Evidence map›Paper›PMID 37876543›Full record

ArticleFrontiers in endocrinology2023

Functional enrichment analysis of mutated genes in children with hyperthyroidism.

Xiaojian Mao, Liangliang Tang, Hongyi Li, Wen Zhang, Li Liu, Heyong Wang, Abdalbari Headar

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2023. 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
0.4field-weighted citation impact, top 33% of its field
1 · What the graph read from it

What it found

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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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3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 2 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Xiaojian MaoDepartment of Genetics and Endocrinology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China.
Liangliang TangCenter of Big Data and Business Itelligent, South China University of Technology, Guangzhou, China.
Hongyi LiDepartment of Genetics and Endocrinology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China.
Wen ZhangDepartment of Genetics and Endocrinology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China.
Li LiuDepartment of Genetics and Endocrinology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China.
Heyong WangCenter of Big Data and Business Itelligent, South China University of Technology, Guangzhou, China.
Abdalbari HeadarCenter of Big Data and Business Itelligent, South China University of Technology, Guangzhou, China.
Guangzhou Medical University · CNSouth China University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Hyperthyroidism in Chinese children is relatively high and has been increasing in recent years, which has a significant impact on their healthy development. Hyperthyroidism is a polygenic disorder that presents greater challenges in terms of prediction and treatment than monogenic diseases. This study aims to elucidate the associated functions and gene sets of mutated genes in children with hyperthyroidism in terms of the gene ontology through GO enrichment analysis and in terms of biological signaling pathways through KEGG enrichment analysis, thereby enhancing our understanding of the expected effects of multiple mutated genes on hyperthyroidism in children. Methods: Whole-exome sequencing was performed on the DNA samples of children with hyperthyroidism. Screening for pathogenic genes related to hyperthyroidism in affected children was performed using the publicly available disease databases Malacards, MutationView, and Clinvar, and the functions and influences of the identified pathogenic genes were analyzed using statistical analysis and the gene enrichment approach. Results: Through GO enrichment analysis, it was found that the most significant gene ontology enrichment was the function "hormone activity" in terms of gene ontology molecular function. The corresponding mutated genes set that has common effects on hyperthyroidism in children included TG, CALCA, POMC, CGA, PTH, GHRL, FBN1, TRH, PRL, LEP, ADIPOQ, INS, GH1. The second most significant gene ontology enrichment was the function "response to peptide hormone" in terms of biological process. The corresponding mutated genes set that has common effects on hyperthyroidism in children included LRP6, TSC2, KANK1, COL1A1, CDKN1B, POMC, STAT1, MEN1, APC, GHRL, TSHR, GJB2, FBN1, GPT, LEP, ADIPOQ, INS, GH1. Through KEGG enrichment analysis, it was found that the most significant biological signaling pathway enrichment was the pathway "Thyroid hormone signaling pathway" function. The corresponding mutated genes set that has common effects on hyperthyroidism in children included NOTCH3, MYH7, TSC2, STAT1, MED13L, MAP2K2, SLCO1C1, SLC16A2, and THRB. The second most significant biological signaling pathway enrichment was the pathway "Hypertrophic cardiomyopathy" in terms of biological process. The corresponding mutated genes set that has common effects on hyperthyroidism in children included IGF1, CACNA1S, MYH7, IL6, TTN, CACNB2, LAMA2, and DMD. Conclusion: The mutated genes in children with hyperthyroidism were closely linked to function involved in "hormone activity" and "response to peptide hormone" in terms of the biological signaling pathway, and to the functional pathways involved in "Thyroid hormone signaling pathway" and "Hypertrophic cardiomyopathy" in terms of the biological signaling pathway.

Indexed as

CardiomyopathiesHyperthyroidismOrganic Anion TransportersSymportersAdaptor Proteins, Signal TransducingChildComputational BiologyCytoskeletal ProteinsHumansMonocarboxylic Acid TransportersPro-OpiomelanocortinThyroid HormonesAdaptor Proteins, Signal TransducingCytoskeletal ProteinsKANK1 protein, humanMonocarboxylic Acid TransportersOrganic Anion TransportersPro-OpiomelanocortinSLC16A2 protein, humanSLCO1C1 protein, humanSymportersThyroid Hormonesbioinformaticsenrichment analysisgene mutationgene ontology enrichmenthyperthyroidism

Identifiers

PMID37876543
PMCPMC10591315
OpenAlexW4387460207

What OpenQuestion holds

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LicenceCC BY
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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.