Background: Drug-induced Sweet’s syndrome (DISS), a rare but serious adverse drug reaction characterized by acute febrile neutrophilic dermatosis, remains difficult to identify due to its low incidence and diverse drug triggers.Methods: Drugs associated with DISS were systematically identified and characterized using data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS; Q1 2004–Q4 2024). Reports were analyzed for baseline characteristics, comorbidities, time-to-onset, drug class distributions, and polypharmacy patterns assessed through drug co-occurrence network analysis. Disproportionality analysis identified candidate drugs, which were refined using the least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression. The main analysis excluded malignancy- and/or immune-related indications, with two sensitivity analyses to assess robustness. A cross-database consistency assessment was conducted in VigiBase, supplemented by PubMed literature review and product label examination.Results: A total of 2,018 DISS cases involving 342 drugs were identified. The median time to onset was 22 (interquartile range: 7–98) days, with 55.60% occurring within 30 days. Ninety drugs demonstrated positive disproportionality signals; a similar pattern was observed in the subset of reports submitted by medical doctors. Of these, 24 remained significant in the main model (area under the curve = 0.815, 95% confidence interval: 0.775–0.856), predominantly comprising antineoplastic and anti-infective agents. Sensitivity analyses produced comparable results. Cross-database assessment in VigiBase identified overlap for 10 signals, while literature review supported associations for 15 drugs and 9 were documented as associated with SS in the product labels.Conclusion: This study provides a comprehensive evaluation of drugs associated with DISS using real-world pharmacovigilance data. The results reveal both established and previously unrecognized drug triggers, offering important insights to support early detection, clinical management, and improved drug safety monitoring from statistical and clinical perspectives.

Background: The aim of this study was to analyze and compare melanoma gene expression profiles in TCGA database through the application of different genes to explore the pathogenesis of melanoma. Furthermore, we confirmed the extent of the role of KYNU in melanoma and whether it can be a potential target for the diagnosis and treatment of melanoma. Methods: The gene expression profiles of melanoma samples were downloaded from TCGA database, and matrix files were synthesized to screen differential genes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis and GCDA broad institute were used to analyze common gene locus mutations and expression changes in melanoma, as well as methylation. In addition, the expression patterns of KYNU in melanoma were quantified by immunohistochemistry, Western blotting, qRT-PCR, software such as GEO DataSets and the Human Protein Atlas, and meta-analysis of skin diseases. KYNU was overexpressed in keratinocytes (HaCaT and HEKα) and melanoma cells (A375 and H1205-lu). CFDA-SE, Annexin V–PI double staining, and PI single staining were used to investigate the mechanism of KYNU in melanoma and its effects on melanoma proliferation, apoptosis, invasion, and migration. Results: The main signaling pathways involved in melanoma were EGF/EGFR–RAS–BRAF–MEK–ERK–CyclinD1/CDK4, Ras–PI3K–PTEN–PKB/AKT, and p14/p16 (CDKN2A)–MDM2–p53–p21–cyclinD1/CDK4/6–Rb/E2F. Moreover, MITF, KIT, CDH1. NRAS, AKT1, EGFR, TP53, KIT, and CDK4 were elevated in melanoma, whereas PTEN, cAMP, and BCL2 were reduced in melanoma. The copy number of tumor-promoting genes increased, while the copy number of tumor suppressor genes decreased. Changes in the copy number of the above tumor genes enriched in chromosomes were found through SNP gene mutations. The genes whose expression was negatively regulated by DNA methylation in melanoma included KRT18, CDK2, JAK3, BCL2, MITF, MET, CXCL10, EGF, SOX10, SOCS3, and KIT. The mutation rate of KYNU was high according to TCGA database. The KYNU level was decreased in melanoma. Overexpression of KYNU can promote changes in apoptotic BCL-2, metabolic KYN, 3-HAA, invasion and migration MMP9, E-cadherin, and other related proteins in melanoma. Fluorescence staining and flow analysis showed that a slower proliferation rate led to a stronger fluorescence intensity. In melanoma tumor cells with a low expression of KYNU, overexpression of KYNU could promote tumor cell apoptosis. IL-10 induced immunoregulatory changes in melanoma. The expression of MMP9 and AMPK decreased in A375, but the change in BCL-2 was not obvious. The expression of BCL-2 decreased significantly in H1205-lu. A375 showed cell-cycle arrest, indicating that IL-10 could slow down the cell cycle of melanoma. Conclusions: These results provide insights into the pathologic mechanisms of melanoma target genes and KYNU as a biomarker and potential therapeutic factor for melanoma.