Evidence map›Paper›PMID 39753882›Full record

ArticleScientific reports2025

Common biomarkers of idiopathic pulmonary fibrosis and systemic sclerosis based on WGCNA and machine learning.

Ning Shan, Yu Shang, Yaowu He, Zhe Wen, Shangwei Ning, Hong Chen

Abstract read
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 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

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

3 citing papers in PubMed.

  1. Review
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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

6 authors.

Ning Shan *Harbin Medical University, Harbin, Heilongjiang Province, China.
Yu Shang *The Second Hospital of Heilongjiang Province, Harbin, Heilongjiang Province, China.
Yaowu He *Harbin Medical University, Harbin, Heilongjiang Province, China.
Zhe WenHarbin Medical University, Harbin, Heilongjiang Province, China.
Shangwei NingHarbin Medical University, Harbin, Heilongjiang Province, China. ningsw@ems.hrbmu.edu.cn.
Hong ChenHarbin Medical University, Harbin, Heilongjiang Province, China. chenhong744563@aliyun.com.

Funding

2022 Heilongjiang Province key research and development plan project JD22C008
6 · The paper itself

Abstract

Interstitial lung disease (ILD) is known to be a major complication of systemic sclerosis (SSc) and a leading cause of death in SSc patients. As the most common type of ILD, the pathogenesis of idiopathic pulmonary fibrosis (IPF) has not been fully elucidated. In this study, weighted correlation network analysis (WGCNA), protein‒protein interaction, Kaplan-Meier curve, univariate Cox analysis and machine learning methods were used on datasets from the Gene Expression Omnibus database. CCL2 was identified as a common characteristic gene of IPF and SSc. The genes associated with CCL2 expression in both diseases were enriched mainly in chemokine-related pathways and lipid metabolism-related pathways according to Gene Set Enrichment Analysis. Single-cell RNA sequencing (sc-RNAseq) revealed a significant difference in CCL2 expression in alveolar epithelial type 1/2 cells, mast cells, ciliated cells, club cells, fibroblasts, M1/M2 macrophages, monocytes and plasma cells between IPF patients and healthy donors. Statistical analyses revealed that CCL2 was negatively correlated with lung function in IPF patients and decreased after mycophenolate mofetil (MMF) treatment in SSc patients. Finally, we identified CCL2 as a common biomarker from IPF and SSc, revealing the common mechanism of these two diseases and providing clues for the study of the treatment and mechanism of these two diseases.

Indexed as

BiomarkersChemokine CCL2Idiopathic Pulmonary FibrosisMachine LearningScleroderma, SystemicFemaleGene Expression ProfilingGene Regulatory NetworksHumansMaleMiddle AgedBiomarkersCCL2 protein, humanChemokine CCL2CCL2GSEAIdiopathic pulmonary fibrosisscRNA sequencing analysisSystemic sclerosisWGCNA

Identifiers

PMID39753882
PMCPMC11699037

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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.