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Journal of immunology research
2022;2022 2069756.

Identification of Immune-Related Genes and Small-Molecule Drugs in Interstitial Cystitis/Bladder Pain Syndrome Based on the Integrative Machine Learning Algorithms and Molecular Docking.

Yiheng Jiang, Xinqing Zhu, Abdullah Y Al-Danakh, Qiwei Chen, Deyong Yang
Author Information
  1. Yiheng Jiang: Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian 116021, China. ORCID
  2. Xinqing Zhu: Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian 116021, China. ORCID
  3. Abdullah Y Al-Danakh: Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian 116021, China. ORCID
  4. Qiwei Chen: Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian 116021, China. ORCID
  5. Deyong Yang: Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian 116021, China. ORCID
PMID: 36619718 DOI: 10.1155/2022/2069756

Abstract

Background: Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic, severely distressing clinical syndrome characterized by bladder pain and pressure perceptions. The origin and pathophysiology of IC/BPS are currently unclear, making it difficult to diagnose and formulate successful treatments. Our study is aimed at investigating the role of immune-related genes in the diagnosis, progression, and therapy of IC/BPS.
Method: The gene expression datasets GSE11783, GSE11839, GSE28242, and GSE57560 were retrieved from the GEO database for further analysis. Immune-related IC/BPS differentially expressed genes (DEGs) were identified by limma. Three distinct machine learning approaches, least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF), were used to find the immune-related IC characteristic genes. Nomogram and receiving operator curves (ROC) were plotted to measure characteristic effectiveness. Using the CMap database and the molecular docking approach, potential small-molecule medicines were found and verified. Consensus cluster analysis was also performed to separate the IC/BPS samples into immunological subtypes.
Results: A total of 24 immune-related IC/BPS-DEGs were identified. When compared to the normal control group, the IC/BPS cohort had significantly more immune cell infiltration. Integrative machine learning methods discovered 5 IC/BPS characteristic genes (RASGRP1, PPBP, RBP4, CR2, and PROS2) that may predict IC/BPS diagnosis and immune cell infiltration. Furthermore, two immunological subgroups with substantial variations in immune cell infiltration across IC/BPS samples were identified, which were named cluster1 and cluster2, with the hallmark genes having greater expression in cluster2. Finally, bumetanide was shown to have the potential to be a medication for the treatment of IC/BPS, and it performed well in terms of its molecular binding with RASGRP1.
Conclusion: We found and validated 5 immune-related IC/BPS genes (RASGRP1, PPBP, RBP4, CR2, and PROS2) and 2 IC/BPS immune subtypes. In addition, bumetanide was discovered to be a potential drug for treating IC/BPS, which may provide new insight into the diagnosis and immune therapy of IC/BPS patients.

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MeSH Term

Humans
Cystitis, Interstitial
Molecular Docking Simulation
Bumetanide
Algorithms
Guanine Nucleotide Exchange Factors
Retinol-Binding Proteins, Plasma

Chemicals

Bumetanide
Guanine Nucleotide Exchange Factors
RBP4 protein, human
Retinol-Binding Proteins, Plasma
Journal Article

Word Cloud

Created with Highcharts 10.0.0IC/BPSgenesimmuneimmune-relateddiagnosisidentifiedcharacteristicpotentialcellinfiltrationRASGRP1InterstitialpainsyndrometherapyexpressiondatabaseanalysismachinelearningoperatormolecularfoundperformedsamplesimmunologicalsubtypesIntegrativediscovered5PPBPRBP4CR2PROS2maycluster2bumetanideBackground:cystitis/bladderchronicseverelydistressingclinicalcharacterizedbladderpressureperceptionsoriginpathophysiologycurrentlyunclearmakingdifficultdiagnoseformulatesuccessfultreatmentsstudyaimedinvestigatingroleprogressionMethod:genedatasetsGSE11783GSE11839GSE28242GSE57560retrievedGEOImmune-relateddifferentiallyexpressedDEGslimmaThreedistinctapproachesleastabsoluteshrinkageselectionLASSOsupportvectormachine-recursivefeatureeliminationSVM-RFErandomforestRFusedfindICNomogramreceivingcurvesROCplottedmeasureeffectivenessUsingCMapdockingapproachsmall-moleculemedicinesverifiedConsensusclusteralsoseparateResults:total24IC/BPS-DEGscomparednormalcontrolgroupcohortsignificantlymethodspredictFurthermoretwosubgroupssubstantialvariationsacrossnamedcluster1hallmarkgreaterFinallyshownmedicationtreatmentwelltermsbindingConclusion:validated2additiondrugtreatingprovidenewinsightpatientsIdentificationImmune-RelatedGenesSmall-MoleculeDrugsCystitis/BladderPainSyndromeBasedMachineLearningAlgorithmsMolecularDocking

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