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Fahira, A.

Publications and source records attributed to Fahira, A..

2 recordsLinked to original sources

The Genetic Immune Basis of Gout: Identification, Functional Characterization, and Therapeutic Implications

Gout, the most common inflammatory arthritis, is still managed mainly as a metabolic disorder, with treatment centered around urate lowering rather than blocking the immune response that drives gout flares. This emphasis reflects a historical view of gout as a disease of excess and diet, reinforced by genome-wide association studies (GWAS) that consistently detect urate transport and metabolism loci, while immune-related loci have failed to reach genome-wide significance. Growing clinical and experimental evidence, however, points to a central role for immune pathways in gout pathogenesis. We set out isolate the immune genetic component of gout. Using a conjunctional false discovery rate (conjFDR) framework, we integrated gout GWAS data with those from eight immune-mediated disorders rheumatoid arthritis, Crohns disease, inflammatory bowel disease, psoriasis, multiple sclerosis, asthma, chronic obstructive pulmonary disease, and systemic lupus erythematosus. We observed robust pleiotropic enrichment across all comparisons and mapped the resulting loci through FUMA to 85 unique credible genes broadly distributed across the genome, with no classical urate transporters present. Additionally, we identified 16 novel genes for gout, many of which are of obvious immune nature. We performed GO and KEGG enrichment analyses, which at a stringent q-value threshold identified adaptive and innate immune pathways, including T and B cell receptor signaling, antigen processing and presentation, NF-{kappa}B signaling, and Jak-STAT signaling. At a nominal p-value threshold, we uncovered additional cytokine-driven processes such as IL6 and IL7 signaling. We confirmed disease relevance with DisGeNET and established causality for 14 genes through Mendelian randomization, including IL1RN, MAP3K11, and SH2B3 genes with existing pharmacological inhibitors. We are the first to genetically isolate the genetic immune component of gout. Our findings show that these immune pathways can be specifically targeted, and immune medication should be incorporated into therapeutic strategies to complement urate-lowering approaches.

immunology↗

Synergizing Network Pharmacology and Pan-Cancer Analysis in TCM Repositioning for Tumor Therapy

BackgroundRepositioning Traditional Chinese Medicine (TCM) for cancer treatment, addressing heterogeneity through synergistic effects, aligns with the evolving trend of combination therapy. However, the complexity of TCM and the lack of methodology hinders the elucidation of TCM treatment mechanisms and potential indications. The research aims to construct develop a comprehensive method combining network pharmacology and pan-cancer analysis (NetPharm-PanCan) for TCM repositioning, exemplified by the Astragali Radix-Curcumae Rhizoma (ARCR) herb pair. MethodThe TCM-component-gene network was constructed using Cytoscape3.7.2 with gene screening based on components from TCMSP and targets predicted via the Swiss Target Prediction database. The core gene set of the ARCR (CGSARCR) was identified by analyzing network via the dual algorithm, namely Degree and MCODE followed by GO and KEGG enrichment analyses. The pan-cancer analysis encompassing Gene Set Variation Analysis (GSVA), immune infiltration correlation, cancer pathway analysis, and Gene Set Enrichment Analysis (GSEA) was unveiled to identify the potential indications. Multivariate cox regression analysis was employed for identifying prognostic genes, followed by the modeling for potential therapeutic indications using the R software. The Metescape database was harnessed to reveal similarities and differences in the mechanisms for prognostic genes associated with potential indications. The XGBoost algorithm was used to construct an indication prediction system according to the analysis results above. ResultThe CGSARCR comprises 28 genes targeting all ARCR components. The pan-cancer analysis revealed that the CGSARCR showed significantly higher tumor scores than normal tissue scores across nine different types of cancer, including THCA, KIRC, LUAD, COAD, BRCA, STAD, ESCA, and others. The CGSARCR correlated strongly with cancer-related pathways and the immune microenvironment. Prognostic models evaluate the potential of these indications as follows: THCA> KIRC> LUAD> COAD> BRCA> STAD> ESCA, with enrichment analysis suggesting KIRC and LUAD as the most potential indications of ARCR. The XGBoost-based system achieved high predictive accuracy (training AUC: 0.985, testing AUC: 0.96, training logloss: 0.21, testing logloss: 0.25). ConclusionThe NetPharm-PanCan method provides a robust, network-based pan-cancer analysis framework for TCM repositioning in cancer research. It provides theoretical foundations and practical tools for TCM-based drug development, with potential applicability to broader drug repurposing efforts.

bioinformatics↗