figure 6

Construction of the molecular regulatory network of key genes. (A) TOP2A, RAD51, and AURKA are regulated by CEBPB; RAD51, CDK1, and BUB1 are regulated by NRF1; and AURKA, RAD51, and CDK1 are regulated by ELF1. (B) TOP2A and AURKA are regulated by hsa-miR-98-5p.

Figure 7

Drug prediction and molecular docking analysis of key genes. (A) Top 10 pairs of drug-key gene interactions. (B) Molecular docking and score of key genes and potential drugs.

Figure 8

Expression levels of AURKA, BUB1, CDK1, RAD51, and TOP2A in the BA group and the control group. (A) AURKA. (B) BUB1. (C) CDK1. (D) RAD51. (E) TOP2A. BA, biliary atresia. ns, not significant, *P < 0.05, **P < 0.01, ***P < 0.001.