Evidence map›Paper›PMID 42265659›Full record

ArticleBMC oral health2026

Identification of aging-related signature genes in temporomandibular joint degeneration based on bioinformatics.

Xinyi Wang, Ziyang Xia, Yanzhang Zhou, Minda Wang, Ting Jiang, Jingwen Yang

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

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xinyi WangDepartment of Prosthodontics, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, NMPA Key Laboratory for Dental Materials, Beijing, China.
Ziyang XiaNational Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, NMPA Key Laboratory for Dental Materials, Beijing, China.
Yanzhang ZhouNational Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, NMPA Key Laboratory for Dental Materials, Beijing, China.
Minda WangNational Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, NMPA Key Laboratory for Dental Materials, Beijing, China.
Ting JiangDepartment of Prosthodontics, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, NMPA Key Laboratory for Dental Materials, Beijing, China.
Jingwen YangDepartment of Prosthodontics, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, NMPA Key Laboratory for Dental Materials, Beijing, China. jingwen.yang@foxmail.com.

Funding

National Natural Science Foundation of China 82201022
6 · The paper itself

Abstract

backgroundDegenerative temporomandibular joint diseases (TMJ-DJD) are increasingly affecting elderly populations, with aging being a significant risk factor driving the TMJ degenerative changes. This study aims to explore the shared characteristics between aging and TMJ degenerative diseases at the tissue level, and to identify aging-related signature genes in TMJ degenerations using bioinformatics approaches.

methodsCondyles from young (2 months, n = 7) and aged (18 months, n = 6) mice were used to construct an aging dataset. Overlapping genes were identified as the intersection of differentially expressed genes (DEGs) from the aging and TMJOA datasets, with exclusion of DEGs from the mechanical stimulation dataset. Temporomandibular joint osteoarthritis-associated and aging-related differentially expressed genes (TMJOA-ARDEGs) were identified through Weighted gene co-expression network analysis. Functional enrichment analysis and Protein-Protein Interaction networks were employed. Machine learning algorithms were subsequently applied to further screen the TMJOA-ARDEGs, yielding eight Hub TMJOA-ARDEGs. An age-related TMJOA mouse model (18 months of age) was established and grouped based on the micro-CT and the modified Mankin scores. The expression changes of key Hub TMJOA-ARDEGs were verified by immunohistochemical staining. The Mann-Whitney U test was used in non-normally distributed data, presented as [median (interquartile range)].

resultsFifty-two TMJOA-ARDEGs were identified; functional enrichment analysis revealed that these genes are involved in circadian rhythm regulation and apoptosis. Eight Hub TMJOA-ARDEGs were identified: ANK1, MELTF, FERMT3, MDFI, CXCL14, EPHA3, SMOC2, and NR1D1. Based on the micro-CT, 12 condyles of aged mice were divided into the TMJOA group (n = 6) and the healthy group (n = 6). Micro-CT revealed bone resorption in the TMJOA group compared to the healthy group, with a decrease in BV/TV (p < 0.05) and an increase in Tb.Sp (p < 0.05). The modified Mankin scores showed that the TMJOA group had a score of 5.61 (2.28), higher than the healthy group's score of 0.83 (1.28), p < 0.01. Among the Hub TMJOA-ARDEGs, NR1D1 is a key gene involved in regulating circadian rhythm. Immunohistochemical staining confirmed that NR1D1 expression was significantly elevated in the TMJOA group compared to the healthy group (p < 0.01).

conclusionNR1D1, a negative transcriptional regulator within the circadian feedback loop, is identified as a candidate gene shared between aging and TMJ degenerative diseases.

Indexed as

AgingComputational BiologyOsteoarthritisTemporomandibular Joint DisordersAnimalsDisease Models, AnimalGene Expression ProfilingMiceAgingBioinformaticsDegenerative joint diseaseSignature genesTemporomandibular jointTMJOA

Identifiers

PMID42265659
PMCPMC13459353

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