Int J Stomatol

Int J Stomatol ›› 2023, Vol. 50 ›› Issue (6): 661-668.doi: 10.7518/gjkq.2023082

• Original Articles • Previous Articles     Next Articles

Relationship between periodontitis and ferroptosis based on bioinformatics analysis

Luo Xiaojie1,2(),Wang Dexu1,2,Chen Xiaotao2()   

  1. 1.Dept. of Stomatology, Xinjiang Medical University, Urumqi 830000, China
    2.Dept. of Stomatology, Xinjiang Uygur Autonomous Region People’s Hospital, Urumqi 830000, China
  • Received:2023-02-18 Revised:2023-06-06 Online:2023-11-01 Published:2023-10-24
  • Contact: Xiaotao Chen E-mail:453540314@qq.com;xiaotaochen@163.com
  • Supported by:
    National Natural Science Foundation of China(82260195);Xinjiang Uygur Autonomous Region Regional Collaborative Innovation Special Fund(2021E02071)

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Abstract:

Objective Bioinformatics methods were used to investigate the correlation between periodontitis and ferroptosis. Methods Dataset GS16134 was downloaded from the GEO database, and the driver and suppressor genes of ferroptosis were downloaded from the ferroptosis database (FerrDb). R soft was used to standardize the data, and the “limma” package was used to screen for differentially expressed genes in periodontitis (P<0.05). GO and KEGG analyses were conducted to analyze the differentially expressed genes and identify their main functions and pathways. Protein interaction network was used to screen for key mRNAs. Results Fifty differentially expressed ferroptosis regulatory genes were screened in periodontitis gingival tissue samples and healthy gingival tissue samples. Results of GO function and KEGG pathway analyses showed that the differentially expressed genes mainly participated in the oxidative stress reaction, and they were mainly concentrated in the ferroptosis pathway. Conclusion Ferroptosis regulatory genes were differentially expressed in periodontitis tissue samples, and these genes primarily functioned in the oxidative stress and iron metabolism pathways, indicating a correlation between the two. Ferroptosis may affect inflammation and even bone remodeling of alveolar bone through lipid peroxidation, as well as abnormal iron metabolism. This study provides new insights into the mechanism of periodontitis development.

Key words: periodontitis, ferroptosis, bioinformation

CLC Number: 

  • R 781.4+2

TrendMD: 

Fig 1

Differential expression of iron death regulatory genes in inflammatory and healthy samples"

Fig 2

GO analysis results of differentially expressed genes"

Tab 1

Results of differentially expressed genes"

GO IDGO TermGene ID数目
GO:006979氧化应激反应ALOX5/SLC7A11/TLR4/NFE2L2/PRDX6/EPAS1/DUOX1/MYB/CYBB/DUOX2/NQO1/GCLC/HMOX1/GCH1/MAP1LC3A15
GO:0062197细胞化学应激反应ALOX5/SLC7A11/TLR4/NFE2L2/PRDX6/EPAS1/MYB/CYBB/ATM/NQO1/HMOX1/GCH1/MAP1LC3A13
GO:0034599细胞氧化应激反应ALOX5/SLC7A11/TLR4/NFE2L2/PRDX6/EPAS1/MYB/CYBB/NQO1/HMOX1/GCH1/MAP1LC3A12

Fig 3

KEGG pathway analysis results of differentially expressed genes"

Tab 2

Pathway analysis results of differentially expressed genes"

Pathway ID描述Gene ID数目
hsa04216铁死亡通路FTH1/SLC7A11/SAT1/CYBB/VDAC2/NCOA4/ACSL4/SLC40A1/SLC3A2/GCLC/HMOX1/ACSL3/MAP1LC3A13
hsa04621NOD样受体信号通路TLR4/CYBB/VDAC2/PANX1/ATG16L1/MAPK14/MAP1LC3A7
hsa04140自噬通路ATG3/ATG16L1/ULK1/WIPI1/HRAS/MAP1LC3A6

Fig 4

Main roles of some differential genes in KEGG iron death pathway"

Fig 5

PPI network of differentially expressed genes constructed by Cytoscape"

Fig 6

15 key genes obtained through the ECODE plugin in Cytoscape"

"

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