Evidence map›Paper›PMID 39224398›Full record

ArticleAdvanced biomedical research2024

Unveiling Potential Biomarkers for Urinary Tract Infection: An Integrated Bioinformatics Approach.

Reza Maddah, Fahimeh Ghanbari, Maziyar Veisi, Eman Koosehlar, Marzieh Shadpirouz, Zarrin Basharat, Alireza Hejrati, Bahareh Shateri Amiri, Lina Hejrati

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Article in Advanced biomedical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Reza MaddahDepartment of Bioprocess Engineering, Institute of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
Fahimeh GhanbariApplied Physiology Research Center, Isfahan University of Medical, Isfahan, Iran.
Maziyar VeisiDepartment of Veterinary Medicine Shahrekord, Shahrekord University, Chaharmahal and Bakhtiari Province, Shahrekord, Iran.
Eman KoosehlarDepartment of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran.
Marzieh ShadpirouzDepartment of Applied Mathematics, Faculty of Mathematical Sciences, Shahrood University of Technology, Semnan, Iran.
Zarrin BasharatAlpha Genomics Private Limited, Islamabad 45710, Pakistan.
Alireza HejratiInternal Medicine Department, School of Medicine, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran.
Bahareh Shateri AmiriInternal Medicine Department, School of Medicine, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran.
Lina HejratiInternal Medicine Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Urinary tract infections (UTIs) are a widespread health concern with high recurrence rates and substantial economic impact, and they can increase the prevalence of antibiotic resistance. This study employed an integrated bioinformatics approach to identify key genes associated with UTI development, offering potential targets for interventions. Materials and Methods: For this study, the microarray dataset GSE124917 from the Gene Expression Omnibus (GEO) database was selected and reanalyzed. The differentially expressed genes (DEGs) between UTIs and healthy samples were identified using the LIMMA package in R software. In this section, Enrichr database was utilized to perform functional enrichment analysis of DEGs. Subsequently, the protein-protein interaction (PPI) network of the DEGs was constructed and visualized through Cytoscape, utilizing the STRING online database. The identification of hub genes was performed using Cytoscape's cytoHubba plug-in employing various methods. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic accuracy of hub genes. Results: Among the outcomes of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, the tumor necrosis factor (TNF) signaling pathway was identified as one of the notable pathways. The PPI network of the DEGs was successfully established and visualized in Cytoscape with the aid of the STRING online database. Using cytoHubba with different methods, we identified seven hub genes (STAT1, IL6, IFIT1, IFIT3, IFIH1, MX1, and IRF7). Based on the ROC analysis, all hub genes showed high diagnostic value. Conclusion: These findings provide a valuable baseline for future research aimed at unraveling the intricate molecular mechanisms behind UTI.

Indexed as

Bioinformaticscomputationalmolecular biologiesurinary tract infections

Identifiers

PMID39224398
PMCPMC11368229

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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.