Identification of diagnostic biomarkers and potential therapeutic drugs in focal segmental glomerulosclerosis with metabolic syndrome by integrating bioinformatics and machine learning DOI Creative Commons
Tianwen Yao, Qingliang Wang, Shisheng Han

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 24, 2024

Abstract Purpose Immune system dysregulation plays a pivotal role in focal segmental glomerulosclerosis (FSGS) and metabolic syndrome (MS). This study aimed to identify core diagnostic genes potential therapeutic drugs for FSGS patients with MS. Methods We obtained two one MS datasets from the GEO database. DEGs module gene were identified via Limma WGCNA. Then, functional enrichment analysis, PPI network construction, machine learning algorithms applied analyze immune-associated genes. Afterwards, nomogram ROC curve used evaluate value screen Finally, immune cell was investigated FSGS, connectivity map (cMAP) analysis conducted small molecule compounds. Results dataset yielded 756 DEGs, integrated 5257 133 intersection of FSGS. Following construction network, 42 node filtered. eight hub through screening, which further evaluated by value. Among them, six had high values. higher level resting natural killer cells, monocytes, activated dendritic cells meanwhile lower levels plasma follicular helper T mast cells. cMAP we ten compounds that might work as Conclusion Six immune-related (STAT3, CX3CR1, CCDC148, TRPC6, CLMP, CDC42EP1), obtained. could provide

Language: Английский

Prediction of COVID-19 mortality using machine learning strategies and a large-scale panel of plasma inflammatory proteins: A cohort study DOI
Luiz Filipe Bastos Mendes,

Henrique Ritter Dal-Pizzol,

Gabriele da Silveira Prestes

et al.

Medicina Intensiva (English Edition), Journal Year: 2025, Volume and Issue: unknown, P. 502200 - 502200

Published: April 1, 2025

Language: Английский

Citations

1
Identification of diagnostic biomarkers and potential therapeutic drugs in focal segmental glomerulosclerosis with metabolic syndrome by integrating bioinformatics and machine learning DOI Creative Commons
Tianwen Yao, Qingliang Wang, Shisheng Han

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 24, 2024

Abstract Purpose Immune system dysregulation plays a pivotal role in focal segmental glomerulosclerosis (FSGS) and metabolic syndrome (MS). This study aimed to identify core diagnostic genes potential therapeutic drugs for FSGS patients with MS. Methods We obtained two one MS datasets from the GEO database. DEGs module gene were identified via Limma WGCNA. Then, functional enrichment analysis, PPI network construction, machine learning algorithms applied analyze immune-associated genes. Afterwards, nomogram ROC curve used evaluate value screen Finally, immune cell was investigated FSGS, connectivity map (cMAP) analysis conducted small molecule compounds. Results dataset yielded 756 DEGs, integrated 5257 133 intersection of FSGS. Following construction network, 42 node filtered. eight hub through screening, which further evaluated by value. Among them, six had high values. higher level resting natural killer cells, monocytes, activated dendritic cells meanwhile lower levels plasma follicular helper T mast cells. cMAP we ten compounds that might work as Conclusion Six immune-related (STAT3, CX3CR1, CCDC148, TRPC6, CLMP, CDC42EP1), obtained. could provide

Language: Английский

Citations

0