Evidence map›Paper›PMID 40280949›Full record

ArticleScientific reports2025

Integrated bioinformatics analysis screened the key genes and pathways of idiopathic pulmonary fibrosis.

Juan Wu, Yangyang Wei, Hong Kang, Xiao Yu, Shuting Wei, Ting Xue, Xiaomei Kong

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Juan Wu *NHC Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, China.
Yangyang Wei *NHC Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, China.
Hong KangNHC Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, China.
Xiao YuNHC Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, China.
Shuting WeiNHC Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, China.
Ting XueNHC Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, China.
Xiaomei KongNHC Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, China. kongxm@sxmu.edu.cn.

Funding

Basic Research Projects of Natural Sciences in Shanxi Province No. 202303021212320
6 · The paper itself

Abstract

To investigate the molecular mechanisms underlying idiopathic pulmonary fibrosis (IPF), we analyzed the GSE173355 and GSE173356 datasets obtained from the NCBI-GEO database. We identified differentially expressed genes (DEGs) and differentially methylated sites. Functional enrichment analysis was conducted for both DEGs and differentially methylated sites. Functional enrichment analysis was performed for both DEGs and differentially methylated sites, alongside an examination of immune-related scores, proportions, and GSVA enrichment scores of immune cells in IPF versus control samples. An integrated gene-methylation association analysis revealed 8 genes with expression levels negatively influenced by methylation. The Rap1 pathway, Focal adhesion, and Axon guidance were significantly enriched among both DEGs and differentially methylated sites. Immune-related scores were notably lower in the IPF group compared to the control group, with marked differences in immune cell proportions and GSVA enrichment scores. Screening of DEGs identified 361 differentially expressed immune-related genes (IRGs). Protein-protein interaction (PPI) network analysis using the STRING database and Cytoscape software unveiled 10 key genes and 3 core subnetworks. RT-qPCR validation in bleomycin-induced IPF mouse model and A549 EMT model confirmed the reliability of most findings. These results provide new insights into IPF pathogenesis and potential therapeutic strategies, necessitating further functional validation.

Indexed as

Computational BiologyIdiopathic Pulmonary FibrosisA549 CellsAnimalsDatabases, GeneticDisease Models, AnimalDNA MethylationGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHumansMiceProtein Interaction MapsSignal TransductionIdiopathic pulmonary fibrosisImmune environmentImmune-related geneIntegrated bioinformaticsMethylation

Identifiers

PMID40280949
PMCPMC12032202

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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.