基于机器学习的严重社区获得性肺炎中铜死亡相关标志物和免疫浸润的鉴定。

Machine learning-based identification of cuproptosis-related markers and immune infiltration in severe community-acquired pneumonia.

机构信息

Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.

Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.

出版信息

Clin Respir J. 2023 Jul;17(7):618-628. doi: 10.1111/crj.13633. Epub 2023 Jun 6.

DOI:10.1111/crj.13633

PMID:37279744

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10363779/

Abstract

BACKGROUND

Severe community-acquired pneumonia (SCAP) is one of the world's most common diseases and a major etiology of acute respiratory distress syndrome (ARDS). Cuproptosis is a novel form of regulated cell death that can occur in various diseases.

METHODS

Our study explored the degree of immune cell infiltration during the onset of severe CAP and identified potential biomarkers related to cuproptosis. Gene expression matrix was obtained from GEO database indexed GSE196399. Three machine learning algorithms were applied: The least absolute shrinkage and selection operator (LASSO), the random forest, and the support vector machine-recursive feature elimination (SVM-RFE). Immune cell infiltration was quantified by single-sample gene set enrichment analysis (ssGSEA) scoring. Nomogram was constructed to verify the applicability of using cuproptosis-related genes to predict the onset of severe CAP and its deterioration toward ARDS.

RESULTS

Nine cuproptosis-related genes were differentially expressed between the severe CAP group and the control group: ATP7B, DBT, DLAT, DLD, FDX1, GCSH, LIAS, LIPT1, and SLC31A1. All 13 cuproptosis-related genes were involved in immune cell infiltration. A three-gene diagnostic model was constructed to predict the onset of severe CAP: GCSH, DLD, and LIPT1.

CONCLUSION

Our study confirmed the involvement of the newly discovered cuproptosis-related genes in the progression of SCAP.

摘要

背景

严重社区获得性肺炎（SCAP）是世界上最常见的疾病之一，也是急性呼吸窘迫综合征（ARDS）的主要病因。铜死亡是一种新发现的细胞死亡形式，可发生在各种疾病中。

方法

本研究探讨了严重 CAP 发病时免疫细胞浸润的程度，并确定了与铜死亡相关的潜在生物标志物。从 GEO 数据库中获取基因表达矩阵，索引为 GSE196399。应用三种机器学习算法：最小绝对收缩和选择算子（LASSO）、随机森林和支持向量机递归特征消除（SVM-RFE）。通过单样本基因集富集分析（ssGSEA）评分量化免疫细胞浸润。构建列线图以验证使用铜死亡相关基因预测严重 CAP 发病及其向 ARDS 恶化的适用性。