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利用机器学习和综合生物信息学方法,识别膀胱癌和炎症性肠病之间可能存在的共享枢纽基因和生物学机制。

Identifying possible hub genes and biological mechanisms shared between bladder cancer and inflammatory bowel disease using machine learning and integrated bioinformatics.

机构信息

Department of Urology, Institute of the Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.

Graduate School of Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, People's Republic of China.

出版信息

J Cancer Res Clin Oncol. 2023 Dec;149(18):16885-16904. doi: 10.1007/s00432-023-05266-0. Epub 2023 Sep 23.

Abstract

BACKGROUND

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.

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.

RESULTS

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.

CONCLUSIONS

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.

摘要

背景

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,其发病机制仍缺乏了解。因此,本研究旨在使用生物信息学方法鉴定 IBD 和 BC 之间相互作用的潜在枢纽基因及其重要途径和病理机制。

方法

分析基因表达综合数据库(GEO)和癌症基因组图谱(TCGA)的数据,以筛选 IBD 和 BC 之间常见的差异表达基因(DEG)。使用“clusterProfiler”软件包对 DEG 进行 GO 术语和京都基因与基因组百科全书(KEGG)途径富集分析。之后,我们对这些 DEG 进行加权基因共表达网络分析(WGCNA),以确定与 BC 显著相关的重要模块和基因。蛋白质-蛋白质相互作用(PPI)网络用于识别枢纽基因。进一步,通过 Cox 分析使用枢纽基因构建预后特征。使用三种分类算法测试十个枢纽 DEG 的有效性。最后,我们分析了 microRNA(miRNA)-mRNA、转录因子(TF)-mRNA 调控网络。

结果

正向调节细胞器分裂、染色体区域、微管结合和细胞周期信号通路是常见 DEG 的主要富集途径。PPI 网络确定了三个具有高连接性的枢纽蛋白(AURKB、CDK1 和 CCNA2)。基于十个枢纽基因的三种机器学习分类算法在 IBD 和 BC 中表现良好(准确率>0.80)。基于十个枢纽基因的稳健预测模型可以准确地对具有各种临床结局的 BC 病例进行分类。基于基因-TF 和基因-miRNA 网络的构建,确定了 9 个 TF 和 6 个 miRNA 作为潜在的关键 TF 和 miRNA。基于基因-药物相互作用分析,有 13 种药物与枢纽基因相互作用。

结论

本研究探讨了 IBD 和 BC 的常见基因特征和潜在发病机制。我们揭示了不平衡的免疫反应、细胞周期途径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 的分子机制仍需要进一步研究。

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