Research and analysis of differential gene expression in CD34 hematopoietic stem cells in myelodysplastic syndromes DOI Creative Commons
Mianzhi Wang, Chang-Sheng Liao, Xudong Wei

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0315408 - e0315408

Published: March 12, 2025

Objective This study aims to investigate and analyze the differentially expressed genes (DEGs) in CD34 + hematopoietic stem cells (HSCs) from patients with myelodysplastic syndromes (MDS) through bioinformatics analysis, ultimate goal of uncovering potential molecular mechanisms underlying pathogenesis MDS. The findings this are expected provide novel insights into clinical treatment strategies for Methods Initially, we downloaded three datasets, GSE81173, GSE4619, GSE58831, public Gene Expression Omnibus (GEO) database as our training sets, selected GSE19429 dataset validation set. To ensure data consistency comparability, standardized sets removed batch effects using ComBat algorithm, thereby integrating them a unified gene expression dataset. Subsequently, conducted differential analysis identify significant changes levels across different disease states. In order enhance prediction accuracy, incorporated six common predictive models trained based on filtered After comprehensive evaluation, ultimately algorithms—Lasso regression, random forest, support vector machine (SVM)—as core models. more precisely pinpoint closely related characteristics, utilized aforementioned learning methods took intersection these results, yielding robust list associated features. Following this, in-depth key set validated results independently Furthermore, performed groups, co-expression enrichment delve deeper roles initiation progression. Through analyses, aim new foundations diagnosis treatment. Figure illustrates preprocessing workflow study. Results Our CD34+ revealed differences patterns compared control group (individuals without MDS). Specifically, two genes, IRF4 ELANE, were notably downregulated HSCs MDS patients, indicating their downregulatory pathological process Conclusion sheds light MDS, particular focus pivotal ELANE pathogenic genes. perspective understanding complexity exploring therapeutic strategies. They may also guide development precise effective treatments, such targeted interventions directed against

Language: Английский

Machine learning potential predictor of idiopathic pulmonary fibrosis DOI Creative Commons
Chenchun Ding, Quan Liao,

Renjie Zuo

et al.

Frontiers in Genetics, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 22, 2025

Introduction Idiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction therefore of critical importance. Methods In this study, we obtained gene expression profiles corresponding clinical data IPF patients from the GEO database. GO enrichment KEGG pathway analyses were performed using R software. To construct an model, employed LASSO-Cox regression analysis SVM-RFE algorithm. PODNL1 PIGA identified as potential associated with onset, their predictive accuracy was confirmed ROC curve in test set. Furthermore, GSEA revealed multiple pathways, while immune function demonstrated significant correlation between onset cell infiltration. Finally, roles validated through vivo vitro experiments qRT-PCR, Western blotting, immunohistochemistry. Results These findings suggest that may serve contribute to its pathogenesis. Discussion This study highlights early biomarker discovery IPF, offering insights into mechanisms diagnostic strategies.

Language: Английский

Citations

0
Therapeutic targets in aging-related osteoarthritis: A focus on the extracellular matrix homeostasis DOI Creative Commons
Hao Wan, Ming‐Fu Chang, Di Shi

et al.

Life Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 123487 - 123487

Published: Feb. 1, 2025

Language: Английский

Citations

0
Research and analysis of differential gene expression in CD34 hematopoietic stem cells in myelodysplastic syndromes DOI Creative Commons
Mianzhi Wang, Chang-Sheng Liao, Xudong Wei

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0315408 - e0315408

Published: March 12, 2025

Objective This study aims to investigate and analyze the differentially expressed genes (DEGs) in CD34 + hematopoietic stem cells (HSCs) from patients with myelodysplastic syndromes (MDS) through bioinformatics analysis, ultimate goal of uncovering potential molecular mechanisms underlying pathogenesis MDS. The findings this are expected provide novel insights into clinical treatment strategies for Methods Initially, we downloaded three datasets, GSE81173, GSE4619, GSE58831, public Gene Expression Omnibus (GEO) database as our training sets, selected GSE19429 dataset validation set. To ensure data consistency comparability, standardized sets removed batch effects using ComBat algorithm, thereby integrating them a unified gene expression dataset. Subsequently, conducted differential analysis identify significant changes levels across different disease states. In order enhance prediction accuracy, incorporated six common predictive models trained based on filtered After comprehensive evaluation, ultimately algorithms—Lasso regression, random forest, support vector machine (SVM)—as core models. more precisely pinpoint closely related characteristics, utilized aforementioned learning methods took intersection these results, yielding robust list associated features. Following this, in-depth key set validated results independently Furthermore, performed groups, co-expression enrichment delve deeper roles initiation progression. Through analyses, aim new foundations diagnosis treatment. Figure illustrates preprocessing workflow study. Results Our CD34+ revealed differences patterns compared control group (individuals without MDS). Specifically, two genes, IRF4 ELANE, were notably downregulated HSCs MDS patients, indicating their downregulatory pathological process Conclusion sheds light MDS, particular focus pivotal ELANE pathogenic genes. perspective understanding complexity exploring therapeutic strategies. They may also guide development precise effective treatments, such targeted interventions directed against

Language: Английский

Citations

0