Sun Peng, Wang Yue, Zhou Sijing, Liang Jiahui, Zhang Binbin, Li Pulin, Han Rui, Fei Guanghe, Cao Chao, Wang Ran
Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Department of Infectious Diseases, Hefei Second People's Hospital, Hefei, China.
Front Cell Infect Microbiol. 2024 Nov 21;14:1453466. doi: 10.3389/fcimb.2024.1453466. eCollection 2024.
In recent years, COVID-19 and tuberculosis have emerged as major infectious diseases, significantly contributing to global mortality as respiratory illnesses. There is increasing evidence of a reciprocal influence between these diseases, exacerbating their incidence, severity, and mortality rates.
This study involved retrieving COVID-19 and tuberculosis data from the GEO database and identifying common differentially expressed genes. Machine learning techniques, specifically random forest analysis, were applied to pinpoint key genes for diagnosing COVID-19. The Cibersort algorithm was employed to estimate immune cell infiltration in individuals with COVID-19. Additionally, single-cell sequencing was used to study the distribution of VNN1 within immune cells, and molecular docking provided insights into potential drugs targeting these critical prognosis genes.
GMNN, SCD, and FUT7 were identified as robust diagnostic markers for COVID-19 across training and validation datasets. Importantly, VNN1 was associated with the progression of severe COVID-19, showing a strong correlation with clinical indicators and immune cell infiltration. Single-cell sequencing demonstrated a predominant distribution of VNN1 in neutrophils, and molecular docking highlighted potential pharmacological targets for VNN1.
This study enhances our understanding of the shared pathogenic mechanisms underlying tuberculosis and COVID-19, providing essential insights that could improve the diagnosis and treatment of severe COVID-19 cases.
近年来，新冠病毒病和结核病已成为主要的传染病，作为呼吸系统疾病，对全球死亡率有重大影响。越来越多的证据表明这两种疾病之间存在相互影响，加剧了它们的发病率、严重程度和死亡率。
本研究涉及从基因表达综合数据库（GEO数据库）中检索新冠病毒病和结核病的数据，并识别共同的差异表达基因。应用机器学习技术，特别是随机森林分析，来确定诊断新冠病毒病的关键基因。采用Cibersort算法估计新冠病毒病患者的免疫细胞浸润情况。此外，利用单细胞测序研究VNN1在免疫细胞中的分布，分子对接为靶向这些关键预后基因的潜在药物提供了见解。
在训练和验证数据集中，GMNN、SCD和FUT7被确定为新冠病毒病的可靠诊断标志物。重要的是，VNN1与重症新冠病毒病的进展相关，与临床指标和免疫细胞浸润有很强的相关性。单细胞测序显示VNN1在中性粒细胞中占主导分布，分子对接突出了VNN1的潜在药理学靶点。
本研究增进了我们对结核病和新冠病毒病共同致病机制的理解，提供了有助于改善重症新冠病毒病病例诊断和治疗的重要见解。
