Identification of Potential Biomarkers for Psoriasis by DNA Methylation and Gene Expression Datasets

Potential mechanisms and drug prediction of Rheumatoid Arthritis and primary Sjögren's Syndrome: A public databases-based study

Rheumatoid arthritis (RA) and primary Sjögren's syndrome (pSS) are the most common systemic autoimmune diseases, and they are increasingly being recognized as occurring in the same patient population. These two diseases share several clinical features and laboratory parameters, but the exact mechanism of their co-pathogenesis remains unclear. The intention of this study was to investigate the common molecular mechanisms involved in RA and pSS using integrated bioinformatic analysis. RNA-seq data for RA and pSS were picked up from the Gene Expression Omnibus (GEO) database. Co-expression genes linked with RA and pSS were recognized using weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. Then, we screened two public disease-gene interaction databases (GeneCards and Comparative Toxicogenomics Database) for common targets associated with RA and pSS. The DGIdb database was used to predict therapeutic drugs for RA and pSS. The Human microRNA Disease Database (HMDD) was used to screen out the common microRNAs associated with RA and pSS. Finally, a common miRNA-gene network was created using Cytoscape. Four hub genes (CXCL10, GZMA, ITGA4, and PSMB9) were obtained from the intersection of common genes from WGCNA, differential gene analysis and public databases. Twenty-four drugs corresponding to hub gene targets were predicted in the DGIdb database. Among the 24 drugs, five drugs had already been reported for the treatment of RA and pSS. Other drugs, such as bortezomib, carfilzomib, oprozomib, cyclosporine and zidovudine, may be ideal drugs for the future treatment of RA patients with pSS. According to the miRNA-gene network, hsa-mir-21 may play a significant role in the mechanisms shared by RA and pSS. In conclusion, we identified commom targets as potential biomarkers in RA and pSS from publicly available databases and predicted potential drugs based on the targets. A new understanding of the molecular mechanisms associated with RA and pSS is provided according to the miRNA-gene network.

Epigenetic characterization of sarcopenia-associated genes based on machine learning and network screening

To screen characteristic genes related to sarcopenia by bioinformatics and machine learning, and to verify the accuracy of characteristic genes in the diagnosis of sarcopenia. Download myopia-related data sets from geo public database, find the differential genes through R language limma package after merging, STRING database to build protein interaction network, and do Go analysis and GSEA analysis to understand the functions and molecular signal pathways that may be affected by the differential genes. Further screen the characteristic genes through LASSO and SVM-RFE machine algorithms, make the ROC curve of the characteristic genes, and obtain the AUC value. 10 differential genes were obtained from the data set, including 7 upregulated genes and 3 downregulated genes. Eight characteristic genes were screened by a machine learning algorithm, and the AUC value of characteristic genes exceeded 0.7. In patients with sarcopenia, the expression of TPPP3, C1QA, LGR5, MYH8, and CDKN1A genes are upregulated, and the expression of SLC38A1, SERPINA5, and HOXB2 genes are downregulated. The above genes have high accuracy in the diagnosis of sarcopenia. The research results provide new ideas for the diagnosis and mechanism research of sarcopenia.

Novel diagnostic biomarkers of oxidative stress, ferroptosis, immune infiltration characteristics and experimental validation in ischemic stroke

Ischemic stroke (IS) is a prominent type of cerebrovascular disease leading to death and disability in an aging society and is closely related to oxidative stress. Gene expression profiling (GSE222551) was derived from Gene Expression Omnibus (GEO), and 1934 oxidative stress (OS) genes were obtained from the GeneCards database. Subsequently, we identified 149 differentially expressed genes related to OS (DEOSGs). Finally, PTGS2, FOS, and RYR1 were identified as diagnostic markers of IS. Moreover, GSE16561 was used to validate the DEOSGs. Two diagnostic genes (PTGS2 and FOS) were significantly highly expressed, while RYR1 was significantly lowly expressed in the IS group. Remarkably, immune infiltration characteristics of these three genes were analyzed, and we found that PTGS2, FOS, and RYR1 were mainly correlated with Mast cells activated, Neutrophils, and Plasma cells, respectively. Next, we intersected three DEOSGs with the ferroptosis gene set, the findings revealed that only PTGS2 was a differentially expressed gene of ferroptosis. High PTGS2 expression levels in the infarcted cortex of middle cerebral artery occlusion (MCAO) rats were confirmed by immunofluorescence (IF), western blotting (WB), and Immunohistochemistry (IHC). Inhibition of PTGS2 clearly improved the neurological outcome of rats by decreasing infarct volume, neurological problems, and modified neurological severity scores following IS compared with the controls. The protective effect of silencing PTGS2 may be related to anti-oxidative stress and ferroptosis. In conclusion, this work may provide a new perspective for the research of IS, and further research based on PTGS2 may be a breakthrough.

Identification of differentially methylated genes for severe acne by genome-wide DNA methylation and gene expression analysis

Severe acne is a chronic inflammatory skin condition that is affected by both genetic and environmental factors. DNA methylation is associated with a variety of inflammatory skin diseases, but its role in severe acne is unclear. In this study, we conducted a two-stage epigenome correlation study using 88 blood samples to identify disease-related differential methylation sites. We found close associations between the DNA methylation at 23 differentially methylated sites (DMSs) and severe acne, including PDGFD, ARHGEF10, etc. Further analysis revealed that differentially methylated genes (PARP8 and MAPKAPK2) were also expressed differently between severe acne and health control groups. These findings lead us to speculation that epigenetic mechanisms may play an important role in the pathogenesis of severe acne.

Signaling pathways and targeted therapies for psoriasis

Psoriasis is a common, chronic, and inflammatory skin disease with a high burden on individuals, health systems, and society worldwide. With the immunological pathologies and pathogenesis of psoriasis becoming gradually revealed, the therapeutic approaches for this disease have gained revolutionary progress. Nevertheless, the mechanisms of less common forms of psoriasis remain elusive. Furthermore, severe adverse effects and the recurrence of disease upon treatment cessation should be noted and addressed during the treatment, which, however, has been rarely explored with the integration of preliminary findings. Therefore, it is crucial to have a comprehensive understanding of the mechanisms behind psoriasis pathogenesis, which might offer new insights for research and lead to more substantive progress in therapeutic approaches and expand clinical options for psoriasis treatment. In this review, we looked to briefly introduce the epidemiology, clinical subtypes, pathophysiology, and comorbidities of psoriasis and systematically discuss the signaling pathways involving extracellular cytokines and intracellular transmission, as well as the cross-talk between them. In the discussion, we also paid more attention to the potential metabolic and epigenetic mechanisms of psoriasis and the molecular mechanistic cascades related to its comorbidities. This review also outlined current treatment for psoriasis, especially targeted therapies and novel therapeutic strategies, as well as the potential mechanism of disease recurrence.

Association between environmental chemicals co-exposure and peripheral blood immune-inflammatory indicators

Chronic inflammation is closely related to chronic inflammatory diseases, autoimmune diseases and cancer. Few studies have evaluated the effects of exposure to multiple chemical combinations on immunoinflammatory related indicators and their possible molecular mechanisms. This study explored the effect of exposure to various chemicals on immune-inflammatory biomarkers and its molecular mechanism. Using data from 1,723 participants in the National Health and Nutrition Examination Survey (NHANES, 2011-2012), the aim was to determine the association between chemical mixtures and immunoinflammatory biomarkers [including White blood cell (Wbc), neutrophil (Neu), lymphocytes (Lym), and Neutrophil-to-lymphocyte ratio (NLR)] using linear regression model, weighted quantile sum regression (WQSR) model, and bayesian nuclear machine regression (BKMR) model. Meanwhile, functional enrichment analysis and protein-protein interaction network establishment were performed to explore the molecular mechanism of inflammation induced by high-weight chemicals. In the linear regression model established for each single chemical, the four immunoinflammatory biomarkers were positively correlated with polycyclic aromatic hydrocarbons (PAHs), negatively correlated with perfluoroalkyl substances (PFASs), and positively or negatively correlated with metallic and non-metallic elements. WQSR model showed that cadmium (Cd), perfluorooctane sulfonic acid (PFOS) and perfluorodecanoic acid (PFDE) had the highest weights. In BKMR analysis, the overall effect of chemical mixtures was significantly associated with Lym and showed an increasing trend. The hub genes in high-weight chemicals inflammation-related genes were interleukin-6 (IL6), tumor necrosis factor (TNF), and interleukin-1B (IL1B), etc. They were mainly enriched in inflammatory response, Cytokine-cytokine receptor interaction, Th17 cell differentiation and IL-17 signaling pathway. The above results show that exposure to environmental chemical cocktails primarily promotes an increase in Lym across the immune-inflammatory spectrum. The mechanism leading to the inflammatory response may be related to the activation of IL-6 amplifier by the co-exposure of environmental chemicals.

Mast cells as important regulators in the development of psoriasis

Psoriasis is a chronic inflammatory immune skin disease mediated by genetic and environmental factors. As a bridge between innate and adaptive immunity, mast cells are involved in the initiation, development, and maintenance of psoriasis by interactions and communication with a variety of cells. The current review describes interactions of mast cells with T cells, Tregs, keratinocytes, adipocytes, and sensory neurons in psoriasis to emphasize the important role of mast cell-centered cell networks in psoriasis.

Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis

Background

Aberrant DNA methylation patterns are of increasing interest in the study of psoriasis mechanisms. This study aims to screen potential diagnostic indicators affected by DNA methylation for psoriasis based on bioinformatics using multiple machine learning algorithms and to preliminarily explore its molecular mechanisms.

Methods

GSE13355, GSE14905, and GSE73894 were collected from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated region- (DMR-) genes between psoriasis and control samples were combined to obtain differentially expressed methylated genes. Subsequently, a protein-protein interaction (PPI) network was established to analyze the interaction between differentially expressed methylated genes. Moreover, the hub genes of psoriasis were screened by the least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM), which were further performed single-gene gene set enrichment analysis (GSEA) to clarify the pathogenesis of psoriasis. The druggable genes were predicted using DGIdb. Finally, the expressions of hub genes in psoriasis lesions and healthy controls were detected by immunohistochemistry (IHC) and quantitative real-time PCR (RT-qPCR).

Results

In this study, a total of 767 DEGs and 896 DMR-genes were obtained. Functional enrichment showed that they were significantly associated with skin development, skin barrier function, immune/inflammatory response, and cell cycle. The combined transcriptomic and DNA methylation data resulted in 33 differentially expressed methylated genes, of which GJB2 was the final identified hub gene for psoriasis, with robust diagnostic power. IHC and RT-qPCR showed that GJB2 was significantly higher in psoriasis samples than those in healthy controls. Additionally, GJB2 may be involved in the development and progression of psoriasis by disrupting the body's immune system, mediating the cell cycle, and destroying the skin barrier, in addition to possibly inducing diseases related to the skeletal aspects of psoriasis. Moreover, OCTANOL and CARBENOXOLONE were identified as promising compounds through the DGIdb database.

Conclusion

The abnormal expression of GJB2 might play a critical role in psoriasis development and progression. The genes identified in our study might serve as a diagnostic indicator and therapeutic target in psoriasis.

Identification and Experimental Validation of Marker Genes between Diabetes and Alzheimer’s Disease

Currently, Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are widely prevalent in the elderly population, and accumulating evidence implies a strong link between them. For example, patients with T2DM have a higher risk of developing neurocognitive disorders, including AD, but the exact mechanisms are still unclear. This time, by combining bioinformatics analysis and in vivo experimental validation, we attempted to find a common biological link between AD and T2DM. We firstly downloaded the gene expression profiling (AD: GSE122063; T2DM: GSE161355) derived from the temporal cortex. To find the associations, differentially expressed genes (DEGs) of the two datasets were filtered and intersected. Based on them, enrichment analysis was carried out, and the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to identify the specific genes. After verifying in the external dataset and in the samples from the AD and type 2 diabetes animals, the shared targets of the two diseases were finally determined. Based on them, the ceRNA networks were constructed. Besides, the logistic regression and single-sample gene set enrichment analysis (ssGSEA) were performed. As a result, 62 DEGs were totally identified between AD and T2DM, and the enrichment analysis indicated that they were much related to the function of synaptic vesicle and MAPK signaling pathway. Based on the evidence from external dataset and RT-qPCR, CARTPT, EPHA5, and SERPINA3 were identified as the marker genes in both diseases, and their clinical significance and biological functions were further analyzed. In conclusion, discovering and exploring the marker genes that are dysregulated in both 2 diseases could help us better comprehend the intrinsic relationship between T2DM and AD, which may inspire us to develop new strategies for facing the dilemmas of clinical or basic research in cognitive dysfunction.

Identification of Potential Biomarkers and Small Molecule Drugs for Cutaneous Melanoma Using Integrated Bioinformatic Analysis

Background: Cutaneous melanoma (CM) is a type of skin cancer with a high fatality rate, and its pathogenesis has not yet been fully elucidated.

Methods: We obtained the gene expression datasets of CM through the Gene Expression Omnibus (GEO) database. Subsequently, robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between CM cases and normal skin controls. Gene functional annotation was performed to explore the potential function of the DEGs. We built the protein–protein interaction (PPI) network by the Interactive Gene database retrieval tool (STRING) and selected hub modules by Molecular Complexity Detection (MCODE). We furthered and validated our results using the TCGA-GTEX dataset. Finally, potential small molecule drugs were predicted by CMap database and verified by molecular docking method.

Results: A total of 135 DEGs were obtained by RRA synthesis analysis. GMPR, EMP3, SLC45A2, PDZD2, NPY1R, DLG5 and ADH1B were screened as potential targets for CM. Furazolidone was screened as a potential small molecule drug for the treatment of CM, and its mechanism may be related to the inhibition of CM cell proliferation by acting on GMPR.

Conclusion: We identified seven prognostic therapeutic targets associated with CM and furazolidone could be used as a potential drug for CM treatment, providing new prognostic markers, potential therapeutic targets and small molecule drugs for the treatment and prevention of CM.

Potential genetic biomarkers predict adverse pregnancy outcome during early and mid-pregnancy in women with systemic lupus erythematosus

BACKGROUND

Effectively predicting the risk of adverse pregnancy outcome (APO) in women with systemic lupus erythematosus (SLE) during early and mid-pregnancy is a challenge. This study was aimed to identify potential markers for early prediction of APO risk in women with SLE.

METHODS

The GSE108497 gene expression dataset containing 120 samples (36 patients, 84 controls) was downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed, and differentially expressed genes (DEGs) were screened to define candidate APO marker genes. Next, three individual machine learning methods, random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator, were combined to identify feature genes from the APO candidate set. The predictive performance of feature genes for APO risk was assessed using area under the receiver operating characteristic curve (AUC) and calibration curves. The potential functions of these feature genes were finally analyzed by conventional gene set enrichment analysis and CIBERSORT algorithm analysis.

RESULTS

We identified 321 significantly up-regulated genes and 307 down-regulated genes between patients and controls, along with 181 potential functionally associated genes in the WGCNA analysis. By integrating these results, we revealed 70 APO candidate genes. Three feature genes, SEZ6, NRAD1, and LPAR4, were identified by machine learning methods. Of these, SEZ6 (AUC = 0.753) showed the highest in-sample predictive performance for APO risk in pregnant women with SLE, followed by NRAD1 (AUC = 0.694) and LPAR4 (AUC = 0.654). After performing leave-one-out cross validation, corresponding AUCs for SEZ6, NRAD1, and LPAR4 were 0.731, 0.668, and 0.626, respectively. Moreover, CIBERSORT analysis showed a positive correlation between regulatory T cell levels and SEZ6 expression (P < 0.01), along with a negative correlation between M2 macrophages levels and LPAR4 expression (P < 0.01).

CONCLUSIONS

Our preliminary findings suggested that SEZ6, NRAD1, and LPAR4 might represent the useful genetic biomarkers for predicting APO risk during early and mid-pregnancy in women with SLE, and enhanced our understanding of the origins of pregnancy complications in pregnant women with SLE. However, further validation was required.