文献类型：期刊文献

英文题名：Elucidating the molecular mechanisms of sepsis: Identifying key aging-related biomarkers and potential therapeutic targets in the treatment of sepsis

作者：Zhou, Jie[1];Liu, Jiao[2];Zhang, Chuanwu[3];Zhou, Yihua[1,6];Zheng, Zemao[4,7];Li, Haoguang[5,8]

机构：[1]Nanchang Univ, Affiliated Hosp 2, Jiangxi Med Coll, Dept Crit Care Med, Nanchang, Peoples R China;[2]Shaoxing Univ, Sch Med, Shaoxing, Peoples R China;[3]Gannan Med Univ, Ganzhou, Peoples R China;[4]Southern Med Univ, Nanfang Hosp, Dept Resp & Crit Care Med, Guangzhou, Peoples R China;[5]Nanchang Univ, Affiliated Hosp 2, Jiangxi Med Coll, Dept Rheumatol & Immunol, Nanchang, Peoples R China;[6]Nanchang Univ, Dept Crit Care Med, Affiliated Hosp 2, Nanchang 330000, Jiangxi, Peoples R China;[7]Southern Med Univ, Nanfang Hosp, Dept Resp & Crit Care Med, Guangzhou, Peoples R China;[8]Nanchang Univ, Affiliated Hosp 2, Jiangxi Med Coll, Dept Rheumatol & Immunol, Nanchang 330006, Peoples R China

年份：2024

卷号：39

期号：6

起止页码：3341

外文期刊名：ENVIRONMENTAL TOXICOLOGY

收录：SCI-EXPANDED(收录号：WOS:001179432100001)、、EI(收录号：20241115718361)、Scopus(收录号：2-s2.0-85186908019)、WOS

语种：英文

外文关键词：aging-related genes; Danshen (radix Salviae); diagnostic model; machine learning; sepsis

外文摘要：Background: Sepsis remains a crucial global health issue characterized by high mortality rates and a lack of specific treatments. This study aimed to elucidate the molecular mechanisms underlying sepsis and to identify potential therapeutic targets and compounds. Methods: High-throughput sequencing data from the GEO database (GSE26440 as the training set and GSE13904 and GSE32707 as the validation sets), weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, alongside a combination of PPI and machine learning methods (LASSO and SVM) were utilized. Results: WGCNA identified the black module as positively correlated, and the green module as negatively correlated with sepsis. Further intersections of these module genes with age-related genes yielded 57 sepsis-related genes. GO and KEGG pathway enrichment analysis, PPI, LASSO, and SVM selected six hub aging-related genes: BCL6, FOS, ETS1, ETS2, MAPK14, and MYC. A diagnostic model was constructed based on these six core genes, presenting commendable performance in both the training and validation sets. Notably, ETS1 demonstrated significant differential expression between mild and severe sepsis, indicating its potential as a biomarker of severity. Furthermore, immune infiltration analysis of these six core genes revealed their correlation with most immune cells and immune-related pathways. Additionally, compounds were identified in the traditional Chinese medicine Danshen, which upon further analysis, revealed 354 potential target proteins. GO and KEGG enrichment analysis of these targets indicated a primary enrichment in inflammation and immune-related pathways. A Venn diagram intersects these target proteins, and our aforementioned six core genes yielded three common genes, suggesting the potential efficacy of Danshen in sepsis treatment through these genes. Conclusions: This study highlights the pivotal roles of age-related genes in the molecular mechanisms of sepsis, offers potential biomarkers, and identifies promising therapeutic compounds, laying a robust foundation for future studies on the treatment of sepsis.