Breast cancer remains a major cause of cancer-related mortality, and exploratory computational workflows can help prioritize immune-associated markers for further investigation. Here, we used the cancer genome atlas breast invasive carcinoma (TCGA-BRCA) bulk transcriptomic data and the public single-cell dataset GSE161529 to examine associations between myeloperoxidase (MPO) expression, clinical outcomes, immune infiltration, methylation, upstream-regulator annotations, single-cell expression patterns, virtual knockdown sensitivity outputs, drug-gene interaction retrieval, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) annotation. MPO expression was lower in breast cancer tissues than in adjacent non-tumor tissues. Higher MPO expression was associated with a longer progression-free interval, whereas its associations with overall survival and disease-specific survival were not statistically significant. Receiver operating characteristic (ROC) analysis suggested tumor-normal separation within the analyzed public dataset, but this should not be interpreted as clinical diagnostic validation. Immune deconvolution and enrichment analyses indicated that MPO expression mainly tracked with immune- and myeloid-related transcriptional features, rather than establishing tumor-intrinsic regulation of the immune microenvironment. At single-cell resolution, the MPO signal was sparse, with only 85 MPO-positive cells detected before k-nearest neighbor (KNN)-based neighborhood expansion. Detectable MPO signal and MPO-associated scores were interpreted cautiously because they may be influenced by sparse expression, cell-type annotation uncertainty, dropout, doublets, or ambient RNA. In silico virtual knockdown suggested candidate immune- and inflammatory-related transcriptional changes, but these results were considered exploratory and require validation. Drug-gene interaction database (DGIdb)-based drug-gene retrieval and ADMET annotation were used only as preliminary chemical annotations and were not interpreted as therapeutic evidence. Overall, this study provides a reproducible in silico workflow for generating hypotheses about MPO-associated immune/myeloid features in breast cancer, which require external cohort validation and experimental confirmation.
Qingrui Li, Fei Teng, Weijia Kong et al.· Journal of Visualized Experi...· 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
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