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已发表论文
通过孟德尔随机化与免疫浸润分析筛选哮喘与脓毒症的共诊断基因及潜在治疗靶点
Authors Chen C, Li X, Zhang X, Hu M, Yang M , Yuan Z, Yao S, Qin S, Qin Y, Xiao Y
Received 30 June 2025
Accepted for publication 28 November 2025
Published 10 December 2025 Volume 2025:18 Pages 1689—1711
DOI https://doi.org/10.2147/JAA.S550648
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Luis Garcia-Marcos
Changhan Chen,1,2,* Xinyi Li,3,* Xupeng Zhang,3 Manlin Hu,3 Meng Yang,3 Zhangchi Yuan,3 Shanhu Yao,4 Sha Qin,5 Yuexiang Qin,1,6 Yuyang Xiao1,3
1Department of Otolaryngology and Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 2National Medical Metabolomics International Collaborative Research Center, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 3Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China; 4Department of Radiology, the Third Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 5Clinical Laboratory of Shenzhen Children’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 6Health Management Medicine Center, the Third Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yuexiang Qin, Department of Otolaryngology and Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China, Email 602466@csu.edu.cn Yuyang Xiao, Department of Otolaryngology and Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China, Email 8303211701@csu.edu.cn
Background: Evidence suggests a bidirectional relationship between asthma and sepsis, but the mechanisms are unclear. This study explores the co-diagnostic genes and molecular links between asthma and sepsis using Mendelian Randomization (MR) and bioinformatics methods, and further validates the findings through clinical data, aiming to identify potential therapeutic targets and drugs.
Methods: Protein Quantitative Trait Loci (pQTL) data from an Icelandic population were used for two-sample MR analysis. Differential expression analysis identified common genes. Gene Ontology and KEGG enrichment analyses explored biological functions. Receiver Operating Characteristic (ROC) curves validated gene performance, and immune cell infiltration was analyzed using CIBERSORT. Drug targets were predicted using DrugSigDB and validated by molecular docking. Clinical data of the three groups of people were collected, and baseline analysis, ROC curve analysis, and comparison of the expression levels of CXCL8 were carried out.
Results: At the genetic level, 435 genes related to asthma and 1,385 genes related to sepsis were identified, with 141 common genes. Further findings showed that there were 247 differentially expressed genes in asthma and 2,878 differentially expressed genes in sepsis, and 65 common differentially expressed genes were enriched in immune and inflammatory pathways. The key gene CXCL8 exhibited excellent diagnostic performance and was closely related to immune cell subsets. Based on the research results, ten potential therapeutic drugs were screened, and seven of them were verified by molecular docking. Clinical sample testing results show that CXCL8 is closely related to asthma and sepsis.
Conclusion: CXCL8 may be a biomarker for both asthma and sepsis. Immune cells like monocytes, macrophages, and T cells may drive comorbid progression. Potential drug candidates were identified, offering new insights for future research on these diseases.
Keywords: MR, machine learning, molecular docking, asthma, sepsis, CXCL8
1Department of Otolaryngology and Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 2National Medical Metabolomics International Collaborative Research Center, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 3Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China; 4Department of Radiology, the Third Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 5Clinical Laboratory of Shenzhen Children’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 6Health Management Medicine Center, the Third Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yuexiang Qin, Department of Otolaryngology and Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China, Email 602466@csu.edu.cn Yuyang Xiao, Department of Otolaryngology and Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China, Email 8303211701@csu.edu.cn
Background: Evidence suggests a bidirectional relationship between asthma and sepsis, but the mechanisms are unclear. This study explores the co-diagnostic genes and molecular links between asthma and sepsis using Mendelian Randomization (MR) and bioinformatics methods, and further validates the findings through clinical data, aiming to identify potential therapeutic targets and drugs.
Methods: Protein Quantitative Trait Loci (pQTL) data from an Icelandic population were used for two-sample MR analysis. Differential expression analysis identified common genes. Gene Ontology and KEGG enrichment analyses explored biological functions. Receiver Operating Characteristic (ROC) curves validated gene performance, and immune cell infiltration was analyzed using CIBERSORT. Drug targets were predicted using DrugSigDB and validated by molecular docking. Clinical data of the three groups of people were collected, and baseline analysis, ROC curve analysis, and comparison of the expression levels of CXCL8 were carried out.
Results: At the genetic level, 435 genes related to asthma and 1,385 genes related to sepsis were identified, with 141 common genes. Further findings showed that there were 247 differentially expressed genes in asthma and 2,878 differentially expressed genes in sepsis, and 65 common differentially expressed genes were enriched in immune and inflammatory pathways. The key gene CXCL8 exhibited excellent diagnostic performance and was closely related to immune cell subsets. Based on the research results, ten potential therapeutic drugs were screened, and seven of them were verified by molecular docking. Clinical sample testing results show that CXCL8 is closely related to asthma and sepsis.
Conclusion: CXCL8 may be a biomarker for both asthma and sepsis. Immune cells like monocytes, macrophages, and T cells may drive comorbid progression. Potential drug candidates were identified, offering new insights for future research on these diseases.
Keywords: MR, machine learning, molecular docking, asthma, sepsis, CXCL8