Identifying health risk determinants and molecular targets in patients with idiopathic pulmonary fibrosis via combined differential and weighted gene co-expression analysis
IntroductionIdiopathic pulmonary fibrosis (IPF) is a rare but debilitating lung disease characterized by excessive fibrotic tissue accumulation, primarily affecting individuals over 50 years of age. Early diagnosis is challenging, and without intervention, the prognosis remains poor. Understanding t...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2024.1496462/full |
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| author | Abu Tayab Moin Md. Asad Ullah Jannatul Ferdous Nipa Mohammad Sheikh Farider Rahman Afsana Emran Md. Minhazul Islam Swapnil Das Tawsif Al Arian Mohammad Mahfuz Enam Elahi Mukta Akter Umme Sadea Rahman Arnab Halder Shoaib Saikat Mohammad Jakir Hosen |
| author_facet | Abu Tayab Moin Md. Asad Ullah Jannatul Ferdous Nipa Mohammad Sheikh Farider Rahman Afsana Emran Md. Minhazul Islam Swapnil Das Tawsif Al Arian Mohammad Mahfuz Enam Elahi Mukta Akter Umme Sadea Rahman Arnab Halder Shoaib Saikat Mohammad Jakir Hosen |
| author_sort | Abu Tayab Moin |
| collection | DOAJ |
| description | IntroductionIdiopathic pulmonary fibrosis (IPF) is a rare but debilitating lung disease characterized by excessive fibrotic tissue accumulation, primarily affecting individuals over 50 years of age. Early diagnosis is challenging, and without intervention, the prognosis remains poor. Understanding the molecular mechanisms underlying IPF pathogenesis is crucial for identifying diagnostic markers and therapeutic targets.MethodsWe analyzed transcriptomic data from lung tissues of IPF patients using two independent datasets. Differentially expressed genes (DEGs) were identified, and their functional roles were assessed through pathway enrichment and tissue-specific expression analysis. Protein-protein interaction (PPI) networks and co-expression modules were constructed to identify hub genes and their associations with disease severity. Machine learning approaches were applied to identify genes capable of differentiating IPF patients from healthy individuals. Regulatory signatures, including transcription factor and microRNA interactions, were also explored, alongside the identification of potential drug targets.ResultsA total of 275 and 167 DEGs were identified across two datasets, with 67 DEGs common to both. These genes exhibited distinct expression patterns across tissues and were associated with pathways such as extracellular matrix organization, collagen fibril formation, and cell adhesion. Co-expression analysis revealed DEG modules correlated with varying IPF severity phenotypes. Machine learning analysis pinpointed a subset of genes with high discriminatory power between IPF and healthy individuals. PPI network analysis identified hub proteins involved in key biological processes, while functional enrichment reinforced their roles in extracellular matrix regulation. Regulatory analysis highlighted interactions with transcription factors and microRNAs, suggesting potential mechanisms driving IPF pathogenesis. Potential drug targets among the DEGs were also identified.DiscussionThis study provides a comprehensive transcriptomic overview of IPF, uncovering DEGs, hub proteins, and regulatory signatures implicated in disease progression. Validation in independent datasets confirmed the relevance of these findings. The insights gained here lay the groundwork for developing diagnostic tools and novel therapeutic strategies for IPF. |
| format | Article |
| id | doaj-art-4a20ca7c3a2c455abbfaa99eaabb4fa4 |
| institution | Kabale University |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Genetics |
| spelling | doaj-art-4a20ca7c3a2c455abbfaa99eaabb4fa42025-01-29T06:45:35ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-01-011510.3389/fgene.2024.14964621496462Identifying health risk determinants and molecular targets in patients with idiopathic pulmonary fibrosis via combined differential and weighted gene co-expression analysisAbu Tayab Moin0Md. Asad Ullah1Jannatul Ferdous Nipa2Mohammad Sheikh Farider Rahman3Afsana Emran4Md. Minhazul Islam5Swapnil Das6Tawsif Al Arian7Mohammad Mahfuz Enam Elahi8Mukta Akter9Umme Sadea Rahman10Arnab Halder11Shoaib Saikat12Mohammad Jakir Hosen13Laboratory of Clinical Genetics, Genomics and Enzyme Research, Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chattogram, BangladeshDepartment of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, BangladeshDepartment of Genetic Engineering and Biotechnology, East West University, Dhaka, BangladeshDepartment of Molecular Biotechnology, Applied Bioscience and Process Engineering, Anhalt University of Applied Sciences, Köthen, GermanyDepartment of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, BangladeshDepartment of Pharmacy, BGC Trust University Bangladesh, Chattogram, BangladeshDepartment of Pharmacy, University of Science and Technology Chittagong (USTC), Chattogram, BangladeshDepartment of Pharmacy, Faculty of Biological Science, Jahangirnagar University, Dhaka, Savar, BangladeshDepartment of Pharmacy, University of Asia Pacific, Dhaka, BangladeshDepartment of Agricultural Extension, Ministry of Agriculture, Dhaka, Bangladesh0Department of Pharmacy, Independent University, Dhaka, BangladeshDepartment of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh1Department of Biochemistry and Biotechnology, Faculty of Bio-Sciences, University of Barishal, Barishal, Bangladesh2Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, BangladeshIntroductionIdiopathic pulmonary fibrosis (IPF) is a rare but debilitating lung disease characterized by excessive fibrotic tissue accumulation, primarily affecting individuals over 50 years of age. Early diagnosis is challenging, and without intervention, the prognosis remains poor. Understanding the molecular mechanisms underlying IPF pathogenesis is crucial for identifying diagnostic markers and therapeutic targets.MethodsWe analyzed transcriptomic data from lung tissues of IPF patients using two independent datasets. Differentially expressed genes (DEGs) were identified, and their functional roles were assessed through pathway enrichment and tissue-specific expression analysis. Protein-protein interaction (PPI) networks and co-expression modules were constructed to identify hub genes and their associations with disease severity. Machine learning approaches were applied to identify genes capable of differentiating IPF patients from healthy individuals. Regulatory signatures, including transcription factor and microRNA interactions, were also explored, alongside the identification of potential drug targets.ResultsA total of 275 and 167 DEGs were identified across two datasets, with 67 DEGs common to both. These genes exhibited distinct expression patterns across tissues and were associated with pathways such as extracellular matrix organization, collagen fibril formation, and cell adhesion. Co-expression analysis revealed DEG modules correlated with varying IPF severity phenotypes. Machine learning analysis pinpointed a subset of genes with high discriminatory power between IPF and healthy individuals. PPI network analysis identified hub proteins involved in key biological processes, while functional enrichment reinforced their roles in extracellular matrix regulation. Regulatory analysis highlighted interactions with transcription factors and microRNAs, suggesting potential mechanisms driving IPF pathogenesis. Potential drug targets among the DEGs were also identified.DiscussionThis study provides a comprehensive transcriptomic overview of IPF, uncovering DEGs, hub proteins, and regulatory signatures implicated in disease progression. Validation in independent datasets confirmed the relevance of these findings. The insights gained here lay the groundwork for developing diagnostic tools and novel therapeutic strategies for IPF.https://www.frontiersin.org/articles/10.3389/fgene.2024.1496462/fullidiopathic pulmonary fibrosistranscriptome analysisdifferentially expressed geneslung tissuedrug targetsbiomarkers |
| spellingShingle | Abu Tayab Moin Md. Asad Ullah Jannatul Ferdous Nipa Mohammad Sheikh Farider Rahman Afsana Emran Md. Minhazul Islam Swapnil Das Tawsif Al Arian Mohammad Mahfuz Enam Elahi Mukta Akter Umme Sadea Rahman Arnab Halder Shoaib Saikat Mohammad Jakir Hosen Identifying health risk determinants and molecular targets in patients with idiopathic pulmonary fibrosis via combined differential and weighted gene co-expression analysis Frontiers in Genetics idiopathic pulmonary fibrosis transcriptome analysis differentially expressed genes lung tissue drug targets biomarkers |
| title | Identifying health risk determinants and molecular targets in patients with idiopathic pulmonary fibrosis via combined differential and weighted gene co-expression analysis |
| title_full | Identifying health risk determinants and molecular targets in patients with idiopathic pulmonary fibrosis via combined differential and weighted gene co-expression analysis |
| title_fullStr | Identifying health risk determinants and molecular targets in patients with idiopathic pulmonary fibrosis via combined differential and weighted gene co-expression analysis |
| title_full_unstemmed | Identifying health risk determinants and molecular targets in patients with idiopathic pulmonary fibrosis via combined differential and weighted gene co-expression analysis |
| title_short | Identifying health risk determinants and molecular targets in patients with idiopathic pulmonary fibrosis via combined differential and weighted gene co-expression analysis |
| title_sort | identifying health risk determinants and molecular targets in patients with idiopathic pulmonary fibrosis via combined differential and weighted gene co expression analysis |
| topic | idiopathic pulmonary fibrosis transcriptome analysis differentially expressed genes lung tissue drug targets biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2024.1496462/full |
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