Differential Expression of Estrogen-Responsive Genes in Women with Psoriasis

Emerging Contaminants: An Important But Ignored Risk Factor for Psoriasis

Industrialization and modernization have changed the environment. A group of emerging contaminants (ECs) has been defined recently. Psoriasis, whose incidence has increased in recent years, is a relapsing immune-mediated disease carrying a heavy disease burden. The erythematous scaly plaque is a typical symptom and occurs on several parts of the body. In addition, psoriasis has many comorbidities, such as psoriatic arthritis, diabetes, and depression, damaging the quality of life of patients. IL-17, IL-12, IL-23, and TNF-alpha are important related cytokines. ECs can influence psoriasis through the immune system and inflammatory responses. Specific mechanisms include increasing pro-inflammatory cytokines such as TNF-α and IL-17, and activating immune cells such as macrophages. And for psoriasis patients, it is suggested to reduce the exposure of most ECs. However, the complex mechanisms involved have not been discussed together and concluded. In this review, we summarize the relationship between ECs and psoriasis, focusing on the immune system, especially the immune cells and cytokines. These results can help guide clinical treatment and long-term management of psoriasis.

Cytokine release syndrome induced by anti-programmed death-1 treatment in a psoriasis patient: A dark side of immune checkpoint inhibitors

In recent years, cancer immunotherapy has introduced novel treatments, such as monoclonal antibodies, which have facilitated targeted therapies against tumor cells. Programmed death-1 (PD-1) is an immune checkpoint expressed in T cells that regulates the immune system's activity to prevent over-activation and tissue damage caused by inflammation. However, PD-1 is also expressed in tumor cells and functions as an immune evasion mechanism, making it a therapeutic target to enhance the immune response and eliminate tumor cells. Consequently, immune checkpoint inhibitors (ICIs) have emerged as an option for certain tumor types. Nevertheless, blocking immune checkpoints can lead to immune-related adverse events (irAEs), such as psoriasis and cytokine release syndrome (CRS), as exemplified in the clinical case presented by Zhou et al involving a patient with advanced gastric cancer who received sintilimab, a monoclonal antibody targeting PD-1. Subsequently, the patient experienced exacerbation of psoriasis and CRS. The objective of this editorial article is to elucidate potential immunologic mechanisms that may contribute to the development of CRS and psoriasis in patients receiving ICIs. It is crucial to acknowledge that while ICIs offer superior safety and efficacy compared to conventional therapies, they can also manifest irAEs affecting the skin, gastrointestinal tract, or respiratory system. In severe cases, these irAEs can lead to life-threatening complications such as circulatory shock or multiorgan failure. Consequently, it is recommended that patients receiving ICIs undergo regular monitoring to identify and manage these adverse events effectively.

Prediction of multi-relational drug-gene interaction via Dynamic hyperGraph Contrastive Learning

Drug-gene interaction prediction occupies a crucial position in various areas of drug discovery, such as drug repurposing, lead discovery and off-target detection. Previous studies show good performance, but they are limited to exploring the binding interactions and ignoring the other interaction relationships. Graph neural networks have emerged as promising approaches owing to their powerful capability of modeling correlations under drug-gene bipartite graphs. Despite the widespread adoption of graph neural network-based methods, many of them experience performance degradation in situations where high-quality and sufficient training data are unavailable. Unfortunately, in practical drug discovery scenarios, interaction data are often sparse and noisy, which may lead to unsatisfactory results. To undertake the above challenges, we propose a novel Dynamic hyperGraph Contrastive Learning (DGCL) framework that exploits local and global relationships between drugs and genes. Specifically, graph convolutions are adopted to extract explicit local relations among drugs and genes. Meanwhile, the cooperation of dynamic hypergraph structure learning and hypergraph message passing enables the model to aggregate information in a global region. With flexible global-level messages, a self-augmented contrastive learning component is designed to constrain hypergraph structure learning and enhance the discrimination of drug/gene representations. Experiments conducted on three datasets show that DGCL is superior to eight state-of-the-art methods and notably gains a 7.6% performance improvement on the DGIdb dataset. Further analyses verify the robustness of DGCL for alleviating data sparsity and over-smoothing issues.

Proteomic Studies of Psoriasis

In this review paper, we discuss the contribution of proteomic studies to the discovery of disease-specific biomarkers to monitor the disease and evaluate available treatment options for psoriasis. Psoriasis is one of the most prevalent skin disorders driven by a Th17-specific immune response. Although potential patients have a genetic predisposition to psoriasis, the etiology of the disease remains unknown. During the last two decades, proteomics became deeply integrated with psoriatic research. The data obtained in proteomic studies facilitated the discovery of novel mechanisms and the verification of many experimental hypotheses of the disease pathogenesis. The detailed data analysis revealed multiple differentially expressed proteins and significant changes in proteome associated with the disease and drug efficacy. In this respect, there is a need for proteomic studies to characterize the role of the disease-specific biomarkers in the pathogenesis of psoriasis, develop clinical applications to choose the most efficient treatment options and monitor the therapeutic response.