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 [39]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.

   Keywords: Bioinformatics, computational, molecular biologies, urinary
   tract infections

INTRODUCTION

   Urinary tract infections (UTIs) are recognized as one of the most
   common bacterial infections, affecting approximately 150 million
   individuals worldwide annually.[[40]1,[41]2] According to data
   estimates, sixty percent of females report having a history of at least
   one UTI, and over 50% of women experience recurrent infections within 6
   months.[[42]3] The most common pathogen is urinary tract pathogenic
   Escherichia coli (UPEC). Approximately seventy-five percent of
   uncomplicated UTIs and sixty-five percent of complicated UTIs are
   caused by UPEC.[[43]4,[44]5] The hemolysin, fimbriae, and iron
   acquisition system are the primary virulence factors of UPEC
   responsible for causing UTIs. This infection can result in a
   significant economic burden on both the public and society. While
   antibiotics are the current standard treatment for UTIs, the rising
   rates of drug resistance have diminished the therapeutic efficacy of
   these drugs for treating UTIs.[[45]6,[46]7] Advancements in technology
   such as microarray and bioinformatics have revolutionized the
   exploration of molecular mechanisms of diseases and improved disease
   diagnosis and treatment. A comprehensive analysis of microarray data
   combined with bioinformatics approaches is essential. By these
   techniques, researchers gain a comprehensive understanding of the
   genetic and molecular factors driving UTIs. This knowledge can aid in
   the development of more accurate diagnostic tools for early detection
   of UTIs, as well as the identification of potential therapeutic
   targets. Furthermore, it can provide insights into personalized
   treatment strategies based on an individual’s genetic profile, allowing
   for more precise and effective interventions.[[47]8,[48]9]

   In this study, we employed bioinformatics methodologies to identify
   pivotal genes implicated in the progression of UTI, which can be
   offered as an intervention target for patients with UTIs.

MATERIAL AND METHODS

Source of data and analysis

   The microarray profile dataset was retrieved from the National Center
   for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO)
   databases.[[49]10] The dataset [50]GSE124917, obtained from the
   Agilent-072363 SurePrint G3 Human GEN v3 8x60K Microarray
   ([51]GPL21185), consisted of a total of six samples. This dataset
   included three normal samples and three samples infected with UPEC. The
   LIMMA package[[52]11] of R software was employed to identify
   differentially expressed genes (DEGs) in the UPEC group compared with
   the corresponding control. DEGs were filtered based on an adjusted P
   value of less than 0.05 and an absolute log-fold change greater than
   1.0 and were considered for downstream analysis. To visualize DEGs that
   were upregulated or downregulated, a volcano plot was generated. This
   plot provides a graphical representation of the fold change and
   statistical significance of the DEGs. A comprehensive list of DEGs can
   be found in Supplementary File 1. This file contains detailed
   information regarding the identified DEGs, including their
   corresponding fold change values, statistical significance, and
   additional relevant annotations ethic commete approval ID
   (IR.IUMS.REC.1402.179).

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
functional enrichment analysis

   The enrichment analysis of DEGs was conducted using the Enrichr online
   tool.[[53]12] This analysis aimed to explore the molecular and
   functional enrichments associated with the DEGs. Within Enrichr, both
   GO and KEGG enrichment analyses were performed on the DEGs.

   The GO enrichment analysis provides insights into the functional
   categorization of DEGs across three domains: biological process (BP),
   molecular function (MF), and cellular component (CC). However, the KEGG
   enrichment analysis identifies the key pathways in which the DEGs are
   likely to be involved. To determine statistical significance, a P value
   threshold of less than 0.05 was applied.

Protein-protein interaction (PPI) network and screening hub genes

   STRING, an online biological database dedicated to predicting PPIs, was
   employed for the construction of the PPI network.[[54]13] To visualize
   the PPI network, Cytoscape software was utilized, which is specifically
   designed for visualizing complex biological networks.[[55]14]
   Furthermore, the cytohubba Cytoscape plug-in[[56]15] was employed for
   the identification of hub genes among the DEGs. The PPI network
   construction involved utilizing the STRING database, with a focus on
   interactions with median confidence scores exceeding 0.7, which were
   deemed statistically significant. Subsequently, Cytoscape was employed
   to visually analyze the constructed PPI network. To identify hub genes,
   the cytoHubba plug-in was employed, utilizing the degree, closeness,
   and betweenness centrality methods, which enabled the sorting of hub
   genes. The intersection of hub genes among these methods was designated
   as hub genes in this study.

Evaluation of the diagnostic value

   To assess the diagnostic effectiveness of these biomarkers, receiver
   operating characteristic (ROC) curves were generated using GraphPad
   Prime 9.[[57]16] The area under the ROC curve (AUC) was then
   calculated, with an AUC value greater than 0.9 indicating that the
   biomarkers possessed favorable diagnostic value.

RESULTS

Data analysis

   In the present investigation, a microarray dataset consisting of
   individuals with UTI and healthy individuals was subjected to analysis
   using the LIMMA package in the R software. The analysis revealed a
   total of 801 DEGs, with 579 genes showing upregulation and 222 genes
   showing downregulation. The DEGs are visually represented in a volcano
   plot, as depicted in [58]Figure 1. The list of DEGs is shown in
   Supplementary File 1.

Figure 1.

   Figure 1
   [59]Open in a new tab

   A volcano plot illustrating the DEGs. In this plot, downregulated genes
   are shown by green points, while upregulated genes are depicted by red
   points

GO and KEGG functional enrichment analysis

   The Enrichr database was employed to conduct GO enrichment and KEGG
   pathway analyses on a set of 1599 DEGs. The GO analysis demonstrated
   significant enrichments of the DEGs in BP terms [[60]Figure 2],
   including the cytokine-mediated signaling pathway (GO: 0019221),
   cellular response to interferon-gamma (GO: 0071346), and cellular
   response to type I interferon (GO: 0071357). Regarding MF terms
   [[61]Figure 2], the DEGs exhibited notable enrichments in ribonucleic
   acid (RNA) polymerase II transcription regulatory region
   sequence-specific deoxyribonucleic acid (DNA) binding (GO: 0000977),
   cis-regulatory region sequence-specific DNA binding (GO: 0000987), and
   RNA polymerase II cis-regulatory region sequence-specific DNA binding
   (GO: 0000978). Furthermore, in terms of CC terms [[62]Figure 2], the
   DEGs were predominantly enriched in the nucleus (GO: 0005634),
   intracellular membrane-bounded organelle (GO: 0043231), and integral
   component of plasma membrane (GO: 0005887).

Figure 2.

   Figure 2
   [63]Open in a new tab

   Results of the Gene Ontology analysis performed on the DEGs. This
   analysis provides insights into the functional categorization
   associated with the identified DEGs

   Additionally, the KEGG pathway analysis highlighted several significant
   pathways [[64]Figure 3], including the tumor necrosis factor (TNF)
   signaling pathway, viral protein interaction with cytokine and cytokine
   receptor, cytokine-cytokine receptor interaction, influenza A, and
   nucleotide oligomerization domain (NOD)-like receptor signaling
   pathway. These pathways were identified as the most important in
   relation to the DEGs.

Figure 3.

   Figure 3
   [65]Open in a new tab

   Outcomes of the KEGG pathway enrichment analysis conducted on the DEGs.
   This analysis provides valuable insights into the specific biological
   pathways that are significantly enriched with the identified DEGs

PPI network and screening hub genes

   A PPI network [[66]Figure 4], comprising 369 nodes and 1808 edges, was
   obtained from the STRING database and subsequently visualized using
   Cytoscape. To identify hub genes within the PPI network, cytoHubba
   analysis was performed. Notably, the analysis identified STAT1, IL6,
   IFIT1, IFIT3, IFIH1, MX1, and IRF7 as hub genes shared among three
   different methods: degree, closeness, and betweenness.

Figure 4.

   Figure 4
   [67]Open in a new tab

   PPI network of genes, wherein the blue ellipses represent individual
   genes, and the gray lines depict the interactions between them.
   Notably, the PPI network consisted of 369 nodes (genes) and 1808 edges
   (interactions)

Evaluation of the diagnostic value

   The accuracy of the prognostic potential of the hub genes was evaluated
   using the ROC curve. The AUC was calculated to assess and compare the
   diagnostic capabilities of the hub genes. The findings revealed
   significant discriminative power, as indicated by estimated AUC values
   ranging from 0.9 to 1 [[68]Figure 5]. Notably, based on the ROC
   analysis, all hub genes exhibited high diagnostic values with AUC
   values exceeding 0.9.

Figure 5.

   Figure 5
   [69]Open in a new tab

   Validation of hub genes through receiver operating characteristic (ROC)
   curves

DISCUSSION

   UTIs are highly prevalent, affecting millions of individuals worldwide
   each year. Recurrence rates among women are high, with a considerable
   proportion experiencing multiple infections within a brief
   period.[[70]17,[71]18] This study utilized a comprehensive approach
   integrating various bioinformatics methodologies to discover key genes
   associated with the development of UTIs, highlighting their potential
   as targets for interventions.

   In this study, KEGG pathway enrichment analysis revealed that the TNF
   signaling pathway exhibits notable significance, suggesting its
   potential importance in the initiation and progression of UTIs. The TNF
   signaling pathway plays an important role in the immune response to
   infections. Upon bacterial attack, macrophages and neutrophils, the
   protective cells of the urinary tract, are recruited to the site of
   infection. These cells produce TNF, which binds to its receptors on
   various cell types, including immune cells, epithelial cells, and
   endothelial cells. Activation of TNF receptors leads to a signaling
   cascade, and activating transcription factors such as nuclear factor
   kappa B (NF-Κb) and activator protein 1 (AP-1), result in increased
   expression of genes related to pro-inflammatory cytokines and
   chemokines. This recruitment and activation of additional immune cells
   aid in bacterial clearance. However, excessive production of TNF can
   also contribute to tissue damage and inflammation. Modulation of the
   TNF signaling pathway may serve as a therapeutic target to reduce
   inflammation and tissue injury. Overall, the TNF signaling pathway
   plays a crucial role in the immune response to UTIs, but dysregulation
   of this pathway can also contribute to tissue damage and
   inflammation.[[72]19]

   The present study identified seven hub genes (STAT1, IL6, IFIT1, IFIT3,
   IFIH1, MX 1, and IRF7) that are significantly associated with the
   progression of UTIs. These findings underscore the crucial role of
   these genes in the development and advancement of UTIs.

   Based on reports, not only has STAT1 been recognized as an essential
   player in the biological response to various Toll-like receptors
   (TLRs), including TLR-4, but it also plays a vital role in the
   modulatory mechanism that contributes to the development of
   inflammatory diseases or infections.[[73]20,[74]21]

   In this regard, C. H. Ho et al. showed that STAT-1 is essential for
   controlling UPEC invasion and infection in uroepithelial cells. They
   also showed that sugar could increase UPEC infection in uroepithelial
   cells by overexpression of the TLR-4, STAT-1, and pro-inflammatory
   IL-6.[[75]22,[76]23]

   In 2016, Cheng et al. found that UPEC infection in the epididymis could
   increase IL-6 and TNF-α through NF-kB signaling with TLR-4 or
   TLR-5.[[77]24] In 2018, Ching et al. demonstrated that IL-6 deficiency
   was linked to a rise in the development of intracellular bacterial
   populations (Ching et al., 2018).[[78]24] According to the study of
   Engelsöy et al. (2019), UPEC infection causes an increase in TNF-α and
   IL-6 gene levels, reducing biofilm formation and hemolytic
   activity.[[79]25] Furthermore, Vegaj-Hernández et al. in 2021 revealed
   that the UPEC proteins FimH and FliC cause the secretion of
   IL-6.[[80]26]

   The signaling pathway associated with IFIT1 and IFIT3 is a key
   component of the host immune response to infection. Bacteria such as
   Escherichia coli (E. coli) can activate TLRs on the surface of
   epithelial cells in the urinary tract, leading to the production of
   IFN-alpha and IFN-beta interferons. These cytokines activate the
   JAK-STAT signaling pathway, which regulates the expression of other
   interferon-stimulated genes, including IFIT-3 and IFIT1. IFIT3 inhibits
   the replication of E. coli and other uropathogenic bacteria and
   modulates the host immune response. In addition to its direct
   antimicrobial effects, IFIT3 signaling also plays a role in the
   activation of adaptive immune cells. IFIT3 promotes the differentiation
   of cluster of differentiation (CD) 8+ cells into cytotoxic T
   lymphocytes (CTLs), which can eliminate infected cells. Overall, the
   signaling pathway associated with IFIT3 is an important part of the
   host immune response to infection.[[81]27,[82]28,[83]29]

   Despite the valuable insights provided by this study, there are some
   limitations that should be acknowledged:

   Reliance on a single dataset: The analysis was conducted using a
   specific microarray dataset ([84]GSE124917), which may limit the
   generalizability of the findings. Including multiple independent
   datasets or replicating the study with different datasets would
   strengthen the robustness of the results. Bioinformatics approach
   limitations: While the bioinformatics approach employed in this study
   is valuable for identifying potential key genes and pathways, it relies
   on computational predictions. The findings should be interpreted with
   caution and further validated through experimental studies. Lack of
   clinical data integration: This study primarily focused on molecular
   and genetic aspects of UTIs. The integration of clinical data, such as
   patient characteristics, treatment regimens, and outcomes, would
   provide a more comprehensive understanding of the disease and
   strengthen the relevance of the findings in a clinical context.
   Addressing these limitations would contribute to a more comprehensive
   understanding of the molecular mechanisms underlying UTI development
   and facilitate the translation of these findings into clinical
   applications. To advance our understanding further and address gaps in
   current knowledge, future studies in this field should focus on several
   key aspects. Firstly, exploring the functional roles of the identified
   hub genes in the pathogenesis of UTIs is essential. Furthermore, the
   validation of the diagnostic potential of the identified hub genes in
   diverse patient populations and clinical settings is crucial. Lastly,
   as the field of bioinformatics continues to evolve, integrating
   multi-omics data and employing advanced computational approaches could
   provide a more holistic view of UTI pathogenesis.

CONCLUSION

   In conclusion, this study brings us one step closer to understanding
   the complex puzzle of UTIs. By unraveling the key regulatory pathways
   involved, we now have a better grasp of how UTIs develop and progress.
   However, there is still much more to learn, and further research is
   crucial to validate these findings. By unraveling the mysteries of this
   condition, we can develop innovative strategies and interventions that
   go beyond simply treating the symptoms. Our collective efforts will
   pave the way for a future where UTIs are more effectively understood,
   prevented, and managed, leading to happier and healthier lives for all.

Financial support and sponsorship

   Nil.

Conflicts of interest

   There are no conflicts of interest.

REFERENCES