Background Psoriasis (PsO): is an immune-mediated inflammatory disease that imposes a significant burden on patients. Many patients experience relapse or inadequate responses, and PsO subtypes also lack effective therapies, highlighting the need for new therapeutic targets. Methods: We performed a proteome-wide Mendelian randomization (MR) to explore potential therapeutic targets for PsO. Protein quantitative trait loci (pQTLs) data were obtained from the Pharma Proteomics Project (54,219 UK Biobank participants, 2923 proteins), and PsO phenotype and subtype data were sourced from FinnGen (10,312 cases; 397,564 controls) for discovery. Replication MR utilized integrated protein data (Iceland and Norfolk) and phenotype data from multiple databases (UK Biobank and GWAS Catalog). Reverse MR and colocalization were used to support causal relationships. Single-cell RNA-seq analysis revealed distinct expression patterns of protein-coding genes across different cell types in PsO biopsy samples and normal skin tissues. Protein-protein interactions (PPI) and molecular docking were used to evaluate druggability. Results: MR analysis identified 13 proteins significantly associated with PsO risk (p < $2.56\times {10}^{-5}$), including 10 proteins associated with PsO subtypes. Decreased levels of eight proteins (IFNLR1, APOF, TDRKH, DDR1, HLA-E, LTA, MOG, and ICAM3) and increased levels of five proteins (IFNGR2, HCG22, IL12B, BTN3A2, and TRIM40) showed protective effects against PsO progression. Robust colocalization ($PP{H}_4$ > 0.9) identified IFNLR1, IFNGR2, APOF, and TDRKH as top candidates. Single-cell RNA sequencing analysis revealed that IFNLR1, IFNGR2, LTA, TDRKH, and DDR1 were specifically expressed in T cells of psoriatic biopsy specimens compared to healthy controls. Molecular docking indicated the druggability of IFNLR1 and IFNGR2. Conclusions: We identified several potential therapeutic targets for PsO, with IFNLR1, IFNGR2, APOF, and TDRKH emerging as promising candidates, particularly IFNLR1 and IFNGR2, which are associated with the IFN family. These findings may provide new perspectives on PsO therapy and pathogenesis.

Identifying novel compound–protein interactions (CPIs) plays a pivotal role in target identification and drug discovery. Although the recent multimodal methods have achieved outstanding advances in CPI prediction, they fail to effectively learn both intra-modality and inter-modality dynamics, which limits their prediction performance. To address the limitation, we propose a novel multimodal tensor fusion CPI prediction framework, named MMTF-CPI, which contains three unimodal learning modules for structure, heterogeneous network and transcriptional profiling modalities, a tensor fusion module and a prediction module. MMTF-CPI is capable of focusing on both intra-modality and inter-modality dynamics with the tensor fusion module. We demonstrated that MMTF-CPI is superior to multiple state-of-the-art multimodal methods across seven datasets. The prediction performance of MMTF-CPI is significantly improved with the tensor fusion module compared to other fusion methods. Moreover, our case studies confirmed the practical value of MMTF-CPI in target identification. Via MMTF-CPI, we also discovered several candidate compounds for the therapy of breast cancer and non-small cell lung cancer.

Glioblastoma multiforme (GBM) is a common malignancy with poor prognosis, and exhausted T (TEX) cells, a subset of T cells characterized by progressive loss of effector functions, play a critical role in its progression. This study aimed to investigate the impact of TEX-related genes on immune function, prognosis, and drug sensitivity in GBM through multiomics analysis. Initially, we identified a novel set of TEX-related genes specific to GBM and screened hub genes (CCL5, IL18, CXCR6, FCER1G, TNFSF13B) using conventional statistical methods combined with machine learning. A prognostic risk model was subsequently constructed based on TCGA data and validated in the CGGA cohort. Single-cell and pharmacogenomic analyses revealed significant differences in tumor microenvironment composition and drug sensitivity between risk groups. Notably, Palbociclib emerged as a potential therapeutic agent targeting the novel discovered biomarker CCL5. RT-qPCR results showed that T cells with low CCL5 expression exhibited reduced expression of immune checkpoint-related genes (PD1, TIM3, LAG3) and increased expression of CD28, suggesting enhanced immune function. In conclusion, our findings highlight five hub genes as prognostic markers that could stratify GBM patients with different immune landscapes and levels of drug sensitivity. Furthermore, experimental results suggest that low CCL5 expression could alleviate T cell exhaustion and represent a promising therapeutic target, offering new strategies for improving GBM prognosis.