Evidence map›Paper›PMID 39881357›Full record

ArticleBMC pharmacology & toxicology2025

Elucidating the role of FBXW4 in osteoporosis: integrating bioinformatics and machine learning for advanced insight.

Jinxiao Li, Jing Li, Man Zheng, Jinxing Liu, Xinyou Zhao

Abstract read
In one paragraph

Article in BMC pharmacology & toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
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

5 authors.

Jinxiao Li *First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250014, China.
Jing Li *Dongying Traditional Chinese Medicine Hospital, Dongying, Shandong, China.
Man ZhengDongying People's Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, 257091, China.
Jinxing LiuFirst Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250014, China. ljx276@sina.com.
Xinyou ZhaoYanzhou District People's Hospital, Jining, Shandong, China. zhaoxinyou2005@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOsteoporosis (OP), often termed the "silent epidemic," poses a substantial public health burden. Emerging insights into the molecular functions of FBXW4 have spurred interest in its potential roles across various diseases.

methodsThis study explored FBXW4 by integrating DEGs from GEO datasets GSE2208, GSE7158, GSE56815, and GSE35956 with immune-related gene compilations from the ImmPort repository. Gene selection was refined using advanced approaches, including LASSO regression and SVM-RFE. Functional enrichment of FBXW4-associated genes was assessed via GSEA and GSVA, identifying significant immune pathway involvement. Immune-related biological processes linked to FBXW4 expression were further evaluated using CIBERSORT and ESTIMATE algorithms. Validation of FBXW4 expression was performed using GSE35956.

resultsA total of 13 hub genes were selected through LASSO and SVM-RFE analyses. Functional assays implicated FBXW4 in antiviral defense, cytokine production, and immune response modulation. Notably, FBXW4 expression correlated positively with several immune cell subsets, including memory B cells, activated memory CD4+ T cells, naive B cells, gamma delta T cells, M0 macrophages, follicular helper T cells, and naive CD4+ T cells, while showing a negative association with neutrophils.

conclusionsThis study uncovers a complex interplay between FBXW4 and immune processes in osteoporosis, suggesting its potential utility as a biomarker for OP diagnosis and monitoring. These findings lay the groundwork for future investigations into the therapeutic and diagnostic potential of FBXW4 in OP.

Indexed as

F-Box ProteinsMachine LearningOsteoporosisComputational BiologyDatabases, GeneticHumansF-Box ProteinsAutoimmune inflammatory disorderFBXW4Lasso regressionOsteoporosis (OP)SVM-RFE

Identifiers

PMID39881357
PMCPMC11781060

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