Evidence map›Paper›PMID 40640934›Full record

ArticleOrphanet journal of rare diseases2025

Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis.

Xingren Liu, Junmei Song, Shujin Guo, Yi Liao, Jun Zou, Liqing Yang, Caiyu Jiang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Orphanet journal of rare diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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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.

Xingren Liu *Department of Respiratory and Critical Care Medicine, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Junmei Song *Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cardiovascular Disease, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Shujin GuoDepartment of Health Management & Institute of Health Management, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Yi LiaoDepartment of Respiratory and Critical Care Medicine, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Jun ZouDepartment of Respiratory and Critical Care Medicine, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Liqing YangDepartment of Respiratory and Critical Care Medicine, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. yangliqing@med.uestc.edu.cn.
Caiyu JiangDepartment of Respiratory and Critical Care Medicine, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. jiangcaiyu@med.uestc.edu.cn.ORCID http://orcid.org/0009-0002-5864-1401

Funding

Discipline Construction Fund of Sichuan Provincial People's Hospital 30420240026-33
6 · The paper itself

Abstract

objectiveEmerging evidence suggests a potential relationship between lipid metabolism and idiopathic pulmonary fibrosis (IPF). This study aimed to identify lipid-related genes implicated in IPF pathogenesis.

methodsLipid-associated genes were retrieved from the GeneCards database and analyzed using unsupervised consensus clustering to classify IPF samples. Weighted gene co-expression network analysis (WGCNA) was performed on the identified clusters to determine core modules and hub genes associated with IPF. Machine learning algorithms were applied to these hub genes to construct diagnostic and prognostic models, which were validated across multiple datasets. Single-cell sequencing was used to investigate the distribution of potential pathogenic genes, and their functional roles were further validated through cellular experiments.

resultsTwo distinct clusters were identified, showing significant differences in lung function parameters and fibrosis-related gene expression. WGCNA revealed that the blue module was strongly associated with IPF and served as the core module. Genes from this module were used to construct diagnostic and prognostic models, which demonstrated strong predictive performance across multiple validation datasets. Single-cell sequencing revealed that KLF4 was highly expressed in lung epithelial cells. Functional assays indicated that knockdown of KLF4 did not affect the proliferation of human alveolar type II epithelial cells but significantly enhanced their migratory capacity, thereby promoting the fibrotic process.

conclusionThis study successfully constructed lipid-related diagnostic and prognostic models for IPF and identified KLF4 as a potential causative gene. These findings provide a foundation for further exploration of lipid metabolism in IPF pathogenesis and potential therapeutic strategies targeting KLF4.

Indexed as

Idiopathic Pulmonary FibrosisLipid MetabolismMachine LearningGene Regulatory NetworksHumansKruppel-Like Factor 4Kruppel-Like Transcription FactorsPrognosisKLF4 protein, humanKruppel-Like Factor 4Kruppel-Like Transcription FactorsDiagnosisIdiopathic pulmonary fibrosisLipidPrognosis

Identifiers

PMID40640934
PMCPMC12247251

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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.