Observational studyMedicine2020
Using bioinformatics approach identifies key genes and pathways in idiopathic pulmonary fibrosis.
Observational study in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Identification of Hub Genes in Idiopathic Pulmonary Fibrosis and Their Association with Lung Cancer by Bioinformatics Analysis.Advances in respiratory medicine · 2023Pooled it
- Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing.Scientific reports · 2023Article
- Diagnosis of Fibrotic Hypersensitivity Pneumonitis: Is There a Role for Biomarkers?Life (Basel, Switzerland) · 2023Review
- An explainable machine learning-driven proposal of pulmonary fibrosis biomarkers.Computational and structural biotechnology journal · 2023Article
- miR-338-3p blocks TGFβ-induced myofibroblast differentiation through the induction of PTEN.American journal of physiology. Lung cellular and molecular physiology · 2022Article
- Genome-wide expression of the residual lung reacting to experimental Pneumonectomy.BMC genomics · 2021Article
- A novel prognostic signature for idiopathic pulmonary fibrosis based on five-immune-related genes.Annals of translational medicine · 2021Article
- Sleep Apnea in Idiopathic Pulmonary Fibrosis: A Molecular Investigation in an Experimental Model of Fibrosis and Intermittent Hypoxia.Life (Basel, Switzerland) · 2021Article
- Bioinformatic analysis of differentially expressed genes and pathways in idiopathic pulmonary fibrosis.Annals of translational medicine · 2021Article
- Identification of Key Candidate Genes Involved in the Progression of Idiopathic Pulmonary Fibrosis.Molecules (Basel, Switzerland) · 2021Article
- Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary FibrosisFrontiers in molecular biosciences · 2021Article
- Mechanism of Fei-Xian Formula in the Treatment of Pulmonary Fibrosis on the Basis of Network Pharmacology Analysis Combined with Molecular Docking Validation.Evidence-based complementary and alternative medicine : eCAM · 2021Article
- The Genomic Response to TGF-β1 Dictates Failed Repair and Progression of Fibrotic Disease in the Obstructed Kidney.Frontiers in cell and developmental biology · 2021Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Idiopathic pulmonary fibrosis is a chronic and irreversible respiratory disease with a high incidence worldwide and no specific treatment. Currently, the etiology and pathogenesis of this disease remain largely unknown. In main purpose of this study, bioinformatics analysis was used to uncover key genes and pathways related to idiopathic pulmonary fibrosis (IPF). Gene expression profiles of GSE2052 and GSE35145 were obtained. After combining the 2 chip groups; then, we normalized the data, eliminating batch difference. R software was used to process and to screen differentially expressed genes (DEGs) between the IPF and normal tissues. Then, functional enrichment analysis of these DEGs was carried out, and a protein-protein interaction network (PPI) was also constructed. A total of 276 DEGs (152 up and 134 down-regulated genes) were identified in the IPF lung samples. The PPI network was established with 227 nodes and 763 edges. The top 10 hub genes were CAM1, CDH1, CXCL12, JUN, CTGF, SERPINE1, CXCL1, EDN1, COL1A2, and SPARC. Analyzing the PPI network modules with close interaction, the 3 key modules in the whole PPI network were screened out. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for the module containing DEGs contained the viral protein interaction with cytokine and the cytokine receptor, the TNF signaling pathway, and the chemokine signaling pathway. The identified key genes and pathways may play an important role in the occurrence and development of IPF, and may be expected to be biomarkers or therapeutic targets for the diagnosis of IPF.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.