Identification of Key Signaling Pathways and Genes in Eosinophilic Asthma and Neutrophilic Asthma by Weighted Gene Co-Expression Network Analysis

Assessing prospective molecular biomarkers and functional pathways in severe asthma based on a machine learning method and bioinformatics analyses

BACKGROUND

Severe asthma, which differs significantly from typical asthma, involves specific molecular biomarkers that enhance our understanding and diagnostic capabilities. The objective of this study is to assess the biological processes underlying severe asthma and to detect key molecular biomarkers.

METHODS

We used Weighted Gene Co-Expression Network Analysis (WGCNA) to detect hub genes in the GSE143303 dataset and indicated their functions and regulatory mechanisms using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) annotations. In the GSE147878 dataset, we used Gene Set Enrichment Analysis (GSEA) to determine the regulatory directions of gene sets. We detected differentially expressed genes in the GSE143303 and GSE64913 datasets, constructed a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and validated the model using the GSE147878 dataset and real-time quantitative PCR (RT-qPCR) to confirm the molecular biomarkers.

RESULTS

Using WGCNA, we discovered modules that were strongly correlated with clinical features, specifically the purple module (r = 0.53) and the midnight blue module (r = -0.65). The hub genes within these modules were enriched in pathways related to mitochondrial function and oxidative phosphorylation. GSEA in the GSE147878 dataset revealed significant enrichment of upregulated gene sets associated with oxidative phosphorylation and downregulated gene sets related to asthma. We discovered 12 commonly regulated genes in the GSE143303 and GSE64913 datasets and developed a LASSO regression model. The model corresponding to lambda.min selected nine genes, including TFCP2L1, KRT6A, FCER1A, and CCL5, which demonstrated predictive value. These genes were significantly upregulated or under expressed in severe asthma, as validated by RT-qPCR.

CONCLUSION

Mitochondrial abnormalities affecting oxidative phosphorylation play a critical role in severe asthma. Key molecular biomarkers like TFCP2L1, KRT6A, FCER1A, and CCL5, are essential for detecting severe asthma. This research significantly enhances the understanding and diagnosis of severe asthma.

Exosomes in asthma: Underappreciated contributors to the pathogenesis and novel therapeutic tools

OBJECTIVE

Asthma, a chronic inflammatory disease with diverse pathomechanisms, presents challenges in developing personalized diagnostic and therapeutic approaches. This review aims to provide a comprehensive overview of the role of exosomes, small extracellular vesicles, in asthma pathophysiology and explores their potential as diagnostic biomarkers and therapeutic tools.

METHODS

A literature search was conducted to identify recent studies investigating the involvement of exosomes in asthma. The retrieved articles were analyzed to extract relevant information on the role of exosomes in maintaining lung microenvironment homeostasis, regulating inflammatory responses, and their diagnostic and therapeutic potential for asthma.

RESULTS

Exosomes secreted by various cell types, have emerged as crucial mediators of intercellular communication in healthy and diseased conditions. Evidence suggest that exosomes play a significant role in maintaining lung microenvironment homeostasis and contribute to asthma pathogenesis by regulating inflammatory responses. Differential exosomal content between healthy individuals and asthmatics holds promise for the development of novel asthma biomarkers. Furthermore, exosomes secreted by immune and nonimmune cells, as well as those detected in biofluids, demonstrate potential in promoting or regulating immune responses, making them attractive candidates for designing new treatment strategies for inflammatory conditions such as asthma.

CONCLUSION

Exosomes, with their ability to modulate immune responses and deliver therapeutic cargo, offer potential as targeted therapeutic tools in asthma management. Further research and clinical trials are required to fully understand the mechanisms underlying exosome-mediated effects and translate these findings into effective diagnostic and therapeutic strategies for asthma patients.

Bioinformatic analysis and experimental validation of the potential gene in the airway inflammation of steroid-resistant asthma

Steroid-resistant asthma is a troublesome clinical problem in public health. The pathogenesis of steroid-resistant asthma is complex and remains to be explored. In our work, the online Gene Expression Omnibus microarray dataset GSE7368 was used to explore differentially expressed genes (DEGs) between steroid-resistant asthma patients and steroid-sensitive asthma patients. Tissue-specific gene expression of DEGs was analyzed using BioGPS. The enrichment analyses were performed using GO, KEGG, and GSEA analysis. The protein-protein interaction network and key gene cluster were constructed using STRING, Cytoscape, MCODE, and Cytohubba. A steroid-resistant neutrophilic asthma mouse model was established using lipopolysaccharide (LPS) and ovalbumin (OVA). An LPS-stimulated J744A.1 macrophage model was prepared to validate the underlying mechanism of the interesting DEG gene using the quantitative reverse transcription-polymerase chain reaction (qRT-PCR). A total of 66 DEGs were identified, most of which were present in the hematologic/immune system. Enrichment analysis displayed that the enriched pathways were the IL-17 signaling pathway, MAPK signal pathway, Toll-like receptor signaling pathway, and so on. DUSP2, as one of the top upregulated DEGs, has not been clearly demonstrated in steroid-resistant asthma. In our study, we observed that the salubrinal administration (DUSP2 inhibitor) reversed neutrophilic airway inflammation and cytokine responses (IL-17A, TNF-α) in a steroid-resistant asthma mouse model. We also found that salubrinal treatment reduced inflammatory cytokines (CXCL10 and IL-1β) in LPS-stimulated J744A.1 macrophages. DUSP2 may be a candidate target for the therapy of steroid-resistant asthma.

Possible Incidental Parkinson's Disease following Asthma: A Nested Case-Control Study in Korea

A connection between asthma and the occurrence of Parkinson's disease (PD) has been suggested, but the findings have been contentious and require verification. In this nested case-control study using data from the Korean National Health Insurance Service-Health Screening Cohort (2002-2019), which comprised 9029 participants with PD and 36,116 matched controls, we explored the relationship between asthma and incident PD. An overlap-weighted logistic regression model was used to measure the probability of asthma and PD. After adjusting for various covariates, we found that asthma was related to a 1.11-fold greater probability of PD (95% confidence interval: 1.06-1.16). A subgroup analysis showed that this effect was independent of age, sex, residential area, or alcohol consumption, and that it was still noticeable even among patients with a high income; those with a normal weight or obesity; those who were non-smokers or current smokers; and those with no history of chronic obstructive pulmonary disease, hypertension, hyperglycemia, hyperlipidemia, or anemia. Thus, these findings may indicate that asthma may slightly augment the likelihood of PD in the Korean adult population regardless of demographic or lifestyle factors, making it difficult to predict PD in asthma patients.

Identification of key genes and signaling pathways associated with dementia with Lewy bodies and Parkinson's disease dementia using bioinformatics

Objective

Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) are collectively known as Lewy body dementia (LBD). Considering the heterogeneous nature of LBD and the different constellations of symptoms with which patients can present, the exact molecular mechanism underlying the differences between these two isoforms is still unknown. Therefore, this study aimed to explore the biomarkers and potential mechanisms that distinguish between PDD and DLB.

Methods

The mRNA expression profile dataset of GSE150696 was acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between 12 DLB and 12 PDD were identified from Brodmann area 9 of human postmortem brains using GEO2R. A series of bioinformatics methods were applied to identify the potential signaling pathways involved, and a protein–protein interaction (PPI) network was constructed. Weighted gene co-expression network analysis (WGCNA) was used to further investigate the relationship between gene co-expression and different LBD subtypes. Hub genes that are strongly associated with PDD and DLB were obtained from the intersection of DEGs and selected modules by WGCNA.

Results

A total of 1,864 DEGs between PDD and DLB were filtered by the online analysis tool GEO2R. We found that the most significant GO- and KEGG-enriched terms are involved in the establishment of the vesicle localization and pathways of neurodegeneration-multiple diseases. Glycerolipid metabolism and viral myocarditis were enriched in the PDD group. A B-cell receptor signaling pathway and one carbon pool by folate correlated with DLB in the results obtained from the GSEA. We found several clusters of co-expressed genes which we designated by colors in our WGCNA analysis. Furthermore, we identified seven upregulated genes, namely, SNAP25, GRIN2A, GABRG2, GABRA1, GRIA1, SLC17A6, and SYN1, which are significantly correlated with PDD.

Conclusion

The seven hub genes and the signaling pathways we identified may be involved in the heterogeneous pathogenesis of PDD and DLB.

Comprehensive analysis of immune-related genes for classification and immune microenvironment of asthma

OBJECTIVES

To determine the effects of immune-related genes (IRGs) and immune landscape of induced sputum, and develop novel, non-invasive diagnostic molecular therapeutic targets for asthma.

METHODS

GSE76262 datasets were used to identify differentially expressed IRGs in asthma. Key IRGs were detected using a protein-protein interaction network. Receiver operating characteristic (ROC) curves were analyzed to investigate the diagnostic value of key IRGs. Gene set enrichment analysis (GSEA) was performed with WebGestalt. Single-sample gene set enrichment analysis and CIBERSORT were used to investigate the immune landscape of induced sputum.

RESULTS

A total of 75 potential IRGs were associated with asthma, most of which were involved in the NF-kappa B signaling pathway. ROC analysis showed AUC values for the hub pathway ranging from 0.676-0.767, with moderate diagnostic value for asthma. We also identified IRGs-related cytokines (TNF-α, IL-1β, IL-8 and IL-6) in 76 asthma and 91 control serum samples to further explore diagnostic efficacy, showing a cumulative AUC of 0.998 for these four related cytokines. Analysis of immune cell infiltration levels showed that follicular helper T cells, activated dendritic cells, activated mast cells and eosinophils were significantly higher and macrophages M0 and macrophages M2 were significantly reduced in sputum from patients with asthma.

CONCLUSIONS

IRGs-related cytokines and immune infiltration may contribute to the diagnosis and immune classification of asthma.

"Liquid biopsy" - extracellular vesicles as potential novel players towards precision medicine in asthma

Extracellular vesicles (EVs) have emerged as vital mediators in intracellular communication in the lung microenvironment. Environmental exposure to various triggers (e.g., viruses, allergens) stimulates the EV-mediated cascade of pro-inflammatory responses that play a key role in the asthma pathomechanism. This complex EV-mediated crosstalk in the asthmatic lung microenvironment occurs between different cell types, including airway epithelial cells and immune cells. The cargo composition of EVs mirrors hereby the type and activation status of the parent cell. Therefore, EVs collected in a noninvasive way (e.g., in nasal lavage, serum) could inform on the disease status as a "liquid biopsy", which is particularly important in the pediatric population. As a heterogeneous disease, asthma with its distinct endotypes and phenotypes requires more investigation to develop novel diagnostics and personalized case management. Filling these knowledge gaps may be facilitated by further EV research. Here, we summarize the contribution of EVs in the lung microenvironment as potential novel players towards precision medicine in the development of asthma. Although rapidly evolving, the EV field is still in its infancy. However, it is expected that a better understanding of the role of EVs in the asthma pathomechanism will open up new horizons for precision medicine diagnostic and therapeutic solutions.

Identification of key modules and hub genes for eosinophilic asthma by weighted gene co-expression network analysis

Objective: Eosinophilic asthma (EA) is one of the most important asthma phenotypes with distinct features. However, its genetic characteristics are not fully understood. This study aimed to investigate the transcriptome features and to identify hub genes of EA.Methods: Differentially expressed genes (DEGs) analysis, weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were performed to construct gene networks and to identify hub genes. Enrichment analyses were performed to investigate the biological processes, pathways and immune status of EA. The hub genes were validated in another dataset. The diagnostic value of the identified hub genes was assessed by receiver operator characteristic curve (ROC) analysis.Results: Compared with NEA, EA had a different gene expression pattern, in which 81 genes were differentially expressed. WGCNA identified two gene modules significantly associated with EA. Intersections of the DEGs and the genes in the modules associated with EA were mainly enriched in chemotaxis and signal transduction by GO and KEGG enrichment analyses. Single-sample gene set enrichment analysis indicated that EA had different immune infiltration and functions compared with NEA. Seven hub genes of EA were identified and validated, including CCL17, CCL26, CD1C, CXCL11, CXCL10, CCL22 and CCR7, all of which have diagnostic value for distinguishing EA from NEA (All AUC >0.7) .Conclusions: This study demonstrated the distinct gene expression patterns, biological processes and immune status of EA. Hub genes of EA were identified and validated. Our study could provide a framework of co-expression gene modules and potential therapeutic targets for EA.

MX2: Identification and systematic mechanistic analysis of a novel immune-related biomarker for systemic lupus erythematosus

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease that involves multiple organs. However, the current SLE-related biomarkers still lack sufficient sensitivity, specificity and predictive power for clinical application. Thus, it is significant to explore new immune-related biomarkers for SLE diagnosis and development.

Methods

We obtained seven SLE gene expression profile microarrays (GSE121239/11907/81622/65391/100163/45291/49454) from the GEO database. First, differentially expressed genes (DEGs) were screened using GEO2R, and SLE biomarkers were screened by performing WGCNA, Random Forest, SVM-REF, correlation with SLEDAI and differential gene analysis. Receiver operating characteristic curves (ROCs) and AUC values were used to determine the clinical value. The expression level of the biomarker was verified by RT‒qPCR. Subsequently, functional enrichment analysis was utilized to identify biomarker-associated pathways. ssGSEA, CIBERSORT, xCell and ImmuCellAI algorithms were applied to calculate the sample immune cell infiltration abundance. Single-cell data were analyzed for gene expression specificity in immune cells. Finally, the transcriptional regulatory network of the biomarker was constructed, and the corresponding therapeutic drugs were predicted.

Results

Multiple algorithms were screened together for a unique marker gene, MX2, and expression analysis of multiple datasets revealed that MX2 was highly expressed in SLE compared to the normal group (all P < 0.05), with the same trend validated by RT‒qPCR (P = 0.026). Functional enrichment analysis identified the main pathway of MX2 promotion in SLE as the NOD-like receptor signaling pathway (NES=2.492, P < 0.001, etc.). Immuno-infiltration analysis showed that MX2 was closely associated with neutrophils, and single-cell and transcriptomic data revealed that MX2 was specifically expressed in neutrophils. The NOD-like receptor signaling pathway was also remarkably correlated with neutrophils (r >0.3, P < 0.001, etc.). Most of the MX2-related interacting proteins were associated with SLE, and potential transcription factors of MX2 and its related genes were also significantly associated with the immune response.

Conclusion

Our study found that MX2 can serve as an immune-related biomarker for predicting the diagnosis and disease activity of SLE. It activates the NOD-like receptor signaling pathway and promotes neutrophil infiltration to aggravate SLE.

Identification of an autophagy-related gene signature for predicting prognosis and immune activity in pancreatic adenocarcinoma

Adenocarcinoma of the pancreas (PAAD) is a cancerous growth that deteriorates rapidly and has a poor prognosis. Researchers are investigating autophagy in PAAD to identify a new biomarker and treatment target. An autophagy-related gene (ARG) model for overall survival (OS) was constructed using multivariate Cox regression analyses. A cohort of the Cancer Genome Atlas (TCGA)-PAAD was used as the training group as a basis for model construction. This prediction model was validated with several external datasets. To evaluate model performance, the analysis with receiver operating characteristic curves (ROC) was performed. The Human Protein Atlas (HPA) and Cancer Cell Line Encyclopedia (CCLE) were investigated to validate the effects of ARGs expression on cancer cells. Comparing the levels of immune infiltration between high-risk and low-risk groups was finished through the use of CIBERSORT. The differentially expressed genes (DEGs) between the low-/high-risk groups were analyzed further via Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, which were used to identify potential small-molecule compounds in Connectivity Map (CMap), followed by half-maximal inhibitory concentration (IC50) examination with PANC-1 cells. The risk score was finally calculated as follows: BAK1 × 0.34 + ITGA3 × 0.38 + BAG3 × 0.35 + APOL1 × 0.26-RAB24 × 0.67519. ITGA3 and RAB24 both emerged as independent prognostic factors in multivariate Cox regression. Each PAAD cohort had a significantly shorter OS in the high-risk group than in the low-risk group. The high-risk group exhibited infiltration of several immune cell types, including naive B cells (p = 0.003), plasma cells (p = 0.044), and CD8 T cells (nearly significant, p = 0.080). Higher infiltration levels of NK cells (p = 0.025), resting macrophages (p = 0.020), and mast cells (p = 0.007) were found in the high-risk group than the low-risk group. The in vitro and in vivo expression of signature ARGs was consistent in the CCLE and HPA databases. The top 3 enriched Gene Ontology biological processes (GO-BPs) were signal release, regulation of transsynaptic signaling, and modulation of chemical synaptic transmission, and the top 3 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were MAPK, cAMP, and cell adhesion molecules. Four potential small-molecule compounds (piperacetazine, vinburnine, withaferin A and hecogenin) that target ARGs were also identified. Taking the results together, our research shows that the ARG signature may serve as a useful prognostic indicator and reveal potential therapeutic targets in patients with PAAD.

Asthma and Post-Asthmatic Fibrosis: A Search for New Promising Molecular Markers of Transition from Acute Inflammation to Pulmonary Fibrosis

Asthma is a heterogeneous pulmonary disorder, the progression and chronization of which leads to airway remodeling and fibrogenesis. To understand the molecular mechanisms of pulmonary fibrosis development, key genes forming the asthma-specific regulome and involved in lung fibrosis formation were revealed using a comprehensive bioinformatics analysis. The bioinformatics data were validated using a murine model of ovalbumin (OVA)-induced asthma and post-asthmatic fibrosis. The performed analysis revealed a range of well-known pro-fibrotic markers (Cat, Ccl2, Ccl4, Ccr2, Col1a1, Cxcl12, Igf1, Muc5ac/Muc5b, Spp1, Timp1) and a set of novel genes (C3, C3ar1, Col4a1, Col4a2, Cyp2e1, Fn1, Thbs1, Tyrobp) mediating fibrotic changes in lungs already at the stage of acute/subacute asthma-driven inflammation. The validation of genes related to non-allergic bleomycin-induced pulmonary fibrosis on asthmatic/fibrotic lungs allowed us to identify new universal genes (Col4a1 and Col4a2) associated with the development of lung fibrosis regardless of its etiology. The similarities revealed in the expression profiles of nodal fibrotic genes between asthma-driven fibrosis in mice and nascent idiopathic pulmonary fibrosis in humans suggest a tight association of identified genes with the early stages of airway remodeling and can be considered as promising predictors and early markers of pulmonary fibrosis.

Identification of GGT5 as a Novel Prognostic Biomarker for Gastric Cancer and its Correlation With Immune Cell Infiltration

Gastric cancer (GC) is a common malignant tumor of the digestive system. Recent studies revealed that high gamma-glutamyl-transferase 5 (GGT5) expression was associated with a poor prognosis of gastric cancer patients. In the present study, we aimed to confirm the expression and prognostic value of GGT5 and its correlation with immune cell infiltration in gastric cancer. First, we compared the differential expression of GGT5 between gastric cancer tissues and normal gastric mucosa in the cancer genome atlas (TCGA) and GEO NCBI databases using the most widely available data. Then, the Kaplan-Meier method, Cox regression, and univariate logistic regression were applied to explore the relationships between GGT5 and clinical characteristics. We also investigated the correlation of GGT5 with immune cell infiltration, immune-related genes, and immune checkpoint genes. Finally, we estimated enrichment of gene ontologies categories and relevant signaling pathways using GO annotations, KEGG, and GSEA pathway data. The results showed that GGT5 was upregulated in gastric cancer tissues compared to normal tissues. High GGT5 expression was significantly associated with T stage, histological type, and histologic grade (p < 0.05). Moreover, gastric cancer patients with high GGT5 expression showed worse 10-years overall survival (p = 0.008) and progression-free intervals (p = 0.006) than those with low GGT5 expression. Multivariate analysis suggested that high expression of GGT5 was an independent risk factor related to the worse overall survival of gastric cancer patients. A nomogram model for predicting the overall survival of GC was constructed and computationally validated. GGT5 expression was positively correlated with the infiltration of natural killer cells, macrophages, and dendritic cells but negatively correlated with Th17 infiltration. Additionally, we found that GGT5 was positively co-expressed with immune-related genes and immune checkpoint genes. Functional analysis revealed that differentially expressed genes relative to GGT5 were mainly involved in the biological processes of immune and inflammatory responses. In conclusion, GGT5 may serve as a promising prognostic biomarker and a potential immunological therapeutic target for GC, since it is associated with immune cell infiltration in the tumor microenvironment.