Identification of differentially expressed genes and signaling pathways in chronic obstructive pulmonary disease via bioinformatic analysis

Transcriptome analysis on pulmonary inflammation between periodontitis and COPD

Objective

The aim of this study is to investigate the correlation between periodontal disease and chronic obstructive pulmonary disease (COPD) from the perspective of gene regulation, as well as the inflammatory pathways involved.

Methods

Forty C57BL/6 mice were randomly divided into four groups: control group, chronic periodontitis (CP) group, COPD group, and CP&COPD group. Lung tissue samples were selected for messenger ribonucleic acid (mRNA) sequencing analysis, and differential genes were screened out. Gene enrichment analysis was carried out, and then crosstalk gene enrichment analysis was conducted to explore the pathogenesis related to periodontal disease and COPD.

Results

Results of enrichment analysis showed that the differentially expressed genes (DEGs) in the CP group were concentrated in response to bacterial origin molecules. The DEGs in the COPD group gene were enriched in positive regulation of B cell activation. The DEGs in the CP&COPD group were concentrated in neutrophil extravasation and neutrophil migration. The mice in the three experimental groups had 19 crosstalk genes, five of which were key genes.

Conclusions

Lcn2, S100a8, S100a9, Irg1, Clec4d are potential crossover genes of periodontal disease and COPD. Lcn2, S100a8, S100a9 are correlated with neutrophils in both diseases. Irg1 and Clec4d may bind to receptors on the surface of lymphocytes to produce cytokines and activate inflammatory pathways, this requires further research.

Effectiveness of Immunotherapy in Non-Small Cell Lung Cancer Patients with a Diagnosis of COPD: Is This a Hidden Prognosticator for Survival and a Risk Factor for Immune-Related Adverse Events?

The interplay between the immune system and chronic obstructive pulmonary disease (COPD) and non-small cell lung cancer (NSCLC) is complex and multifaceted. In COPD, chronic inflammation and oxidative stress can lead to immune dysfunction that can exacerbate lung damage, further worsening the respiratory symptoms. In NSCLC, immune cells can recognise and attack the cancer cells, which, however, can evade or suppress the immune response by various mechanisms, such as expressing immune checkpoint proteins or secreting immunosuppressive cytokines, thus creating an immunosuppressive tumour microenvironment that promotes cancer progression and metastasis. The interaction between COPD and NSCLC further complicates the immune response. In patients with both diseases, COPD can impair the immune response against cancer cells by reducing or suppressing the activity of immune cells, or altering their cytokine profile. Moreover, anti-cancer treatments can also affect the immune system and worsen COPD symptoms by causing lung inflammation and fibrosis. Immunotherapy itself can also cause immune-related adverse events that could worsen the respiratory symptoms in patients with COPD-compromised lungs. In the present review, we tried to understand the interplay between the two pathologies and how the efficacy of immunotherapy in NSCLC patients with COPD is affected in these patients.

Role of early growth response 1 in inflammation-associated lung diseases

Early growth response 1 (EGR1), which is involved in cell proliferation, differentiation, apoptosis, adhesion, migration, and immune and inflammatory responses, is a zinc finger transcription factor. EGR1 is a member of the EGR family of early response genes and can be activated by external stimuli such as neurotransmitters, cytokines, hormones, endotoxins, hypoxia, and oxidative stress. EGR1 expression is upregulated during several common respiratory diseases, such as acute lung injury/acute respiratory distress syndrome, chronic obstructive pulmonary disease, asthma, pneumonia, and novel coronavirus disease 2019. Inflammatory response is the common pathophysiological basis of these common respiratory diseases. EGR1 is highly expressed early in the disease, amplifying pathological signals from the extracellular environment and driving disease progression. Thus, EGR1 may be a target for early and effective intervention in these inflammation-associated lung diseases.

Impact of sub-acute acrolein inhalation on the molecular regulation of mitochondrial metabolism in rat lung

Nowadays, mitochondria are recognized as key players in the pathogenesis of a variety of smoking-associated lung diseases. Acrolein, a component of cigarette smoke, is a known driver of biological mechanisms underlying smoking-induced respiratory toxicity. The impact of sub-acute acrolein inhalation in vivo on key processes controlling mitochondrial homeostasis in cells of the airways however is unknown. In this study, we investigated the activity/abundance of a myriad of molecules critically involved in mitochondrial metabolic pathways and mitochondrial quality control processes (mitochondrial biogenesis and mitophagy) in the lungs of Sprague-Dawley rats that were sub-acutely exposed to filtered air or 3 ppm acrolein by whole-body inhalation (5 h/day, 5 days/week for 4 weeks). Acrolein exposure induced a general inflammatory response in the lung as gene expression analysis revealed an increased expression of Icam1 and Cinc1 (p < 0.1; p < 0.05). Acrolein significantly decreased enzyme activity of hydroxyacyl-Coenzyme A dehydrogenase (p < 0.01), and decreased Pdk4 transcript levels (p < 0.05), suggestive of acrolein-induced changes in metabolic processes. Investigation of constituents of the mitochondrial biogenesis pathways and mitophagy machinery revealed no pronounced alterations. In conclusion, sub-acute inhalation of acrolein did not affect the regulation of mitochondrial metabolism and quality control, which is in contrast to more profound changes after acute exposure in other studies.

Plasma Extracellular Vesicle miRNA Profiles Distinguish Chronic Obstructive Pulmonary Disease Exacerbations and Disease Severity

Purpose

Molecular biomarkers for chronic obstructive pulmonary disease (COPD) severity have been difficult to identify. We aimed to assess extracellular vesicle miRNAs’ potential as a blood biomarker in discriminating disease severity in participants with COPD.

Patients and Methods

Plasma extracellular vesicles (EVs) were obtained from two COPD cohorts (n = 20 during an exacerbation event, n = 20 during stable state), with varying disease severity (GOLD stages). The miRCURY LNA miRNA Serum/Plasma assay, specific to 179 targets, was used to evaluate EV miRNA expression. The miRNAs that were significantly dysregulated were further assessed for discriminatory power using ROC curve analysis, as well as their role in relevant biological pathways.

Results

One miRNA was significantly dysregulated between moderate GOLD participants compared to severe/very severe GOLD participants, with an AUC of 0.798, p = 0.01 for miR-374b-5p. Five miRNAs were significantly dysregulated between exacerbating and stable COPD participants, with miR-223-3p resulting in the highest AUC (0.755, p = 0.006) for a single miRNA, with a combination of three miRNAs (miR-92b-3p, miR-374a-5p and miR-106b-3p) providing the highest discriminatory power (AUC 0.820, p = 0.001). The “cytokine–cytokine receptor interaction” (hsa04060 pathway) was the most significant KEGG pathway enriched for three out of the five miRNAs associated with COPD exacerbations.

Conclusion

This initial small-scale study suggests that the bioactive cargo (miRNAs) in plasma EVs holds specific biological information for the severity of airflow obstruction and COPD exacerbations, warranting further investigation.

Differences of core genes in liver fibrosis and hepatocellular carcinoma: Evidence from integrated bioinformatics and immunohistochemical analysis

BACKGROUND

Liver fibrosis and hepatocellular carcinoma (HCC) are common adverse consequences of chronic liver injury. The interaction of various risk factors may cause them to happen. Identification of specific biomarkers is of great significance for understanding the occurrence, development mechanisms, and determining the novel tools for diagnosis and treatment of both liver fibrosis and HCC.

AIM

To identify liver fibrosis-related core genes, we analyzed the differential expression pattern of core genes in liver fibrosis and HCC.

METHODS

Gene expression profiles of three datasets, GSE14323, GSE36411, and GSE89377, obtained from the Gene Expression Omnibus (GEO) database, were analyzed, and differentially expressed genes (DEGs) between patients with liver cirrhosis and healthy controls were identified by screening via R software packages and online tool for Venn diagrams. The WebGestalt online tool was used to identify DEGs enriched in biological processes, molecular functions, cellular components, and Kyoto Encyclopedia of Genes and Genomes pathways. The protein–protein interactions of DEGs were visualized using Cytoscape with STRING. Next, the expression pattern of core genes was analyzed using Western blot and immunohistochemistry in a carbon tetrachloride (CCl₄)-induced liver cirrhosis mouse model and in patient liver samples. Finally, Kaplan-Meier curves were constructed using the Kaplan-Meier plotter online server.

RESULTS

Forty-five DEGs (43 upregulated and 2 downregulated genes) associated with liver cirrhosis were identified from three GEO datasets. Ten hub genes were identified, which were upregulated in liver cirrhosis. Western blot and immunohistochemical analyses of the three core genes, decorin (DCN), dermatopontin (DPT), and SRY-box transcription factor 9 (SOX9), revealed that they were highly expressed in the CCl₄-induced liver cirrhosis mouse model. The expression levels of DCN and SOX 9 were positively correlated with the degree of fibrosis, and SOX 9 level in HCC patients was significantly higher than that in fibrosis patients. However, high expression of DPT was observed only in patients with liver fibrosis, and its expression in HCC was low. The gene expression profiling interactive analysis server (GEPIA) showed that SOX9 was significantly upregulated whereas DCN and DPT were significantly downregulated in patients with HCC. In addition, the Kaplan-Meier curves showed that HCC patients with higher SOX9 expression had significantly lower 5-year survival rate, while patients with higher expression of DCN or DPT had significantly higher 5-year survival rates.

CONCLUSION

The expression levels of DCN, DPT, and SOX9 were positively correlated with the degree of liver fibrosis but showed different correlations with the 5-year survival rates of HCC patients.

α-Arrestins and Their Functions: From Yeast to Human Health

α-Arrestins, also called arrestin-related trafficking adaptors (ARTs), constitute a large family of proteins conserved from yeast to humans. Despite their evolutionary precedence over their extensively studied relatives of the β-arrestin family, α-arrestins have been discovered relatively recently, and thus their properties are mostly unexplored. The predominant function of α-arrestins is the selective identification of membrane proteins for ubiquitination and degradation, which is an important element in maintaining membrane protein homeostasis as well as global cellular metabolisms. Among members of the arrestin clan, only α-arrestins possess PY motifs that allow canonical binding to WW domains of Rsp5/NEDD4 ubiquitin ligases and the subsequent ubiquitination of membrane proteins leading to their vacuolar/lysosomal degradation. The molecular mechanisms of the selective substrate’s targeting, function, and regulation of α-arrestins in response to different stimuli remain incompletely understood. Several functions of α-arrestins in animal models have been recently characterized, including redox homeostasis regulation, innate immune response regulation, and tumor suppression. However, the molecular mechanisms of α-arrestin regulation and substrate interactions are mainly based on observations from the yeast Saccharomyces cerevisiae model. Nonetheless, α-arrestins have been implicated in health disorders such as diabetes, cardiovascular diseases, neurodegenerative disorders, and tumor progression, placing them in the group of potential therapeutic targets.

The S100 Protein Family as Players and Therapeutic Targets in Pulmonary Diseases

The S100 protein family consists of over 20 members in humans that are involved in many intracellular and extracellular processes, including proliferation, differentiation, apoptosis, Ca₂⁺ homeostasis, energy metabolism, inflammation, tissue repair, and migration/invasion. Although there are structural similarities between each member, they are not functionally interchangeable. The S100 proteins function both as intracellular Ca²⁺ sensors and as extracellular factors. Dysregulated responses of multiple members of the S100 family are observed in several diseases, including the lungs (asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, cystic fibrosis, pulmonary hypertension, and lung cancer). To this degree, extensive research was undertaken to identify their roles in pulmonary disease pathogenesis and the identification of inhibitors for several S100 family members that have progressed to clinical trials in patients for nonpulmonary conditions. This review outlines the potential role of each S100 protein in pulmonary diseases, details the possible mechanisms observed in diseases, and outlines potential therapeutic strategies for treatment.

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

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.

Breast Cancer Case Identification Based on Deep Learning and Bioinformatics Analysis

Mastering the molecular mechanism of breast cancer (BC) can provide an in-depth understanding of BC pathology. This study explored existing technologies for diagnosing BC, such as mammography, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) and summarized the disadvantages of the existing cancer diagnosis. The purpose of this article is to use gene expression profiles of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to classify BC samples and normal samples. The method proposed in this article triumphs over some of the shortcomings of traditional diagnostic methods and can conduct BC diagnosis more rapidly with high sensitivity and have no radiation. This study first selected the genes most relevant to cancer through weighted gene co-expression network analysis (WGCNA) and differential expression analysis (DEA). Then it used the protein-protein interaction (PPI) network to screen 23 hub genes. Finally, it used the support vector machine (SVM), decision tree (DT), Bayesian network (BN), artificial neural network (ANN), convolutional neural network CNN-LeNet and CNN-AlexNet to process the expression levels of 23 hub genes. For gene expression profiles, the ANN model has the best performance in the classification of cancer samples. The ten-time average accuracy is 97.36% (±0.34%), the F1 value is 0.8535 (±0.0260), the sensitivity is 98.32% (±0.32%), the specificity is 89.59% (±3.53%) and the AUC is 0.99. In summary, this method effectively classifies cancer samples and normal samples and provides reasonable new ideas for the early diagnosis of cancer in the future.

Identification of the hub genes and prognostic indicators of gastric cancer and correlation of indicators with tumor-infiltrating immune cell levels

Aims: To identify the hub genes and prognostic indicators of gastric cancer (GC) and determine the correlation between prognostic indicators and the tumor-infiltrating immune cell levels so as to provide useful information for future GC diagnosis and treatment. Methods: The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset and two microarray datasets were used to screen the overlapping differentially expressed genes (DEGs) between normal gastric and GC tissue samples. Hub genes were screened via protein-protein interaction networks and module analysis of the overlapping DEGs. Their expression was validated at the cell level and tissue level using the ONCOMINE database. The prognostic indicators of overall survival (OS) and disease-free survival was identified by Cox proportional hazards regression analysis based on tumor grade and cancer stage. The expression of hub genes was validated at the cell level. The correlation of prognostic indicators with the tumor-infiltrating immune cell levels was analyzed using Tumor IMmune Estimation Resource. Results: Ten hub genes, namely CDC6, CDC20, BUB1B, TOP2A, CDK1, AURKA, CCNA2, CCNB1, MAD2L1, and KIF11, were screened and their upregulation in the GC tissue was verified. Three prognostic factors, namely LUM, VCAN, and EFNA4, were identified; their expression was higher in GC cells than in normal cells. LUM, VCAN, and EFNA4 were correlated with tumor-infiltrating immune cell levels in GC. Significance: The identified hub genes and prognostic indicators of GC could be useful indicators for future GC diagnosis and treatment.

EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models

Pulmonary hypertension (PH) is a common disease that affects the normal functioning of the human pulmonary arteries. The peripheral blood mononuclear cells (PMBCs) served as an ideal source for a minimally invasive disease diagnosis. This study hypothesized that the transcriptional fluctuations in the PMBCs exposed to the PH arteries may stably reflect the disease. However, the dimension of a human transcriptome is much higher than the number of samples in all the existing datasets. So, an ensemble feature selection algorithm, EnRank, was proposed to integrate the ranking information of four popular feature selection algorithms, i.e., T-test (Ttest), Chi-squared test (Chi2), ridge regression (Ridge), and Least Absolute Shrinkage and Selection Operator (Lasso). Our results suggested that the EnRank-detected biomarkers provided useful information from these four feature selection algorithms and achieved very good prediction accuracy in predicting the PH patients. Many of the EnRank-detected biomarkers were also supported by the literature.

m6A RNA methylation regulators could contribute to the occurrence of chronic obstructive pulmonary disease

N6‐methyladenosine (m6A) RNA methylation, the most prevalent internal chemical modification of mRNA, has been reported to participate in the progression of various tumours via the dynamic regulation of m6A RNA methylation regulators. However, the role of m6A RNA methylation regulators in chronic obstructive pulmonary disease (COPD) has never been reported. This study aimed to determine the expression and potential functions of m6A RNA methylation regulators in COPD. Four gene expression data sets were acquired from Gene Expression Omnibus. Gene ontology function, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, weighted correlation network analysis and protein‐protein interaction network analysis were performed. The correlation analyses of m6A RNA methylation regulators and key COPD genes were also performed. We found that the mRNA expressions of IGF2BP3, FTO, METTL3 and YTHDC2, which have the significant associations with some key genes enriched in the signalling pathway and biological processes that promote the development progression of COPD, are highly correlated with the occurrence of COPD. In conclusion, six central m6A RNA methylation regulators could contribute to the occurrence of COPD. This study provides important evidence for further examination of the role of m6A RNA methylation in COPD.

Association Between Serum S100A8/S100A9 Heterodimer and Pulmonary Function in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

BACKGROUND

Many studies have indicated that S100A8 and S100A9 may be involved in the development and progression of chronic obstructive pulmonary disease (COPD). However, there has been no clinical study analyzing the role of the serum S100A8/S100A9 heterodimer in COPD patients. The aim of this study was to analyze the correlation of the serum S100A8/S100A9 heterodimer with pulmonary function in COPD patients during acute exacerbation (AE-COPD) based on a cross-sectional study.

METHODS

A total of 131 AE-COPD patients and matched healthy subjects were recruited. Pulmonary function, arterial blood gas values, and serum inflammatory cytokines were measured.

RESULTS

Serum S100A8/S100A9 was increased in AE-COPD patients. AE-COPD patients were ranked into different grades based on FEV1%. Serum S100A8/S100A9 was higher in Grade 4 than in Grade 1-2 and Grade 3 patients with AE-COPD. Univariate regression analysis found that serum S100A8/S100A9 was negatively correlated with FEV1% in AE-COPD patients. Furthermore, serum S100A8/S100A9 was positively associated with MCP-1 in AE-COPD patients. Further stratified analysis revealed that serum S100A8/S100A9 was negatively associated with FEV1/FVC in Grade 3 (OR 0.629, P < 0.05) and in Grade 4 (OR 0.347, P < 0.05). In addition, there was a positive relationship between serum S100A8/S100A9 and PaCO2 in Grade 3 (OR 1.532, P < 0.05) and Grade 4 (OR 1.925, P < 0.01).

CONCLUSION

S100A8/S100A9 was negatively associated with pulmonary function in AE-COPD patients, indicating that the serum S100A8/S100A9 heterodimer may be involved in the progression of AE-COPD, and may be a relevant serum biomarker in the diagnosis for AE-COPD.