Microarray Data Analysis for Transcriptome Profiling

Identification of oxidative stress-related biomarkers in uterine leiomyoma: a transcriptome-combined Mendelian randomization analysis

Background

Oxidative stress has been implicated in the pathogenesis of uterine leiomyoma (ULM) with an increasing incidence. This study aimed to identify potential oxidative stress-related biomarkers in ULM using transcriptome data integrated with Mendelian randomization (MR) analysis.

Methods

Data from GSE64763 and GSE31699 in the Gene Expression Omnibus (GEO) were included in the analysis. Oxidative stress-related genes (OSRGs) were identified, and the intersection of differentially expressed genes (DEGs), Weighted Gene Co-expression Network Analysis (WGCNA) genes, and OSRGs was used to derive differentially expressed oxidative stress-related genes (DE-OSRGs). Biomarkers were subsequently identified via MR analysis, followed by Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis. Nomograms, regulatory networks, and gene-drug interaction networks were constructed based on the identified biomarkers.

Results

A total of 883 DEGs were identified between ULM and control samples, from which 42 DE-OSRGs were screened. MR analysis revealed four biomarkers: ANXA1, CD36, MICB, and PRDX6. Predictive nomograms were generated based on these biomarkers. ANXA1, CD36, and MICB were significantly enriched in chemokine signaling and other pathways. Notably, ANXA1 showed strong associations with follicular helper T cells, resting mast cells, and M0 macrophages. CD36 was positively correlated with resting mast cells, while MICB was negatively correlated with macrophages. Additionally, ANXA1 displayed strong binding energy with amcinonide, and MICB with ribavirin.

Conclusion

This study identified oxidative stress-related biomarkers (ANXA1, CD36, MICB, and PRDX6) in ULM through transcriptomic and MR analysis, providing valuable insights for ULM therapeutic research.

Addressing Cardiovascular Toxicity Risk of Electronic Nicotine Delivery Systems in the Twenty-First Century: "What Are the Tools Needed for the Job?" and "Do We Have Them?"

Cigarette smoking is positively and robustly associated with cardiovascular disease (CVD), including hypertension, atherosclerosis, cardiac arrhythmias, stroke, thromboembolism, myocardial infarctions, and heart failure. However, after more than a decade of ENDS presence in the U.S. marketplace, uncertainty persists regarding the long-term health consequences of ENDS use for CVD. New approach methods (NAMs) in the field of toxicology are being developed to enhance rapid prediction of human health hazards. Recent technical advances can now consider impact of biological factors such as sex and race/ethnicity, permitting application of NAMs findings to health equity and environmental justice issues. This has been the case for hazard assessments of drugs and environmental chemicals in areas such as cardiovascular, respiratory, and developmental toxicity. Despite these advances, a shortage of widely accepted methodologies to predict the impact of ENDS use on human health slows the application of regulatory oversight and the protection of public health. Minimizing the time between the emergence of risk (e.g., ENDS use) and the administration of well-founded regulatory policy requires thoughtful consideration of the currently available sources of data, their applicability to the prediction of health outcomes, and whether these available data streams are enough to support an actionable decision. This challenge forms the basis of this white paper on how best to reveal potential toxicities of ENDS use in the human cardiovascular system-a primary target of conventional tobacco smoking. We identify current approaches used to evaluate the impacts of tobacco on cardiovascular health, in particular emerging techniques that replace, reduce, and refine slower and more costly animal models with NAMs platforms that can be applied to tobacco regulatory science. The limitations of these emerging platforms are addressed, and systems biology approaches to close the knowledge gap between traditional models and NAMs are proposed. It is hoped that these suggestions and their adoption within the greater scientific community will result in fresh data streams that will support and enhance the scientific evaluation and subsequent decision-making of tobacco regulatory agencies worldwide.

Integrative Analysis for Elucidating Transcriptomics Landscapes of Glucocorticoid-Induced Osteoporosis

Osteoporosis is the most common bone metabolic disease, characterized by bone mass loss and bone microstructure changes due to unbalanced bone conversion, which increases bone fragility and fracture risk. Glucocorticoids are clinically used to treat a variety of diseases, including inflammation, cancer and autoimmune diseases. However, excess glucocorticoids can cause osteoporosis. Herein we performed an integrated analysis of two glucocorticoid-related microarray datasets. The WGCNA analysis identified 3 and 4 glucocorticoid-related gene modules, respectively. Differential expression analysis revealed 1047 and 844 differentially expressed genes in the two datasets. After integrating differentially expressed glucocorticoid-related genes, we found that most of the robust differentially expressed genes were up-regulated. Through protein-protein interaction analysis, we obtained 158 glucocorticoid-related candidate genes. Enrichment analysis showed that these genes are significantly enriched in the osteoporosis related pathways. Our results provided new insights into glucocorticoid-induced osteoporosis and potential candidate markers of osteoporosis.