Exploration of the Shared Gene Signatures and Molecular Mechanisms Between Systemic Lupus Erythematosus and Pulmonary Arterial Hypertension: Evidence From Transcriptome Data

Comprehensive bioinformatics analysis uncover molecular pathways shared between osteoarthritis and atherosclerosis

BACKGROUND

There is growing evidence of an association between osteoarthritis (OA) and atherosclerosis (AS). However, their mechanisms are not yet fully understood. The aim of this study was to investigate the common genetic and molecular mechanisms underlying the common pathogenesis of OA and AS.

METHODOLOGY

Gene expression profiles of OA (GSE51588) and AS (GSE100927) were obtained from the Gene Expression Omnibus (GEO) database. After identifying shared differentially expressed genes (DEGs) and hub genes, we performed multifaceted bioinformatics analyses, including functional annotation, co-expression analysis, TF-mRNA and ceRNA regulatory network construction, pharmacogenetic prediction, and receiver operator characteristic (ROC) curve assessment. In addition, the immune infiltration of OA and AS was analyzed and compared based on the ssGSEA algorithm, and the correlation between hub genes and infiltrating immune cells was evaluated in OA and AS, respectively.

RESULT

A total of 48 up-regulated and 43 down-regulated public DEGs were screened between GSE51588 and GSE100927, and functional enrichment analysis emphasized the important role of immune and inflammatory pathways in OA and AS. After protein-protein interaction (PPI) network construction, a total of 9 hub genes (CCR5, IFIT2, MMP1, CXCL9, RSAD2, IFIH1, TNF, IFIT3, and TBX21) were identified as key genes. Targeting the key genes we identified several molecular drug candidates against OA combined with AS related. Additionally diagnostic efficacy assessment using 9 central genes showed great diagnostic value (area under the curve from 0.710 to 0.973). Immune infiltration study also revealed coordinated changes in immune cell profiles in OA and AS diseases.

CONCLUSION

After a series of bioinformatics analysis and validation, CCR5, IFIT2, MMP1, CXCL9, RSAD2, IFIH1, TNF, IFIT3 and TBX21 were identified as common hub genes for the development of OA and AS. This study provides a new perspective on the common molecular mechanisms between OA and AS, and offers new insights into the potential pathogenesis of OA combined with AS and the direction of treatment.

Exploration of common molecular mechanisms of psoriatic arthritis and aging based on integrated bioinformatics and single-cell RNA-seq analysis

OBJECTIVE

This study investigated the key genes shared between Psoriatic arthritis (PSA) and aging.

METHODS

By integrating and analyzing single-cell RNA sequencing data from the synovial fluid of PsA patients, peripheral blood of senescent patients, and the normal population, the subpopulation of cells that were jointly upregulated in both was obtained as the core cellular subpopulation. We analyzed the proposed chronology of this core cellular subpopulations and the function of cellular communication, screened the differentially expressed genes in the core cellular subpopulations compared with other categories, analyzed the causal relationship between the differentially expressed genes and PsA by Mendelian randomization and analyzed the enriched pathways of key genes.

RESULTS

T cell subsets were represented in a significant proportion of both PsA and senescent patients, in which CD8-CM was expressed up-regulated in both PsA and senescent populations, and a total of 98 differentially expressed genes were obtained, and a Mendelian randomization study revealed that TGFBR3, PPP3CC, and APOBEC3G were causally associated with PsA. Colocalization analysis was performed to identify co-localized association signals in the PsA GWAS results and expression quantitative trait Loci (eQTL) dataset of key genes, and metabolic pathways that were predominantly enriched for key genes were analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG).

CONCLUSIONS

In this study, we found that CD8-CM expression was up-regulated in PsA and senescent populations, and identified key genes for PsA and senescence: TGFBR3, PPP3CC and APOBEC3G. This provides new insights into the pathogenesis and combined treatment of PsA and aging.

Deciphering the transcriptomic landscape of systemic lupus erythematosus-associated pulmonary arterial hypertension

BACKGROUND

Systemic lupus erythematosus (SLE) is an autoimmune disease that involves multi-organ damage. Pulmonary arterial hypertension (PAH) is one of the life-threatening complications of SLE. The underlying cause of systemic lupus erythematosus-associated pulmonary arterial hypertension has not been fully comprehended. Besides the mechanisms implicated in the development of PAH, such as damage to the endothelial cells, the aberrant activation of the immune system also plays a substantial role in the pathogenesis of SLE-PAH.

METHODS

In this study, peripheral blood samples from 100 patients with SLE-PAH and 95 patients of SLE without PAH (SLE-nonPAH) were obtained for RNA sequencing and comprehensive transcriptomic analysis. Pathway enrichment analysis was performed based on differentially expressed genes (DEGs) between SLE-PAH and SLE-nonPAH to elucidate the mechanisms potentially driving the development of PAH in SLE patients. Utilizing consensus non-negative matrix factorization (cNMF), we also conducted a detailed analysis to identify distinct subgroups within the SLE-PAH population. Meanwhile, the protein-protein interaction (PPI) analysis was performed and hub genes among the SLE-PAH subgroups were detected. Common transcription factors (TFs) of detected hub genes were also discovered to serve as potential therapeutic targets.

RESULTS

Inflammatory signaling pathways, notably those involving interferon and TNFα, were found to play an important role in the SLE-PAH. Utilizing cNMF method, three unique subgroups of SLE-PAH patients were delineated, each characterized by a distinct level of inflammatory activity. Specifically, the high inflammation subgroup, marked by the activity of Interleukin-6 (IL-6), exhibited a milder form of PAH. In contrast, the subgroup with moderate inflammation displayed the most pronounced PAH symptoms. Further disease enrichment analysis revealed that, beyond the dysregulated inflammatory pathways, patients with the most severe PAH exhibited distinct pathogenic transcriptomic profiles that disrupted vascular smooth muscle homeostasis, a critical component of vascular health. In the most severely affected subgroup, 13 hub genes were identified. Additionally, two transcription factors commonly associated with these genes, KLF1 and GATA1, were discovered, which may serve as potential therapeutic targets.

CONCLUSION

Our investigation establishes inflammation as a key driver in the development of SLE-PAH. Patients who presented with concurrent dysregulations in inflammatory responses along with PAH-specific pathogenic markers exhibited a marked increase in the severity of their PAH. The insights gleaned from our transcriptomic analysis reveal the intricate interplay between inflammatory mechanisms and the molecular substrates of PAH. This nuanced understanding paves the way for more targeted and effective therapeutic approaches for SLE-PAH.

A study on the genetic comorbidity between autoimmune diseases and pulmonary hypertension: an observational study and POST-GWAS analysis

Background: The causal relationship between various prevalent autoimmune diseases (ADs) and pulmonary hypertension (PH) has yet to be fully understood, and the contribution of genetic factors to their coexistence remains largely unexplored. Methods: We utilized Post-GWAS and the Medical Information Mart for Intensive Care (MIMIC) database to investigate the relationship between autoimmune diseases and PH. Results: After a series of MR Analyses, only Type 1 diabetes Mellitus (T1DM) (OR = 1.06, 95% CI 0.99-1.13, P = 0.083; OR = 1.07, 95% CI 1.02-1.13, P = 0.005) and primary biliary cholangitis (PBC) (OR = 1.10, 95% CI 1.05-1.15, P = 8.22E-5; OR = 1.08, 95% CI 1.03-1.14, P = 0.002) emerged as significantly correlated with PH. Additionally, reverse MR indicated that PH could trigger the development of systemic lupus erythematosus (SLE) (OR=1.090, 95% CI = 1.014-1.171, P = 0.014). An observational study using real-world data found a clear association between rheumatoid arthritis and increased risk of PH after adjusting confounding various variables (OR = 1.39, 95% CI 1.11-1.75, P = 0.005). Furthermore, the genetic correlation results between the diseases: T1DM & PAH: P (LDSC) = 1.20e-11, P (GNOVA) = 3.36e-08; PBC & PAH: P(LDSC) = 9.40e-07, P (GNOVA) = 5.17e-05. Conclusions: Our study indicates a genetic correlation and shared risk genes between PH and autoimmune diseases, offering insights into the mechanisms underlying their co-occurrence and potential implications for future therapeutic strategies.

Exploration and Identification of Potential Biomarkers and Immune Cell Infiltration Analysis in Synovial Tissue of Rheumatoid Arthritis

INTRODUCTION

Rheumatoid arthritis (RA) is a prevalent autoimmune disease with synovial inflammation and hyperplasia, which can potentially cause degradation of articular cartilage, ultimately causing joint deformity, and impaired function. However, exact mechanisms underlying RA remain incompletely understood. This study seeks to uncover genomic signatures and potential biomarkers of RA, along with exploring the biological processes involved.

METHODS

Six microarray datasets from RA patients, osteoarthritis (OA) and healthy controls (HC) of synovial tissue were obtained from the Gene Expression Omnibus (GEO) database for integrated analysis. Differentially expressed genes (DEGs) between groups were identified by "limma" package. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out. Protein-protein interaction (PPI) network was analyzed by STRING and presented by Cytoscape. Weighted gene co-expression network analysis (WGCNA) was conducted to discover and construct the co-expression gene modules correlated with clinical phenotype. CytoHubba and MCODE were utilized for screening hub genes. Additionally, immune cell infiltration analysis was conducted utilizing CIBERSORT algorithm. The correlation of hub genes with immune cells were examined through Pearson Correlation Analysis.

RESULTS

The overlapped 92 up-regulated genes were determined between RA versus normal controls and RA versus OA, which were primarily enriched in immune response, lymphocyte activation, and chemokine signaling pathway. By integrating WGCNA, Cytohubba and MCODE algorithms, 16 hub genes were identified including CXCL13, ITK, CXCL9, CCR5, CCR7, NKG7, CCR7, and CD52. We validated the diagnostic significance of these markers in RA by qRT-PCR. Moreover, the analysis of immune cell infiltration demonstrated a positive association between these hub genes with B cell naïve, plasma cell, T cells follicular helper, and macrophages M1. The abundance of these cells was markedly greater in RA compared to OA and normal controls.

CONCLUSION

This research ultimately identified 5 potential diagnostic biomarkers of RA in the synovial tissue, namely NKG7, CD52, ITK, CXCL9, and GZMA. These findings have enhanced our comprehension of RA pathogenesis and identified promising diagnostic and therapeutic targets of RA.

The causal relationship between systemic lupus erythematosus and juvenile myoclonic epilepsy: A Mendelian randomization study and mediation analysis

This study aimed to investigate the causal relationship between systemic lupus erythematosus (SLE) and juvenile myoclonic epilepsy (JME). Univariable and reverse Mendelian randomization (MR) analyses were performed to investigate the potential causal associations between SLE, systemic autoimmune disorders (SADs), and JME. Two-step mediation MR analysis was further performed to explore indirect factors that may influence the relationship between SLE and JME. Summary data on SADs were extracted from the Integrative Epidemiology Unit Open genome-wide association study database, and summary statistics for JME were acquired from the International League Against Epilepsy Consortium. The inverse-variance weighted (IVW) method was used for primary analysis, supplemented by MR-Egger and weighted median. In the univariable MR analysis, IVW results indicated a causal relationship between SLE and an increased risk of JME (odds ratio = 1.0030, 95% confidence interval, 1.0004-1.0057; p = 0.023). The subsequent mediation MR analysis showed that inflammatory cytokines may not be the mediating factors between SLE and JME, while the inverse MR analysis found no significant relationship. Our study indicated that genetic susceptibility to SLE was causally linked to JME. However, subsequent mediation analysis failed to identify the potential mediators that could influence this relationship. Moreover, evidence suggested that other SADs were not causally associated with JME. This study may provide guidance for screening risk factors for seizures and exploring potential treatments in SLE and JME, and even all SADs and JME.

Investigation of the Shared Biomarkers in Heterotopic Ossification Between Ossification of the Ligamentum Flavum and Ankylosing Spondylitis

STUDY DESIGN

Bioinformatics analysis of Gene Expression Omnibus (GEO).

OBJECTIVE

Ossification of the ligamentum flavum (OLF) and ankylosing spondylitis (AS) represent intricate conditions marked by the gradual progression of endochondral ossification. This investigation endeavors to unveil common biomarkers associated with heterotopic ossification and explore the potential molecular regulatory mechanisms.

METHODS

Microarray and RNA-sequencing datasets retrieved from the Gene Expression Omnibus (GEO) repository were harnessed to discern differentially expressed genes (DEGs) within the OLF and AS datasets. Subsequently, Weighted Gene Co-expression Network Analysis (WGCNA) was implemented to pinpoint co-expression modules linked to OLF and AS. Common genes were further subjected to an examination of functional pathway enrichment. Moreover, hub intersection genes were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by an evaluation of diagnostic performance in external OLF and AS cohorts. Lastly, an analysis of immune cell infiltration was conducted to scrutinize the correlation of immune cell presence with shared biomarkers in OLF and AS.

RESULTS

A total of 1353 and 91 Differentially Expressed Genes (DEGs) were identified in OLF and AS, respectively. Using the Weighted Gene Co-expression Network Analysis (WGCNA), 2 modules were found to be notably significant for OLF and AS. The integrative bioinformatic analysis revealed 3 hub genes (MAB21L2, MEGF10, ISLR) as shared risk biomarkers, with MAB21L2 being the central focus. Receiver Operating Characteristic (ROC) analysis exhibited a strong diagnostic potential for these hub genes. Gene Ontology (GO) analysis indicated their involvement in the positive regulation of myoblast proliferation. Notably, MAB21L2 was singled out as the optimal common biomarker for OLF and AS. Furthermore, an analysis of immune infiltration demonstrated a correlation between MAB21L2 expression and changes in immune cells. Activated CD8 T cells were identified as shared differential immune infiltrating cells significantly linked to MAB21L2 in both OLF and AS.

CONCLUSION

This study represents the first instance of identifying MAB21L2 as a prospective diagnostic marker for patients contending with OLF associated with AS. The research results indicate that the ECM-receptor interaction and the cell-cell adhesion may play a role in both disease processes. This newfound knowledge not only enhances our understanding of the pathogenesis behind spinal ligament ossification but also uncovers potential targets for therapeutic interventions.

Immune thrombocytopenia in patients with systemic lupus erythematosus

Immune thrombocytopenia (ITP) is a common hematological manifestation of systemic lupus erythematosus (SLE). The diversity of its clinical features and treatment responses may reveal the complex pathophysiological mechanisms of the disease. To enhance the therapeutic response rate and improve the prognosis for SLE patients with concurrent ITP, while reducing adverse events during the treatment process, it is crucial to accurately identify and apply clinical parameters to predict patients' responses to treatment. In addition to conventional therapeutic approaches such as glucocorticoids, immunosuppressants, and intravenous immunoglobulin (IVIG), a range of emerging therapies are gradually becoming the focus of research. These innovative therapeutic strategies include thrombopoietin receptor agonists (TPO-RAs), targeted therapies against B-cells, and plasma cell-targeted treatments. With a deepening understanding of the role of platelets in immune and inflammatory responses, novel platelet-targeted therapeutic agents in the field of SLE-ITP treatment may demonstrate significant potential. Despite this, to ensure the clinical efficacy and safety of these therapeutic approaches, we must rely on rigorously designed randomized controlled trials (RCTs) for further validation. This article provides a systematic review of the pathogenesis of systemic lupus erythematosus (SLE) complicated by immune thrombocytopenia (ITP) and conducts a comprehensive overview of current treatment strategies. The article also provides an in-depth exploration of the key biomarkers that may influence the therapeutic response in SLE-ITP patients. This comprehensive analysis aims to elucidate the factors that potentially affect the efficacy of treatments and contribute to a more personalized approach to patient care.

Identification of CD19 as a shared biomarker via PPARγ/β-catenin/Wnt3a pathway linking psoriasis and major depressive disorder

BACKGROUND

Psoriasis, a chronic inflammatory skin disorder, is frequently linked with metabolic, cardiovascular, and psychological comorbidities. Recent research has highlighted the correlation between psoriasis and major depressive disorder (MDD); however, the underlying mechanism remains unclear.

METHODS

Commonly differentially expressed genes (DEGs) in psoriasis and MDD were identified and visualized using data from the GEO database. Subsequently, functional enrichment analysis was conducted using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Genemania. The hub gene was selected through LASSO and Random Forest algorithms, validated in clinical tissues using Student's t-test and Receiver Operating Characteristic curve. To investigate the hub gene's function in disease phenotype, we established imiquimod (IMQ)-induced psoriasiform dermatitis and chronic unpredictable mild stress (CUMS) mouse models. Lentiviral shRNA interference was topically applied in mice, and downstream pathways were validated at the mRNA and protein levels.

RESULTS

A total of 395 overlapping DEGs were identified from GSE121212 and GSE54568 datasets, and twenty core genes were extracted. Functional enrichment analysis revealed that the core genes were significantly associated with the Wnt signaling pathway, neurodegeneration, and energy metabolism. CD19 was identified as the hub gene through algorithms, and external validation showed remarkable AUC values of 0.69 and 0.74, respectively. The level of CD19 increased significantly in IMQ-treated and CUMS-treated mice. Suppression of CD19 significantly alleviated the phenotypes of IMQ-induced psoriasiform dermatitis and CUMS-induced depressive-like behaviors by regulating the PPARγ/β-catenin/Wnt3a pathway.

CONCLUSION

CD19 may serve as a common biomarker or therapeutic target of psoriasis and MDD via PPARγ/β-catenin/Wnt3a pathway.

Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers

BACKGROUND

To utilize machine learning for identifying treatment response genes in diabetic foot ulcers (DFU).

METHODS

Transcriptome data from patients with DFU were collected and subjected to comprehensive analysis. Initially, differential expression analysis was conducted to identify genes with significant changes in expression levels between DFU patients and healthy controls. Following this, enrichment analyses were performed to uncover biological pathways and processes associated with these differentially expressed genes. Machine learning algorithms, including feature selection and classification techniques, were then applied to the data to pinpoint key genes that play crucial roles in the pathogenesis of DFU. An independent transcriptome dataset was used to validate the key genes identified in our study. Further analysis of single-cell datasets was conducted to investigate changes in key genes at the single-cell level.

RESULTS

Through this integrated approach, SCUBE1 and RNF103-CHMP3 were identified as key genes significantly associated with DFU. SCUBE1 was found to be involved in immune regulation, playing a role in the body's response to inflammation and infection, which are common in DFU. RNF103-CHMP3 was linked to extracellular interactions, suggesting its involvement in cellular communication and tissue repair mechanisms essential for wound healing. The reliability of our analysis results was confirmed in the independent transcriptome dataset. Additionally, the expression of SCUBE1 and RNF103-CHMP3 was examined in single-cell transcriptome data, showing that these genes were significantly downregulated in the cured DFU patient group, particularly in NK cells and macrophages.

CONCLUSION

The identification of SCUBE1 and RNF103-CHMP3 as potential biomarkers for DFU marks a significant step forward in understanding the molecular basis of the disease. These genes offer new directions for both diagnosis and treatment, with the potential for developing targeted therapies that could enhance patient outcomes. This study underscores the value of integrating computational methods with biological data to uncover novel insights into complex diseases like DFU. Future research should focus on validating these findings in larger cohorts and exploring the therapeutic potential of targeting SCUBE1 and RNF103-CHMP3 in clinical settings.

Assessing personalized molecular portraits underlying endothelial-to-mesenchymal transition within pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a progressive and rapidly fatal disease with an intricate etiology. Identifying biomarkers for early PAH lesions based on the exploration of subtle biological processes is significant for timely diagnosis and treatment. In the present study, nine distinct cell populations identified based on gene expression profiles revealed high heterogeneity in cell composition ratio, biological function, distribution preference, and communication patterns in PAH. Notably, compared to other cells, endothelial cells (ECs) showed prominent variation in multiple perspectives. Further analysis demonstrated the endothelial-to-mesenchymal transition (EndMT) in ECs and identified a subgroup exhibiting a contrasting phenotype. Based on these findings, a machine-learning integrated program consisting of nine learners was developed to create a PAH Endothelial-to-mesenchymal transition Signature (PETS). This study identified cell populations underlying EndMT and furnished a potential tool that might be valuable for PAH diagnosis and new precise therapies.

Sex Dimorphism in Pulmonary Arterial Hypertension Associated With Autoimmune Diseases

Pulmonary hypertension is a rare, incurable, and progressive disease. Although there is increasing evidence that immune disorders, particularly those associated with connective tissue diseases, are a strong predisposing factor in the development of pulmonary arterial hypertension (PAH), there is currently a lack of knowledge about the detailed molecular mechanisms responsible for this phenomenon. Exploring this topic is crucial because patients with an immune disorder combined with PAH have a worse prognosis and higher mortality compared with patients with other PAH subtypes. Moreover, data recorded worldwide show that the prevalence of PAH in women is 2× to even 4× higher than in men, and the ratio of PAH associated with autoimmune diseases is even higher (9:1). Sexual dimorphism in the pathogenesis of cardiovascular disease was explained for many years by the action of female sex hormones. However, there are increasing reports of interactions between sex hormones and sex chromosomes, and differences in the pathogenesis of cardiovascular disease may be controlled not only by sex hormones but also by sex chromosome pathways that are not dependent on the gonads. This review discusses the role of estrogen and genetic factors including the role of genes located on the X chromosome, as well as the potential protective role of the Y chromosome in sexual dimorphism, which is prominent in the occurrence of PAH associated with autoimmune diseases. Moreover, an overview of animal models that could potentially play a role in further investigating the aforementioned link was also reviewed.

Shared genes of polycystic ovary syndrome and sedentary behavior as a novel immune landscape biomarker for endometrial cancer

Endometrial cancer (EC) is associated with significant risk factors such as polycystic ovarian syndrome (PCOS) and sedentary behavior. In our study, we aim to employ machine learning algorithms to investigate the potential molecular processes that underlie their interaction and explore their respective roles in the diagnosis and immunotherapy of EC. The GEO database provides access to microarray data, which was utilized in this study to identify gene expression modules associated with PCOS and sedentary behavior, using weighted gene expression network analysis (WGCNA). Cluego software was then employed to investigate the energy enrichment of shared pathways in both PCOS and sedentary individuals, and differential gene analysis was used to confirm another two databases. The miRNAs-mRNAs controlled network was constructed to verify the pathway. The immune-related factors of the shared pathway in EC were then analyzed. Finally, to validate our findings, we conducted cell experiments using EC cell lines (AN3CA, KLE, Ishikawa, RL95-2, and HEC-1A). We found that increased intracellular aromatic compound anabolism is a common feature of both PCOS and sedentary individuals. We then developed a disease pathway model that was based on the common genetic characteristics of PCOS and sedentary behavior. We utilized pathway typing in EC samples and found a significant survival difference between the two subgroups, with the upregulated expression type exhibiting an immune-hot phenotype. Finally, the experimental results confirmed the expression of the hub gene (NAA15) in EC. The findings of our study suggest that genes related to the intracellular aromatic compound metabolic pathway can be used for immunotherapy of EC.

Identification of ENTPD1 as a novel biomarker linking allergic rhinitis and systemic lupus erythematosus

Several studies reveal that allergic rhinitis (AR) is a significant risk factor of systemic lupus erythematosus (SLE). However, studies investigating the common pathogenesis linking AR and SLE are lacking. Our study aims to search for the shared biomarkers and mechanisms that may provide new therapeutic targets for preventing AR from developing SLE. GSE50223 for AR and GSE103760 for SLE were downloaded from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs). The Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to explore the functions of shared DEGs. Hub genes were screened by cytoHubba (a plugin of Cytoscape) and validated in another two datasets. Gene set enrichment analysis (GSEA) and single-sample Gene set enrichment analysis (ssGSEA) algorithm were applied to understand the functions of hub gene. ENTPD1 was validated as a hub gene between AR and SLE. GSEA results revealed that ENTPD1 was associated with KRAS_SIGNALING_UP pathway in AR and related to HYPOXIA, TGF_BETA_SIGNALING and TNFA_SIGNALING_VIA_NFKB pathways in SLE. The expression of ENTPD1 was positively correlated with activated CD8 T cell in both diseases. Thus, ENTPD1 may be a novel therapeutic target for preventing AR from developing SLE.

Uncovering the shared neuro-immune-related regulatory mechanisms between spinal cord injury and osteoarthritis

Adults with spinal cord injury (SCI), a destructive neurological injury, have a significantly higher incidence of osteoarthritis (OA), a highly prevalent chronic joint disorder. This study aimed to dissect the neuroimmune-related regulatory mechanisms of SCI and OA using bioinformatics analysis. Using microarray data from the Gene Expression Omnibus database, differentially expressed genes (DEGs) were screened between SCI and sham samples and between OA and control samples. Common DEGs were used to construct a protein-protein interaction (PPI) network. Weighted gene co-expression network analysis (WGCNA) was used to mine SCI- and OA-related modules. Shared miRNAs were identified, and target genes were predicted using the Human MicroRNA Disease Database (HMDD) database. A miRNA-gene-pathway regulatory network was constructed with overlapping genes, miRNAs, and significantly enriched pathways. Finally, the expression of the identified genes and miRNAs was verified using RT-qPCR. In both the SCI and OA groups, 185 common DEGs were identified, and three hub clusters were obtained from the PPI network. WGCNA revealed three SCI-related modules and two OA-related modules. There were 43 overlapping genes between the PPI network clusters and the WGCNA network modules. Seventeen miRNAs shared between patients with SCI and OA were identified. A regulatory network consisting of five genes, six miRNAs, and six signaling pathways was constructed. Upregulation of CD44, TGFBR1, CCR5, and IGF1, while lower levels of miR-125b-5p, miR-130a-3p, miR-16-5p, miR-204-5p, and miR-204-3p in both SCI and OA were successfully verified using RT-qPCR. Our study suggests that a miRNA-gene-pathway network is implicated in the neuroimmune-related regulatory mechanisms of SCI and OA. CD44, TGFBR1, CCR5, and IGF1, and their related miRNAs (miR-125b-5p, miR-130a-3p, miR-16-5p, miR-204-5p, and miR-204-3p) may serve as promising biomarkers and candidate therapeutic targets for SCI and OA.

Identification of genes related to ribosomal proteins in colorectal cancer: exploring their potential as biomarkers, prognostic indicators, and therapeutic targets

BACKGROUND

Colorectal cancer (CRC) ranks as the third most commonly diagnosed cancer in both females and males, underscoring the need for the identification of effective biomarkers.

METHODS AND RESULTS

We assessed the expression levels of ribosomal proteins (RPs) at both mRNA and protein levels. Subsequently, leveraging the STRING database, we constructed a protein-protein interaction network and identified hub genes. The co-expression network of differentially expressed genes associated with CRC and their target hub RPs was constructed using the weighted gene co-expression network analysis algorithm. Gene ontology and molecular signatures database were conducted to gain insights into the biological roles of genes associated with the identified module. To confirm the results, the expression level of the candidate genes in the CRC samples compared to the adjacent healthy was evaluated by the RT-qPCR method. Our findings indicated that the genes related to RPs were predominantly enriched in biological processes associated with Myc Targets, Oxidative Phosphorylation, and cell proliferation. Also, results demonstrated that elevated levels of GRWD1, MCM5, IMP4, and RABEPK that related to RPs were associated with poor prognostic outcomes for CRC patients. Notably, IMP4 and RABEPK exhibited higher diagnostic value. Moreover, the expression of IMP4 and RABEPK showed a significant association with drug resistance using cancer cell line encyclopedia and genomics of drug sensitivity in cancer databases. Also, the results showed that the expression level of IMP4 and RABEPK in cancerous samples was significantly higher compared to the adjacent healthy ones.

CONCLUSION

The general results of this study have shown that many genes related to RPs are increased in cancer and could be associated with the death rate of patients. We also highlighted the therapeutic and prognostic potentials of RPs genes in CRC.

Prediction of shared gene signatures and biological mechanisms between polycystic ovary syndrome and asthma: Based on weighted gene coexpression network analysis

OBJECTIVE

Several clinical studies have shown an association between polycystic ovary syndrome (PCOS) and asthma; however, the molecular link between these conditions remains unclear. In this study, we conducted a reanalysis and repurposing of existing databases in order to depict the common key genes, related signaling pathways, and similarity of the immune microenvironment between PCOS and asthma.

METHODS

PCOS and asthma data sets were downloaded, and common signal pathways were identified by using gene set enrichment analysis. Identified common susceptibility genes were explored by intersecting the weighted gene coexpression network analysis module genes for both diseases. Then, we performed protein-protein interaction, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses of the common susceptibility genes. Finally, we analyzed the immune environment of PCOS and asthma.

RESULTS

We identified five hub genes, namely, MMP9, CDC42, CD44, CD19, and BCL2L1, and uncovered that these five hub genes showed a tendency to be upregulated in both PCOS and asthma and possessed good diagnostic ability. In addition, we revealed that both PCOS and asthma were significantly enriched in the FcεRI-mediated signaling pathway. Moreover, we found that both PCOS and asthma exhibited infiltration of similar types of immune cells, such as monocytes, suggesting that the two diseases have similar pathological features.

CONCLUSION

PCOS and asthma share common causative genes with a similar immune environment. Taken together, we uncovered previously unsuspected traits for comprehensive diagnosis and treatment of PCOS and asthma in the future.

Naringenin nanoparticles targeting cyclin B1 suppress the progression of rheumatoid arthritis-associated lung cancer by inhibiting fibroblast-to-myofibroblast transition

There is growing evidence of an elevated risk of lung cancer in patients with rheumatoid arthritis. The poor prognosis of rheumatoid arthritis-associated lung cancer and the lack of therapeutic options pose an even greater challenge to the clinical management of patients. This study aimed to identify potential molecular targets associated with the progression of rheumatoid arthritis-associated lung cancer and examine the efficacy of naringenin nanoparticles targeting cyclin B1. Mendelian randomizatio analysis revealed that rheumatoid arthritis has a positive correlation with the risk of lung cancer. Cyclin B1 was significantly upregulated in patients with rheumatoid arthritis-associated lung cancer and was significantly overexpressed in synovial tissue fibroblasts. Furthermore, the overexpression of cyclin B1 in rheumatoid arthritis fibroblast-like synoviocytes, which promotes their proliferation and fibroblast-to-myofibroblast transition, can significantly contribute to the growth and infiltration of lung cancer cells. Importantly, our prepared naringenin nanoparticles targeting cyclin B1 effectively attenuated proliferation and fibroblast-to-myofibroblast transition by blocking cells at the G2/M phase. In vivo experiments, naringenin nanoparticles targeting cyclin B1 significantly alleviated the development of collagen-induced arthritis and lung orthotopic tumors. Collectively, our results reveal that naringenin nanoparticles targeting cyclin B1 can suppress the progression of rheumatoid arthritis-associated lung cancer by inhibiting fibroblast-to-myofibroblast transition. These findings provide new insights into the treatment of rheumatoid arthritis-associated lung cancer therapy.

Identification of the shared hub gene signatures and molecular mechanisms between HIV-1 and pulmonary arterial hypertension

The close link between HIV-1 infection and the occurrence of pulmonary arterial hypertension (PAH). However, the underlying molecular mechanisms of their interrelation remain unclear. The microarray data of HIV-1 and PAH were downloaded from GEO database. We utilized WGCNA to identify shared genes between HIV-1 and PAH, followed by conducting GO and pathway enrichment analyses. Subsequently, differentially genes analysis was performed using external validation datasets to further filter hub genes. Immunoinfiltration analysis was performed using CIBERSORT. Finally, hub gene expression was validated using scRNA-seq data. We identified 109 shared genes through WGCNA, primarily enriched in type I interferon (IFN) pathways. By taking the intersection of WGCNA important module genes and DEGs, ISG15 and IFI27 were identified as pivotal hub genes. Immunoinfiltration analysis and scRNA-seq results indicated the significant role of monocytes in the shared molecular mechanisms of HIV-1 and PAH. In summary, our study illustrated the possible mechanism of PAH secondary to HIV-1 and showed that the heightened IFN response in HIV-1 might be a crucial susceptibility factor for PAH, with monocytes being pivotal cells involved in the type I IFN response pathway. This provides potential new insights for further investigating the molecular mechanisms connecting HIV-1 and PAH.

Transcriptionally activates CCL28 expression to inhibit M2 polarization of macrophages and prevent immune escape in colorectal cancer cells

OBJECTIVE

This study aimed to investigate the potential molecular mechanism of SPDEF in immune evasion of colorectal cancer (CRC) and examine its impact on macrophage M2 polarization using the TCGA and GEO databases.

METHODS

By combining TCGA and GEO databases, differential gene expression between CRC samples and standard tissue samples was analyzed to screen for immune-related genes (IRGs) associated with the prognosis of CRC patients. A predictive risk model was constructed based on 18 key IRGs, which were then validated using the GEO dataset. The relationship between transcription factors and IRGs was further explored to investigate their regulatory network in CRC. In vivo and in vitro experiments were carried out to validate these regulatory relationships and explore the function of SPDEF and CCL28 in CRC.

RESULTS

Twelve key IRGs associated with clinical and pathological characteristics of CRC patients were identified. Among them, CCL28 significantly impacted macrophage infiltration in CRC cells and may be a critical factor in immune evasion. In both in vitro and in vivo experiments, overexpression of SPDEF upregulated CCL28 expression, thereby suppressing M2 polarization of macrophages and inhibiting CRC cell proliferation and tumor growth. Notably, interference with CCL28 could reverse the effect of SPDEF overexpression.

CONCLUSION

SPDEF can suppress immune evasion of CRC cells by activating CCL28, which is achieved through the modulation of M2 polarization of macrophages. This provides a new research direction and potential therapeutic target for immunotherapy in CRC.

Prevalence, risk factors and echocardiographic predictors of pulmonary hypertension in systemic lupus erythematosus: towards a screening protocol

BACKGROUND

Systemic lupus erythematosus (SLE) significantly affects the lungs and heart, and pulmonary hypertension (PH) is a severe manifestation that leads to considerable morbidity and mortality.

OBJECTIVES

We aimed to determine the prevalence and risk factors of probable SLE-PH, assess the main echocardiographic predictors and develop a potential screening strategy.

METHODS

A prospective single-centre study was conducted on 201 patients with SLE who underwent transthoracic echocardiography. Patients meeting PH criteria were referred for right heart catheterisation (RHC).

RESULTS

Among patients, 88.56% were women, 85.57% were of Spanish origin and 43.78% had structural heart disease. Out of these, 16 (7.96%) had intermediate or high probability criteria for PH according to European Society of Cardiology (ESC) 2022. Six RHCs confirmed PH with a prevalence of 2.99% for SLE-PH and 1.99% for SLE-pulmonary arterial hypertension (PAH).

KEY RISK FACTORS

Key risk factors included age, cardiorespiratory symptoms, serositis, anti-Ro, cardiac biomarkers and altered pulmonary function tests (PFTs). PH was linked to a higher Systemic Lupus International Collaborative Clinics/American College of Rheumatology Damage Index (SDI) (mean SDI 4.75 vs 2.05, p<0.001) and increased mortality risk in a 2-year follow-up (12.50% vs 1.08%, p=0.002).

CONCLUSION

In our cohort, 7.96% of patients with SLE had an intermediate or high PH probability. By RHC, six patients (2.99%) met the ESC/European Respiratory Society criteria for PH and four (1.99%) for PAH. The main risk factors were older age, cardiorespiratory symptoms, serositis, anti-Ro, cardiac biomarkers and altered PFTs. PH was a severe SLE complication, suggesting the need for earlier diagnosis through data-driven screening to reduce associated morbidity and mortality.

G6PD is a prognostic biomarker correlated with immune infiltrates in lung adenocarcinoma and pulmonary arterial hypertension

BACKGROUND

Lung adenocarcinoma (LUAD) with Pulmonary arterial hypertension (PAH) shows a poor prognosis. Detecting related genes is imperative for prognosis prediction.

METHODS

The gene expression profiles of LUAD and PAH were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, respectively. The co-expression modules associated with LUAD and PAH were evaluated using the Weighted Gene Co-Expression Network Analysis (WGCNA). The relationship between key gene expression with immune-cell infiltration and the tumor immune microenvironment (TIME) was evaluated. We confirmed the mRNA and protein levels in vivo and vitro. G6PD knockdown was used to conduct the colony formation assay, transwell invasion assay, and scratch wound assay of A549 cells. EDU staining and CCK8 assay were performed on G6PD knockdown HPASMCs. We identified therapeutic drug molecules and performed molecular docking between the key gene and small drug molecules.

RESULTS

Three major modules and 52 overlapped genes were recognized in LUAD and PAH. We identified the key gene G6PD, which was significantly upregulated in LUAD and PAH. In addition, we discovered a significant difference in infiltration for most immune cells between high- and low-G6PD expression groups. The mRNA and protein expressions of G6PD were significantly upregulated in LUAD and PAH. G6PD knockdown decreased proliferation, cloning, and migration of A549 cells and cell proliferation in HPASMCs. We screened five potential drug molecules against G6PD and targeted glutaraldehyde by molecular docking.

CONCLUSIONS

This study reveals that G6PD is an immune-related biomarker and a possible therapeutic target for LUAD and PAH patients.

Exploration of the Shared Gene Signatures and Molecular Mechanisms between Chronic Bronchitis and Antineutrophil Cytoplasmic Antibody-associated Glomerulonephritis: Evidence from Transcriptome Data

BACKGROUND

Chronic Bronchitis (CB) is a recurrent and persistent pulmonary inflammation disease. Growing evidence suggests an association between CB and Anti-neutrophil Cytoplasmic Antibody-associated Glomerulonephritis (ANCA-GN). However, the precise mechanisms underlying their association remain unclear.

AIMS

The purpose of this study was to further explore the molecular mechanism of the occurrence of chronic bronchitis (CB) associated with anti-neutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA- GN).

OBJECTIVE

Our study aimed to investigate the potential shared pathogenesis of CB-associated ANCA-GN.

METHODS

Datasets of ANCA (GSE108113 and GSE104948) and CB (GSE151052 and GSE162635) were obtained from the Gene Expression Omnibus (GEO) datasets. Firstly, GSE108113 and GSE151052 were analyzed to identify common differentially expressed genes (DEGs) by Limma package. Based on common DEGs, protein-protein interaction (PPI) network and functional enrichment analyses, including GO, KEGG, and GSEA, were performed. Then, hub genes were identified by degree algorithm and validated in GSE104948 and GSE162635. Further PPI network and functional enrichment analyses were performed on hub genes. Additionally, a competitive ceRNA network was constructed through miRanda and spongeScan. Transcription factors (TFs) were predicted and verified using the TRRUST database. Furthermore, the CIBERSORT algorithm was employed to explore immune cell infiltration. The Drug Gene Interaction Database (DGIDB) was utilized to predict small-molecular compounds of CB and ANCA-GN.

RESULTS

A total of 963 DEGs were identified in the integrated CB dataset, and 610 DEGs were identified in the integrated ANCA-GN dataset. Totally, we identified 22 common DEGs, of which 10 hub genes (LYZ, IRF1, PIK3CG, IL2RG, NT5E, ARG2, HBEGF, NFATC2, ALPL, and FKBP5) were primarily involved in inflammation and immune responses. Focusing on hub genes, we constructed a ceRNA network composed of 323 miRNAs and 348 lncRNAs. Additionally, five TFs (SP1, RELA, NFKB1, HIF1A, and SP3) were identified to regulate the hub genes. Furthermore, immune cell infiltration results revealed immunoregulation in CB and ANCA-GN. Finally, some small-molecular compounds (Daclizumab, Aldesleukin, and NT5E) were predicted to predominantly regulate inflammation and immunity, especially IL-2.

CONCLUSION

Our study explores the inflammatory-immune pathways underlying CB-associated ANCA-GN and emphasizes the importance of NETs and lymphocyte differentiation, providing novel insights into the shared pathogenesis and therapeutic targets.

Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis

BACKGROUND

In recent years, research on the pathogenesis of systemic lupus erythematosus (SLE) has made great progress. However, the prognosis of the disease remains poor, and high sensitivity and accurate biomarkers are particularly important for the early diagnosis of SLE.

METHODS

SLE patient information was acquired from three Gene Expression Omnibus (GEO) databases and used for differential gene expression analysis, such as weighted gene coexpression network (WGCNA) and functional enrichment analysis. Subsequently, three algorithms, random forest (RF), support vector machine-recursive feature elimination (SVM-REF) and least absolute shrinkage and selection operation (LASSO), were used to analyze the above key genes. Furthermore, the expression levels of the final core genes in peripheral blood from SLE patients were confirmed by real-time quantitative polymerase chain reaction (RT-qPCR) assay.

RESULTS

Five key genes (ABCB1, CD247, DSC1, KIR2DL3 and MX2) were found in this study. Moreover, these key genes had good reliability and validity, which were further confirmed by clinical samples from SLE patients. The receiver operating characteristic curves (ROC) of the five genes also revealed that they had critical roles in the pathogenesis of SLE.

CONCLUSION

In summary, five key genes were obtained and validated through machine-learning analysis, offering a new perspective for the molecular mechanism and potential therapeutic targets for SLE.

Store-operated calcium entry mediates hyperalgesic responses during neuropathy

Neuropathic pain (NP), resulting from nerve injury, alters neural plasticity in spinal cord and brain via the release of inflammatory mediators. The remodeling of store-operated calcium entry (SOCE) involves the refilling of calcium in the endoplasmic reticulum via STIM1 and Orai1 proteins and is crucial for maintaining neural plasticity and neurotransmitter release. The mechanism underlying SOCE-mediated NP remains largely unknown. In this study, we found SOCE-mediated calcium refilling was significantly higher during neuropathic pain, and the major component Orai1 was specifically co-localized with neuronal markers. Intrathecal injection of SOCE antagonist SKF96365 remarkably alleviated nerve injury- and formalin-induced pain and suppressed c-Fos expression in response to innocuous mechanical stimulation. RNA sequencing revealed that SKF96365 altered the expression of spinal transcription factors, including Fos, Junb, and Socs3, during neuropathic pain. In order to identify the genes critical for SKF96365-induced effects, we performed weighted gene co-expression network analysis (WGCNA) to identify the genes most correlated with paw withdrawal latency phenotypes. Of the 16 modules, MEsalmon module was the most highly correlated with SKF96365 induced effects. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the enriched genes of MEsalmon module were significantly related to Toll-like receptor signaling, steroid biosynthesis, and chemokine signaling, which may mediate the analgesic effect caused by SKF9636 treatment. Additionally, the SOCE antagonist YM-58483 produced similar analgesic effects in nerve injury- and formalin-induced pain. Our results suggest that manipulation of spinal SOCE signaling might be a promising target for pain relief by regulating neurotransmitter production and spinal transcription factor expression.

Identification of the shared gene signatures between pulmonary fibrosis and pulmonary hypertension using bioinformatics analysis

Pulmonary fibrosis (PF) and pulmonary hypertension (PH) have common pathophysiological features, such as the significant remodeling of pulmonary parenchyma and vascular wall. There is no effective specific drug in clinical treatment for these two diseases, resulting in a worse prognosis and higher mortality. This study aimed to screen the common key genes and immune characteristics of PF and PH by means of bioinformatics to find new common therapeutic targets. Expression profiles are downloaded from the Gene Expression Database. Weighted gene co-expression network analysis is used to identify the co-expression modules related to PF and PH. We used the ClueGO software to enrich and analyze the common genes in PF and PH and obtained the protein-protein interaction (PPI) network. Then, the differential genes were screened out in another cohort of PF and PH, and the shared genes were crossed. Finally, RT-PCR verification and immune infiltration analysis were performed on the intersection genes. In the result, the positive correlation module with the highest correlation between PF and PH was determined, and it was found that lymphocyte activation is a common feature of the pathophysiology of PF and PH. Eight common characteristic genes (ACTR2, COL5A2, COL6A3, CYSLTR1, IGF1, RSPO3, SCARNA17 and SEL1L) were gained. Immune infiltration showed that compared with the control group, resting CD4 memory T cells were upregulated in PF and PH. Combining the results of crossing characteristic genes in ImmPort database and RT-PCR, the important gene IGF1 was obtained. Knocking down IGF1 could significantly reduce the proliferation and apoptosis resistance in pulmonary microvascular endothelial cells, pulmonary smooth muscle cells, and fibroblasts induced by hypoxia, platelet-derived growth factor-BB (PDGF-BB), and transforming growth factor-β1 (TGF-β1), respectively. Our work identified the common biomarkers of PF and PH and provided a new candidate gene for the potential therapeutic targets of PF and PH in the future.

Helicobacter pylori infection increases the risk of carotid plaque formation: Clinical samples combined with bioinformatics analysis

Objective

Infection with Helicobacter pylori (H. pylori) may increase atherosclerosis, which can lead to carotid plaque formation. Our study examined the relationship between H. pylori infection and carotid plaque formation, and its underlying mechanisms.

Methods

A total of 36,470 people who underwent physical examination in Taizhou Hospital Health Examination Center from June 2017 to June 2022 were included in this study. All people participated in the urease test, neck ultrasound, blood pressure detection, anthropometric measurement and biochemical laboratory examination. In addition, the GSE27411 and GSE28829 datasets in the Gene Expression Omnibus (GEO) database were used to analyze the mechanism of H. pylori infection and atherosclerosis progression.

Results

H. pylori infection, sex, age, blood lipids, blood pressure, fasting blood glucose, glycated hemoglobin and body mass index were risk factors for carotid plaque formation. An independent risk factor was still evident in the multivariate logistic regression analysis, indicating H. pylori infection. Furthermore, after weighted gene coexpression network analysis (WGCNA), we discovered 555 genes linked to both H. pylori infection and the advancement of atherosclerosis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed a strong correlation between these genes and immunity, infection, and immune disorders. SsGSEA analysis showed that H. pylori infection and atherosclerosis included changes in the immune microenvironment. Finally, three genes MS4A6A, ADAMDEC1 and AQP9 were identified to be involved in the formation of atherosclerosis after H. pylori infection. Conclusion: Our research affirms that H. pylori is a unique contributor to the formation of carotid plaque, examines the immune microenvironment associated with H. pylori infection and advanced carotid atherosclerosis, and offers fresh perspectives on how H. pylori infection leads to atherosclerosis.

Membrane metalloendopeptidase (MME) is positively correlated with systemic lupus erythematosus and may inhibit the occurrence of breast cancer

BACKGROUND

Patients with systemic lupus erythematosus (SLE) have a lower risk of breast cancer (BRCA) than the general population. In this study, we explored the underlying molecular mechanism that is dysregulated in both diseases.

METHODS

Weighted gene coexpression network analysis (WGCNA) was executed with the SLE and BRCA datasets from the Gene Expression Omnibus (GEO) website and identified the potential role of membrane metalloendopeptidase (MME) in both diseases. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of related proteins and miRNAs were performed to investigate the potential molecular pathways.

RESULTS

WGCNA revealed that MME was positively related to SLE but negatively related to BRCA. In BRCA, MME expression was significantly decreased in tumor tissues, especially in luminal B and infiltrating ductal carcinoma subtypes. Receiver operating characteristic (ROC) analysis identified MME as a valuable diagnostic biomarker of BRCA, with an area under the curve (AUC) value equal to 0.984 (95% confidence interval = 0.976-0.992). KEGG enrichment analysis suggested that MME-related proteins and targeted miRNAs may reduce the incidence of BRCA in SLE patients via the PI3K/AKT/FOXO signaling pathway. Low MME expression was associated with favorable relapse-free survival (RFS) but no other clinical outcomes and may contribute to resistance to chemotherapy in BRCA, with an AUC equal to 0.527 (P value < 0.05).

CONCLUSIONS

In summary, MME expression was significantly decreased in BRCA but positively correlated with SLE, and it might reduce the incidence of BRCA in SLE patients via the PI3K/AKT/FOXO signaling pathway.

Exploring the shared molecular mechanisms between systemic lupus erythematosus and primary Sjögren's syndrome based on integrated bioinformatics and single-cell RNA-seq analysis

Background

Systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) are common systemic autoimmune diseases that share a wide range of clinical manifestations and serological features. This study investigates genes, signaling pathways, and transcription factors (TFs) shared between SLE and pSS.

Methods

Gene expression profiles of SLE and pSS were obtained from the Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to identify shared genes related to SLE and pSS. Overlapping genes were then subject to Gene Ontology (GO) and protein-protein interaction (PPI) network analyses. Cytoscape plugins cytoHubba and iRegulon were subsequently used to screen shared hub genes and predict TFs. In addition, gene set variation analysis (GSVA) and CIBERSORTx were used to calculate the correlations between hub genes and immune cells as well as related pathways. To confirm these results, hub genes and TFs were verified in microarray and single-cell RNA sequencing (scRNA-seq) datasets.

Results

Following WGCNA and limma analysis, 152 shared genes were identified. These genes were involved in interferon (IFN) response and cytokine-mediated signaling pathway. Moreover, we screened six shared genes, namely IFI44L, ISG15, IFIT1, USP18, RSAD2 and ITGB2, out of which three genes, namely IFI44L, ISG15 and ITGB2 were found to be highly expressed in both microarray and scRNA-seq datasets. IFN response and ITGB2 signaling pathway were identified as potentially relevant pathways. In addition, STAT1 and IRF7 were identified as common TFs in both diseases.

Conclusion

This study revealed IFI44L, ISG15 and ITGB2 as the shared genes and identified STAT1 and IRF7 as the common TFs of SLE and pSS. Notably, the IFN response and ITGB2 signaling pathway played vital roles in both diseases. Our study revealed common pathogenetic characteristics of SLE and pSS. The particular roles of these pivotal genes and mutually overlapping pathways may provide a basis for further mechanistic research.

Screening of Potential Circulating Diagnostic Biomarkers and Molecular Mechanisms of Systemic Lupus Erythematosus-Related Myocardial Infarction by Integrative Analysis

Background

The risk of acute myocardial infarction (AMI) is elevated in patients with systemic lupus erythematosus (SLE), and it is of great clinical value to identify potential molecular mechanisms and diagnostic markers of AMI associated with SLE by analyzing public database data and transcriptome sequencing data.

Methods

AMI and SLE-related sequencing datasets GSE62646, GSE60993, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database and divided into prediction and validation cohorts. To identify the key genes associated with AMI related to SLE, WGCNA and DEGs analysis were performed for the prediction and validation cohorts, respectively. The related signaling pathways were identified by GO/KEGG enrichment analysis. Peripheral blood mononuclear cells (PBMCs) from patients with AMI were collected for transcriptome sequencing to validate the expression of key genes in patients with AMI. Least absolute shrinkage and selection operator (LASSO) regression analysis was applied to screen diagnostic biomarkers. The diagnostic efficacy of biomarkers was validated by ROC analysis, and the CIBERSORTx platform was used to analyze the composition of immune cells in AMI and SLE.

Results

A total of 108 genes closely related to AMI and SLE were identified in the prediction cohort, and GO/KEGG analysis showed significantly enriched signaling pathways. The results of differential analysis in validation cohort were consistent with them. By transcriptional sequencing of PBMCs from peripheral blood of AMI patients, combined with the results of prediction and validation cohort analysis, seven genes were finally screened out. LASSO analysis finally identifies DYSF, LRG1 and CSF3R as diagnostic biomarkers of SLE-related-AMI. CIBERSORTx analysis revealed that the biomarkers were highly correlated with neutrophils.

Conclusion

Neutrophil degranulation and NETs formation play important roles in SLE-related AMI, and DYSF, LRG1 and CSF3R were identified as important diagnostic markers for the development and progression of SLE-related AMI.

Exploring the key genetic association between chronic pancreatitis and pancreatic ductal adenocarcinoma through integrated bioinformatics

Background: Pancreatic ductal adenocarcinoma (PDAC) develops rapidly and has a poor prognosis. It has been demonstrated that pancreatic ductal adenocarcinoma and chronic pancreatitis (CP) have a close connection. However, the underlying mechanisms for chronic pancreatitis transforming into pancreatic ductal adenocarcinoma are still unclear. The purpose of this study was to identify real hub genes in the development of chronic pancreatitis and pancreatic ductal adenocarcinoma. Methods: RNA-seq data of chronic pancreatitis and pancreatic ductal adenocarcinoma were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between chronic pancreatitis and pancreatic ductal adenocarcinoma. GEO2R and a Venn diagram were used to identify differentially expressed genes. Then visualized networks were constructed with ClueGO, and modules of PPI network were calculated by MCODE plugin. Further validation of the results was carried out in two additional cohorts. Analyses of CEL-coexpressed genes and regulators including miRNAs and transcription factors were performed by using the corresponding online web tool. Finally, the influence of CEL in the tumor immune microenvironment (TIME) was assessed by immune contextual analysis. Results: With the help of WGCNA and GEO2R, four co-expression modules and six hub genes were identified, respectively. ClueGO enrichment analysis and MCODE cluster analysis revealed that the dysfunctional transport of nutrients and trace elements might contribute to chronic pancreatitis and pancreatic ductal adenocarcinoma development. The real hub gene CEL was identified with a markedly low expression in pancreatic ductal adenocarcinoma in external validation sets. According to the miRNA-gene network construction, hsa-miR-198 may be the key miRNA. A strong correlation exists between CEL and TIME after an evaluation of the influence of CEL in TIME. Conclusion: Our study revealed the dysfunctional transport of nutrients and trace elements may be common pathogenesis of pancreatic ductal adenocarcinoma and chronic pancreatitis. Examination on these common pathways and real hub genes may shed light on the underlying mechanism.

Identification of Shared Immune Cells and Immune-Related Co-Disease Genes in Chronic Heart Failure and Systemic Lupus Erythematosus Based on Transcriptome Sequencing

Purpose

The purpose was to identify shared immune cells and co-disease genes in chronic heart failure (HF) and systemic lupus erythematosus (SLE), as well as explore the potential mechanisms of action between HF and SLE.

Methods

A collection of peripheral blood mononuclear cells (PBMCs) from ten patients with HF and SLE and ten normal controls (NC) was used for transcriptome sequencing. Differentially expressed genes (DEGs) analysis, enrichment analysis, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and machine learning were applied for the screening of shared immune cells and co-disease genes in HF and SLE. Gene expression analysis and correlation analysis were used to explore the potential mechanisms of co-disease genes and immune cells in HF and SLE.

Results

In this study, it was found that two immune cells, T cells CD4 naïve and Monocytes, displayed similar expression patterns in HF and SLE at the same time. By taking intersection of the above immune cell-associated genes with the DEGs common to both HF and SLE, four immune-associated co-disease genes, CCR7, RNASE2, RNASE3 and CXCL10, were finally identified. CCR7, as one of the four key genes, was significantly down-regulated in HF and SLE, while the rest three key genes were all significantly up-regulated in both diseases.

Conclusion

T cells CD4 naïve and Monocytes were first revealed as possible shared immune cells of HF and SLE, and CCR7, RNASE2, RNASE3 and CXCL10 were identified as possible key genes common to HF and SLE as well as potential biomarkers or therapeutic targets for HF and SLE.

Exploration of the potential common pathogenic mechanisms in COVID-19 and silicosis by using bioinformatics and system biology

Silicosis is an occupational lung disease that is common worldwide. In recent years, coronavirus disease 2019 (COVID-19) has provided daunting challenges to public healthcare systems globally. Although multiple studies have shown a close link between COVID-19 and other respiratory diseases, the inter-relational mechanisms between COVID-19 and silicosis remain unclear. This study aimed to explore the shared molecular mechanisms and drug targets of COVID-19 and silicosis. Gene expression profiling identified four modules that were most closely associated with both diseases. Furthermore, we performed functional analysis and constructed a protein-protein interaction network. Seven hub genes (budding uninhibited by benzimidazoles 1 [BUB1], protein regulator of cytokinesis 1 [PRC1], kinesin family member C1 [KIFC1], ribonucleotide reductase regulatory subunit M2 [RRM2], cyclin-dependent kinase inhibitor 3 [CDKN3], Cyclin B2 [CCNB2], and minichromosome maintenance complex component 6 [MCM6]) were involved in the interaction between COVID-19 and silicosis. We investigated how diverse microRNAs and transcription factors regulate these seven genes. Subsequently, the correlation between the hub genes and infiltrating immune cells was explored. Further in-depth analyses were performed based on single-cell transcriptomic data from COVID-19, and the expression of hub-shared genes was characterized and located in multiple cell clusters. Finally, molecular docking results reveal small molecular compounds that may improve COVID-19 and silicosis. The current study reveals the common pathogenesis of COVID-19 and silicosis, which may provide a novel reference for further research.

Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Background

Acute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive.

Methods

Four public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers.

Results

Seven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability.

Conclusion

Our study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC.

Shared Genes of PPARG and NOS2 in Alzheimer’s Disease and Ulcerative Colitis Drive Macrophages and Microglia Polarization: Evidence from Bioinformatics Analysis and Following Validation

Emerging evidence shows that peripheral systemic inflammation, such as inflammatory bowel disease (IBD), has a close even interaction with central nervous disorders such as Alzheimer’s disease (AD). This study is designed to further clarify the relationship between AD and ulcerative colitis (UC, a subclass of IBD). The GEO database was used to download gene expression profiles for AD (GSE5281) and UC (GSE47908). Bioinformatics analysis included GSEA, KEGG pathway, Gene Ontology (GO), WikiPathways, PPI network, and hub gene identification. After screening the shared genes, qRT-PCR, Western blot, and immunofluorescence were used to verify the reliability of the dataset and further confirm the shared genes. GSEA, KEGG, GO, and WikiPathways suggested that PPARG and NOS2 were identified as shared genes and hub genes by cytoHubba in AD and UC and further validated via qRT-PCR and Western blot. Our work identified PPARG and NOS2 are shared genes of AD and UC. They drive macrophages and microglia heterogeneous polarization, which may be potential targets for treating neural dysfunction induced by systemic inflammation and vice versa.

Systemic Lupus Erythematosus and Pulmonary Hypertension

Pulmonary Hypertension (PH) is a common manifestation in patients with Systemic Lupus Erythematosus (SLE) and varies from asymptomatic to life-threatening disease. PH can result not only from immune system dysregulation, but also from various conditions, including cardiorespiratory disorders and thromboembolic diseases. Most commonly, SLE-related PH presents with non-specific symptoms, such as progressive dyspnea on exertion, generalized fatigue and weakness and eventually dyspnea at rest. Prompt diagnosis of SLE-related PH and early identification of the underlying pathogenetic mechanisms is demanded in order to introduce targeted therapy to prevent irreversible pulmonary vascular damage. In most cases the management of PH in SLE patients is similar to idiopathic pulmonary arterial hypertension (PAH). Furthermore, specific diagnostic tools like biomarkers or screening protocols, to establish early diagnosis seem to be not available yet. Although, the survival rates for patients with SLE-related PH vary between studies, it is evident that PH presence negatively affects the survival of SLE patients.

Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis

BACKGROUND

Uremia is one of the most challenging problems in medicine and an increasing public health issue worldwide. Patients with uremia suffer from accelerated atherosclerosis, and atherosclerosis progression may trigger plaque instability and clinical events. As a result, cardiovascular and cerebrovascular complications are more likely to occur. This study aimed to identify diagnostic biomarkers in uremic patients with unstable carotid plaques (USCPs).

METHODS

Four microarray datasets (GSE37171, GSE41571, GSE163154, and GSE28829) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in uremia and USCP. Weighted gene co-expression network analysis (WGCNA) was used to determine the respective significant module genes associated with uremia and USCP. Moreover, a protein-protein interaction (PPI) network and three machine learning algorithms were applied to detect potential diagnostic genes. Subsequently, a nomogram and a receiver operating characteristic curve (ROC) were plotted to diagnose USCP with uremia. Finally, immune cell infiltrations were further analyzed.

RESULTS

Using the Limma package and WGCNA, the intersection of 2795 uremia-related DEGs and 1127 USCP-related DEGs yielded 99 uremia-related DEGs in USCP. 20 genes were selected as candidate hub genes via PPI network construction. Based on the intersection of genes from the three machine learning algorithms, three hub genes (FGR, LCP1, and C5AR1) were identified and used to establish a nomogram that displayed a high diagnostic performance (AUC: 0.989, 95% CI 0.971-1.000). Dysregulated immune cell infiltrations were observed in USCP, showing positive correlations with the three hub genes.

CONCLUSION

The current study systematically identified three candidate hub genes (FGR, LCP1, and C5AR1) and established a nomogram to assist in diagnosing USCP with uremia using various bioinformatic analyses and machine learning algorithms. Herein, the findings provide a foothold for future studies on potential diagnostic candidate genes for USCP in uremic patients. Additionally, immune cell infiltration analysis revealed that the dysregulated immune cell proportions were identified, and macrophages could have a critical role in USCP pathogenesis.

Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning

BACKGROUND

Systemic lupus erythematosus (SLE) has become a major public health problem over the years, and atherosclerosis (AS) is one of the main complications of SLE associated with serious cardiovascular consequences in this patient population. The present study aimed to identify potential biomarkers for SLE patients with AS.

METHODS

Five microarray datasets (GSE50772, GSE81622, GSE100927, GSE28829, GSE37356) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS. Weighted gene coexpression network analysis (WGCNA) was used to identify significant module genes associated with SLE. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (Lasso, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest) were applied to identify hub genes. Subsequently, we generated a nomogram and receiver operating characteristic curve (ROC) for predicting the risk of AS in SLE patients. Finally, immune cell infiltrations were analyzed, and Consensus Cluster Analysis was conducted based on Single Sample Gene Set Enrichment Analysis (ssGSEA) scores.

RESULTS

Five hub genes (SPI1, MMP9, C1QA, CX3CR1, and MNDA) were identified and used to establish a nomogram that yielded a high predictive performance (area under the curve 0.900-0.981). Dysregulated immune cell infiltrations were found in AS, with positive correlations with the five hub genes. Consensus clustering showed that the optimal number of subtypes was 3. Compared to subtypes A and B, subtype C presented higher expression of the five hub genes, immune cell infiltration levels and immune checkpoint expression.

CONCLUSION

Our study systematically identified five candidate hub genes (SPI1, MMP9, C1QA, CX3CR1, MNDA) and established a nomogram that could predict the risk of AS with SLE using various bioinformatic analyses and machine learning algorithms. Our findings provide the foothold for future studies on potential crucial genes for AS in SLE patients. Additionally, the dysregulated immune cell proportions and immune checkpoint expressions in AS with SLE were identified.

Genetic damage and potential mechanism exploration under different air pollution patterns by multi-omics

Ambient air pollution was classified as carcinogenic to humans (Group 1) for lung cancer. DNA damage was an important first step in the process of carcinogenesis, and could also be induced by air pollution. In this study, intratracheal instillation and real-time air exposure system were combined to establish SHP (short-term high-level PM_2.5) and LLPO (long-term low-level PM_2.5 and O₃) exposure patterns, respectively. Hierarchical levels of genetic biomarkers were analyzed to explore DNA damage effects in rats. Representative DNA repair genes from different repair pathways were selected to explore the relative expression levels. The methylation level of differentially expressed repair genes were also determined. Besides, miRNA sequencing and non-targeted metabolomic analysis were performed in rat lungs. KEGG and multi-omics analysis were used to explore the potential mechanism of genetic damage under different air pollution patterns. We found that LLPO exposure induced DSBs and chromosome damage. SHP exposure could induce DSBs and DNA oxidative damage, and the effects of genetic damage under this pollution pattern could be repaired by natural repair. Repair genes involved in two pattern were different. SHP exposure could induce higher methylation levels of RAD51, which might be a potential epigenetic mechanism for high-level PM_2.5 induced down-regulated expression of RAD51 and DSBs. Besides, 29 overlapped alterations in metabolic pathways were identified by metabolomic and miRNA sequencing, including purine metabolism and pyrimidine metabolism after LLPO exposure. Differential miRNAs expression in lung tissue were associated with apoptosis, DNA damage and damage repair. We concluded that under different air pollution patterns, DNA damage biomarkers and activated targets of DNA damage repair network were both different. The genetic damage effects caused by high-level short-term PM_2.5 can be alleviated by natural repair. We provided possible mechanisms by multi-omics which could explain the increased carcinogenic risk caused by air pollution.

Triptolide in the treatment of systemic lupus erythematosus - regulatory effects on miR-146a in B cell TLR7 signaling pathway in mice

Objective: To clarify the mechanism of triptolide (TP) in alleviating the conditions underlying SLE.

Methods: Eight-week-old MRL/lpr mice were randomly divided into a model group (n = 5), low-dose TP (TP-L) group (n = 5), and high-dose TP (TP-H) group (n = 5). Mice in these groups were gavaged with normal saline, low-dose TP solution, and high-dose TP solution for 8 weeks, respectively. The expression levels of anti-dsDNA, IgG, IgM, IgA, C3, C4, and CREA, BUN, ALT, AST, ALB, and ALP indexes in the serum of mice were detected. The proportion of CD19⁺CD138⁺B220⁻ cells in the spleen and the pathological changes of kidney tissue in the mice were also evaluated. The possible signaling pathways and microRNA (miRNA) targets of TP in the treatment of SLE were analyzed using network pharmacology. The expressions of TLR7 mRNA and miR-146a in Raji cells (a B lymphocyte line) were detected using qPCR before and after intervention with a miR-146a inhibitor. The protein expression levels of TLR7, MyD88, p-IRAK1, and p-NF-κBp65 were detected using western blot analysis.

Results: TP could significantly decrease the levels of ds-DNA and IgG, alleviate pathological injury in renal tissue, and upregulate miR-146a expression in the B cells of MRL/lpr mice without obvious liver and kidney toxicity. Network pharmacology analysis showed that TP could mainly regulate the Toll-like receptor signaling pathway, and NF-κB signaling pathway, among others. miRNA target prediction suggested that TP could regulate miRNAs such as miR-146a. In vitro cell experiments further confirmed that TP could significantly upregulate miR-146a expression and downregulate the expression of TLR7 mRNA and protein levels TLR7, MyD88, p-IRAK1, and p-NF-κBp65. After intervention with a miR-146a inhibitor, TP had no obvious inhibitory effects on TLR7, MyD88, p-IRAK1, and p-NF-κBp65 expression.

Conclusion: TP may exert therapeutic effects on SLE by regulating miR-146a expression, inhibiting the TLR7/NF-κB signaling pathway, and affecting B cell activation.

Bioinformatics analysis of potential pathogenesis and risk genes of immunoinflammation-promoted renal injury in severe COVID-19

Renal injury secondary to COVID-19 is an important factor for the poor prognosis of COVID-19 patients. The pathogenesis of renal injury caused by aberrant immune inflammatory of COVID-19 remains unclear. In this study, a total of 166 samples from 4 peripheral blood transcriptomic datasets of COVID-19 patients were integrated. By using the weighted gene co-expression network (WGCNA) algorithm, we identified key genes for mild, moderate, and severe COVID-19. Subsequently, taking these genes as input genes, we performed Short Time-series Expression Miner (STEM) analysis in a time consecutive ischemia-reperfusion injury (IRI) -kidney dataset to identify genes associated with renal injury in COVID-19. The results showed that only in severe COVID-19 there exist a small group of genes associated with the progression of renal injury. Gene enrichment analysis revealed that these genes are involved in extensive immune inflammation and cell death-related pathways. A further protein-protein interaction (PPI) network analysis screened 15 PPI-hub genes: ALOX5, CD38, GSF3R, LGR, RPR1, HCK, ITGAX, LYN, MAPK3, NCF4, SELP, SPI1, WAS, TLR2 and TLR4. Single-cell sequencing analysis indicated that PPI-hub genes were mainly distributed in neutrophils, macrophages, and dendritic cells. Intercellular ligand-receptor analysis characterized the activated ligand-receptors between these immune cells and parenchyma cells in depth. And KEGG enrichment analysis revealed that viral protein interaction with cytokine and cytokine receptor, necroptosis, and Toll-like receptor signaling pathway may be potentially essential for immune cell infiltration leading to COVID-19 renal injury. Finally, we validated the expression pattern of PPI-hub genes in an independent data set by random forest. In addition, we found that the high expression of these genes was correlated with a low glomerular filtration rate. Including them as risk genes in lasso regression, we constructed a Nomogram model for predicting severe COVID-19. In conclusion, our study explores the pathogenesis of renal injury promoted by immunoinflammatory in severe COVID-19 and extends the clinical utility of its key genes.

Integrated Bioinformatic Analysis of the Shared Molecular Mechanisms Between Osteoporosis and Atherosclerosis

Background

Osteoporosis and atherosclerosis are common in the elderly population, conferring a heavy worldwide burden. Evidence links osteoporosis and atherosclerosis but the exact underlying common mechanism of its occurrence is unclear. The purpose of this study is to further explore the molecular mechanism between osteoporosis and atherosclerosis through integrated bioinformatic analysis.

Methods

The microarray data of osteoporosis and atherosclerosis in the Gene Expression Omnibus (GEO) database were downloaded. The Weighted Gene Co-Expression Network Analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to identify the co-expression genes related to osteoporosis and atherosclerosis. In addition, the common gene targets of osteoporosis and atherosclerosis were analyzed and screened through three public databases (CTD, DISEASES, and GeneCards). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Metascape. Then, the common microRNAs (miRNAs) in osteoporosis and atherosclerosis were screened out from the Human microRNA Disease Database (HMDD) and the target genes of whom were predicted through the miRTarbase. Finally, the common miRNAs-genes network was constructed by Cytoscape software.

Results

The results of common genes analysis showed that immune and inflammatory response may be a common feature in the pathophysiology of osteoporosis and atherosclerosis. Six hub genes (namely, COL1A1, IBSP, CTSD, RAC2, MAF, and THBS1) were obtained via taking interaction of different analysis results. The miRNAs-genes network showed that has-let-7g might play an important role in the common mechanisms between osteoporosis and atherosclerosis.

Conclusion

This study provides new sights into shared molecular mechanisms between osteoporosis and atherosclerosis. These common pathways and hub genes may offer promising clues for further experimental studies.

Exploration of the molecular mechanisms, shared gene signatures, and MicroRNAs between systemic lupus erythematosus and diffuse large B cell lymphoma by bioinformatics analysis

BACKGROUND

Systemic lupus erythematosus (SLE) is a complex heterogeneous systemic autoimmune disease. Previous studies have shown that SLE may be related to diffuse large B cell lymphoma (DLBCL), but the mechanism of their relationship is still unclear. The present study aimed to explore the common genetic molecular mechanisms, core shared genes, and miRNAs between SLE and DLBCL as well as to investigate the diagnostic markers of DLBCL.

METHODS

The SLE and DLBCL microarray data were downloaded from the comprehensive Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules. Four core shared genes were screened out by various algorithms and validated in other cohorts. Finally, we constructed a common core gene-miRNA network using the human microRNA disease database (HMDD) and TarBase.

RESULTS

Using WGCNA, four modules were identified as important modules for SLE and DLBCL. Enrichment analysis of the shared genes showed that the highly activated NF-κB pathway was a common feature of the pathophysiology. Four core shared genes, namely, PSMB10, PSMB4, TAF10, and NFΚBIA, were screened out. These core shared genes were significantly upregulated in both diseases, and they may be potential diagnostic markers of DLBCL. The core gene-miRNA network showed that miR-155-5p, regulating the shared NF-κB pathway, may play an important role in the susceptibility of SLE patients to DLBCL.

CONCLUSION

The present study revealed that NF-κB pathway in SLE may be a crucial susceptible factor for DLBCL. In addition, we identified PSMB10, PSMB4, TAF10, NFΚBIA and miR-155 involved in the common pathogenesis as potential biomarkers and therapeutic targets for DLBCL.

Exploration of Shared Gene Signatures and Molecular Mechanisms Between Periodontitis and Nonalcoholic Fatty Liver Disease

Background: Periodontitis is associated with periodontal tissue damage and teeth loss. Nonalcoholic fatty liver disease (NAFLD) has an intimate relationship with periodontitis. Nevertheless, interacted mechanisms between them have not been clear. This study was intended for the exploration of shared gene signatures and latent therapeutic targets in periodontitis and NAFLD.

Methods: Microarray datasets of periodontitis and NAFLD were obtained from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was utilized for the acquisition of modules bound up with NAFLD and periodontitis. We used ClueGO to carry out biological analysis on shared genes to search their latent effects in NAFLD and periodontitis. Another cohort composed of differential gene analysis verified the results. The common microRNAs (miRNAs) in NAFLD and periodontitis were acquired in the light of the Human microRNA Disease Database (HMDD). According to miRTarbase, miRDB, and Targetscan databases, latent target genes of miRNAs were forecasted. Finally, the miRNAs–mRNAs network was designed.

Results: Significant modules with periodontitis and NAFLD were obtained via WGCNA. GO enrichment analysis with GlueGo indicated that damaged migration of dendritic cells (DCs) might be a common pathophysiologic feature of NAFLD and periodontitis. In addition, we revealed common genes in NAFLD and periodontitis, including IGK, IGLJ3, IGHM, MME, SELL, ENPP2, VCAN, LCP1, IGHD, FCGR2C, ALOX5AP, IGJ, MMP9, FABP4, IL32, HBB, FMO1, ALPK2, PLA2G7, MNDA, HLA-DRA, and SLC16A7. The results of differential analysis in another cohort were highly accordant with the findings of WGCNA. We established a comorbidity model to explain the underlying mechanism of NAFLD secondary to periodontitis. Finally, the analysis of miRNA pointed out that hsa-mir-125b-5p, hsa-mir-17-5p, and hsa-mir-21-5p might provide potential therapeutic targets.

Conclusion: Our study initially established a comorbidity model to explain the underlying mechanism of NAFLD secondary to periodontitis, found that damaged migration of DCs might be a common pathophysiological feature of NAFLD and periodontitis, and provided potential therapeutic targets.

Altered Cellular Immunity and Differentially Expressed Immune-Related Genes in Patients With Systemic Sclerosis-Associated Pulmonary Arterial Hypertension

Systemic sclerosis (SSc) is the most common connective tissue disease causing pulmonary hypertension (PAH). However, the cause and potential immune molecular events associated with PAH are still unclear. Therefore, it is particularly essential to analyze the changes in SSc-PAH–related immune cells and their immune-related genes. Three microarray datasets (GSE22356, GSE33463, and GSE19617) were obtained by the Gene Expression Omnibus (GEO). Compared with SSc, we found neutrophils have a statistically higher abundance, while T-cell CD4 naive and T-cell CD4 memory resting have a statistically lower abundance in peripheral blood mononuclear cells (PBMCs). Moreover, the results of Gene Set Enrichment Analysis (GSEA) showed there is a differential enrichment of multiple pathways between SSc and SSc-PAH. By combining differentiated expressed genes (DEGs) and immune-related genes (IRGs), fifteen IRGs were selected. In addition, we also analyzed the first five rich Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and the most abundant Gene Ontology (GO)-molecular functional terms. Furthermore, interleukin-7 receptor (IL-7R), tyrosine–protein kinase (LCK), histone deacetylase 1 (HDAC1), and epidermal growth factor receptor (EGFR) genes were identified as hub genes via protein–protein interaction (PPI) network analysis. The Comparative Toxic Genomics Database (CTD) analysis result showed that LCK, HDAC1, and EGFR have a higher score with SSc. Coexpression network analysis confirmed that IL-7R, LCK, and HDAC1 are key genes related to immune regulation in SSc without PAH and are involved in T-cell immune regulation. Subsequently, using GSE22356 and GSE33463 as the test sets and GSE19617 as the verification set, it was verified that the mRNA expression levels of the three central genes of SSc-PAH were significantly lower than those of the SSc without PAH samples. Consistent with previous predictions, the expressions of IL-7R, LCK, and HDAC1 are positively correlated with the numbers of T-cell CD4 naive and T-cell CD4 memory, while the expressions of IL-7R and LCK are negatively correlated with the numbers of neutrophils in the peripheral blood. Therefore, this evidence may suggest that these three immune-related genes: IL-7R, LCK, and HDAC1, may be highly related to the immunological changes in SSc-PAH. These three molecules can reduce T cells in SSc-PAH PBMCs through the regulation of T-cell activation, which suggests that these three molecules may be involved in the development of SSc-PAH. Meanwhile, the low expression of IL-7R, LCK, and HDAC1 detected in the peripheral blood of SSc may indicate the possibility of PAH and hopefully become a biomarker for the early detection of SSc-PAH. Finally, 49 target miRNAs of 3 specifically expressed hub genes were obtained, and 49 mRNA–miRNA pairs were identified, which provided directions for our further research.

Osteoarticular Involvement-Associated Biomarkers and Pathways in Psoriasis: The Shared Pathway With Ankylosing Spondylitis

Psoriatic arthritis (PsA) is a unique immune-mediated disease with cutaneous and osteoarticular involvement. However, only a few studies have explored the susceptibility of osteoarticular involvement in psoriasis (Ps) at the genetic level. This study investigated the biomarkers associated with osteoarticular participation and potential shared molecular mechanisms for PsA and ankylosing spondylitis (AS).

Exploring the common gene signatures and pathogeneses of obesity with Alzheimer's disease via transcriptome data

BACKGROUND

Obesity is a complex condition that influences several organ systems and physiologic systems. Obesity (OB) is closely linked to Alzheimer's disease (AD). However, the interrelationship between them remains unclear. The purpose of this study is to explore the key genes and potential molecular mechanisms in obesity and AD.

METHODS

The microarray data for OB and AD were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene correlation network analysis (WGCNA) was used to delineate the co-expression modules related to OB and AD. The shared genes existing in obesity and AD were identified through biological process analyses using the DAVID website, which then constructed the Protein-Protein Interaction (PPI) Network and selected the hub genes by Cytoscape. The results were validated in other microarray data by differential gene analysis. Moreover, the hub gene expressions were further determined in mice by qPCR.

RESULTS

The WGCNA identifies five modules and four modules as significant modules with OB and AD, respectively. Functional analysis of shared genes emphasized that inflammation response and mitochondrial functionality were common features in the pathophysiology of OB and AD. The results of differential gene analysis in other microarray data were extremely similar to them. Then six important hub genes were selected and identified using cytoHubba, including MMP9, PECAM1, C3AR1, IL1R1, PPARGC1α, and COQ3. Finally, we validated the hub gene expressions via qPCR.

CONCLUSIONS

Our work revealed the high inflammation/immune response and mitochondrial impairment in OB patients, which might be a crucial susceptibility factor for AD. Meanwhile, we identified novel gene candidates such as MMP9, PECAM1, C3AR1, IL1R1, PPARGC1α, and COQ3 that could be used as biomarkers or potential therapeutic targets for OB with AD.

Comprehensive Analysis to Identify Key Genes Involved in Advanced Atherosclerosis

Background

The study was aimed at finding accurate and effective therapeutic targets and deepening our understanding of the mechanisms of advanced atherosclerosis (AA).

Methods

We downloaded the gene expression datasets GSE28829, GSE120521, and GSE43292 from Gene Expression Omnibus. Weighted gene coexpression network analysis (WGCNA) was performed for GSE28829, and functional enrichment analysis and protein–protein interaction network analysis were conducted on the key module. Significant genes in the key module were analyzed by molecular complex detection, and genes in the most important subnetwork were defined as hub genes. Multiple dataset analyses for hub genes were conducted. Genes that overlapped between hub genes and differentially expressed genes (DEGs) of GSE28829 and GSE120521 were defined as key genes. Further validation for key genes was performed using GSE28829 and GSE43292. Gene set enrichment analysis (GSEA) was applied to key genes.

Results

A total of 77 significant genes in the key module of GSE28829 were screened out that were mainly associated with inflammation and immunity. The subnetwork was obtained from significant genes, and 18 genes in this module were defined as hub genes, which were related to immunity and expressed in multiple diseases, particularly systemic lupus erythematosus. Some hub genes were regulated by SPI1 and associated with the blood, spleen, and lung. After overlapping with DEGs of GSE28829 and GSE120521, a total of 10 genes (HCK, ITGAM, CTSS, TYROBP, LAPTM5, FCER1G, ITGB2, NCF2, AIF1, and CD86) were identified as key genes. All key genes were validated and evaluated successfully and were related to immune response pathways.

Conclusion

Our study suggests that the key genes related to immune and inflammatory responses are involved in the development of AA. This may deepen our understanding of the mechanisms of and provide valuable therapeutic targets for AA.