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

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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.

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Data availability

All data generated or analysed during this study are included in this published article. The data are not publicly available due to data protection reasons.

Abbreviations

AUC:

Area under the curve

AURKB:

Aurora kinases B

BC:

Bladder cancer

BP:

Biological processes

CC:

Cellular component

CD:

Crohn’s disease

CDKs:

Cyclin‐dependent kinases

DCA:

Decision curve analysis

DEGs:

Differentially expressed genes

DGIdb:

Drug Gene Interaction Database

FC:

Foldchange

FDR:

False Discovery Rate

GEO:

Gene expression omnibus

GO:

Gene ontology

GS:

Gene significance

HPA:

Human Protein Atlas

IBD:

Inflammatory bowel disease

IHC:

Immunohistochemistry

KEGG:

Kyoto encyclopedia of genes and genomes

MCODE:

Molecular complex detection

MDM2:

Mouse double minute 2

ME:

Module eigengene

MF:

Molecular function

MM:

Module membership

OS:

Overall survival

PPI:

Protein–protein interaction

ROC:

Receiver operating characteristic

SVM:

Support vector machine

TFCP2L1:

Transcription factor CP2‐like protein 1

TFs:

Transcription factors

TCGA:

The cancer genome atlas

UALCAN:

University of ALabama at Birmingham CANcer data analysis Portal

UC:

Ulcerative colitis

WGCNA:

Weighted gene co-expression network analysis

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Funding

The study was funded by the National Key Research and Development Program of China (Grant Numbers: 2018YFC2002202) and the Chinese Academy of Medical Sciences (Grant Numbers: BJ-2022-237).

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Author notes

  1. Jianye Wang, Jianlong Wang and Yaoguang Zhang contributed equally to this work and share the last authorship.

Authors and Affiliations

  1. 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

    Jianyong Liu, Pengjie Wu, Jianye Wang, Jianlong Wang & Yaoguang Zhang

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

    Jianyong Liu, Pengjie Wu, Jianye Wang, Jianlong Wang & Yaoguang Zhang

  3. Beijing Hospital Continence Center, Beijing, People’s Republic of China

    Jianyong Liu, Pengjie Wu, Jianye Wang, Jianlong Wang & Yaoguang Zhang

  4. Department of Urology, Peking University People’s Hospital, Beijing, 100044, People’s Republic of China

    Shicong Lai

  5. No. 1 DaHua Road, Dong Dan, Beijing, 100730, China

    Jianye Wang, Jianlong Wang & Yaoguang Zhang

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  1. Jianyong Liu
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Contributions

JL: project development, data collection, data analysis, manuscript writing. PW: data collection, data analysis, manuscript editing. SL: data collection, data analysis, manuscript editing. Jianye Wang: project development, data analysis, manuscript editing. Jianlong Wang: project development, data analysis, manuscript editing. YZ: project development, data analysis, manuscript editing.

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Correspondence to Jianye Wang, Jianlong Wang or Yaoguang Zhang.

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Liu, J., Wu, P., Lai, S. et al. Identifying possible hub genes and biological mechanisms shared between bladder cancer and inflammatory bowel disease using machine learning and integrated bioinformatics. J Cancer Res Clin Oncol 149, 16885–16904 (2023). https://doi.org/10.1007/s00432-023-05266-0

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