Structural changes of the nasal mucosa.
<div><p>This study was designed to identify immune-related biomarkers associated with allergic rhinitis (AR) and construct a robust a diagnostic model. Two datasets (GSE5010 and GSE50223) were downloaded from the NCBI GEO database, containing 38 and 84 blood CD4 + T cell samples, respect...
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| منشور في: |
2025
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| _version_ | 1852017020147597312 |
|---|---|
| author | MaoMeng Wang (22177417) |
| author2 | Shuang Wang (46453) XinHua Lin (22177420) XiaoJing Lv (22177423) XueXia Liu (13114949) Hua Zhang (12549) |
| author2_role | author author author author author |
| author_facet | MaoMeng Wang (22177417) Shuang Wang (46453) XinHua Lin (22177420) XiaoJing Lv (22177423) XueXia Liu (13114949) Hua Zhang (12549) |
| author_role | author |
| dc.creator.none.fl_str_mv | MaoMeng Wang (22177417) Shuang Wang (46453) XinHua Lin (22177420) XiaoJing Lv (22177423) XueXia Liu (13114949) Hua Zhang (12549) |
| dc.date.none.fl_str_mv | 2025-09-03T17:38:59Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0329549.g017 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Structural_changes_of_the_nasal_mucosa_/30045268 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Medicine Cell Biology Genetics Molecular Biology Immunology Biological Sciences not elsewhere classified support vector machine subsequently developed utilizing revealing significant differences potential clinical applications least absolute shrinkage eliminate batch effects 7 ), followed surrogate variable analysis demonstrating diagnostic efficiencies advancing novel diagnostic ncbi geo database differentially expressed genes related biomarkers associated disease risk scores distinguishing ar patients weighted gene co including &# 8220 highlight key genes ar mouse model expression network analysis diagnostic model gene co &# 8220 key genes expression network subsequent analysis string database selected genes relevant genes three disease related pathways related modules identify disease expression levels diagnosing ar xlink "> version 3 version 1 validation sets two datasets successfully established selection operator r package qpcr experiments linear models healthy controls eight types control groups cell samples assessed using |
| dc.title.none.fl_str_mv | Structural changes of the nasal mucosa. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>This study was designed to identify immune-related biomarkers associated with allergic rhinitis (AR) and construct a robust a diagnostic model. Two datasets (GSE5010 and GSE50223) were downloaded from the NCBI GEO database, containing 38 and 84 blood CD4 + T cell samples, respectively. To eliminate batch effects, the surrogate variable analysis (sva) R package (version 3.38.0) was employed, enabling the integration of data for subsequent analysis. Immune cell infiltration profiles were assessed using the Gene Set Variation Analysis (GSVA) R package (version 1.36.3). A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. Protein-protein interaction (PPI) networks were constructed based on the STRING database to highlight key genes. A diagnostic model was subsequently developed utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm and Support Vector Machine (SVM) method, with its discriminative capacity assessed via Receiver Operating Characteristic (ROC) curves. A total of twenty-eight immune cell types were analyzed, revealing significant differences in eight types between the AR and control groups. Through WGCNA, three disease-related modules comprising 4278 candidate genes were identified. Differential expression analysis identified 326 significant DEGs, of which 257 overlapped with WGCNA-selected genes. These genes exhibited significant enrichment in immune-related pathways, including “cytokine-cytokine receptor interaction” and “chemokine signaling pathway.” Gene Set Enrichment Analysis (GSEA) further uncovered 12 KEGG pathways significantly associated with disease risk scores. Drug screening identified 24 small molecule drugs related to key genes. A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively. An AR mouse model was successfully established, and the expression levels of relevant genes were validated through RT-qPCR experiments. The five-gene diagnostic model established in this study exhibits strong predictive ability in distinguishing AR patients from healthy controls, with potential clinical applications in diagnosing AR and advancing novel diagnostic and therapeutic strategies.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_44ea01d57ca25819d2cec42e88ba7aed |
| identifier_str_mv | 10.1371/journal.pone.0329549.g017 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30045268 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Structural changes of the nasal mucosa.MaoMeng Wang (22177417)Shuang Wang (46453)XinHua Lin (22177420)XiaoJing Lv (22177423)XueXia Liu (13114949)Hua Zhang (12549)BiochemistryMedicineCell BiologyGeneticsMolecular BiologyImmunologyBiological Sciences not elsewhere classifiedsupport vector machinesubsequently developed utilizingrevealing significant differencespotential clinical applicationsleast absolute shrinkageeliminate batch effects7 ), followedsurrogate variable analysisdemonstrating diagnostic efficienciesadvancing novel diagnosticncbi geo databasedifferentially expressed genesrelated biomarkers associateddisease risk scoresdistinguishing ar patientsweighted gene coincluding &# 8220highlight key genesar mouse modelexpression network analysisdiagnostic modelgene co&# 8220key genesexpression networksubsequent analysisstring databaseselected genesrelevant genesthree diseaserelated pathwaysrelated modulesidentify diseaseexpression levelsdiagnosing arxlink ">version 3version 1validation setstwo datasetssuccessfully establishedselection operatorr packageqpcr experimentslinear modelshealthy controlseight typescontrol groupscell samplesassessed using<div><p>This study was designed to identify immune-related biomarkers associated with allergic rhinitis (AR) and construct a robust a diagnostic model. Two datasets (GSE5010 and GSE50223) were downloaded from the NCBI GEO database, containing 38 and 84 blood CD4 + T cell samples, respectively. To eliminate batch effects, the surrogate variable analysis (sva) R package (version 3.38.0) was employed, enabling the integration of data for subsequent analysis. Immune cell infiltration profiles were assessed using the Gene Set Variation Analysis (GSVA) R package (version 1.36.3). A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. Protein-protein interaction (PPI) networks were constructed based on the STRING database to highlight key genes. A diagnostic model was subsequently developed utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm and Support Vector Machine (SVM) method, with its discriminative capacity assessed via Receiver Operating Characteristic (ROC) curves. A total of twenty-eight immune cell types were analyzed, revealing significant differences in eight types between the AR and control groups. Through WGCNA, three disease-related modules comprising 4278 candidate genes were identified. Differential expression analysis identified 326 significant DEGs, of which 257 overlapped with WGCNA-selected genes. These genes exhibited significant enrichment in immune-related pathways, including “cytokine-cytokine receptor interaction” and “chemokine signaling pathway.” Gene Set Enrichment Analysis (GSEA) further uncovered 12 KEGG pathways significantly associated with disease risk scores. Drug screening identified 24 small molecule drugs related to key genes. A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively. An AR mouse model was successfully established, and the expression levels of relevant genes were validated through RT-qPCR experiments. The five-gene diagnostic model established in this study exhibits strong predictive ability in distinguishing AR patients from healthy controls, with potential clinical applications in diagnosing AR and advancing novel diagnostic and therapeutic strategies.</p></div>2025-09-03T17:38:59ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0329549.g017https://figshare.com/articles/figure/Structural_changes_of_the_nasal_mucosa_/30045268CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300452682025-09-03T17:38:59Z |
| spellingShingle | Structural changes of the nasal mucosa. MaoMeng Wang (22177417) Biochemistry Medicine Cell Biology Genetics Molecular Biology Immunology Biological Sciences not elsewhere classified support vector machine subsequently developed utilizing revealing significant differences potential clinical applications least absolute shrinkage eliminate batch effects 7 ), followed surrogate variable analysis demonstrating diagnostic efficiencies advancing novel diagnostic ncbi geo database differentially expressed genes related biomarkers associated disease risk scores distinguishing ar patients weighted gene co including &# 8220 highlight key genes ar mouse model expression network analysis diagnostic model gene co &# 8220 key genes expression network subsequent analysis string database selected genes relevant genes three disease related pathways related modules identify disease expression levels diagnosing ar xlink "> version 3 version 1 validation sets two datasets successfully established selection operator r package qpcr experiments linear models healthy controls eight types control groups cell samples assessed using |
| status_str | publishedVersion |
| title | Structural changes of the nasal mucosa. |
| title_full | Structural changes of the nasal mucosa. |
| title_fullStr | Structural changes of the nasal mucosa. |
| title_full_unstemmed | Structural changes of the nasal mucosa. |
| title_short | Structural changes of the nasal mucosa. |
| title_sort | Structural changes of the nasal mucosa. |
| topic | Biochemistry Medicine Cell Biology Genetics Molecular Biology Immunology Biological Sciences not elsewhere classified support vector machine subsequently developed utilizing revealing significant differences potential clinical applications least absolute shrinkage eliminate batch effects 7 ), followed surrogate variable analysis demonstrating diagnostic efficiencies advancing novel diagnostic ncbi geo database differentially expressed genes related biomarkers associated disease risk scores distinguishing ar patients weighted gene co including &# 8220 highlight key genes ar mouse model expression network analysis diagnostic model gene co &# 8220 key genes expression network subsequent analysis string database selected genes relevant genes three disease related pathways related modules identify disease expression levels diagnosing ar xlink "> version 3 version 1 validation sets two datasets successfully established selection operator r package qpcr experiments linear models healthy controls eight types control groups cell samples assessed using |