The information of datasets used in this study.
<div><p>Objective</p><p>Psoriatic arthritis (PsA) and rheumatoid arthritis (RA) are the most common types of inflammatory musculoskeletal disorders that share overlapping clinical features and complications. The aim of this study was to identify shared marker genes and mechan...
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2024
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| _version_ | 1852025351338721280 |
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| author | Kaiyi Zhou (2553352) |
| author2 | Siyu Luo (11856014) Qinxiao Wang (20141557) Sheng Fang (709608) |
| author2_role | author author author |
| author_facet | Kaiyi Zhou (2553352) Siyu Luo (11856014) Qinxiao Wang (20141557) Sheng Fang (709608) |
| author_role | author |
| dc.creator.none.fl_str_mv | Kaiyi Zhou (2553352) Siyu Luo (11856014) Qinxiao Wang (20141557) Sheng Fang (709608) |
| dc.date.none.fl_str_mv | 2024-11-07T18:33:17Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0313344.t002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/The_information_of_datasets_used_in_this_study_/27631453 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Cell Biology Genetics Molecular Biology Immunology Developmental Biology Cancer Hematology Biological Sciences not elsewhere classified receiver operating characteristic nf -&# 954 lymphocyte antigen 96 least absolute shrinkage inflammatory musculoskeletal disorders constructed using data b signaling pathways gsea analysis indicated gene expression omnibus gap junction function cell sequencing data enhanced vegf signaling seven overlapping degs decision curve analysis immune cell function two marker genes tf network suggested potential diagnostic markers study identified rpl22l1 vegf signaling results indicated potential association marker genes high expression gap junctions diagnostic capacity cell infiltration cell development cell communication immune landscape immune infiltrates immune cells xlink "> utilized datasets transcription factor thereby exploring shared biomarkers selection operator rheumatoid arthritis rfe ). psoriatic arthritis nk cells mechanistic similarities machine learning like receptors like 1 key biomarkers investigations demonstrated findings based experimental approaches dca ). comprehensive understanding common types cellular subpopulations cells examined |
| dc.title.none.fl_str_mv | The information of datasets used in this study. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Objective</p><p>Psoriatic arthritis (PsA) and rheumatoid arthritis (RA) are the most common types of inflammatory musculoskeletal disorders that share overlapping clinical features and complications. The aim of this study was to identify shared marker genes and mechanistic similarities between PsA and RA.</p><p>Methods</p><p>We utilized datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. To identify the marker genes, we applied two machine learning algorithms: the least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE). Subsequently, we assessed the diagnostic capacity of the identified marker genes using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). A transcription factor (TF) network was constructed using data from JASPAR, HumanTFDB, and GTRD. We then employed CIBERSORT to analyze the abundance of immune infiltrates in PsA and RA, assessing the relationship between marker genes and immune cells. Additionally, cellular subpopulations were identified by analyzing single-cell sequencing data from RA, with T cells examined for trajectory and cellular communication using Monocle and CellChat, thereby exploring their linkage to marker genes.</p><p>Results</p><p>A total of seven overlapping DEGs were identified between PsA and RA. Gene enrichment analysis revealed that these genes were associated with mitochondrial respiratory chain complex IV, Toll-like receptors, and NF-κB signaling pathways. Both machine learning algorithms identified Ribosomal Protein L22-like 1 (RPL22L1) and Lymphocyte Antigen 96 (LY96) as potential diagnostic markers for PsA and RA. These markers were validated using test sets and experimental approaches. Furthermore, GSEA analysis indicated that gap junctions may play a crucial role in the pathogenesis of both conditions. The TF network suggested a potential association between marker genes and core enrichment genes related to gap junctions. The application of CIBERSORT and single-cell RNA sequencing provided a comprehensive understanding of the role of marker genes in immune cell function. Our results indicated that RPL22L1 and LY96 are involved in T cell development and are associated with T cell communication with NK cells and monocytes. Notably, high expression of both RPL22L1 and LY96 was linked to enhanced VEGF signaling in T cells.</p><p>Conclusion</p><p>Our study identified RPL22L1 and LY96 as key biomarkers for PsA and RA. Further investigations demonstrated that these two marker genes are closely associated with gap junction function, T cell infiltration, differentiation, and VEGF signaling. Collectively, these findings provide new insights into the diagnosis and treatment of PsA and RA.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_cf05e1663a70953d63949e565b76c2a9 |
| identifier_str_mv | 10.1371/journal.pone.0313344.t002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27631453 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The information of datasets used in this study.Kaiyi Zhou (2553352)Siyu Luo (11856014)Qinxiao Wang (20141557)Sheng Fang (709608)BiophysicsCell BiologyGeneticsMolecular BiologyImmunologyDevelopmental BiologyCancerHematologyBiological Sciences not elsewhere classifiedreceiver operating characteristicnf -&# 954lymphocyte antigen 96least absolute shrinkageinflammatory musculoskeletal disordersconstructed using datab signaling pathwaysgsea analysis indicatedgene expression omnibusgap junction functioncell sequencing dataenhanced vegf signalingseven overlapping degsdecision curve analysisimmune cell functiontwo marker genestf network suggestedpotential diagnostic markersstudy identified rpl22l1vegf signalingresults indicatedpotential associationmarker geneshigh expressiongap junctionsdiagnostic capacitycell infiltrationcell developmentcell communicationimmune landscapeimmune infiltratesimmune cellsxlink ">utilized datasetstranscription factorthereby exploringshared biomarkersselection operatorrheumatoid arthritisrfe ).psoriatic arthritisnk cellsmechanistic similaritiesmachine learninglike receptorslike 1key biomarkersinvestigations demonstratedfindings basedexperimental approachesdca ).comprehensive understandingcommon typescellular subpopulationscells examined<div><p>Objective</p><p>Psoriatic arthritis (PsA) and rheumatoid arthritis (RA) are the most common types of inflammatory musculoskeletal disorders that share overlapping clinical features and complications. The aim of this study was to identify shared marker genes and mechanistic similarities between PsA and RA.</p><p>Methods</p><p>We utilized datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. To identify the marker genes, we applied two machine learning algorithms: the least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE). Subsequently, we assessed the diagnostic capacity of the identified marker genes using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). A transcription factor (TF) network was constructed using data from JASPAR, HumanTFDB, and GTRD. We then employed CIBERSORT to analyze the abundance of immune infiltrates in PsA and RA, assessing the relationship between marker genes and immune cells. Additionally, cellular subpopulations were identified by analyzing single-cell sequencing data from RA, with T cells examined for trajectory and cellular communication using Monocle and CellChat, thereby exploring their linkage to marker genes.</p><p>Results</p><p>A total of seven overlapping DEGs were identified between PsA and RA. Gene enrichment analysis revealed that these genes were associated with mitochondrial respiratory chain complex IV, Toll-like receptors, and NF-κB signaling pathways. Both machine learning algorithms identified Ribosomal Protein L22-like 1 (RPL22L1) and Lymphocyte Antigen 96 (LY96) as potential diagnostic markers for PsA and RA. These markers were validated using test sets and experimental approaches. Furthermore, GSEA analysis indicated that gap junctions may play a crucial role in the pathogenesis of both conditions. The TF network suggested a potential association between marker genes and core enrichment genes related to gap junctions. The application of CIBERSORT and single-cell RNA sequencing provided a comprehensive understanding of the role of marker genes in immune cell function. Our results indicated that RPL22L1 and LY96 are involved in T cell development and are associated with T cell communication with NK cells and monocytes. Notably, high expression of both RPL22L1 and LY96 was linked to enhanced VEGF signaling in T cells.</p><p>Conclusion</p><p>Our study identified RPL22L1 and LY96 as key biomarkers for PsA and RA. Further investigations demonstrated that these two marker genes are closely associated with gap junction function, T cell infiltration, differentiation, and VEGF signaling. Collectively, these findings provide new insights into the diagnosis and treatment of PsA and RA.</p></div>2024-11-07T18:33:17ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0313344.t002https://figshare.com/articles/dataset/The_information_of_datasets_used_in_this_study_/27631453CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/276314532024-11-07T18:33:17Z |
| spellingShingle | The information of datasets used in this study. Kaiyi Zhou (2553352) Biophysics Cell Biology Genetics Molecular Biology Immunology Developmental Biology Cancer Hematology Biological Sciences not elsewhere classified receiver operating characteristic nf -&# 954 lymphocyte antigen 96 least absolute shrinkage inflammatory musculoskeletal disorders constructed using data b signaling pathways gsea analysis indicated gene expression omnibus gap junction function cell sequencing data enhanced vegf signaling seven overlapping degs decision curve analysis immune cell function two marker genes tf network suggested potential diagnostic markers study identified rpl22l1 vegf signaling results indicated potential association marker genes high expression gap junctions diagnostic capacity cell infiltration cell development cell communication immune landscape immune infiltrates immune cells xlink "> utilized datasets transcription factor thereby exploring shared biomarkers selection operator rheumatoid arthritis rfe ). psoriatic arthritis nk cells mechanistic similarities machine learning like receptors like 1 key biomarkers investigations demonstrated findings based experimental approaches dca ). comprehensive understanding common types cellular subpopulations cells examined |
| status_str | publishedVersion |
| title | The information of datasets used in this study. |
| title_full | The information of datasets used in this study. |
| title_fullStr | The information of datasets used in this study. |
| title_full_unstemmed | The information of datasets used in this study. |
| title_short | The information of datasets used in this study. |
| title_sort | The information of datasets used in this study. |
| topic | Biophysics Cell Biology Genetics Molecular Biology Immunology Developmental Biology Cancer Hematology Biological Sciences not elsewhere classified receiver operating characteristic nf -&# 954 lymphocyte antigen 96 least absolute shrinkage inflammatory musculoskeletal disorders constructed using data b signaling pathways gsea analysis indicated gene expression omnibus gap junction function cell sequencing data enhanced vegf signaling seven overlapping degs decision curve analysis immune cell function two marker genes tf network suggested potential diagnostic markers study identified rpl22l1 vegf signaling results indicated potential association marker genes high expression gap junctions diagnostic capacity cell infiltration cell development cell communication immune landscape immune infiltrates immune cells xlink "> utilized datasets transcription factor thereby exploring shared biomarkers selection operator rheumatoid arthritis rfe ). psoriatic arthritis nk cells mechanistic similarities machine learning like receptors like 1 key biomarkers investigations demonstrated findings based experimental approaches dca ). comprehensive understanding common types cellular subpopulations cells examined |