The role of the Oncostatin M/ OSM receptor β axis in activating dermal microvascular endothelial cells in Systemic Sclerosis

Marden, Grace; Wan, Qianqian; Wilks, James; Nevin, Katherine; Feeney, Maria; Wisniacki, Nicolas; Trojanowski, Marcin; Bujor, Andreea M.; Stawski, Lukasz; Trojanowska, Maria

doi:10.21203/rs.3.rs-21302/v2

Cited by 1 publication

(1 citation statement)

References 53 publications

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“…In addition, OSM protected against cardiac I/R injury by regulating apoptosis, insulin sensitivity and mitochondrial biogenesis in diabetic mice [37]. Some studies have reported that OSM activates endothelial cells [38] and smooth muscle cells [39], but to date, the role of OSM in PAH and the underlying mechanisms remain to be studied. There is an imbalance between vasodilation and vasoconstriction favouring vasoconstriction with an increase in circulating vasoconstrictors and a decrease in circulating vasodilators (i.e., prostacyclin and prostaglandin) during PAH development.…”

Section: Discussionmentioning

confidence: 99%

Identification of biomarkers related to copper metabolism in patients with pulmonary arterial hypertension

et al. 2023

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Background The pathogenesis of pulmonary arterial hypertension (PAH) and associated biomarkers remain to be studied. Copper metabolism is an emerging metabolic research direction in many diseases, but its role in PAH is still unclear. Methods PAH-related datasets were downloaded from the Gene Expression Omnibus database, and 2067 copper metabolism-related genes (CMGs) were obtained from the GeneCards database. Differential expression analysis and the Venn algorithm were used to acquire the differentially expressed CMGs (DE-CMGs). DE-CMGs were then used for the coexpression network construction to screen candidate key genes associated with PAH. Furthermore, the predictive performance of the model was verified by receiver operating characteristic (ROC) analysis, and genes with area under the curve (AUC) values greater than 0.8 were selected as diagnostic genes. Then support vector machine, least absolute shrinkage and selection operator regression, and Venn diagrams were applied to detect biomarkers. Moreover, gene set enrichment analysis was performed to explore the function of the biomarkers, and immune-related analyses were utilized to study the infiltration of immune cells. The drug-gene interaction database was used to predict potential therapeutic drugs for PAH using the biomarkers. Biomarkers expression in clinical samples was verified by real-time quantitative PCR. Results Four biomarkers (DDIT3, NFKBIA, OSM, and PTGER4) were screened. The ROC analysis showed that the 4 biomarkers performed well (AUCs > 0.7). The high expression groups for the 4 biomarkers were enriched in protein activity-related pathways including protein export, spliceosome and proteasome. Furthermore, 8 immune cell types were significantly different between the two groups, including naive B cells, memory B cells, and resting memory CD4 T cells. Afterward, a gene-drug network was constructed. This network illustrated that STREPTOZOCIN, IBUPROFEN, and CELECOXIB were shared by the PTGER4 and DDIT3. Finally, the results of RT-qPCR in clinical samples further confirmed the results of the public database for the expression of NFKBIA and OSM. Conclusion In conclusion, four biomarkers (DDIT3, NFKBIA, OSM, and PTGER4) with considerable diagnostic values were identified, and a gene-drug network was further constructed. The results of this study may have significant implications for the development of new diagnostic biomarkers and actionable targets to expand treatment options for PAH patients.

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Section: Discussionmentioning

confidence: 99%