Author:
Chen Liyi,Liu Chong,Liang Tuo,Ye Zhen,Huang Shengsheng,Chen Jiarui,Sun Xuhua,Yi Ming,Zhou Chenxing,Jiang Jie,Chen Tianyou,Li Hao,Chen Wuhua,Guo Hao,Chen Wenkang,Yao Yuanlin,Liao Shian,Yu Chaojie,Wu Shaofeng,Fan Binguang,Gan Zhaoping,Zhan Xinli
Abstract
PurposeThe purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB).MethodsClinical data were collected to construct a nomogram model. C-index, calibration curve, ROC curve, and DCA curve were used to assess the predictive ability and accuracy of the model. Additionally, 10 intervertebral disc samples were collected for protein identification. Bioinformatics was used to analyze differentially expressed proteins (DEPs), including immune cells analysis, Gene Ontology (GO) and KEGG pathway enrichment analysis, and protein-protein interaction networks (PPI).ResultsThe nomogram predicted risk of STB ranging from 0.01 to 0.994. The C-index and AUC in the training set were 0.872 and 0.862, respectively. The results in the external validation set were consistent with the training set. Immune cells scores indicated that B cells naive in STB tissues were significantly lower than non-TB spinal tissues. Hub proteins were calculated by Degree, Closeness, and MCC methods. The main KEGG pathway included Coronavirus disease-COVID-19. There were 9 key proteins in the intersection of COVID-19-related proteins and hub proteins. There was a negative correlation between B cells naive and RPL19. COVID-19-related proteins were associated with immune genes.ConclusionLymphocytes were predictive factors for the diagnosis of STB. Immune cells showed low expression in STB. Nine COVID-19-related proteins were involved in STB mechanisms. These nine key proteins may suppress the immune mechanism of STB by regulating the expression of immune genes.
Funder
National Natural Science Foundation of China
Subject
Immunology,Immunology and Allergy
Cited by
5 articles.
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