Evidence map›Paper›PMID 40376303›Full record

ArticleFrontiers in genetics2025

Identification of potential crucial genes and mechanisms associated with metabolically unhealthy obesity based on the gene expression profile.

Qingqing Wang, Silu Wang, Zhanyu Zhuang, Xueting Wu, Hongkun Gao, Tianyi Zhang, Guorong Zou, Xing Ge, Yapeng Liu

Abstract read
In one paragraph

Article in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Unraveling Obesity: A Five-Year Integrative Review of Transcriptomic Data.International journal of molecular sciences · 2025
    Review
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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Qingqing WangDepartment of Nephrology, Xuzhou Children's Hospital, Xuzhou, Jiangsu, China.
Silu WangSchool of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Zhanyu ZhuangDepartment of Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Xueting WuDepartment of Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Hongkun GaoSchool of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Tianyi ZhangSchool of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Guorong ZouDepartment of Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Xing GeSchool of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Yapeng LiuYunlong District Center for Disease Control and Prevention, Xuzhou, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Obesity is an epidemic and systemic metabolic disease that seriously endangers human health. This study aimed to understand the transcriptomic characteristics of the blood of metabolically unhealthy obesity (MUO) and provide insight into the target genes of differently expressed microRNAs in the occurrence and development of MUO. Methods: The GSE146869, GSE145412, GSE23561, and GSE169290 datasets were analyzed to understand the transcriptome characteristics of the blood of MUO and provide insights into the target genes of differently expressed microRNAs (DEMs) in MUO. Functional and pathway enrichment analyses and gene interaction network analyses were performed to profile the function of differentially expressed genes (DEGs). In addition, miRNet 2.0, TransmiR v2.0, RNA22, TargetScan 7.2, miRDB, and miRWalk databases were used to predict the target genes of effective microRNAs. Results: A total of 189 co-DEGs were identified in at least two datasets. The 156 co-upregulated genes were enriched into 29 biological process (BP) terms and 12 KEGG pathways. Among the 29 BP terms, the immune- and metabolism-related BP terms were enriched. The 33 co-downregulated genes were enriched into two BP terms, including apoptotic process and regulation of the apoptotic process, with no KEGG pathway. The hub genes Conclusion: A network consisting of 18 microRNAs and 85 target genes might serve as a risk factor for metabolically unhealthy obesity.

Indexed as

bioinformatics analysisdifferentially expressed genesdifferentially expressed microRNAsmetabolically unhealthy obesitymetabolic syndrome

Identifiers

PMID40376303
PMCPMC12078199

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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.