Mapping rheumatoid arthritis susceptibility through integrative bioinformatics and genomics

Rheumatoid arthritis (RA) is an autoimmune disease that influences several organs and tissues, especially the synovial joints, and is associated with multiple genetic and environmental factors. Numerous databases provide information on the relationship between a specific gene and the disease pathogenesis. However, it is important to further prioritize biological risk genes for downstream development and validation.  This study aims to map RA-association genetic variation using genome-wide association study (GWAS) databases and prioritize influential genes in RA pathogenesis based on functional annotations. These functional annotations include missense/nonsense mutations, cis-expression quantitative trait locus (cis-eQTL), overlap knockout mouse phenotype (KMP), protein-protein interaction (PPI), molecular pathway analysis (MPA), and primary immunodeficiency (PID). 119 genetic variants mapped had a potential high risk for RA based on functional scoring. The top eight risk genes of RA are TYK2 and IFNGR2, followed by TNFRSF1A, IL12RB1 and CD40, C5, NCF2, and IL6R. These candidate genes are potential biomarkers for RA that can aid drug discovery and disease diagnosis

Mapping Rheumatoid Arthritis Susceptibility through Integrative Bioinformatics and Genomics

Rheumatoid arthritis (RA) is an autoimmune disease that influences several organs and tissues, especially the synovial joints, and is associated with multiple genetic and environmental factors. Numerous databases provide information on the relationship between a specific gene and the disease pathogenesis. However, it is important to further prioritize biological risk genes for downstream development and validation. This study aims to map RA-association genetic variation using genome-wide association study (GWAS) databases and prioritize influential genes in RA pathogenesis based on functional annotations. These functional annotations include missense/nonsense mutations, cis-expression quantitative trait locus (cis-eQTL), overlap knockout mouse phenotype (KMP), protein-protein interaction (PPI), molecular pathway analysis (MPA), and primary immunodeficiency (PID). 119 genetic variants mapped had a potential high risk for RA based on functional scoring. The top eight risk genes of RA are TYK2 and IFNGR2, followed by TNFRSF1A, IL12RB1 and CD40, C5, NCF2, and IL6R. These candidate genes are potential biomarkers for RA that can aid drug discovery and disease diagnosis

Cutting-Edge Therapies for Lung Cancer

Lung cancer remains a formidable global health challenge that necessitates inventive strategies to improve its therapeutic outcomes. The conventional treatments, including surgery, chemotherapy, and radiation, have demonstrated limitations in achieving sustained responses. Therefore, exploring novel approaches encompasses a range of interventions that show promise in enhancing the outcomes for patients with advanced or refractory cases of lung cancer. These groundbreaking interventions can potentially overcome cancer resistance and offer personalized solutions. Despite the rapid evolution of emerging lung cancer therapies, persistent challenges such as resistance, toxicity, and patient selection underscore the need for continued development. Consequently, the landscape of lung cancer therapy is transforming with the introduction of precision medicine, immunotherapy, and innovative therapeutic modalities. Additionally, a multifaceted approach involving combination therapies integrating targeted agents, immunotherapies, or traditional cytotoxic treatments addresses the heterogeneity of lung cancer while minimizing its adverse effects. This review provides a brief overview of the latest emerging therapies that are reshaping the landscape of lung cancer treatment. As these novel treatments progress through clinical trials are integrated into standard care, the potential for more effective, targeted, and personalized lung cancer therapies comes into focus, instilling renewed hope for patients facing challenging diagnoses

Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis

Dermatomyositis (DM) is an autoimmune disease that is classified as a type of idiopathic inflammatory myopathy, which affects human skin and muscles. The most common clinical symptoms of DM are muscle weakness, rash, and scaly skin. There is currently no cure for DM. Genetic factors are known to play a pivotal role in DM progression, but few have utilized this information geared toward drug discovery for the disease. Here, we exploited genomic variation associated with DM and integrated this with genomic and bioinformatic analyses to discover new drug candidates. We first integrated genome-wide association study (GWAS) and phenome-wide association study (PheWAS) catalogs to identify disease-associated genomic variants. Biological risk genes for DM were prioritized using strict functional annotations, further identifying candidate drug targets based on druggable genes from databases. Overall, we analyzed 1239 variants associated with DM and obtained 43 drugs that overlapped with 13 target genes (JAK2, FCGR3B, CD4, CD3D, LCK, CD2, CD3E, FCGR3A, CD3G, IFNAR1, CD247, JAK1, IFNAR2). Six drugs clinically investigated for DM, as well as eight drugs under pre-clinical investigation, are candidate drugs that could be repositioned for DM. Further studies are necessary to validate potential biomarkers for novel DM therapeutics from our findings