ArticleFrontiers in immunology2024
Transcriptomics in idiopathic pulmonary fibrosis unveiled: a new perspective from differentially expressed genes to therapeutic targets.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 16 citations in OpenAlex.
- Public Transcriptomic Data Mining for SCLC: From Candidate Ma rkers to Therapeutic Exploration.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- A systematic review of artificial intelligence-based diagnosis models for idiopathic pulmonary fibrosis.Journal of thoracic disease · 2026Review
- Integrative bioinformatics and machine learning reveal an association of LTF and MMP8 with systemic inflammation and lung injury.Respiratory research · 2026Article
- Review
- Profibrotic macrophage populations and cell communications in pulmonary fibrosis.Journal of physiology and biochemistry · 2026Review
- Mitochondria-Related Pathogenic Genes in Paediatric Asthma: A Multi-Omics Mendelian Randomization Study.Journal of cellular and molecular medicine · 2026Article
- ZG16B: A key regulator of tumor progression and immune microenvironment modulation in cancer (Review).International journal of molecular medicine · 2026Review
- Keratinocyte-Associated Biomarkers Reveal Pathogenic Mechanisms in Acne.FASEB bioAdvances · 2026Article
- Integrated Serum Pharmacochemistry, Network Pharmacology, and Transcriptomics Reveal the Mechanisms and Active Constituents of Qingfei Huoxue Decoction Against Bleomycin-Induced Pulmonary Fibrosis.Drug design, development and therapy · 2026Article
- An hiPSC-derived multi-lineage lung model exhibiting proximal-distal epithelial features for modeling pulmonary fibrosis.Frontiers in cell and developmental biology · 2026Article
- Investigating the Biomarkers for Alzheimer's Disease: Insights from Microarray Analysis, Mendelian Randomization, and Experimental Validation.Current medicinal chemistry · 2026Article
- Differentially expressed genes and therapeutic targets for nonalcoholic fatty liver disease from transcriptomic techniques.Medicine · 2025Article
- Development of a prognostic prediction signature for idiopathic pulmonary fibrosis by integrating multiple programmed cell death-related genes and machine learning algorithms.Journal of thoracic disease · 2025Article
- Yangke powder alleviates OVA-induced allergic asthma by inhibiting the PI3K/AKT/NF-κB signaling pathway.Chinese medicine · 2025Article
- Identification of novel biomarkers and prognostic model for neuroblastoma using Mendelian randomization and transcriptomic analysis.Discover oncology · 2025Article
- Identifying Lipid Metabolism-Related Therapeutic Targets and Diagnostic Markers for Lung Adenocarcinoma by Mendelian Randomization and Machine Learning Analysis.Thoracic cancer · 2025Article
- Article
- Integrated Analysis of Bulk RNA Sequencing, eQTL, GWAS, and Single-Cell RNA Sequencing Reveals Key Genes in Hepatocellular Carcinoma.Journal of cellular and molecular medicine · 2025Article
- Integrative Analysis of GEO Datasets and Mendelian Randomization Reveals a Potential Role ofISOC1 in Renal Cell Carcinoma.Journal of Cancer · 2025Article
- The role of age-related genes in idiopathic pulmonary fibrosis and molecular docking analysis of their drug targets.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The underlying molecular pathways of idiopathic pulmonary fibrosis (IPF), a progressive lung condition with a high death rate, are still mostly unknown. By using microarray datasets, this study aims to identify new genetic targets for IPF and provide light on the genetic factors that contribute to the development of IPF. Method: We conducted a comprehensive analysis of three independent IPF datasets from the Gene Expression Omnibus (GEO) database, employing R software for data handling and normalization. Our evaluation of the relationships between differentially expressed genes (DEGs) and IPF included differential expression analysis, expression quantitative trait loci (eQTL) analysis, and Mendelian Randomization(MR) analyses. Additionally, we used Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to explore the functional roles and pathways of these genes. Finally, we validated the results obtained for the target genes. Results: We identified 486 highly expressed genes and 468 lowly expressed genes that play important roles in IPF. MR analysis identified six significantly co-expressed genes associated with IPF, specifically C12orf75, SPP1, ZG16B, LIN7A, PPP1R14A, and TLR2. These genes participate in essential biological processes and pathways, including macrophage activation and neural system regulation. Additionally, CIBERSORT analysis indicated a unique immune cell distribution in IPF, emphasized the significance of immunological processes in the disease. The MR analysis was consistent with the results of the analysis of variance in the validation cohort, which strengthens the reliability of our MR findings. Conclusion: Our findings provide new insights into the molecular basis of IPF and highlight the promise of therapeutic interventions. They emphasize the potential of targeting specific molecular pathways for the treatment of IPF, laying the foundation for further research and clinical work.
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