最近的研究表明，炎症性肠病（IBD）与膀胱癌（BC）的发病率有关。但是对其发病机制的理解还存在不足。因此，本研究旨在利用生物信息学方法鉴定IBD和BC之间的潜在关键基因及其重要通路和病理机制的相互作用。使用来自Gene Expression Omnibus（GEO）和癌症基因组图谱（TCGA）的数据来分析IBD和BC之间的共同差异表达基因（DEGs）。使用"clusterProfiler"包对DEGs进行GO term和Kyoto Encyclopedia of Genes and Genomes（KEGG）通路富集分析。然后，在这些DEGs上进行加权基因共表达网络分析（WGCNA），以确定与BC显著相关的关键模块和基因。使用蛋白质相互作用（PPI）网络来识别关键基因。进一步，利用Cox分析使用这些关键基因来建立预后签名。使用三种分类算法测试了十个关键DEGs的有效性。最后，分析了微小RNA（miRNA）-mRNA和转录因子（TF）-mRNA的调控网络。共同DEGs的主要富集通路包括细胞器分裂的正调控、染色体区域、微管结合和细胞周期信号通路。PPI网络识别到了具有高连接性的三个关键蛋白（AURKB，CDK1和CCNA2）。基于十个关键基因的三种机器学习分类算法在IBD和BC的分类中表现良好（准确度>0.80）。基于这十个关键基因的稳健预测模型能够准确分类具有不同临床结局的BC病例。根据基因-TFs和基因-miRNAs网络构建，确定了9个TFs和6个miRNAs作为潜在的关键TFs和miRNAs。基于基因-药物相互作用分析，有13种药物与关键基因有相互作用。本研究探讨了IBD和BC的共同基因标志和潜在发病机制。我们揭示了免疫反应失衡、细胞周期通路和中性粒细胞浸润可能是IBD和BC的共同发病机制。治疗IBD和CC的分子机制需要进一步研究。© 2023年作者(s)，在Springer Nature的Springer-Verlag GmbH Germany专有许可下。

Recent studies have shown that inflammatory bowel disease (IBD) is associated with bladder cancer (BC) incidence. But there is still a lack of understanding regarding its pathogenesis. Thus, this study aimed to identify potential hub genes and their important pathways and pathological mechanisms of interactions between IBD and BC using bioinformatics methods.The data from Gene Expression Omnibus (GEO) and the cancer genome atlas (TCGA) were analyzed to screen common differentially expressed genes (DEGs) between IBD and BC. The "clusterProfiler" package was used to analyze GO term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment in DEGs. After that, we conducted a weighted gene co-expression network analysis (WGCNA) on these DEGs to determine the vital modules and genes significantly related to BC. Protein-protein interaction (PPI) networks was used to identify hub genes. Further, the hub genes were used to develop a prognostic signature by Cox analysis. The validity of the ten hub DEGs was tested using three classification algorithms. Finally, we analyzed the microRNAs (miRNA)-mRNA, transcription factors (TFs)-mRNA regulatory network.Positive regulation of organelle fission, chromosomal region, tubulin binding, and cell cycle signaling pathway were the major enriched pathways for the common DEGs. PPI networks identified three hub proteins (AURKB, CDK1, and CCNA2) with high connectivity. Three machine-learning classification algorithms based on ten hub genes performed well for IBD and BC (accuracy > 0.80). The robust predictive model based on the ten hub genes could accurately classify BC cases with various clinical outcomes. Based on the gene-TFs and gene-miRNAs network construction, 9 TFs and 6 miRNAs were identified as potential critical TFs and miRNAs. There are 13 drugs that interact with the hub gene based on gene-drug interaction analysis.This study explored common gene signatures and the potential pathogenesis of IBD and BC. We revealed that an unbalanced immune response, cell cycle pathway, and neutrophil infiltration might be the common pathogenesis of IBD and BC. Molecular mechanisms for the treatment of IBD and CC still require further investigation.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.