Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 May 21:12:670971.
doi: 10.3389/fimmu.2021.670971. eCollection 2021.

Identification of Inflammation-Related Biomarker Lp-PLA2 for Patients With COPD by Comprehensive Analysis

Mingming Deng  1   2   3   4   5 , Yan Yin  6 , Qin Zhang  6 , Xiaoming Zhou  7 , Gang Hou  1   2   3   4   5
Affiliations

Identification of Inflammation-Related Biomarker Lp-PLA2 for Patients With COPD by Comprehensive Analysis

Mingming Deng et al. Front Immunol. .

Abstract

Purpose: Chronic obstructive pulmonary disease (COPD) is a complex and persistent lung disease and lack of biomarkers. The aim of this study is to screen and verify effective biomarkers for medical practice.

Methods: Differential expressed genes analysis and weighted co-expression network analysis were used to explore potential biomarker. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene set enrichment analysis (GSEA) analysis were used to explore potential mechanism. CIBERSORTx website was used to evaluate tissue-infiltrating immune cells. Enzyme-linked immunosorbent assay (ELISA) was used to assess the concentrations of the Lp-PLA2 in serum.

Results: Ten genes were selected via combined DEGs and WGCNA. Furthermore, PLA2G7 was choose based on validation from independent datasets. Immune infiltrate and enrichment analysis suggest PLA2G7 may regulate immune pathway via macrophages. Next, Lp-PLA2(coded by PLA2G7 gene) level was upregulated in COPD patients, increased along with The Global Average of COPD (GOLD) stage. In additional, Lp-PLA2 level was significant correlate with FEV1/FVC, BMI, FFMI, CAT score, mMRC score and 6MWD of COPD patients. Finally, the predictive efficiency of Lp-PLA2 level (AUC:0.796) and derived nomogram model (AUC:0.884) in exercise tolerance was notably superior to that of the sit-to-stand test and traditional clinical features.

Conclusion: Lp-PLA2 is a promising biomarker for COPD patients and is suitable for assessing exercise tolerance in clinical practice.

Keywords: COPD; Lp-PLA2; PLA2G7; biomarker; exercise tolerance.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Differential expressed genes analysis and Volcano map (A) and heatmap (B) of differential expressed genes; (C) Chord plot depicting the relationship between genes and Gene ontology (GO) terms of biological process; (D) GSEA showed eight pathways enriched in COPD patients.
Figure 2
Figure 2
Analysis of immune landscape associated with COPD. Heatmap (A) and violin plot (B) showing the distribution of 22 types of immune cells in normal smoker and COPD patients in GSE76925; (C) The relationship between FVC% predicted and immune cell infiltration level; red: statistically significant (P < 0.05); (D) The relationship between FEV1/FVC and immune cell infiltration level; red: statistically significant (P < 0.05).
Figure 3
Figure 3
Weighted co-expression network analysis. (A) Sample dendrogram and trait heat map; (B) Analysis of the scale-free fit index (left) and the mean connectivity (right) for various soft-thresholding powers; (C) Clustering dendrograms of genes based on a dissimilarity measure (1-TOM); (D) Module-trait associations were evaluated by correlations between module eigengenes and sample traits. (E–G) Scatterplot of Gene Significance for COPD, FEV1/FVC, FEV1% predicted vs. Module Membership in brown module.
Figure 4
Figure 4
Selection and validation of hub gene. (A) Venn diagrams to indicate 10 shared genes from brown module and DEGs; (B) Validation of hub genes in the dataset GSE38974; (C) Validation of hub genes in the dataset GSE69818.
Figure 5
Figure 5
Function analysis and validation of PLA2G7. (A) The relationship between PLA2G7 and the clinical characteristics (age, BMI, FVC%predicted and FEV1/FVC) of COPD patients; (B) GSEA results showed that several immune-related pathways were significantly associated with PLA2G7; (C) Relationship between PLA2G7 expression and immune cell infiltration level; red: statistically significant (P < 0.05); (D) PLA2G7 expression was increased in blood of COPD patients based on GSE42057 (left) and GSE56766 (right); (E) PLA2G7 expression were up-regulated in alveolar macrophages of COPD samples based on GSE130928 (left) and GSE13896 (right).
Figure 6
Figure 6
The clinical value of Lp-PLA2 level for COPD patients. (A) Lp-PLA2 level in serum tended to be higher in COPD patients than healthy smokers; (B) Lp-PLA2 level increase along with GOLD stage; (C) Lp-PLA2 level was negative correlate with FEV1/FVC (r=-0.528, p<0.001); (D) Lp-PLA2 level was positive correlate with mMRC score (r=0.339, p<0.001) (left) and CAT score (r=0.339, p<0.001) (right); (E) Lp-PLA2 level was negative correlate with BMI (r=-0.312, p=0.002) (left) and FFMI (r=-0.336, p=0.002) (right); (F) Lp-PLA2 level was negative correlate with 6MWD (r=-0.578, p=0.002); (G) ROC curve analysis of the Lp-PLA2 level, the 5STS score and the 30STS score for predicting 6MWD <350 m. ns, not statistically significant, **P < 0.01 and ***P < 0.001.
Figure 7
Figure 7
Construction of nomogram model. (A) Nomogram predicting 6MWD<350m for COPD patients; (B) Calibration curves for nomogram predicted 6MWD<350m for COPD patients; (C) ROC curve analysis show highest AUC value was seen for the nomogram model; (D) Decision curve analysis (DCA) shows the net benefit in 6MWD<350m predictions was the highest in nomogram model.
See this image and copyright information in PMC

References

    1. Rabe KF, Watz H. Chronic Obstructive Pulmonary Disease. Lancet (London England) (2017) 389:1931–40. 10.1016/S0140-6736(17)31222-9 - DOI - PubMed
    1. Nguyen JMK, Robinson DN, Sidhaye VK. Why New Biology Must be Uncovered to Advance Therapeutic Strategies for Chronic Obstructive Pulmonary Disease. Am J Physiol Lung Cell Mol Physiol (2020) 320(1):L1–11. 10.1152/ajplung.00367.2020 - DOI - PMC - PubMed
    1. Matsunaga K, Harada M, Suizu J, Oishi K, Asami-Noyama M, Hirano T. Comorbid Conditions in Chronic Obstructive Pulmonary Disease: Potential Therapeutic Targets for Unmet Needs. J Clin Med (2020) 9:3078–98. 10.3390/jcm9103078 - DOI - PMC - PubMed
    1. O’Donnell DE, James MD, Milne KM, Neder JA. The Pathophysiology of Dyspnea and Exercise Intolerance in Chronic Obstructive Pulmonary Disease. Clin Chest Med (2019) 40:343–66. 10.1016/j.ccm.2019.02.007 - DOI - PubMed
    1. Agarwala P, Salzman SH. Six-Minute Walk Test: Clinical Role, Technique, Coding, and Reimbursement. Chest (2020) 157:603–11. 10.1016/j.chest.2019.10.014 - DOI - PMC - PubMed

Publication types

MeSH terms

Substances