Evidence map›Paper›PMID 38197976›Full record

ArticleWorld journal of urology2024

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

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

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Article in World journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
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4citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Yuchen Gao *Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Ding Liu *Department of Urology, Shanghai Tenth People's Hospital, Nanjing Medical University, Shanghai, 200072, China.
Hongmin ZhouDepartment of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Yunze DongDepartment of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Xiao XuDepartment of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Xiangcheng ZhanDepartment of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Nueraihemaiti YimingniyiziDepartment of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Xudong YaoDepartment of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Tiancheng XieDepartment of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China. xysxtc@163.com.
Yunfei XuDepartment of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China. xuyunfeish@163.com.

Funding

National Natural Science Foundation of China 81971371National Natural Science Foundation of China 82101671the Research Project of Shanghai Municipal Health Commission 201940105
6 · The paper itself

Abstract

purposeKidney 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.

methodsThe 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.

resultsIn 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.

conclusionThe 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.

Indexed as

Kidney CalculiMicroRNAsBiomarkersCell MovementComputational BiologyHumansBiomarkersMicroRNAsBioinformatic analysisDifferentially expressed genesDrug-gene interaction networkGene–miRNA interactionsKidney stone diseaseLCN2

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.