Investigation on epigenomic signature for chronic obstructive pulmonary disease - an in silico approach DOI

Saranyadevi Subburaj,

S. Saravana Babu,

Gokulkrishnan Anandhavenkadasamy

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Abstract Chronic obstructive pulmonary disease is an advanced and debilitating respiratory condition characterized by chronic inflammation airflow constraint. Despite its global prevalence, precise diagnostic prognostic biomarkers remain unidentified. This research aims to identify potential epigenomic for integrating DNA methylation differential gene expression analyses. Publicly available datasets were utilized, ‘R’ programming was used the analysis significantly differentially methylated regions, performed in ‘R’, revealing expressed genes associated with this disease. Functional enrichment pathway analyses carried out using DAVID bioinformatics tool, Reactome revealed key biological processes pathways involved pathogenesis. Additionally, protein-protein interaction networks constructed utilizing a string database recognize hub therapeutic relevance. Our work provides understanding of epigenetic landscape disease, identifying 8 candidate that could aid as tools or targets. Gene ontology show mechanism pathways, showing efficiency biomarker integrative approach not only advances molecular underpinnings but also paves way personalized treatment strategies.

Language: Английский

Investigation on epigenomic signature for chronic obstructive pulmonary disease - an in silico approach DOI

Saranyadevi Subburaj,

S. Saravana Babu,

Gokulkrishnan Anandhavenkadasamy

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Abstract Chronic obstructive pulmonary disease is an advanced and debilitating respiratory condition characterized by chronic inflammation airflow constraint. Despite its global prevalence, precise diagnostic prognostic biomarkers remain unidentified. This research aims to identify potential epigenomic for integrating DNA methylation differential gene expression analyses. Publicly available datasets were utilized, ‘R’ programming was used the analysis significantly differentially methylated regions, performed in ‘R’, revealing expressed genes associated with this disease. Functional enrichment pathway analyses carried out using DAVID bioinformatics tool, Reactome revealed key biological processes pathways involved pathogenesis. Additionally, protein-protein interaction networks constructed utilizing a string database recognize hub therapeutic relevance. Our work provides understanding of epigenetic landscape disease, identifying 8 candidate that could aid as tools or targets. Gene ontology show mechanism pathways, showing efficiency biomarker integrative approach not only advances molecular underpinnings but also paves way personalized treatment strategies.

Language: Английский

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