Identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co-expression network analysis and LASSO.
<h4>Background</h4>Psoriasis is an inflammatory skin disease, and current treatments have their own limitations, including moderate treatment effectiveness, poor compliance, and potential safety risks, etc. Therefore, the primary focus of this study is to explore novel molecular targets...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317666 |
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| author | Chenguang Wang Zhiyong Liu Yan He Yashu Zhang Shiqi Chen Yuhao Zhou Wenqing Yang Lijun Fan |
| author_facet | Chenguang Wang Zhiyong Liu Yan He Yashu Zhang Shiqi Chen Yuhao Zhou Wenqing Yang Lijun Fan |
| author_sort | Chenguang Wang |
| collection | DOAJ |
| description | <h4>Background</h4>Psoriasis is an inflammatory skin disease, and current treatments have their own limitations, including moderate treatment effectiveness, poor compliance, and potential safety risks, etc. Therefore, the primary focus of this study is to explore novel molecular targets and improve the diagnosis and treatment of psoriasis patients.<h4>Method</h4>In this study, comprehensive bioinformatics analysis was performed on the expression profiles of tissue samples from patients with psoriasis in the clinical trial of TYK2/JAK1 inhibitor treatment (NCT02310750). Weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression were performed to identify characteristic genes and construct the diagnostic models. Gene set enrichment analysis (GSEA) was used to identify the biological processes of psoriasis characteristic gene sets. GO and KEGG pathway analysis were combined to elucidate the potential biological significance of differentially expressed genes (DEGs). The accuracy of biomarker identification was further validated using immune cell infiltration and receiver operating characteristic (ROC) curves based on external data (GSE6710\GSE30999\GSE14905).<h4>Results</h4>A total of 5 genes (DEFB103A, OAS3, OASL, SAMD9, STAT1) were co-identified as characteristic genes in psoriasis progression and treatment. The feature of the immune cell infiltration was highly consistent with association of characteristic biomarkers with immune cells. A total of 14 up-regulated genes and 5 down-regulated genes were identified in respective modules (AUC NL/LS = 0.9783; AUC pre/post = 0.9395; AUC external = 0.9469). In addition, 8 genes (DEFB103A, OASL, HERC6, ISG15, MKI67, MX1, MXD1, SCO2) were considered to have statistically significant differences in sensitivity of short-term treatment for psoriasis.<h4>Conclusion</h4>The research findings provide an understanding of the role of novel biomarkers and offer a perspective for further in-depth investigation into the progression and treatment of psoriasis. |
| format | Article |
| id | doaj-art-d17062d02e17443288482cc426a734dd |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-d17062d02e17443288482cc426a734dd2025-08-20T03:27:37ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e031766610.1371/journal.pone.0317666Identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co-expression network analysis and LASSO.Chenguang WangZhiyong LiuYan HeYashu ZhangShiqi ChenYuhao ZhouWenqing YangLijun Fan<h4>Background</h4>Psoriasis is an inflammatory skin disease, and current treatments have their own limitations, including moderate treatment effectiveness, poor compliance, and potential safety risks, etc. Therefore, the primary focus of this study is to explore novel molecular targets and improve the diagnosis and treatment of psoriasis patients.<h4>Method</h4>In this study, comprehensive bioinformatics analysis was performed on the expression profiles of tissue samples from patients with psoriasis in the clinical trial of TYK2/JAK1 inhibitor treatment (NCT02310750). Weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression were performed to identify characteristic genes and construct the diagnostic models. Gene set enrichment analysis (GSEA) was used to identify the biological processes of psoriasis characteristic gene sets. GO and KEGG pathway analysis were combined to elucidate the potential biological significance of differentially expressed genes (DEGs). The accuracy of biomarker identification was further validated using immune cell infiltration and receiver operating characteristic (ROC) curves based on external data (GSE6710\GSE30999\GSE14905).<h4>Results</h4>A total of 5 genes (DEFB103A, OAS3, OASL, SAMD9, STAT1) were co-identified as characteristic genes in psoriasis progression and treatment. The feature of the immune cell infiltration was highly consistent with association of characteristic biomarkers with immune cells. A total of 14 up-regulated genes and 5 down-regulated genes were identified in respective modules (AUC NL/LS = 0.9783; AUC pre/post = 0.9395; AUC external = 0.9469). In addition, 8 genes (DEFB103A, OASL, HERC6, ISG15, MKI67, MX1, MXD1, SCO2) were considered to have statistically significant differences in sensitivity of short-term treatment for psoriasis.<h4>Conclusion</h4>The research findings provide an understanding of the role of novel biomarkers and offer a perspective for further in-depth investigation into the progression and treatment of psoriasis.https://doi.org/10.1371/journal.pone.0317666 |
| spellingShingle | Chenguang Wang Zhiyong Liu Yan He Yashu Zhang Shiqi Chen Yuhao Zhou Wenqing Yang Lijun Fan Identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co-expression network analysis and LASSO. PLoS ONE |
| title | Identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co-expression network analysis and LASSO. |
| title_full | Identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co-expression network analysis and LASSO. |
| title_fullStr | Identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co-expression network analysis and LASSO. |
| title_full_unstemmed | Identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co-expression network analysis and LASSO. |
| title_short | Identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co-expression network analysis and LASSO. |
| title_sort | identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co expression network analysis and lasso |
| url | https://doi.org/10.1371/journal.pone.0317666 |
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