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

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

Shuyang Chen  1 , Zheng Zhou  1 , Yajun Wang  1 , Shujing Chen  1 , Jinjun Jiang  2
Affiliations

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

Shuyang Chen et al. Clin Respir J. 2023 Jul.

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.

Keywords: ARDS; bioinformatics; cuproptosis; severe CAP.

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Conflict of interest statement

The authors declare no conflicts of interest.

This research was conducted using publically available datasets. No potentially identifiable human images or data is presented in this study.

Figures

FIGURE 1
FIGURE 1
Heat map of differentially expressed cuproptosis‐related genes in GSE196399. The red color indicates an upregulation, whereas the green color indicates an downregulation.
FIGURE 2
FIGURE 2
Immune infiltration analysis. (A) Heat map of immune pathways in severe community‐acquired pneumonia (SCAP) group and control group. (B) Heat map of immune cells in SCAP group and control group. (C) Correlation matrix of immune cells. (D) Correlation matrix of immune pathways. (E) Comparison of the degree of immune cell infiltration in SCAP group and control group. (F) Comparison of the degree of immune pathways involvement in SCAP group and control group. (G) Correlation analysis of cuproptosis‐related genes and immune cells and pathways. *p < 0.05, **p < 0.01, ***p < 0.001, ns no significance.
FIGURE 3
FIGURE 3
Machine learning‐based selection and identification of cuproptosis‐related genes (CRGs) associated with severe community‐acquired pneumonia (SCAP). (A) Ten cross‐validations of adjusted parameter selection in the least absolute shrinkage and selection operator (LASSO) model. (B) LASSO coefficient analysis. (C) Ranking of the relative importance of genes. (D) Venn diagram indicating the characteristic genes selected by LASSO, random forest and SVM‐RFE algorithms. (E) Comparison of signature gene expression between SCAP group and control group. (F, G, H) ROC curves for estimating the diagnostic performance of the signature genes DLD, GCSH, and LIPT1. *p < 0.05, **p < 0.01, ***p < 0.001, ns no significance.
FIGURE 4
FIGURE 4
Diagnostic efficacy of signature cuproptosis‐related genes. (A) Receiver operating characteristic (ROC) curve indicating the diagnostic efficacy of signature genes. (B) Construction of column line graph integrating signature cuproptosis‐related genes (CRGs) for severe community‐acquired pneumonia (SCAP).
See this image and copyright information in PMC

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