Evidence map›Paper›PMID 39893187›Full record

ArticleScientific reports2025

Integrating bulk and single-cell RNA sequencing data: unveiling RNA methylation and autophagy-related signatures in chronic obstructive pulmonary disease patients.

Shi-Xia Liao, Lan-Ying Zhang, Ling-Mei Shi, Huai-Yu Hu, Yan-Hui Gu, Ting-Hua Wang, Yao Ouyang, Peng-Peng Sun

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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

8 authors.

Shi-Xia LiaoDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, 563003, China.
Lan-Ying ZhangDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, 563003, China.
Ling-Mei ShiDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, 563003, China.
Huai-Yu HuShenQi Ethnic Medicine College of Guizhou Medical University, Zunyi, 550000, China.
Yan-Hui GuDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, 563003, China.
Ting-Hua WangInstitute of Neurological Disease, West China Hospital, Sichuan University, Chengdu, 610041, China.
Yao OuyangDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, 563003, China. ouyanyao@zmu.edu.cn.
Peng-Peng SunDepartment of Osteopathy, Affiliated Hospital of Zunyi Medical University, Zunyi, 563003, China. sunpenghk@126.com.

Funding

National Natural Science Foundation of China National Natural Science Foundation of ChinaScience and Technology Joint Fund of Affiliated Hospital of Zunyi Medical College of Zunyi City Science and Technology Bureau No. (2018)71
6 · The paper itself

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous lung disease influenced by epigenetic modifications, particularly RNA methylation. Emerging evidence also suggests that autophagy plays a crucial role in immune cell infiltration and is implicated in COPD progression. This study aimed to investigate key RNA methylation regulators and explore the roles of RNA methylation and autophagy in COPD pathogenesis. We analyzed tissue-based bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) datasets from COPD and non-COPD patients, sourced from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified between COPD and non-COPD samples, and protein-protein interaction networks were constructed. Univariate logistic regression identified shared genes between DEGs and RNA methylation gene sets. Functional enrichment analyses, including Gene Ontology (GO), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA), were performed. Weighted gene co-expression network analysis (WGCNA) and immune infiltration analysis were conducted. Integration with scRNA-seq data further elucidated changes in immune cell composition, and cell communication analysis assessed interactions between macrophages and other immune cells. AddModuleScore analysis quantified RNA methylation and autophagy effects. Finally, a COPD mouse model was used to validate the expression of critical RNA methylation genes (FTO and IGF2BP2) in lung macrophages via RT-qPCR and flow cytometry. As revealed, we identified 13 RNA methylation-related genes enriched in translation and methylation processes. GSEA and GSVA revealed significant enrichment of these genes in immune and autophagy pathways. WGCNA analysis pinpointed key hub genes linking RNA methylation and autophagy. Integrated scRNA-seq analysis demonstrated a marked reduction of macrophages in COPD, with FTO and IGF2BP2 emerging as critical RNA methylation regulators. Macrophages with elevated RNA methylation and autophagy scores had increased interactions with other immune cells. In COPD mouse models, decreased expression of FTO and IGF2BP2 in lung macrophages was validated. Taken together, this study highlights the significant roles of RNA methylation in relation to autophagy pathways in the context of COPD. We identified key RNA methylation-related hub genes, such as FTO and IGF2BP2, which were found to have decreased expression in COPD macrophages. These findings provide novel genetic insights into the epigenetic mechanisms of COPD and suggest potential avenues for developing diagnostic and therapeutic strategies.

Indexed as

AutophagyPulmonary Disease, Chronic ObstructiveRNAAnimalsEpigenesis, GeneticFemaleGene Expression ProfilingGene Regulatory NetworksHumansMaleMethylationMiceProtein Interaction MapsRNA MethylationSequence Analysis, RNASingle-Cell AnalysisRNAAutophagyCOPDMacrophagesRNA methylationSingle cell RNA sequencing

Identifiers

PMID39893187
PMCPMC11787343

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Registered trials

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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.