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Identification of biomarkers and potential therapeutic targets of kidney stone disease using bioinformatics

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World Journal of Urology Aims and scope Submit manuscript

A Letter to the Editor to this article was published on 29 April 2024

Abstract

Purpose

Kidney stone disease (KSD) is a common urological disease, but its pathogenesis remains unclear. In this study, we screened KSD-related hub genes using bioinformatic methods and predicted the related pathways and potential drug targets.

Methods

The GSE75542 and GSE18160 datasets in the Gene Expression Omnibus (GEO) were selected to identify common differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify enriched pathways. Finally, we constructed a hub gene–miRNA network and drug–DEG interaction network.

Results

In total, 44 upregulated DEGs and 1 downregulated DEG were selected from the GEO datasets. Signaling pathways, such as leukocyte migration, chemokine activity, NF-κB, TNF, and IL-17, were identified in GO and KEGG. We identified 10 hub genes using Cytohubba. In addition, 21 miRNAs were predicted to regulate 4 or more hub genes, and 10 drugs targeted 2 or more DEGs. LCN2 expression was significantly different between the GEO datasets. Quantitative real-time polymerase chain reaction (qRT–PCR) analyses showed that seven hub gene expressions in HK-2 cells with CaOx treatment were significantly higher than those in the control group.

Conclusion

The 10 hub genes identified, especially LCN2, may be involved in kidney stone occurrence and development, and may provide new research targets for KSD diagnosis. Furthermore, KSD-related miRNAs may be targeted for the development of novel drugs for KSD treatment.

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Data availability

The datasets supporting this research are presented in the article.

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Acknowledgements

This research was funded by the National Natural Science Foundation of China (No. 81971371 and 82101671) and the Research Project of Shanghai Municipal Health Commission (No. 201940105). We would like to thank the GEO database for providing the data.

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Author notes

  1. Yuchen Gao and Ding Liu share the first authorship.

Authors and Affiliations

  1. Department of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, China

    Yuchen Gao, Hongmin Zhou, Yunze Dong, Xiao Xu, Xiangcheng Zhan, Nueraihemaiti Yimingniyizi, Xudong Yao, Tiancheng Xie & Yunfei Xu

  2. Department of Urology, Shanghai Tenth People’s Hospital, Nanjing Medical University, Shanghai, 200072, China

    Ding Liu

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  1. Yuchen Gao
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Contributions

Conceptualization, X.Y. and Y.X.; methodology, Y.G. and Y.X.; software, H.Z. and Y.G.; validation, N.Y. and D.L.; formal analysis, Y.G. and D.L.; investigation, X.Z.; resources, T.X.; data curation, Y.D.; writing—original draft preparation, Y.G. and D.L.; writing—review and editing, T.X., Y.G. and Y.X.; visualization, X.X.; supervision, D.L. and Y.X.; project administration, T.X. and Y.X.; funding acquisition, T.X. and Y.X. All authors have read and agreed to the published version of the manuscript. Y.G. and D.L. contributed equally to this work.

Corresponding authors

Correspondence to Tiancheng Xie or Yunfei Xu.

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Gao, Y., Liu, D., Zhou, H. et al. Identification of biomarkers and potential therapeutic targets of kidney stone disease using bioinformatics. World J Urol 42, 17 (2024). https://doi.org/10.1007/s00345-023-04704-5

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