Background Recessive dystrophic epidermolysis bullosa (RDEB), Kindler syndrome (KS) and xeroderma pigmentosum complementation group C (XPC) are three cancer‐prone genodermatoses whose causal genetic mutations cannot fully explain, on their own, the array of associated phenotypic manifestations. Recent evidence highlights the role of the stromal microenvironment in the pathology of these disorders. Objectives To investigate, by means of comparative gene expression analysis, the role played by dermal fibroblasts in the pathogenesis of RDEB, KS and XPC. Methods We conducted RNA‐Seq analysis, which included a thorough examination of the differentially expressed genes, a functional enrichment analysis and a description of affected signalling circuits. Transcriptomic data were validated at the protein level in cell cultures, serum samples and skin biopsies. Results Interdisease comparisons against control fibroblasts revealed a unifying signature of 186 differentially expressed genes and four signalling pathways in the three genodermatoses. Remarkably, some of the uncovered expression changes suggest a synthetic fibroblast phenotype characterized by the aberrant expression of extracellular matrix (ECM) proteins. Western blot and immunofluorescence in situ analyses validated the RNA‐Seq data. In addition, enzyme‐linked immunosorbent assay revealed increased circulating levels of periostin in patients with RDEB. Conclusions Our results suggest that the different causal genetic defects converge into common changes in gene expression, possibly due to injury‐sensitive events. These, in turn, trigger a cascade of reactions involving abnormal ECM deposition and underexpression of antioxidant enzymes. The elucidated expression signature provides new potential biomarkers and common therapeutic targets in RDEB, XPC and KS. What's already known about this topic? Recessive dystrophic epidermolysis bullosa (RDEB), Kindler syndrome (KS) and xeroderma pigmentosum complementation group C (XPC) are three genodermatoses with high predisposition to cancer development. Although their causal genetic mutations mainly affect epithelia, the dermal microenvironment likely contributes to the physiopathology of these disorders. What does this study add? We disclose a large overlapping transcription profile between XPC, KS and RDEB fibroblasts that points towards an activated phenotype with high matrix‐synthetic capacity. This common signature seems to be independent of the primary causal deficiency, but reflects an underlying derangement of the extracellular matrix via transforming growth factor‐β signalling activation and oxidative state imbalance. What is the translational message? This study broadens the current knowledge about the pathology of these diseases and highlights new targets and biomarkers for effective therapeutic intervention. It is suggested that high levels of circulating periostin could represent a potential biomarker in RDEB.

Psoriasis is a complex inflammatory skin disease that presents a wide variety of clinical manifestations. Human β defensin‐2 (hBD‐2) is highly up‐regulated in psoriatic lesions and has been defined as a biomarker for disease activity. We explored the potential benefits of targeting hBD‐2 by topical application of DEFB4‐siRNA‐containing SECosomes in a bioengineered skin‐humanized mouse model for psoriasis. A significant improvement in the psoriatic phenotype was observed by histological examination, with a normalization of the skin architecture and a reduction in the number and size of blood vessels in the dermal compartment. Treatment leads to the recovery of transglutaminase activity, filaggrin expression and stratum corneum appearance to the levels similar to those found in normal regenerated human skin. The availability of a reliable skin‐humanized mouse model for psoriasis in conjunction with the use of the SECosome technology may provide a valuable preclinical tool for identifying potential therapeutic targets for this disease.

Drug discovery and development remains a complex and time-consuming process, often hindered by high costs and low success rates. In the big data era, artificial intelligence (AI) has emerged as a promising tool to accelerate and optimize these processes, particularly in the field of oncology. This review explores the application of AI-based methods for drug repurposing and natural product-inspired drug design in cancer, focusing on their potential to address the challenges and limitations of traditional drug discovery approaches. We delve into various AI-based approaches (machine learning, deep learning, and others) that are currently being employed for these purposes, and the role of experimental techniques in these approaches. By systematically reviewing the literature, we aim to provide a comprehensive overview of the current state of AI-assisted cancer drug discovery workflows, highlighting AI’s contributions to accelerating drug development, reducing costs, and improving therapeutic outcomes. This review also discusses the challenges and opportunities associated with the integration of AI into the drug discovery pipeline, such as data quality, interpretability, and ethical considerations.