Integrating bioinformatics to analyse hub genes and levels of immune infiltration in acne
Objective To explore the association of hub genes and immune infiltration with acne pathogenesis by integrating and analyzing multi-chip data. Methods Gene microarray datasets were obtained from the GEO database, and differentially expressed genes (DEGs) in the acne phenotype-related gene module wer...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
editoiral office of Journal of Diagnosis and Therapy on Dermato-venereology
2022-10-01
|
| Series: | Pifu-xingbing zhenliaoxue zazhi |
| Subjects: | |
| Online Access: | http://pfxbzlx.gdvdc.com/CN/10.3969/j.issn.1674-8468.2022.05.005 |
| _version_ | 1797732690307317760 |
|---|---|
| author | Wenlong FAN Hongxin WANG Dongyu CHEN Xiaoyu YANG Qiao HUANG Min HU Suyue PAN Pu WANG Yuqing HE |
| author_facet | Wenlong FAN Hongxin WANG Dongyu CHEN Xiaoyu YANG Qiao HUANG Min HU Suyue PAN Pu WANG Yuqing HE |
| author_sort | Wenlong FAN |
| collection | DOAJ |
| description | Objective To explore the association of hub genes and immune infiltration with acne pathogenesis by integrating and analyzing multi-chip data. Methods Gene microarray datasets were obtained from the GEO database, and differentially expressed genes (DEGs) in the acne phenotype-related gene module were screened using the "limma" R package and weighted gene co-expression network analysis (WGCNA). Moreover, GO functional enrichment and KEGG pathway analysis were performed. STRING online database and Cytoscape software were used to construct protein interaction network and to screen hub genes. Analysis of immune cell infiltration and correlation of hub genes with immune cell infiltration were based on the CIBERSORT algorithm. Results A total of 154 DEGs were screened. The DEGs associated with signaling receptor binding, cytokine activity and chemokine receptor binding were mainly enriched in pathways such as viral protein interaction with cytokine and cytokine receptors, cytokine-cytokine receptor interactions, chemokine signaling, NF-κB signaling and IL-17 signaling. The PPI network was constructed with 139 nodes and 1 597 edges, identifying two hub genes (CCR5, CXCL8). Among the 22 infiltrating immune cells, lesional site exhibited more neutrophils, activated mast cells, activated dendritic cells and activated CD4 memory T cells, but fewer regulatory T cells, resting dendritic cells and resting mast cells, in comparison to normal controls. Conclusion Changes in hub genes and immune cell infiltration, as identified by bioinformatics methods, may play an important role in the pathogenesis of acne. |
| first_indexed | 2024-03-12T12:17:08Z |
| format | Article |
| id | doaj.art-df02b22c9ac84165a2532d4761e068a0 |
| institution | Directory Open Access Journal |
| issn | 1674-8468 |
| language | zho |
| last_indexed | 2024-03-12T12:17:08Z |
| publishDate | 2022-10-01 |
| publisher | editoiral office of Journal of Diagnosis and Therapy on Dermato-venereology |
| record_format | Article |
| series | Pifu-xingbing zhenliaoxue zazhi |
| spelling | doaj.art-df02b22c9ac84165a2532d4761e068a02023-08-30T11:38:45Zzhoeditoiral office of Journal of Diagnosis and Therapy on Dermato-venereologyPifu-xingbing zhenliaoxue zazhi1674-84682022-10-0129541242010.3969/j.issn.1674-8468.2022.05.005Integrating bioinformatics to analyse hub genes and levels of immune infiltration in acneWenlong FAN0Hongxin WANG1Dongyu CHEN2Xiaoyu YANG3Qiao HUANG4Min HU5Suyue PAN6Pu WANG7Yuqing HE8Department of Epidemiology and Health Statistics, School of Public Health, Institute of Medical Systems Biology, Guangdong Medical University, Dongguan 523808, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Institute of Medical Systems Biology, Guangdong Medical University, Dongguan 523808, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Institute of Medical Systems Biology, Guangdong Medical University, Dongguan 523808, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Institute of Medical Systems Biology, Guangdong Medical University, Dongguan 523808, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Institute of Medical Systems Biology, Guangdong Medical University, Dongguan 523808, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Institute of Medical Systems Biology, Guangdong Medical University, Dongguan 523808, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Institute of Medical Systems Biology, Guangdong Medical University, Dongguan 523808, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Institute of Medical Systems Biology, Guangdong Medical University, Dongguan 523808, ChinaDepartment of Epidemiology and Health Statistics, School of Public Health, Institute of Medical Systems Biology, Guangdong Medical University, Dongguan 523808, ChinaObjective To explore the association of hub genes and immune infiltration with acne pathogenesis by integrating and analyzing multi-chip data. Methods Gene microarray datasets were obtained from the GEO database, and differentially expressed genes (DEGs) in the acne phenotype-related gene module were screened using the "limma" R package and weighted gene co-expression network analysis (WGCNA). Moreover, GO functional enrichment and KEGG pathway analysis were performed. STRING online database and Cytoscape software were used to construct protein interaction network and to screen hub genes. Analysis of immune cell infiltration and correlation of hub genes with immune cell infiltration were based on the CIBERSORT algorithm. Results A total of 154 DEGs were screened. The DEGs associated with signaling receptor binding, cytokine activity and chemokine receptor binding were mainly enriched in pathways such as viral protein interaction with cytokine and cytokine receptors, cytokine-cytokine receptor interactions, chemokine signaling, NF-κB signaling and IL-17 signaling. The PPI network was constructed with 139 nodes and 1 597 edges, identifying two hub genes (CCR5, CXCL8). Among the 22 infiltrating immune cells, lesional site exhibited more neutrophils, activated mast cells, activated dendritic cells and activated CD4 memory T cells, but fewer regulatory T cells, resting dendritic cells and resting mast cells, in comparison to normal controls. Conclusion Changes in hub genes and immune cell infiltration, as identified by bioinformatics methods, may play an important role in the pathogenesis of acne.http://pfxbzlx.gdvdc.com/CN/10.3969/j.issn.1674-8468.2022.05.005acnehub genesweighted gene co-expression network analysisimmune infiltrationpathogenesis |
| spellingShingle | Wenlong FAN Hongxin WANG Dongyu CHEN Xiaoyu YANG Qiao HUANG Min HU Suyue PAN Pu WANG Yuqing HE Integrating bioinformatics to analyse hub genes and levels of immune infiltration in acne Pifu-xingbing zhenliaoxue zazhi acne hub genes weighted gene co-expression network analysis immune infiltration pathogenesis |
| title | Integrating bioinformatics to analyse hub genes and levels of immune infiltration in acne |
| title_full | Integrating bioinformatics to analyse hub genes and levels of immune infiltration in acne |
| title_fullStr | Integrating bioinformatics to analyse hub genes and levels of immune infiltration in acne |
| title_full_unstemmed | Integrating bioinformatics to analyse hub genes and levels of immune infiltration in acne |
| title_short | Integrating bioinformatics to analyse hub genes and levels of immune infiltration in acne |
| title_sort | integrating bioinformatics to analyse hub genes and levels of immune infiltration in acne |
| topic | acne hub genes weighted gene co-expression network analysis immune infiltration pathogenesis |
| url | http://pfxbzlx.gdvdc.com/CN/10.3969/j.issn.1674-8468.2022.05.005 |
| work_keys_str_mv | AT wenlongfan integratingbioinformaticstoanalysehubgenesandlevelsofimmuneinfiltrationinacne AT hongxinwang integratingbioinformaticstoanalysehubgenesandlevelsofimmuneinfiltrationinacne AT dongyuchen integratingbioinformaticstoanalysehubgenesandlevelsofimmuneinfiltrationinacne AT xiaoyuyang integratingbioinformaticstoanalysehubgenesandlevelsofimmuneinfiltrationinacne AT qiaohuang integratingbioinformaticstoanalysehubgenesandlevelsofimmuneinfiltrationinacne AT minhu integratingbioinformaticstoanalysehubgenesandlevelsofimmuneinfiltrationinacne AT suyuepan integratingbioinformaticstoanalysehubgenesandlevelsofimmuneinfiltrationinacne AT puwang integratingbioinformaticstoanalysehubgenesandlevelsofimmuneinfiltrationinacne AT yuqinghe integratingbioinformaticstoanalysehubgenesandlevelsofimmuneinfiltrationinacne |