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利用大规模人类转录组的综合生物信息学分析和实验验证的新见解:自噬在牙周炎中的作用
Authors Liu F , Zhu Z, Zou H, Huang Z, Xiao S, Li Z
Received 1 October 2024
Accepted for publication 21 December 2024
Published 30 December 2024 Volume 2024:17 Pages 11861—11880
DOI https://doi.org/10.2147/JIR.S492048
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Tara Strutt
Fen Liu,1 Zhipeng Zhu,1 Huaxi Zou,2 Zhen Huang,1 Shengkai Xiao,1 Zhihua Li1
1School of Stomatology, Jiangxi Medical College, Nanchang University, Jiangxi Provincial Key Laboratory of Oral Diseases, Jiangxi Provincial Clinical Research Center for Oral Disease, Nanchang, Jiangxi, People’s Republic of China; 2Department of Cardiovascular Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
Correspondence: Zhihua Li, School of Stomatology, Jiangxi Medical College, Nanchang University, Jiangxi Provincial Key Laboratory of Oral Diseases, Jiangxi Provincial Clinical Research Center for Oral Disease, No. 49 FuZhou Road, Nanchang, Jiangxi, People’s Republic of China, Email lwlq323@163.com
Objective: Autophagy plays a crucial role in the pathophysiology of periodontitis, yet its precise involvement in the disease process remains elusive. The aim of the present study was thus to investigate the involvement of autophagy in the pathology of periodontitis. This investigation involved transcriptomic analysis of a broad range of human samples and complemented by in vitro experimentation.
Materials and Methods: We analyzed the transcriptomes of human gingival tissues from individuals with periodontitis and health controls to identify the differential expression of autophagy-related genes (DEARGs) and to investigate their potential interactions and functional pathways. Additionally, protein-protein interaction (PPI) networks were constructed to identify key functional modules and hub genes. Experimental validation of autophagy regulation in periodontitis and identification of key autophagy-regulating genes was accomplished through in vitro cellular experiments. Subsequently, a comprehensive analysis of immune cell infiltrate utilizing the CIBERSORT algorithm was performed. Finally, leveraging the DSigDB database, potential candidate drugs for periodontitis treatment targeting autophagy were predicted.
Results: A total of 79 genes have been identified as DEARGs in periodontitis. An intricate interplay among the DEARGs and their impact on the regulatory mechanisms of autophagy within the context of periodontitis was observed. Subsequently, 10 hub genes were discerned through the establishment of a PPI network. Furthermore, dysregulated autophagic activity in periodontitis was validated, and 9 key genes (APP, KDR, IL1B, CXCL12, CXCR4, IL6, FOS, LCK, and SHC1) were identified through in vitro experiments. Our analysis unveiled an association between these genes and altered immune cell infiltration in periodontitis. Additionally, we predicted potential therapeutic agents such as curcumin, 27-hydroxycholesterol, and Trolox, showing promise in the treatment of periodontitis by modulating the autophagic process.
Conclusion: This study identified nine key genes for autophagy regulation and potential therapeutic agents in periodontitis. These findings not only enhance our comprehension of the pathological mechanisms of periodontitis but also provide substantial evidence for the advancement of novel therapeutic strategies.
Keywords: periodontitis, transcriptome, autophagy, key genes, diagnosis, database