Background Depression is highly prevalent in patients with ulcerative colitis (UC), affecting prognosis and patients’ quality of life. While related studies grow rapidly, a systematic review of the development trends in this field is still lacking. Based on this, the present study used bibliometric and visualization analysis methods to systematically examine the research landscape, hot topics, and future directions in the global field of UC comorbid with depression. Methods Relevant literature published between January 1, 2006 and December 31, 2025 in the Web of Science Core Collection (WOSCC) and Scopus databases was retrieved. CiteSpace and VOSviewer software were used to analyze countries, journals, authors, references, and keywords, and co-occurrence network maps were generated. For complementary analysis, clinical trial articles published in PubMed over the same time period were retrieved. Results A total of 2,069 papers were included in the visualization analysis, with an additional 28 records identified through supplementary searching. In 2021, the number of publications increased significantly and has since remained at a relatively high level. International academic exchanges in UC comorbid with depression research are active, with the United States, the United Kingdom, and Canada being the core research forces in this field. Inflammatory Bowel Diseases is the most published and most representative professional journal. Bernstein, Charles N. and Mikocka-Walus, Antonina have published the highest number of studies, while Ananthakrishnan, Ashwin N. and Ford, Alexander C. have the highest academic influence. The co-citation network of references suggests that research in this field is based on backgrounds such as pain management, perceived stress, and psychiatry. Keyword analysis shows that core research hotspots focus on epidemiological studies, drug safety, and side effect management. Multidisciplinary combined treatment involving gastroenterology, psychiatry, and endocrinology is a frontier research direction. Conclusion External environmental stressors and intrinsic metabolic dysregulation are jointly reshaping the frontier landscape of this field. Beyond traditional epidemiological investigations, patient quality of life and drug safety have emerged as current research priorities. Future research should focus on generating a greater volume of clinical evidence and promoting multidisciplinary collaborative care, with the goal of improving patients’ quality of life and long-term prognosis.
Yang Yang, Yifei Wang, Shuxin Zhang et al.· Frontiers in Medicine· 0 citations
Objective Rheumatoid arthritis (RA) is a chronic autoimmune joint disease driven by dysregulated immune cells and transcription factors. Despite known molecular alterations, systematic screening of key biomarkers and their link to the immune microenvironment remains lacking, particularly regarding extensive multi-algorithm cross-validation across multiple independent cohorts. This study employs bioinformatics and machine learning to identify potential RA biomarkers, aiming to support diagnosis and targeted therapy. Methods Multiple RA-related transcriptomic datasets derived from synovial tissue were integrated from the GEO database to screen differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was performed to identify RA-associated modules. A total of 107 parameter and algorithm permutations from 11 distinct machine learning approaches were employed to screen key feature genes. The optimal model was selected based on average AUC values across training and validation sets, and the final three genes were identified by integrating individual diagnostic performance, biological relevance, and experimental validation. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, while decision curve analysis (DCA) and confusion matrices were applied to validate the clinical net benefit and classification performance of the model. Immune infiltration analysis was used to assess alterations in immune cell composition within the RA microenvironment. Collagen-induced arthritis (CIA) was used to establish rat models of RA in Sprague-Dawley (SD) rats with a modest sample size (control n = 4, CIA n = 6). Ankle joint tissues were harvested for pathological examination, and the key targets were further validated by immunohistochemistry, serving as a preliminary biological corroboration of the computational findings. Results Through differential expression analysis and WGCNA, a set of RA-related candidate genes was identified. After combined screening using 107 parameter and algorithm permutations and ROC curve evaluation, FOSL2, JUN, and EGR1 were ultimately determined as potential biomarkers for RA. These genes demonstrated good individual diagnostic accuracy (AUC > 0.8). Immune infiltration analysis consistently revealed significant enrichment of mast cells in the RA microenvironment. The CIA model rats were successfully established, and immunohistochemistry results showed significantly high expression of FOSL2, JUN, and EGR1 in the synovial tissue. Conclusion This study identifies FOSL2, JUN, and EGR1 as potential markers for RA, supporting their potential roles in RA pathogenesis and clinical application.
Yuxin Han, Pengrui Wang, Yifei Wang et al.· Journal of Inflammation Rese...· 0 citations