PloS one · 2025
Identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co-expression network analysis and LASSO.
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Wang Chenguang, Liu Zhiyong, He Yan, Zhang Yashu, Chen Shiqi, Zhou Yuhao, Yang Wenqing, Fan Lijun
Аннотация
BACKGROUND: 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.
METHOD: 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).
RESULTS: 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.
CONCLUSION: 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.
METHOD: 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).
RESULTS: 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.
CONCLUSION: 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.