May 2015 in “Journal of The American Academy of Dermatology” This study suggests that blood microarray biomarkers may help predict individual treatment response in psoriasis patients, highlighting a distinct blood signature related to inflammation, interferon, and myeloid lineage transcripts.
2 citations
,
August 2022 in “Emergency medicine international” This study found that a key gene signature, including FGF11, highlights the immunologic nature of keloid lesions, distinguishing them from normal fibroblasts and scars.
11 citations
,
July 2022 in “Frontiers in Immunology” This study identified four immune-related signaling molecules (LGR5, PTN, JAG1, and DKK1) associated with keloid, suggesting their potential role in its pathogenesis and as targets for new treatments.
June 2023 in “Research Square (Research Square)” This study identified shared gene expression changes and immune cell infiltration patterns that may contribute to hair loss in alopecia areata and cutaneous lupus erythematosus, but also highlighted factors that might preserve hair in psoriasis patients.
1 citations
,
December 2025 in “Scientific Reports” In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.