Integrated Bioinformatics Analysis Reveals Marker Genes and Potential Therapeutic Targets for Pulmonary Arterial Hypertension

Bioinformatics analysis of hypoxia associated genes and inflammatory cytokine profiling in COPD-PH

Pulmonary hypertension (PH) in chronic obstructive pulmonary disease (COPD) is associated with worse clinical outcomes and decreased survival rates. In absence of disease specific diagnostic/therapeutic targets and unclear pathophysiology, there is an urgent need for the identification of potential genetic/molecular markers and disease associated pathways. The present study aims to use a bioinformatics approach to identify and validate hypoxia-associated gene signatures in COPD-PH patients. Additionally, hypoxia-related inflammatory profile is also explored in these patients. Microarray dataset obtained from the Gene Expression Omnibus repository was used to identify differentially expressed genes (DEGs) in a hypoxic PH mice model. The top three hub genes identified were further validated in COPD-PH patients, with chemokine (C-X-C motif) ligand 9 (CXCL9) and CXCL12 showing significant changes in comparison to healthy controls. Furthermore, multiplexed analysis of 10 inflammatory cytokines, tumor necrosis factor alpha (TNF-α), transforming growth factor β (TGF-β), interleukin 1-beta (IL-1β), IL-4, IL-5, IL-6, IL-13, IL-17, IL-18 and IL-21 was also performed. These markers showed significant changes in COPD-PH patients as compared to controls. They also exhibited the ability to differentially diagnose COPD-PH patients in comparison to COPD. Additionally, IL-6 and IL-17 showed significant positive correlation with systolic pulmonary artery pressure (sPAP). This study is the first report to assess the levels of CXCL9 and CXCL12 in COPD-PH patients and also explores their link with the inflammatory profile of these patients. Our findings could be extended to better understand the underlying disease mechanism and possibly used for tailoring therapies exclusive for the disease.

Analysis and validation of biomarkers of immune cell-related genes in postmenopausal osteoporosis: An observational study

Postmenopausal osteoporosis (PMOP) is a common metabolic inflammatory disease. In conditions of estrogen deficiency, chronic activation of the immune system leads to a hypo-inflammatory phenotype and alterations in its cytokine and immune cell profile, although immune cells play an important role in the pathology of osteoporosis, studies on this have been rare. Therefore, it is important to investigate the role of immune cell-related genes in PMOP. PMOP-related datasets were downloaded from the Gene Expression Omnibus database. Immune cells scores between high bone mineral density (BMD) and low BMD samples were assessed based on the single sample gene set enrichment analysis method. Subsequently, weighted gene co-expression network analysis was performed to identify modules highly associated with immune cells and obtain module genes. Differential analysis between high BMD and low BMD was also performed to obtain differentially expressed genes. Module genes are intersected with differentially expressed genes to obtain candidate genes, and functional enrichment analysis was performed. Machine learning methods were used to filter out the signature genes. The receiver operating characteristic (ROC) curves of the signature genes and the nomogram were plotted to determine whether the signature genes can be used as a molecular marker. Gene set enrichment analysis was also performed to explore the potential mechanism of the signature genes. Finally, RNA expression of signature genes was validated in blood samples from PMOP patients and normal control by real-time quantitative polymerase chain reaction. Our study of PMOP patients identified differences in immune cells (activated dendritic cell, CD56 bright natural killer cell, Central memory CD4 T cell, Effector memory CD4 T cell, Mast cell, Natural killer T cell, T follicular helper cell, Type 1 T-helper cell, and Type 17 T-helper cell) between high and low BMD patients. We obtained a total of 73 candidate genes based on modular genes and differential genes, and obtained 5 signature genes by least absolute shrinkage and selection operator and random forest model screening. ROC, principal component analysis, and t-distributed stochastic neighbor embedding down scaling analysis revealed that the 5 signature genes had good discriminatory ability between high and low BMD samples. A logistic regression model was constructed based on 5 signature genes, and both ROC and column line plots indicated that the model accuracy and applicability were good. Five signature genes were found to be associated with proteasome, mitochondria, and lysosome by gene set enrichment analysis. The real-time quantitative polymerase chain reaction results showed that the expression of the signature genes was significantly different between the 2 groups. HIST1H2AG, PYGM, NCKAP1, POMP, and LYPLA1 might play key roles in PMOP and be served as the biomarkers of PMOP.

Prognostic value of RRM1 and its effect on chemoresistance in pancreatic cancer

PURPOSE

Pancreatic cancer (PC) remains a lethal disease, and gemcitabine resistance is prevalent. However, the biomarkers suggestive of gemcitabine resistance remain unclear.

METHODS

Bioinformatic tools identified ribonucleotide reductase catalytic subunit M1 (RRM1) in gemcitabine-related datasets. A cox regression model revealed the predictive value of RRM1 with clinical features. An external clinical cohort confirmed the prognostic value of RRM1. RRM1 expression was validated in gemcitabine-resistant cells in vitro and in orthotopic PC model. CCK8, flow cytometry, transwell migration, and invasion assays were used to explore the effect of RRM1 on gemcitabine-resistant cells. The CIBERSORT algorithm investigated the impact of RRM1 on immune infiltration.

RESULTS

The constructed nomogram based on RRM1 effectively predicted prognosis and was further validated. Moreover, patients with higher RRM1 had shorter overall survival. RRM1 expression was significantly higher in PC tissue and gemcitabine-resistant cells in vitro and in vivo. RRM1 knockdown reversed gemcitabine resistance, inhibited migration and invasion. The infiltration levels of CD4 + T cells, CD8 + T cells, neutrophils, and plasma cells correlated markedly with RRM1 expression, and communication between tumor and immune cells probably depends on NF-κB/mTOR signaling.

CONCLUSION

RRM1 may be a potential marker for prognosis and a target marker for gemcitabine resistance in PC.

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods

Aim: As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Methods: Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with "limma" package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the "Consensus Cluster Plus" package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Results: Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including TCEAL4, ISG15, RWDD1, ALG5, MRPL20, JTB and LITAF. The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Conclusion: Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.

Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis

Background

Prostate cancer (PCa) has the second highest morbidity and mortality rates in men. Concurrently, novel diagnostic and prognostic biomarkers of PCa remain crucial.

Methods

This study utilized integrated bioinformatics method to identify and validate the potential hub genes with high diagnostic and prognostic value for PCa.

Results

Four Gene Expression Omnibus (GEO) datasets including 123 PCa samples and 76 normal samples were screened and a total of 368 differentially expressed genes (DEGs), including 120 up-regulated DEGs and 248 down-regulated DEGs, were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were majorly enriched in focal adhesion, chemical carcinogenesis, drug metabolism, and cytochrome P450 pathways. Then, 11 hub genes were identified from the protein-protein interaction (PPI) network of the DEGs; 7 of the 11 genes showed the ability of distinguishing PCa from normal prostate based on receiver operating characteristic (ROC) curve analysis. And 5 of the 11 genes were correlated with clinical attributes. Lower CAV1, KRT5, SNAI2 and MYLK expression level were associated with higer Gleason score, advanced pathological T stage and N stage. Lower KRT5 and MYLK expression level were significantly correlated with poor disease-free survival, and lower KRT5 and PTGS2 expression level were significantly related to biochemical recurrence (BCR) status of PCa patients.

Conclusions

In conclusion, CAV1, KRT5, SNAI2, and MYLK show potential clinical diagnostic and prognostic value and could be used as novel candidate biomarkers and therapeutic targets for PCa.

Combined biological effects and lung proteomics analysis in mice reveal different toxic impacts of electronic cigarette aerosol and combustible cigarette smoke on the respiratory system

Combustible cigarettes produce many toxic substances that have been linked to diseases, such as lung cancer and chronic obstructive pulmonary disease. For those smokers unable or unwilling to quit, electronic cigarettes (e-cigarettes) could be used as an alternative to cigarettes. However, the effects and mechanisms of e-cigarette aerosol (ECA) on respiratory function have not been fully elucidated, and in vivo studies of its safety are limited compared to cigarette smoke (CS). In this article, we chose nicotine levels as dosing references and C57BL/6 mice for a 10-week subchronic inhalation toxicity study. A comprehensive set of toxicological endpoints was used to study the effect of exposure. Both CS (6 mg/kg) and ECA (6 or 12 mg/kg) inhalation had decreased the animal’s lung function and increased levels of inflammation markers, along with pathological changes in the airways and lungs, with ECA displaying a relatively small effect at the same dose. Proteomic analysis of lung tissue showed greater overall protein changes by CS than that of ECA, with more severe inflammatory network perturbations. Compared with ECA, KEGG analysis of CS revealed upregulation of more inflammatory and virus-related pathways. Protein–protein interactions (PPI) showed that both ECA and CS significantly changed ribosome and complement system-related proteins in mouse lung tissue. The results support that e-cigarette aerosol is less harmful to the respiratory system than cigarette smoke at the same dose using this animal model, thus providing additional evidence for the relative safety of e-cigarettes.

Bioinformatics and Experimental Analyses Reveal NFIC as an Upstream Transcriptional Regulator for Ischemic Cardiomyopathy

Ischemic cardiomyopathy (ICM) caused by coronary artery disease always leads to myocardial infarction and heart failure. Identification of novel transcriptional regulators in ICM is an effective method to establish new diagnostic and therapeutic strategies. In this study, we used two RNA-seq datasets and one microarray dataset from different studies, including 25 ICM and 21 non-failing control (NF) samples of human left ventricle tissues for further analysis. In total, 208 differentially expressed genes (DEGs) were found by combining two RNA-seq datasets with batch effects removed. GO and KEGG analyses of DEGs indicated that the response to wounding, positive regulation of smooth muscle contraction, chromatin, PI3K-Akt signaling pathway, and transporters pathways are involved in ICM. Simple Enrichment Analysis found that NFIC-binding motifs are enriched in promoter regions of downregulated genes. The Gene Importance Calculator further proved that NFIC is vital. NFIC and its downstream genes were verified in the validating microarray dataset. Meanwhile, in rat cardiomyocyte cell line H9C2 cells, two genes (Tspan1 and Hopx) were confirmed, which decreased significantly along with knocking down Nfic expression. In conclusion, NFIC participates in the ICM process by regulating TSPAN1 and HOPX. NFIC and its downstream genes may be marker genes and potential diagnostic and therapeutic targets for ICM.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.