Evidence map›Paper›PMID 41695716›Full record

ArticlePeerJ2026

Capg enhances proliferation, adipogenesis, and inflammatory response in preadipocytes: insights from bioinformatics analysis and functional validation.

Luyao Zhang, Botao Sang, Sainan Li, Ying Li, Dachuan Guo, Qinan Ma, Xiangfei Liu, Xiaoshuo Li, Beidong Chen, Deping Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Luyao ZhangNational Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Botao SangBeijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Medical School, University of Chinese Academy of Sciences, Beijing, China.
Sainan LiNational Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Ying LiBeijing Hospital, National Center of Gerontology, Chinese Academy of Medical Sciences, Institute of Geriatric Medicine, Beijing University, Beijing, China.
Dachuan GuoNational Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Qinan MaBeijing Hospital, National Center of Gerontology, Chinese Academy of Medical Sciences, Institute of Geriatric Medicine, Beijing University, Beijing, China.
Xiangfei LiuBeijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Xiaoshuo LiBeijing Hospital, National Center of Gerontology, Chinese Academy of Medical Sciences, Institute of Geriatric Medicine, Beijing University, Beijing, China.
Beidong ChenThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China.
Deping LiuNational Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Various associations between adipose tissue and atherosclerosis (AS) have been revealed. This study aims to identify biomarkers in the epididymal adipose tissue of AS mice and to explore their effects on adipose tissue inflammation and adipogenesis. Methods: The gene expression profiles of epididymal adipose tissue (GSE57659 and GSE76812) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEG) screened by Limma R package and genes searched by weighted gene correlation network analysis (WGCNA) were performed to classify common genes associated with AS. The Protein-Protein interaction (PPI) network was constructed by Cytoscape software, and hub genes were eventually determined by the Cytohubba plugin. Finally, one of these hub genes was selected. The cell proliferation ability was assessed using the CCK8 assay. Oil Red O staining and Western blot were employed to evaluate the lipid content in adipocytes. The extent of the inflammatory response in adipocytes was determined by Enzyme-Linked Immunosorbent Assay (ELISA). Results: A total of 125 DEGs were identified between the control group and the atherosclerosis group. Among these, 34 genes were selected based on two key modules identified through WGCNA. Subsequently, five key nodes were identified, namely Conclusion: CAPG promotes the proliferation and differentiation of adipocyte precursor cells. Additionally, CAPG enhances the inflammatory response in adipocytes, potentially serving as a key molecule mediating obesity-related atherosclerosis.

Indexed as

AdipocytesAdipogenesisAtherosclerosisCell ProliferationInflammation3T3-L1 CellsAnimalsComputational BiologyGene Expression ProfilingMaleMiceProtein Interaction MapsAdipogenesisAdipose tissueAtherosclerosisCAPGInflammation

Identifiers

PMID41695716
PMCPMC12903893

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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.