Evidence map›Paper›PMID 39777262›Full record

ArticleFrontiers in genetics2024

Predictive models of sarcopenia based on inflammation and pyroptosis-related genes.

Xiaoqing Li, Cheng Wu, Xiang Lu, Li Wang

Abstract read
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Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Research progress on BTG2 in non‑tumor diseases (Review).International journal of molecular medicine · 2026
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Xiaoqing LiDepartment of Geriatrics, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Cheng WuDepartment of Geriatrics, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Xiang LuDepartment of Geriatrics, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Li WangDepartment of Geriatrics, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Sarcopenia is a prevalent condition associated with aging. Inflammation and pyroptosis significantly contribute to sarcopenia. Methods: Two sarcopenia-related datasets (GSE111016 and GSE167186) were obtained from the Gene Expression Omnibus (GEO), followed by batch effect removal post-merger. The "limma" R package was utilized to identify differentially expressed genes (DEGs). Subsequently, LASSO analysis was conducted on inflammation and pyroptosis-related genes (IPRGs), resulting in the identification of six hub IPRGs. A novel skeletal muscle aging model was developed and validated using an independent dataset. Additionally, Gene Ontology (GO) enrichment analysis was performed on DEGs, along with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and gene set enrichment analysis (GSEA). ssGSEA was employed to assess differences in immune cell proportions between healthy muscle groups in older versus younger adults. The expression levels of the six core IPRGs were quantified via qRT-PCR. Results: A total of 44 elderly samples and 68 young healthy samples were analyzed for DEGs. Compared to young healthy muscle tissue, T cell infiltration levels in aged muscle tissue were significantly reduced, while mast cell and monocyte infiltration levels were relatively elevated. A new diagnostic screening model for sarcopenia based on the six IPRGs demonstrated high predictive efficiency (AUC = 0.871). qRT-PCR results indicated that the expression trends of these six IPRGs aligned with those observed in the database. Conclusion: Six biomarkers-BTG2, FOXO3, AQP9, GPC3, CYCS, and SCN1B-were identified alongside a diagnostic model that offers a novel approach for early diagnosis of sarcopenia.

Indexed as

inflammation and pyroptosis-related genesLASSOnomogram modelpredictive modelsarcopenia

Identifiers

PMID39777262
PMCPMC11703911

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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.