Screening and identification of tissue-infiltrating immune cells and genes for patients with emphysema phenotype of COPD

Identification of Oxidative Stress-Associated Biomarkers in Chronic Obstructive Pulmonary Disease: An Integrated Bioinformatics Analysis

Purpose

Chronic obstructive pulmonary disease (COPD) is among the three leading causes of death worldwide, with its prevalence, morbidity, and mortality rates increasing annually. Oxidative stress (OS) is a key mechanism in COPD development, making the identification of OS-related biomarkers beneficial for improving its diagnosis and treatment.

Methods

The genetic data from patients with COPD and controls were obtained from the Gene Expression Omnibus database to identify OS-related genes (OSRGs). Functional enrichment analysis was conducted using the Kyoto encyclopedia of genes and genomes signaling pathway and gene ontology (GO). Protein-protein interaction networks were constructed to identify the core genes, which were further evaluated using receiver operating characteristic (ROC) curves. Diagnostic models were developed based on the core genes. Besides, the correlation between the expression of the core genes and the immune cells was analyzed using single-sample gene set enrichment analysis. Drug-gene interactions were explored to predict target drugs, and related microribonucleic acid (miRNA) and transcription factors (TFs) were identified using miRNet.

Results

In this study, we identified 299 differential genes, including 16 OSRGs. Among these, five core genes-heat shock protein family A (Hsp70) member 1A (HSPA1A), glutamate-cysteine ligase modifier subunit, interleukin-1 beta (IL-1β), intercellular adhesion molecule 1 (ICAM1), and glutamate-cysteine ligase catalytic subunit (GCLC)-were screened and validated using ROC curve analysis. The results of GO enrichment analysis were mainly focused on the OS response, the negative regulation of the exogenous apoptosis signaling pathway, and the regulation of the apoptosis signaling pathway. Additionally, 33 target drugs were predicted, including ofloxacin, cisplatin, and pegolimumab, among others. Meanwhile, the regulatory networks comprising 33 miRNAs related to the core genes and 38 TFs associated with HSPA1A, IL-1β, ICAM1, and GCLC were constructed. A diagnostic model based on the five genes was constructed and validated with an area under the curve of 0.981 (95% confidence interval: 0.941-1.000).

Conclusion

This study identifies potential biomarkers for diagnosing COPD, new potential targets, and new directions for drug development and treatment.

Integrating bulk and single-cell RNA sequencing data: unveiling RNA methylation and autophagy-related signatures in chronic obstructive pulmonary disease patients

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous lung disease influenced by epigenetic modifications, particularly RNA methylation. Emerging evidence also suggests that autophagy plays a crucial role in immune cell infiltration and is implicated in COPD progression. This study aimed to investigate key RNA methylation regulators and explore the roles of RNA methylation and autophagy in COPD pathogenesis. We analyzed tissue-based bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) datasets from COPD and non-COPD patients, sourced from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified between COPD and non-COPD samples, and protein-protein interaction networks were constructed. Univariate logistic regression identified shared genes between DEGs and RNA methylation gene sets. Functional enrichment analyses, including Gene Ontology (GO), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA), were performed. Weighted gene co-expression network analysis (WGCNA) and immune infiltration analysis were conducted. Integration with scRNA-seq data further elucidated changes in immune cell composition, and cell communication analysis assessed interactions between macrophages and other immune cells. AddModuleScore analysis quantified RNA methylation and autophagy effects. Finally, a COPD mouse model was used to validate the expression of critical RNA methylation genes (FTO and IGF2BP2) in lung macrophages via RT-qPCR and flow cytometry. As revealed, we identified 13 RNA methylation-related genes enriched in translation and methylation processes. GSEA and GSVA revealed significant enrichment of these genes in immune and autophagy pathways. WGCNA analysis pinpointed key hub genes linking RNA methylation and autophagy. Integrated scRNA-seq analysis demonstrated a marked reduction of macrophages in COPD, with FTO and IGF2BP2 emerging as critical RNA methylation regulators. Macrophages with elevated RNA methylation and autophagy scores had increased interactions with other immune cells. In COPD mouse models, decreased expression of FTO and IGF2BP2 in lung macrophages was validated. Taken together, this study highlights the significant roles of RNA methylation in relation to autophagy pathways in the context of COPD. We identified key RNA methylation-related hub genes, such as FTO and IGF2BP2, which were found to have decreased expression in COPD macrophages. These findings provide novel genetic insights into the epigenetic mechanisms of COPD and suggest potential avenues for developing diagnostic and therapeutic strategies.

Identification and experimental validation of PYCARD as a crucial PANoptosis-related gene for immune response and inflammation in COPD

Chronic inflammatory and immune responses play key roles in the development and progression of chronic obstructive pulmonary disease (COPD). PANoptosis, as a unique inflammatory cell death modality, is involved in the pathogenesis of many inflammatory diseases. We aim to identify critical PANoptosis-related biomarkers and explore their potential effects on respiratory tract diseases and immune infiltration landscapes in COPD. Total microarray data consisting of peripheral blood and lung tissue datasets associated with COPD were obtained from the GEO database. PANoptosis-associated genes in COPD were identified by intersecting differentially expressed genes (DEGs) with genes involved in pyroptosis, apoptosis, and necroptosis after normalizing and removing the batch effect. Furthermore, GO, KEGG, PPI network, WGCNA, LASSO-COX, and ROC curves analysis were conducted to screen and verify hub genes, and the correlation between PYCARD and infiltrated immune cells was analyzed. The effect of PYCARD on respiratory tract diseases and the potential small-molecule agents for the treatment of COPD were identified. PYCARD expression was verified in the lung tissue of CS/LPS-induced COPD mice. PYCARD was a critical PANoptosis-related gene in all COPD patients. PYCARD was positively related to NOD-like receptor signaling pathway and promoted immune cell infiltration. Moreover, PYCARD was significantly activated in COPD mice mainly by targeting PANoptosis. PANoptosis-related gene PYCARD is a potential biomarker for COPD diagnosis and treatment.

Inflammatory and Immune Mechanisms in COPD: Current Status and Therapeutic Prospects

Background

Chronic obstructive pulmonary disease (COPD) currently ranks among the top three causes of mortality worldwide, presenting as a prevalent and complex respiratory ailment. Ongoing research has underscored the pivotal role of immune function in the onset and progression of COPD. The immune response in COPD patients exhibits abnormalities, characterized by diminished anti-infection capacity due to immune senescence, heightened activation of neutrophils and macrophages, T cell infiltration, and aberrant B cell activity, collectively contributing to airway inflammation and lung injury in COPD.

Objective

This review aimed to explore the pivotal role of the immune system in COPD and its therapeutic potential.

Methods

We conducted a review of immunity and COPD published within the past decade in the Web of Science and PubMed databases, sorting through and summarizing relevant literature.

Results

This article examines the pivotal roles of the immune system in COPD. Understanding the specific functions and interactions of these immune cells could facilitate the development of novel therapeutic strategies and interventions aimed at controlling inflammation, enhancing immune function, and mitigating the impact of respiratory infections in COPD patients.

Systematic analysis of various RNA transcripts and construction of biological regulatory networks at the post-transcriptional level for chronic obstructive pulmonary disease

BACKGROUND

Although chronic inflammation, oxidative stress, airway remodeling, and protease-antiprotease imbalance have been implicated in chronic obstructive pulmonary disease (COPD), the exact pathogenesis is still obscure. Gene transcription and post-transcriptional regulation have been taken into account as key regulators of COPD occurrence and development. Identifying the hub genes and constructing biological regulatory networks at the post-transcriptional level will help extend current knowledge on COPD pathogenesis and develop potential drugs.

METHODS

All lung tissues from non-smokers (n = 6), smokers without COPD (smokers, n = 7), and smokers with COPD (COPD, n = 7) were collected to detect messenger RNA (mRNA), microRNA (miRNA), circular RNA (circRNA), and long non-coding RNA (lncRNA) expression and identify the hub genes. Biological regulatory networks were constructed at the post-transcriptional level, including the RNA-binding protein (RBP)-hub gene interaction network and the competitive endogenous RNA (ceRNA) network. In addition, we assessed the composition and abundance of immune cells in COPD lung tissue and predicted potential therapeutic drugs for COPD. Finally, the hub genes were confirmed at both the RNA and protein levels.

RESULTS

Among the 20 participants, a total of 121169 mRNA transcripts, 1871 miRNA transcripts, 4244 circRNA transcripts, and 122130 lncRNA transcripts were detected. There were differences in the expression of 1561 mRNAs, 48 miRNAs, 33 circRNAs, and 545 lncRNAs between smokers and non-smokers, as well as 1289 mRNAs, 69 miRNAs, 32 circRNAs, and 433 lncRNAs between smokers and COPD patients. 18 hub genes were identified in COPD. TGF-β signaling and Wnt/β-catenin signaling may be involved in the development of COPD. Furthermore, the circRNA/lncRNA-miRNA-mRNA ceRNA networks and the RBP-hub gene interaction network were also constructed. Analysis of the immune cell infiltration level revealed that M2 macrophages and activated NK cells were increased in COPD lung tissues. Finally, we identified that the ITK inhibitor and oxybutynin chloride may be effective in treating COPD.

CONCLUSIONS

We identified several novel hub genes involved in COPD pathogenesis. TGF-β signaling and Wnt/β-catenin signaling were the most dysregulated pathways in COPD patients. Our study constructed post-transcriptional biological regulatory networks and predicted small-molecule drugs for the treatment of COPD, which enhanced the existing understanding of COPD pathogenesis and suggested an innovative direction for the therapeutic intervention of the disease.

Multi-omics and immune cells' profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of Predictive, Preventive, and Personalized Medicine

Background

Intensive care unit admission (ICUA) triage has been urgent need for solving the shortage of ICU beds, during the coronavirus disease 2019 (COVID-19) surge. In silico analysis and integrated machine learning (ML) approach, based on multi-omics and immune cells (ICs) profiling, might provide solutions for this issue in the framework of predictive, preventive, and personalized medicine (PPPM).

Methods

Multi-omics was used to screen the synchronous differentially expressed protein-coding genes (SDEpcGs), and an integrated ML approach to develop and validate a nomogram for prediction of ICUA. Finally, the independent risk factor (IRF) with ICs profiling of the ICUA was identified.

Results

Colony-stimulating factor 1 receptor (CSF1R) and peptidase inhibitor 16 (PI16) were identified as SDEpcGs, and each fold change (FC_ij) of CSF1R and PI16 was selected to develop and validate a nomogram to predict ICUA. The area under curve (AUC) of the nomogram was 0.872 (95% confidence interval (CI): 0.707 to 0.950) on the training set, and 0.822 (95% CI: 0.659 to 0.917) on the testing set. CSF1R was identified as an IRF of ICUA, expressed in and positively correlated with monocytes which had a lower fraction in COVID-19 ICU patients.

Conclusion

The nomogram and monocytes could provide added value to ICUA prediction and targeted prevention, which are cost-effective platform for personalized medicine of COVID-19 patients. The log₂fold change (log₂FC) of the fraction of monocytes could be monitored simply and economically in primary care, and the nomogram offered an accurate prediction for secondary care in the framework of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00317-5.