Metabolism-related proteins as biomarkers for predicting prognosis in polycystic ovary syndrome DOI Creative Commons
Nan Ding,

Ruifang Wang,

Peili Wang

et al.

Proteome Science, Journal Year: 2024, Volume and Issue: 22(1)

Published: Dec. 19, 2024

Abstract Objective The study aimed to explore the role of metabolism-related proteins and their correlation with clinical data in predicting prognosis polycystic ovary syndrome (PCOS). Methods This research involves a secondary analysis proteomic derived from endometrial samples collected our group, which includes 33 PCOS patients 7 control subjects. A comprehensive identification 4425 were conducted screened differentially expressed (DEPs). Gene Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) enrichment analyses subsequently performed on DEPs. To identify independent prognostic proteins, univariate Cox regression LASSO applied. expression levels these then used develop model, predictive accuracy evaluated through receiver operating characteristic (ROC) curves, decision curve (DCA), calibration curves. Furthermore, we also investigate between proteins. Results identified 285 DEPs groups. GO revealed significant involvement metabolic processes, while KEGG pathway highlighted pathways such as glycolysis/gluconeogenesis glucagon signaling. Ten key (ACSL5, ANPEP, CYB5R3, ENOPH1, GLS, GLUD1, LDHB, PLCD1, PYCR2, PYCR3) predictors prognosis. Patients separated into high low-risk groups according risk score. ROC curves for outcomes at 6, 28, 37 weeks demonstrated excellent performance, AUC values 0.98, 1.0, respectively. nomogram constructed provided reliable tool pregnancy outcomes. DCA indicated net benefit model across various thresholds, confirmed model’s accuracy. Additionally, found BMI exhibited negative GLS ( r =-0.44, p = 0.01) CHO showed positive LDHB 0.35, 0.04). Conclusion provide valuable insights PCOS. protein based offers robust stratification personalized management patients.

Language: Английский

Expression of AMPK and PLIN2 in the regulation of lipid metabolism and oxidative stress in bitches with open cervix pyometra DOI Creative Commons
Xin Deng, Hongxing Liu, Wei Zhao

et al.

BMC Veterinary Research, Journal Year: 2025, Volume and Issue: 21(1)

Published: March 13, 2025

Abstract The pathogenesis of canine pyometra is multifactorial, involving hormonal imbalances, aberrant immune responses, and metabolic dysregulation includes lipid metabolism oxidative stress. This study focuses on stress, revealing the key regulatory role AMPK PLIN2 in pyometra. Bitches with open cervix (n:8) healthy bitches undergoing elective ovariohysterectomy (n:4) were enrolled to study. In experiment one, serum tissue levels Malondialdehyde (MDA) Superoxide Dismutase (SOD) activity assessed. Additionally, uterine histopathological analysis, expressions determined through immunohistochemistry. Furthermore, inflammation, metabolism-related factors evaluated using Western blot analysis. two, primary cell cultures prepared from endometrial cells dogs control group. Cultured epithelial treated lipopolysaccharide (LPS) along oleic acid (OA) induce an inflammatory response. Tissue MDA SOD greater Accumulated droplets observed phosphorylation expression significantly increased related synthesis proteins such as ACC1, FASN, SREBP-1c, was upregulated, while PPARα PGC1α downregulated activation not only restores PGC1α, but also effectively alleviates inflammation stress elucidated, thus contributing dogs.

Language: Английский

Citations

1
Metabolism-related proteins as biomarkers for predicting prognosis in polycystic ovary syndrome DOI Creative Commons
Nan Ding,

Ruifang Wang,

Peili Wang

et al.

Proteome Science, Journal Year: 2024, Volume and Issue: 22(1)

Published: Dec. 19, 2024

Abstract Objective The study aimed to explore the role of metabolism-related proteins and their correlation with clinical data in predicting prognosis polycystic ovary syndrome (PCOS). Methods This research involves a secondary analysis proteomic derived from endometrial samples collected our group, which includes 33 PCOS patients 7 control subjects. A comprehensive identification 4425 were conducted screened differentially expressed (DEPs). Gene Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) enrichment analyses subsequently performed on DEPs. To identify independent prognostic proteins, univariate Cox regression LASSO applied. expression levels these then used develop model, predictive accuracy evaluated through receiver operating characteristic (ROC) curves, decision curve (DCA), calibration curves. Furthermore, we also investigate between proteins. Results identified 285 DEPs groups. GO revealed significant involvement metabolic processes, while KEGG pathway highlighted pathways such as glycolysis/gluconeogenesis glucagon signaling. Ten key (ACSL5, ANPEP, CYB5R3, ENOPH1, GLS, GLUD1, LDHB, PLCD1, PYCR2, PYCR3) predictors prognosis. Patients separated into high low-risk groups according risk score. ROC curves for outcomes at 6, 28, 37 weeks demonstrated excellent performance, AUC values 0.98, 1.0, respectively. nomogram constructed provided reliable tool pregnancy outcomes. DCA indicated net benefit model across various thresholds, confirmed model’s accuracy. Additionally, found BMI exhibited negative GLS ( r =-0.44, p = 0.01) CHO showed positive LDHB 0.35, 0.04). Conclusion provide valuable insights PCOS. protein based offers robust stratification personalized management patients.

Language: Английский

Citations

0