Bioinformatics identification and validation of aging key genes in hormonal osteonecrosis of the femoral head
- VernacularTitle:激素性股骨头坏死衰老关键基因的生物信息学鉴定和验证
- Author:
Boyuan QIU
1
;
Fei LIU
;
Siwen TONG
;
Zhixue OU
;
Weiwei WANG
Author Information
- Publication Type:Journal Article
- Keywords: hormonal osteonecrosis of the femoral head; aging; WGCNA analysis; machine learning; immune infiltration analysis; experimental validation
- From: Chinese Journal of Tissue Engineering Research 2025;29(26):5608-5620
- CountryChina
- Language:Chinese
- Abstract: BACKGROUND:Hormonal osteonecrosis of the femoral head is strongly associated with aging,but the regulatory targets and mechanisms are still unclear.Through bioinformatics combined with machine learning analysis and experimental verification,the key genes of hormonal osteonecrosis of the femoral head mediated by cell senescence will be identified,which will provide new ideas for the prevention and treatment of hormonal osteonecrosis of the femoral head.OBJECTIVE:To screen and validate the senescence core genes of hormonal osteonecrosis of the femoral head using bioinformatics analysis to explore its mechanism of action.METHODS:The GSE123568 dataset was obtained from the GPL15207 platform of the GEO database,which contained the gene expression profiles of peripheral serum samples of 30 hormonal osteonecrosis of the femoral head patients and 10 healthy controls.Data on 279 cellular senescence-related genes were obtained from the CellAge database.Differential analysis and weighted correlation network analysis(WGCNA)were performed on hormonal osteonecrosis of the femoral head gene profiles,and both were intersected with senescence-related genes and then concatenated to obtain hormonal osteonecrosis of the femoral head senescence potential genes,and GO and KEGG analyses were performed.The machine learning method screened out the pivotal genes,constructed nomogram model,and performed consensus clustering and immune infiltration analysis.Finally,clinical femoral samples were collected for validation by qPCR and western blot assay.RESULTS AND CONCLUSION:(1)41 potential genes were obtained,which were mainly enriched in biological processes such as aging and oxidative stress response,as well as FoxO and tumor necrosis factor signaling pathways.(2)The pivotal genes catalase,connective tissue growth factor,forkhead box protein O3,insulin receptor substrate 2,and mitogen-activated protein kinase kinase 11 were obtained after machine learning identification,and the predictive ability of nomogram model was good.(3)The patients were classified into three groups,namely a,b and c,by the consensus clustering analysis.Catalase,forkhead box protein O3,insulin receptor substrate 2,and mitogen-activated protein kinase kinase 11 were differentially expressed among the three molecular subtypes(P＜0.05).Results of immune infiltration showed that the abundance of immune cells,such as activated CD4+T cells,activated CD8+T cells,and eosinophils,differed among the three molecular subclasses(P＜0.05).(4)The results of qPCR and western blot assay showed that the expression of catalase,connective tissue growth factor,forkhead box protein O3,and mitogen-activated protein kinase kinase 11 was lower in hormonal osteonecrosis of the femoral head group compared to the control group(P＜0.05),and the expression of insulin receptor substrate 2 was elevated(P＜0.05).(5)It is concluded that through in-depth analysis combined with bioinformatics and machine learning,and further experimental verification,five hormonal osteonecrosis of the femoral head age-related hub genes were finally identified.These genes are catalase,connective tissue growth factor,forkhead box o3,insulin receptor substrate 2,and serine/threonine kinase 11.These genes may provide potential molecular targets for the prevention and treatment of hormonal osteonecrosis of the femoral head in the future by regulating the cellular aging process.
