Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Rheumatoid Arthritis by Bioinformatics Analysis

Integration of single-cell and bulk RNA sequencing revealed immune heterogeneity and its association with disease activity in rheumatoid arthritis patients

Rheumatoid arthritis (RA) is a chronic, inflammatory, systemic autoimmune disease characterized by cartilage, bone damage, synovial inflammation, hyperplasia, autoantibody production, and systemic features. To obtain an overall profile of the immune environment in RA patients and its association with clinical features, we performed single-cell transcriptome and T-cell receptor sequencing of mononuclear cells from peripheral blood (PBMC) and synovial fluid (SF) from RA patients, integrated with two large cohorts with bulk RNA sequencing for further validation and investigation. Dendritic cells (DCs) exhibited relatively high functional heterogeneity and tissue specificity in relation to both antigen presentation and proinflammatory functions. Peripheral helper T cells (TPHs) are likely to originate from synovial tissue, undergo activation and exhaustion, and are subsequently released into the peripheral blood. Notably, among all immune cell types, TPHs were found to have the most intense associations with disease activity. In addition, CD8 effector T cells could be clustered into two groups with different cytokine expressions and play distinct roles in RA development. By integrating single-cell data with bulk sequencing from two large cohorts, we identified interactions among TPHs, CD8 cells, CD16 monocytes, and DCs that strongly contribute to the proinflammatory local environment in RA joints. Of note, the swollen 28-joint counts exhibited a more pronounced association with this immune environment compared to other disease activity indexes. The immune environment alternated significantly from PBMCs to SF, which indicated that a series of immune cells was involved in proinflammatory responses in the local joints of RA patients. By integrating single-cell data with two large cohorts, we have uncovered associations between specific immune cell populations and clinical features. This integration provides a rapid and precise methodology for assessing local immune activation, offering valuable insights into the pathophysiological mechanisms at play in RA.

Cathepsin S (CTSS) in IgA nephropathy: an exploratory study on its role as a potential diagnostic biomarker and therapeutic target

Introduction

IgA nephropathy (IgAN), a prevalent form of glomerulonephritis globally, exhibits complex pathogenesis. Cathepsins, cysteine proteases within lysosomes, are implicated in various physiological and pathological processes, including renal conditions. Prior observational studies have suggested a potential link between cathepsins and IgAN, yet the precise causal relationship remains unclear.

Methods

We conducted a comprehensive bidirectional and multivariable Mendelian randomization (MR) study using publicly available genetic data to explore the causal association between cathepsins and IgAN systematically. Additionally, immunohistochemical (IHC) staining and enzyme-linked immunosorbent assay (ELISA) were employed to evaluate cathepsin expression levels in renal tissues and serum of IgAN patients. We investigated the underlying mechanisms via gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Molecular docking and virtual screening were also performed to identify potential drug candidates through drug repositioning.

Results

Univariate MR analyses demonstrated a significant link between increased cathepsin S (CTSS) levels and a heightened risk of IgAN. This was evidenced by an odds ratio (OR) of 1.041 (95% CI=1.009-1.073, P=0.012) as estimated using the inverse variance weighting (IVW) method. In multivariable MR analysis, even after adjusting for other cathepsins, elevated CTSS levels continued to show a strong correlation with an increased risk of IgAN (IVW P=0.020, OR=1.037, 95% CI=1.006-1.069). However, reverse MR analyses did not establish a causal relationship between IgAN and various cathepsins. IHC and ELISA findings revealed significant overexpression of CTSS in both renal tissues and serum of IgAN patients compared to controls, and this high expression was unique to IgAN compared with several other primary kidney diseases such as membranous nephropathy, minimal change disease and focal segmental glomerulosclerosis. Investigations into immune cell infiltration, GSEA, and GSVA highlighted the role of CTSS expression in the immune dysregulation observed in IgAN. Molecular docking and virtual screening pinpointed Camostat mesylate, c-Kit-IN-1, and Mocetinostat as the top drug candidates for targeting CTSS.

Conclusion

Elevated CTSS levels are associated with an increased risk of IgAN, and this enzyme is notably overexpressed in IgAN patients' serum and renal tissues. CTSS could potentially act as a diagnostic biomarker, providing new avenues for diagnosing and treating IgAN.

[Identification of potential biomarkers and immunoregulatory mechanisms of rheumatoid arthritis based on multichip co-analysis of GEO database]

OBJECTIVE

To identify the biomarkers for early rheumatoid arthritis (RA) diagnosis and explore the possible immune regulatory mechanisms.

METHODS

The differentially expressed genesin RA were screened and functionally annotated using the limma, RRA, batch correction, and clusterProfiler. The protein-protein interaction network was retrieved from the STRING database, and Cytoscape 3.8.0 and GeneMANIA were used to select the key genes and predicting their interaction mechanisms. ROC curves was used to validate the accuracy of diagnostic models based on the key genes. The disease-specific immune cells were selected via machine learning, and their correlation with the key genes were analyzed using Corrplot package. Biological functions of the key genes were explored using GSEA method. The expression of STAT1 was investigated in the synovial tissue of rats with collagen-induced arthritis (CIA).

RESULTS

We identified 9 core key genes in RA (CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL), which regulate synovial inflammation primarily through cytokines-related pathways. ROC curve analysis showed a high predictive accuracy of the 9 core genes, among which STAT1 had the highest AUC (0.909). Correlation analysis revealed strong correlations of CD3G, ITGAL, LCK, CD8A, and STAT1 with disease-specific immune cells, and STAT1 showed the strongest correlation with M1-type macrophages (R=0.68, P=2.9e-08). The synovial tissues of the ankle joints of CIA rats showed high expressions of STAT1 and p-STAT1 with significant differential expression of STAT1 between the nucleus and the cytoplasm of the synovial fibroblasts. The protein expressions of p-STAT1 and STAT1 in the cell nuclei were significantly reduced after treatment.

CONCLUSION

CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL may serve as biomarkers for early diagnosis of RA. Gene-immune cell pathways such as CD3G/CD8A/LCK-γδ T cells, ITGAL-Tfh cells, and STAT1-M1-type macrophages may be closely related with the development of RA.

Joint effects of CD8A and ICOS in Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS)

BACKGROUND

Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS) are complex disorders with unclear origins, underscoring the need for in-depth molecular investigations into their mechanisms. The main aim of this study is to identify the shared key genes between LQTS and BWS, shedding light on potential common molecular pathways underlying these syndromes.

METHODS

The LQTS and BWS datasets are available for download from the GEO database. Differential expression genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) was used to detect significant modules and central genes. Gene enrichment analysis was performed. CIBERSORT was used for immune cell infiltration analysis. The predictive protein interaction (PPI) network of core genes was constructed using STRING, and miRNAs regulating central genes were screened using TargetScan.

RESULTS

Five hundred DEGs associated with Long QT Syndrome and Beckwith-Wiedemann Syndrome were identified. GSEA analysis revealed enrichment in pathways such as T cell receptor signaling, MAPK signaling, and adrenergic signaling in cardiac myocytes. Immune cell infiltration indicated higher levels of memory B cells and naive CD4 T cells. Four core genes (CD8A, ICOS, CTLA4, LCK) were identified, with CD8A and ICOS showing low expression in the syndromes and high expression in normal samples, suggesting potential inverse regulatory roles.

CONCLUSION

The expression of CD8A and ICOS is low in long QT syndrome and Beckwith-Wiedemann syndrome, indicating their potential as key genes in the pathogenesis of these syndromes. The identification of shared key genes between LQTS and BWS provides insights into common molecular mechanisms underlying these disorders, potentially facilitating the development of targeted therapeutic strategies.

Sinomenine treats rheumatoid arthritis by inhibiting MMP9 and inflammatory cytokines expression: bioinformatics analysis and experimental validation

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease marked by inflammatory cell infiltration and joint damage. The Chinese government has approved the prescription medication sinomenine (SIN), an effective anti-inflammation drug, for treating RA. This study evaluated the possible anti-inflammatory actions of SIN in RA based on bioinformatics analysis and experiments. Six microarray datasets were acquired from the gene expression omnibus (GEO) database. We used R software to identify differentially expressed genes (DEGs) and perform function evaluations. The CIBERSORT was used to calculate the abundance of 22 infiltrating immune cells. The weighted gene co-expression network analysis (WGCNA) was used to discover genes associated with M1 macrophages. Four public datasets were used to predict the genes of SIN. Following that, function enrichment analysis for hub genes was performed. The cytoHubba and least absolute shrinkage and selection operator (LASSO) were employed to select hub genes, and their diagnostic effectiveness was predicted using the receiver operator characteristic (ROC) curve. Molecular docking was undertaken to confirm the affinity between the SIN and hub gene. Furthermore, the therapeutic efficacy of SIN was validated in LPS-induced RAW264.7 cells line using Western blot and Enzyme-linked immunosorbent assay (ELISA). The matrix metalloproteinase 9 (MMP9) was identified as the hub M1 macrophages-related biomarker in RA using bioinformatic analysis and molecular docking. Our study indicated that MMP9 took part in IL-17 and TNF signaling pathways. Furthermore, we found that SIN suppresses the MMP9 protein overexpression and pro-inflammatory cytokines, including tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in the LPS-induced RAW264.7 cell line. In conclusion, our work sheds new light on the pathophysiology of RA and identifies MMP9 as a possible RA key gene. In conclusion, the above findings demonstrate that SIN, from an emerging research perspective, might be a potential cost-effective anti-inflammatory medication for treating RA.

New Classification of Rheumatoid Arthritis Based on Immune Cells and Clinical Characteristics

Background

Rheumatoid arthritis (RA) is a chronic systemic immune disease characterized by joint synovitis, but there are differences in clinical manifestations and serum test results among different patients.

Methods

This is a bioinformatics study. We first obtained the gene expression profile of RA and normal synovium from the database, and screened the differentially expressed immune related genes for enrichment analysis. Subsequently, we classified RA into three subtypes by unsupervised clustering of serum gene expression profiles based on immune enrichment scores. Then, the enrichment and clinical characteristics of different subtypes were analyzed. Finally, according to the infiltration of different subtypes of immune cells, diagnostic markers were screened and verified by qRT-PCR.

Results

C1 subtype is related to the increase of neutrophils, C-reactive protein and erythrocyte sedimentation rate, and joint pain is more significant in patients. C2 subtype is related to the expression of CD8+T cells and Tregs, and patients have mild joint pain symptoms. The RF value of C3 subtype is higher, and the expression of various immune cells is increased. CD4 T cells, NK cells activated, macrophages M1 and neutrophils are immune cells significantly infiltrated in synovium and serum of RA patients. IFNGR1, TRAC, IFITM1 can be used as diagnostic markers of different subtypes.

Conclusion

In this study, RA patients were divided into different immune molecular subtypes based on gene expression profile, and immune diagnostic markers were screened, which provided a new idea for the diagnosis and treatment of RA.

Assessing the causal role of immune traits in rheumatoid arthritis by bidirectional Mendelian randomization analysis

Rheumatoid arthritis (RA) is one of the most common autoimmune joint disorders that leads to cartilage degradation. However, its specific correlation with immune cells has not been thoroughly clarified. Based on the two-sample Mendelian randomization (MR) analysis, the association between RA and 731 immune phenotypes which include morphological parameters (MP), relative cell (RC), median fluorescence intensities (MFI), and absolute cells (AC) was comprehensively determined. After false discovery rate correction, RA and immunophenotypes were statistically associated with each other. It was observed that four immune phenotypes, including 1 MPs, 8 RCs, 15 MFIs, and 10 ACs were causally associated with the risk of RA. Meanwhile, several identified immune traits could serve as independent factors for RA and be robust against pleiotropy. While considering the role of RA in immune traits, the involvement of RA in multiple immunophenotypes including CD62L- myeloid DC AC, CD3 on secreting Treg, CD3 on activated and secreting Treg, and CD3 on CD4 Treg was revealed. This study is the first comprehensive evaluation of the interaction between immune response and RA risk, thus providing therapeutic strategies for RA from an immunological perspective.

Unveiling the link between lactate metabolism and rheumatoid arthritis through integration of bioinformatics and machine learning

Rheumatoid arthritis (RA) is a persistent autoimmune condition characterized by synovitis and joint damage. Recent findings suggest a potential link to abnormal lactate metabolism. This study aims to identify lactate metabolism-related genes (LMRGs) in RA and investigate their correlation with the molecular mechanisms of RA immunity. Data on the gene expression profiles of RA synovial tissue samples were acquired from the gene expression omnibus (GEO) database. The RA database was acquired by obtaining the common LMRDEGs, and selecting the gene collection through an SVM model. Conducting the functional enrichment analysis, followed by immuno-infiltration analysis and protein-protein interaction networks. The results revealed that as possible markers associated with lactate metabolism in RA, KCNN4 and SLC25A4 may be involved in regulating macrophage function in the immune response to RA, whereas GATA2 is involved in the immune mechanism of DC cells. In conclusion, this study utilized bioinformatics analysis and machine learning to identify biomarkers associated with lactate metabolism in RA and examined their relationship with immune cell infiltration. These findings offer novel perspectives on potential diagnostic and therapeutic targets for RA.

Mast Cells and Their Related Gene HK-1 are Closely Associated with Discogenic Low Back Pain: A Bioinformatics and Clinical Sample Study

Background

Low back pain (LBP) is primarily caused by intervertebral disc degeneration (IVDD). Immune cells penetrating nucleus pulposus (NP) tissues may play an important role in generating IVDD and LBP.

Methods

The clinical data from 100 cases of IVDD patients was initially analyzed retrospectively. Subsequently, peripheral blood and NP tissues from 41 IVDD patients were gathered for a validated investigation. Among them, ribosome-removed-RNA sequencing (RNA-seq) was performed on 10 cases of NP tissues of specific classifications (VAS 3 and Pfirrmann 3 were used as the controls, while patients with VAS 6 and Pfirrmann 5 were used as the experimental group). Differentially expressed genes (DEGs) were identified for the subsequent bioinformatics analysis. Further methods to confirm the underlying cause of discogenic LBP included mast cell immunohistochemistry (IHC), 12 cytokine detection, Western blot (WB), and real-time polymerase chain reaction (RT-PCR).

Results

Discogenic LBP and IVDD severity are strongly associated, and immunological cell infiltration has been demonstrated to be a significant factor in LBP by bioanalytical research. Tryptase-positive mast cells were found to be significantly more abundant in the VAS 6 NP tissues of IVDD patients than in the VAS 3 NP tissues. It was initially demonstrated that IVDD and LBP were significantly impacted by hemokinin-1 (HK-1), the mast cell-related gene. Furthermore, blood levels of interleukin 12 p70 (IL-12P70) are noticeably elevated and strongly correlated with HK-1, indicating that HK-1 may be involved in the regulation of mast cell activity and IL-12P70 production.

Conclusion

The severity of LBP was observed to be positively correlated with the IVDD Pfirrmann grading. Further research indicates that patients with IVDD may experience persistent low back pain due to HK-1 activation of mast cells and the release of the cytokine IL12P70. This work will offer new insights into the diagnosis and treatment of discogenic LBP.

Causal associations between rheumatoid arthritis, cataract and glaucoma in European and East Asian populations: A bidirectional two-sample mendelian randomization study

BACKGROUND

Previous studies have indicated a heightened susceptibility to cataract and glaucoma among rheumatoid arthritis (RA) patients, while it remains uncertain whether RA is causally associated with cataract and glaucoma. A two-sample mendelian randomization (MR) analysis was used to investigate the causal associations between RA, cataract and glaucoma in European and East Asian populations.

METHODS

In the European population, genome-wide association study (GWAS) summary statistics for cataract (372,386 individuals) and glaucoma (377,277 individuals) were obtained from the FinnGen consortium (R9), while RA summary data were derived from a meta-analysis of GWAS encompassing 97173 samples. In the East Asian population, summary data for cataract (212453 individuals), glaucoma (212453 individuals), and RA (22515 individuals) were sourced from the IEU Open GWAS project. Inverse-variance weighted (IVW, random-effects) method served as the primary analysis, complemented by MR‒Egger regression, weighted median, weighted mode and simple mode methods. Additionally, various sensitivity tests, including Cochran's Q test, MR‒Egger intercept, MR pleiotropy Residual Sum and Outlier test and leave-one-out test were performed to detect the heterogeneity, horizontal pleiotropy and stability of the analysis results.

RESULTS

Following stringent screening, the number of selected instrumental variables ranged from 8 to 56. The IVW results revealed that RA had an increased risk of cataract (OR = 1.041, 95% CI = 1.019-1.064; P = 2.08×10-4) and glaucoma (OR = 1.029, 95% CI = 1.003-1.057; P = 2.94×10-2) in European populations, and RA displayed a positive association with cataract (OR = 1.021, 95% CI = 1.004-1.039; P = 1.64×10-2) in East Asian populations. Other methods also supported those results by IVW, and sensitivity tests showed that our analysis results were credible and stable.

CONCLUSIONS

This study revealed a positive causality between RA and the increased risk of cataract and glaucoma, which provides guidance for the early prevention of cataracts and glaucoma in patients with RA and furnishes evidence for the impact of RA-induced inflammation on ophthalmic diseases.

Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study

Background

Rheumatoid arthritis (RA) remains one of the most prevalent chronic joint diseases. However, due to the heterogeneity among RA patients, there are still no robust diagnostic and therapeutic biomarkers for the diagnosis and treatment of RA.

Methods

We retrieved RA-related and pan-cancer information datasets from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. Unsupervised cluster analysis was also used to decipher the heterogeneity of RA. A four-signature-based nomogram was constructed and verified to specifically diagnose RA and osteoarthritis (OA) from normal tissues. Consequently, RA-HFLS cell was utilized to investigate the biological role of CRTAM in RA. In addition, comparisons of diagnostic efficacy and biological roles among CRTAM and other classic biomarkers of RA were also performed.

Results

Immune and stromal components were highly enriched in RA. Chemokine- and Th cell-related signatures were significantly activated in RA tissues. Four promising and novel biomarkers, including CRTAM, PTTG1IP, ITGB2, and MMP13, were identified and verified, which could be treated as novel treatment and diagnostic targets for RA. Nomograms based on the four signatures might aid in distinguishing and diagnosing RA, which reached a satisfactory performance in both training (AUC = 0.894) and testing (AUC = 0.843) cohorts. Two distinct subtypes of RA patients were identified, which further verified that these four signatures might be involved in the immune infiltration process. Furthermore, knockdown of CRTAM could significantly suppress the proliferation and invasion ability of RA cell line and thus could be treated as a novel therapeutic target. CRTAM owned a great diagnostic performance for RA than previous biomarkers including MMP3, S100A8, S100A9, IL6, COMP, LAG3, and ENTPD1. Mechanically, CRTAM could also be involved in the progression through immune dysfunction, fatty acid metabolism, and genomic instability across several cancer subtypes.

Conclusion

CRTAM, PTTG1IP, ITGB2, and MMP13 were highly expressed in RA tissues and might function as pivotal diagnostic and treatment targets by deteriorating the immune dysfunction state. In addition, CRTAM might fuel cancer progression through immune signals, especially among RA patients.

MULTIPLE MACHINE LEARNING METHODS AND COMPARATIVE TRANSCRIPTOMICS IDENTIFY PIVOTAL GENES FOR ISCHEMIA-REPERFUSION INJURY IN HUMAN DONOR TISSUE UNDERGOING ORTHOTOPIC LIVER TRANSPLANTATION

ABSTRACT

Background: Hepatic ischemia-reperfusion injury (HIRI) is a major complication affecting patient prognosis during the period after orthotopic liver transplantation (OLT). Although an increasing number of scientists have investigated the molecular biology of ischemia-reperfusion injury (IRI) during OLT in animal and cellular models in recent years, studies using comprehensive and high-quality sequencing results from human specimens to screen for key molecules are still lacking. Aims: The objective of this study is to explore the molecular biological pathways and key molecules associated with HIRI during OLT through RNA sequencing and related bioinformatics analysis techniques. Methods: The study was done by performing mRNA sequencing on liver tissue samples obtained from 15 cases of in situ liver transplantation patients who experienced ischemia and reperfusion injury within 1 year at Guizhou Medical University, and combined with bioinformatics analysis and machine learning methods, we identified the genes and transcription factors that are closely associated with IRI during in situ liver transplantation surgery. Results: There were 877 differentially expressed genes (DEGs) identified in the included liver samples, of which 817 DEGs were upregulated and 60 were downregulated. Functional enrichment analysis revealed significant enrichment of immune-related terms, such as inflammation, defense responses, responses to cytokines, immune system processes, and cellular activation. In addition, core gene enrichment analysis after cytoHubba screening suggested that liver reperfusion injury might be associated with translation-related elements as a pathway together with protein translation processes. Machine learning with the weighted correlation network analysis screening method identified PTGS2, IRF1, and CDKN1A as key genes in the reperfusion injury process. Conclusions: This study demonstrated that the pathways and genomes whose expression is altered throughout the reperfusion process might be critical for the progression of HIRI during OLT.

Longevity-Associated Transcription Factor ATF7 Promotes Healthspan by Suppressing Cellular Senescence and Systematic Inflammation

Aging is characterized by persistent low-grade systematic inflammation, which is largely responsible for the occurrence of various age-associated diseases. We and others have previously reported that long-lived people (such as centenarians) can delay the onset of or even escape certain major age-related diseases. Here, by screening blood transcriptome and inflammatory profiles, we found that long-lived individuals had a relatively lower inflammation level (IL6, TNFα), accompanied by up-regulation of activating transcription factor 7 (ATF7). Interestingly, ATF7 expression was gradually reduced during cellular senescence. Loss of ATF7 induced cellular senescence, while overexpression delayed senescence progress and senescence-associated secretory phenotype (SASP) secretion. We showed that the anti-senescence effects of ATF7 were achieved by inhibiting nuclear factor kappa B (NF-κB) signaling and increasing histone H3K9 dimethylation (H3K9me2). In Caenorhabditis elegans, ATF7 overexpression significantly suppressed aging biomarkers and extended lifespan. Our findings suggest that ATF7 is a longevity-promoting factor that lowers cellular senescence and inflammation in long-lived individuals.

Quercetin is a Potential Therapy for Rheumatoid Arthritis via Targeting Caspase-8 Through Ferroptosis and Pyroptosis

Background

Rheumatoid arthritis (RA) is one of the most common chronic inflammatory autoimmune diseases. However, the underlying molecular mechanisms of its pathogenesis are unknown. This study aimed to identify the common biomarkers of ferroptosis and pyroptosis in RA and screen potential drugs.

Methods

The RA-related differentially expressed genes (DEGs) in GSE55235 were screened by R software and intersected with ferroptosis and pyroptosis gene libraries to obtain differentially expressed ferroptosis-related genes (DEFRGs) and differentially expressed pyroptosis-related genes (DEPRGs). We performed Gene Ontology (GO), Kyoto Encyclopedia of the Genome (KEGG), ClueGO, and Protein-Protein Interaction (PPI) analysis for DEFRGs and DEPRGs and validated them by machine learning. The microRNA/transcription factor (TF)-hub genes regulatory network was further constructed. The key gene was validated using the GSE77298 validation set, cellular validation was performed in in vitro experiments, and immune infiltration analysis was performed using CIBERSORT. Network pharmacology was used to find key gene-targeting drugs, followed by molecular docking and molecular dynamics simulations to analyze the binding stability between small-molecule drugs and large-molecule proteins.

Results

Three hub genes (CASP8, PTGS2, and JUN) were screened via bioinformatics, and the key gene (CASP8) was validated and obtained through the validation set, and the diagnostic efficacy was verified to be excellent through the receiver operating characteristic (ROC) curves. The ferroptosis and pyroptosis phenotypes were constructed by fibroblast-like synoviocytes (FLS), and caspase-8 was detected and validated as a common biomarker for ferroptosis and pyroptosis in RA, and quercetin can reduce caspase-8 levels. Quercetin was found to be a potential target drug for caspase-8 by network pharmacology, and the stability of their binding was further verified using molecular docking and molecular dynamics simulations.

Conclusion

Caspase-8 is an important biomarker for ferroptosis and pyroptosis in RA, and quercetin is a potential therapy for RA via targeting caspase-8 through ferroptosis and pyroptosis.

Construction of an acute myeloid leukemia prognostic model based on m6A-related efferocytosis-related genes

Background

One of the most prevalent hematological system cancers is acute myeloid leukemia (AML). Efferocytosis-related genes (ERGs) and N6-methyladenosine (m6A) have an important significance in the progression of cancer, and the metastasis of tumors.

Methods

The AML-related data were collected from The Cancer Genome Atlas (TCGA; TCGA-AML) database and Gene Expression Omnibus (GEO; GSE9476, GSE71014, and GSE13159) database. The "limma" R package and Venn diagram were adopted to identify differentially expressed ERGs (DE-ERGs). The m6A related-DE-ERGs were obtained by Spearman analysis. Subsequently, univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) were used to construct an m6A related-ERGs risk signature for AML patients. The possibility of immunotherapy for AML was explored. The pRRophetic package was adopted to calculate the IC50 of drugs for the treatment of AML. Finally, the expression of characterized genes was validated by quantitative reverse transcription-PCR (qRT-PCR).

Results

Based on m6A related-DE-ERGs, a prognostic model with four characteristic genes (UCP2, DOCK1, SLC14A1, and SLC25A1) was constructed. The risk score of model was significantly associated with the immune microenvironment of AML, with four immune cell types, 14 immune checkpoints, 20 HLA family genes and, immunophenoscore (IPS) all showing differences between the high- and low-risk groups. A total of 56 drugs were predicted to differ between the two groups, of which Erlotinib, Dasatinib, BI.2536, and bortezomib have been reported to be associated with AML treatment. The qRT-PCR results showed that the expression trends of DOCK1, SLC14A1 and SLC25A1 were consistent with the bioinformatics analysis.

Conclusion

In summary, 4 m6A related- ERGs were identified and the corresponding prognostic model was constructed for AML patients. This prognostic model effectively stratified the risk of AML patients.

Integrative Analysis of Single-Cell and Bulk RNA Sequencing Reveals Prognostic Characteristics of Macrophage Polarization-Related Genes in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a group of cancers with poor prognosis. The combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) can identify important genes involved in cancer development and progression from a broader perspective.

Methods

The scRNA-seq data and bulk RNA-seq data of LUAD were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Analyzing scRNA-seq for core cells in the GSE131907 dataset, and the uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Macrophage polarization-associated subtypes were acquired from the TCGA-LUAD dataset after analysis, followed by further identification of differentially expressed genes (DEGs) in the TCGA-LUAD dataset (normal/LUAD tissue samples, two subtypes). Venn diagrams were utilized to visualize differentially expressed and highly variable macrophage polarization-related genes. Subsequently, a prognostic risk model for LUAD patients was constructed by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO), and the model was investigated for stability in the external data GSE72094. After analyzing the correlation between the trait genes and significantly mutated genes, the immune infiltration between the high/low-risk groups was then examined. The Monocle package was applied to analyze the pseudo-temporal trajectory analysis of different cell clusters in macrophage clusters. Subsequently, cell clusters of data macrophages were selected as key cell clusters to explore the role of characteristic genes in different cell populations and to identify transcription factors (TFs) that affect signature genes. Finally, qPCR were employed to validate the expression levels of prognosis signature genes in LUAD.

Results

424 macrophage highly variable genes, 3920 DEGs, and 9561 DEGs were obtained from macrophage clusters, the macrophage polarization-related subtypes, and normal/LUAD tissue samples, respectively. Twenty-eight differentially expressed and highly mutated MPRGs were obtained. A prognostic risk model with 7 DE-MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) was constructed. This prognostic model still has a good prediction effect in the GSE72094 dataset. ZNF536 and DNAH9 were mutated in the low-risk group, while COL11A1 was mutated in the high-risk group, and they were highly correlated with the characteristic genes. A total of 11 immune cells were significantly different in the high/low-risk groups. Five cell types were again identified in the macrophage cluster, and then NK cells: CD56hiCD62L+ differentiated earlier and were present mainly on 2 branches. While macrophages were present on 2 branches and differentiated later. It was found that the expression levels of BCLAF1 and MAX were higher in cluster 1, which might be the TFs affecting the expression of the characteristic genes. Moreover, qPCR confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis.

Conclusion

Seven MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) were identified as prognostic genes for LUAD and revealed the mechanisms of MPRGs at the single-cell level.

Comprehensive analysis and validation reveal potential MYCN regulatory biomarkers associated with neuroblastoma prognosis

Neuroblastoma (NB) is an embryonic malignant tumor that occurs in the sympathetic nervous system. The treatment results of patients in the high-risk group are poor, and relapse and treatment failure can occur even with multiple combination treatments. The proto-oncogene MYCN is a BHLH Transcription Factor used as an independent prognostic factor for NB. The proportion of MYCN amplification in tumor tissues of high-risk patients reaches 40-50%. Hence, exploring new MYCN target genes is a meaningful approach in developing treatment for high-risk NB patients. The microarray datasets were obtained from Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and miRPathDB were used for enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and for modular analysis. The miRNet and NetworkAnalyst databases were used to predict and construct gene-miRNA and gene-TFs networks. The R2 database was used for expression, correlation, and prognostic analyses. The diagnostic value of the biomarkers was predicted by ROC analysis, and RT-qPCR was used to validate the identified hub genes. Finally, using specific MYCN siRNA and overexpressing plasmids, the correlation between the identified hub genes and MYCN was investigated. Our results showed that FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are novel biomarkers that affect the prognosis of the NB patients. In addition, FBXO9, RNF19B, and TRIM36 were preliminarily confirmed as potential target genes of MYCN. Overall, FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are expected to become novel biomarkers for the treatment of high-risk NB patients.Communicated by Ramaswamy H. Sarma.

Therapeutic Effects of Upadacitinib on Experimental Autoimmune Uveitis: Insights From Single-Cell Analysis

Purpose

This study aimed to elucidate the impact of upadacitinib, a Janus kinase 1 (JAK1)-specific inhibitor, on experimental autoimmune uveitis (EAU) and explore its underlying mechanisms.

Methods

We utilized single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to investigate the JAK/signal transducer and activator of transcription (STAT) pathway in peripheral blood mononuclear cells (PBMCs) of 12 patients with Vogt-Koyanagi-Harada (VKH) disease and cervical draining lymph node (CDLN) cells of EAU. After treating EAU with upadacitinib, we analyzed immune cell gene expression and cell-cell communication by integrating scRNA data. Additionally, we applied flow cytometry and western blot to analyze the CDLN cells.

Results

The JAK/STAT pathway was found to be upregulated in patients with VKH disease and EAU. Upadacitinib effectively alleviated EAU symptoms, reduced JAK1 protein expression, and suppressed pathogenic CD4 T cell (CD4TC) proliferation and pathogenicity while promoting Treg proliferation. The inhibition of pathogenic CD4TCs by upadacitinib was observed in both flow cytometry and scRNA data. Additionally, upadacitinib was found to rescue the interferon-stimulated gene 15 (ISG15)+ CD4TCs and CD8 T and B cell ratios and reduce expression of inflammatory-related genes. Upadacitinib demonstrated the ability to inhibit abnormally activated cell-cell communication, particularly the CXCR4-mediated migration pathway, which has been implicated in EAU pathogenesis. CXCR4 inhibitors showed promising therapeutic effects in EAU.

Conclusions

Our findings indicate that the JAK1-mediated signaling pathway is significantly upregulated in uveitis, and upadacitinib exhibits therapeutic efficacy against EAU. Furthermore, targeting the CXCR4-mediated migration pathway could be a promising therapeutic strategy.

Identification of the Immune Landscapes and Follicular Helper T Cell-Related Genes for the Diagnosis of Age-Related Macular Degeneration

BACKGROUND

Age-related macular degeneration (AMD) is a progressive ocular ailment causing age-associated vision deterioration, characterized by dysregulated immune cell activity. Notably, follicular helper T (Tfh) cells have emerged as pivotal contributors to AMD pathogenesis. Nonetheless, investigations into Tfh-associated gene biomarkers for this disorder remain limited.

METHODS

Utilizing gene expression data pertinent to AMD procured from the Gene Expression Omnibus (GEO) repository, we employed the "DESeq2" R software package to standardize and preprocess expression levels. Concurrently, CIBERSORT analysis was utilized to compute the infiltration proportions of 22 distinct immune cell types. Subsequent to weighted gene correlation network analysis (WGCNA), coupled with differential expression scrutiny, we pinpointed genes intricately linked with Tfh cells. These potential genes underwent further screening using the MCODE function within Cytoscape software. Ultimately, a judicious selection of pivotal genes from these identified clusters was executed through the LASSO algorithm. Subsequently, a diagnostic nomogram was devised based on these selected genes.

RESULTS

Evident Tfh cell disparities between AMD and control cohorts were observed. Our amalgamated analysis, amalgamating differential expression data with co-expression patterns, unveiled six genes closely associated with Tfh cells in AMD. Subsequent employment of the LASSO algo-rithm facilitated identification of the most pertinent genes conducive to predictive modeling. From these, GABRB3, MFF, and PROX1 were elected as prospective diagnostic biomarkers for AMD.

CONCLUSIONS

This investigation discerned three novel biomarker genes, linked to inflammatory mechanisms and pivotal in diagnosing AMD. Further exploration of these genes holds potential to foster novel therapeutic modalities and augment comprehension of AMD's disease trajectory.

MFAP2 enhances cisplatin resistance in gastric cancer cells by regulating autophagy

Background

Cisplatin (CDDP) is of importance in cancer treatment and widely used in advanced gastric cancer (GC). However, its clinical usage is limited due to its resistance, and the regulatory mechanism of CDDP resistance in GC has not yet been fully elucidated. In this study, we first conducted a comprehensive study to investigate the role of MFAP2 through bioinformatics analysis.

Methods

The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were applied to downloadgene expression data and clinicopathologic data, and the differentially expressed genes (DEGs) were further analyzed. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and survival analysis were conducted. Furthermore, according to the clinicopathological characteristics of TCGA, clinical correlation analysis was conducted, and a receiver operating characteristic curve (ROC) was plotted.

Results

We revealed that FAP, INHBA and MFAP2 were good diagnostic factors of GC. However, the mechanism of MFAP2 in GC remains elusive, especially in the aspect of chemotherapy resistance. We developed the CDDP-resistant cell line, and found that MFAP2 was upregulated in CDDP-resistant cells, and MFAP2-knockdown improved CDDP sensitivity. Finally, we found that MFAP2 enhanced CDDP resistance by inducing autophagy in drug-resistant cell lines.

Conclusions

The above results suggested that MFAP2 could affect the chemotherapy resistance by altering the level of autophagy in GC patients as a potential therapeutic target.

Role of ceRNA network in inflammatory cells of rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease caused by inflammatory cells. Various inflammatory cells involved in RA include fibroblast-like synoviocytes, macrophages, CD4+T-lymphocytes, B lymphocytes, osteoclasts and chondrocytes. The close interaction between various inflammatory cells leads to imbalance of immune response and disorder of the expression of mRNA in inflammatory cells. It helps to drive production of pro-inflammatory cytokines and stimulate specific antigen-specific T- and B-lymphocytes to produce autoantibodies which is an important pathogenic factor for RA. Competing endogenous RNA (ceRNA) can regulate the expression of mRNA by competitively binding to miRNA. The related ceRNA network is a new regulatory mechanism for RNA interaction. It has been found to be involved in the regulation of abnormal biological processes such as proliferation, apoptosis, invasion and release of inflammatory factors of RA inflammatory cells. Understanding the ceRNA network in 6 kinds of RA common inflammatory cells provides a new idea for further elucidating the pathogenesis of RA, and provides a theoretical basis for the discovery of new biomarkers and effective therapeutic targets.

Predictive model, miRNA-TF network, related subgroup identification and drug prediction of ischemic stroke complicated with mental disorders based on genes related to gut microbiome

Background

Patients with comorbid schizophrenia, depression, drug use, and multiple psychiatric diagnoses have a greater risk of carotid revascularization following stroke. The gut microbiome (GM) plays a crucial role in the attack of mental illness and IS, which may become an index for the diagnosis of IS. A genomic study of the genetic commonalities between SC and IS, as well as its mediated pathways and immune infiltration, will be conducted to determine how schizophrenia contributes to the high prevalence of IS. According to our study, this could be an indicator of ischemic stroke development.

Methods

We selected two datasets of IS from the Gene Expression Omnibus (GEO), one for training and the other for the verification group. Five genes related to mental disorders and GM were extracted from Gene cards and other databases. Linear models for microarray data (Limma) analysis was utilized to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. It was also used to conduct machine learning exercises such as random forest and regression to identify the best candidate for immune-related central genes. Protein-protein interaction (PPI) network and artificial neural network (ANN) were established for verification. The receiver operating characteristic (ROC) curve was drawn for the diagnosis of IS, and the diagnostic model was verified by qRT-PCR. Further immune cell infiltration analysis was performed to study the IS immune cell imbalance. We also performed consensus clustering (CC) to analyze the expression of candidate models under different subtypes. Finally, miRNA, transcription factors (TFs), and drugs related to candidate genes were collected through the Network analyst online platform.

Results

Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93-0.71) and the verification group (AUC 0.81, CI 0.90-0.72) had a good phenotype in the qRT-PCR test. And in verification group 2 we validated between the two groups with and without carotid-related ischemic cerebrovascular events (AUC 0.87, CI 1-0.64). Furthermore, we investigated cytokines in both GSEA and immune infiltration and verified cytokine-related responses by flow cytometry, particularly IL-6, which played an important role in IS occurrence and progression. Therefore, we speculate that mental illness may affect the development of IS in B cells and IL-6 in T cells. MiRNA (hsa-mir-129-2-3p, has-mir-335-5p, and has-mir-16-5p) and TFs (CREB1, FOXL1), which may be related to IS, were obtained.

Conclusion

Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93-0.71) and the verification group (AUC 0.81, CI 0.90-0.72) had a good phenotype in the qRT-PCR test. And in verification group 2 we validated between the two groups with and without carotid-related ischemic cerebrovascular events (AUC 0.87, CI 1-0.64). MiRNA (hsa-mir-129-2-3p, has-mir-335-5p, and has-mir-16-5p) and TFs (CREB1, FOXL1), which may be related to IS, were obtained.

Macrophages in immunoregulation and therapeutics

Macrophages exist in various tissues, several body cavities, and around mucosal surfaces and are a vital part of the innate immune system for host defense against many pathogens and cancers. Macrophages possess binary M1/M2 macrophage polarization settings, which perform a central role in an array of immune tasks via intrinsic signal cascades and, therefore, must be precisely regulated. Many crucial questions about macrophage signaling and immune modulation are yet to be uncovered. In addition, the clinical importance of tumor-associated macrophages is becoming more widely recognized as significant progress has been made in understanding their biology. Moreover, they are an integral part of the tumor microenvironment, playing a part in the regulation of a wide variety of processes including angiogenesis, extracellular matrix transformation, cancer cell proliferation, metastasis, immunosuppression, and resistance to chemotherapeutic and checkpoint blockade immunotherapies. Herein, we discuss immune regulation in macrophage polarization and signaling, mechanical stresses and modulation, metabolic signaling pathways, mitochondrial and transcriptional, and epigenetic regulation. Furthermore, we have broadly extended the understanding of macrophages in extracellular traps and the essential roles of autophagy and aging in regulating macrophage functions. Moreover, we discussed recent advances in macrophages-mediated immune regulation of autoimmune diseases and tumorigenesis. Lastly, we discussed targeted macrophage therapy to portray prospective targets for therapeutic strategies in health and diseases.

Identifying TNF and IL6 as potential hub genes and targeted drugs associated with scleritis: A bio-informative report

Background

Scleritis is a serious inflammatory eye disease that can lead to blindness. The etiology and pathogenesis of scleritis remain unclear, and increasing evidence indicates that some specific genes and proteins are involved. This study aimed to identify pivotal genes and drug targets for scleritis, thus providing new directions for the treatment of this disease.

Methods

We screened candidate genes and proteins associated with scleritis by text-mining the PubMed database using Python, and assessed their functions by using the DAVID database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to identify the functional enrichment of these genes and proteins. Then, the hub genes were identified with CytoHubba and assessed by protein-protein interaction (PPI) network analysis. And the serum from patients with active scleritis and healthy subjects were used for the validation of hub genes. Finally, the DGIdb database was used to predict targeted drugs for the hub genes for treating scleritis.

Results

A total of 56 genes and proteins were found to be linked to scleritis, and 65 significantly altered pathways were identified in the KEGG analysis (FDR < 0.05). Most of the top five pathways involved the categories “Rheumatoid arthritis,” “Inflammatory bowel disease”, “Type I diabetes mellitus,” and “Graft-versus-host disease”. TNF and IL6 were considered to be the top 2 hub genes through CytoHubba. Based on our serum samples, hub genes are expressed at high levels in active scleritis. Five scleritis-targeting drugs were found among 88 identified drugs.

Conclusions

This study provides key genes and drug targets related to scleritis through bioinformatics analysis. TNF and IL6 are considered key mediators and possible drug targets of scleritis. Five drug candidates may play an important role in the diagnosis and treatment of scleritis in the future, which is worthy of the further experimental and clinical study.

Identification and preliminary validation of synovial tissue-specific genes and their-mediated biological mechanisms in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease. It is well known that the formation of positive feedback between synovial hyperplasia and inflammatory infiltration is intimately associated with the occurrence and development of RA. However, the exact mechanisms still remain unknown, making the early diagnosis and therapy of RA difficult. This study was designed to identify prospective diagnostic and therapeutic biomarkers, as well as their-mediated biological mechanisms in RA.

METHODS

Three microarray datasets (GSE36700, GSE77298 and GSE153015) and two RNA-sequencing datasets (GSE89408 and GSE112656) of synovial tissues, as well as three other microarray datasets (GSE101193, GSE134087 and GSE94519) of peripheral blood were downloaded for integrated analysis. The differently expressed genes (DEGs) were identified by "limma" package of R software. Then, weight gene co-expression analysis and gene set enrichment analysis were performed to investigate synovial tissue-specific genes and their-mediated biological mechanisms in RA. The expression of candidate genes and their diagnostic value for RA were verified by quantitative real-time PCR and receiver operating characteristic (ROC) curve, respectively. Relevant biological mechanisms were explored through cell proliferation and colony formation assay. The suggestive anti-RA compounds were discovered by CMap analysis.

RESULTS

We identified a total of 266 DEGs, which were mainly enriched in cellular proliferation and migration, infection and inflammatory immune signaling pathways. Bioinformatics analysis and molecular validation revealed 5 synovial tissue-specific genes, which exhibited excellent diagnostic value for RA. The infiltration level of immune cells in RA synovial tissue was significantly higher than that in control individuals. Moreover, preliminary molecular experiments suggested that these characteristic genes may be responsible for the high proliferation potential of RA fibroblast-like synoviocytes (FLSs). Finally, 8 small molecular compounds with anti-RA potential were obtained.

CONCLUSIONS

We have proposed 5 potential diagnostic and therapeutic biomarkers (CDK1, TTK, HMMR, DLGAP5, and SKA3) in synovial tissues that may contribute to the pathogenesis of RA. These findings may shed light on the early diagnosis and therapy of RA.

Role and mechanism of fibroblast-activated protein-α expression on the surface of fibroblast-like synoviocytes in rheumatoid arthritis

Fibroblast-activated protein-α (FAP) is a type II integrated serine protease expressed by activated fibroblasts during fibrosis or inflammation. Fibroblast-like synoviocytes (FLSs) in rheumatoid arthritis (RA) synovial sites abundantly and stably overexpress FAP and play important roles in regulating the cellular immune, inflammatory, invasion, migration, proliferation, and angiogenesis responses in the synovial region. Overexpression of FAP is regulated by the initial inflammatory microenvironment of the disease and epigenetic signaling, which promotes RA development by regulating FLSs or affecting the signaling cross-linking FLSs with other cells at the local synovium and inflammatory stimulation. At present, several treatment options targeting FAP are in the process of development. This review discusses the basic features of FAP expressed on the surface of FLSs and its role in RA pathophysiology and advances in targeted therapies.

Identification of a novel immune landscape signature as effective diagnostic markers related to immune cell infiltration in diabetic nephropathy

Background

The study aimed to identify core biomarkers related to diagnosis and immune microenvironment regulation and explore the immune molecular mechanism of diabetic nephropathy (DN) through bioinformatics analysis.

Methods

GSE30529, GSE99325, and GSE104954 were merged with removing batch effects, and different expression genes (DEGs) were screened at a criterion |log2FC| >0.5 and adjusted P <0.05. KEGG, GO, and GSEA analyses were performed. Hub genes were screened by conducting PPI networks and calculating node genes using five algorithms with CytoHubba, followed by LASSO and ROC analysis to accurately identify diagnostic biomarkers. In addition, two different GEO datasets, GSE175759 and GSE47184, and an experiment cohort with 30 controls and 40 DN patients detected by IHC, were used to validate the biomarkers. Moreover, ssGSEA was performed to analyze the immune microenvironment in DN. Wilcoxon test and LASSO regression were used to determine the core immune signatures. The correlation between biomarkers and crucial immune signatures was calculated by Spearman analysis. Finally, cMap was used to explore potential drugs treating renal tubule injury in DN patients.

Results

A total of 509 DEGs, including 338 upregulated and 171 downregulated genes, were screened out. “chemokine signaling pathway” and “cell adhesion molecules” were enriched in both GSEA and KEGG analysis. CCR2, CX3CR1, and SELP, especially for the combination model of the three genes, were identified as core biomarkers with high diagnostic capabilities with striking AUC, sensitivity, and specificity in both merged and validated datasets and IHC validation. Immune infiltration analysis showed a notable infiltration advantage for APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. In addition, the correlation analysis showed that CCR2, CX3CR1, and SELP were strongly and positively correlated with checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. Finally, dilazep was screened out as an underlying compound for DN analyzed by CMap.

Conclusions

CCR2, CX3CR1, and SELP are underlying diagnostic biomarkers for DN, especially in their combination. APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation may participate in the occurrence and development of DN. At last, dilazep may be a promising drug for treating DN.

Identification of the candidate genes of diagnosing rheumatoid arthritis using the single-cell sequencing technology and T cell subclusters analysis of patients with rheumatoid arthritis

Objectives: This study aims to analyze the heterogeneity among different cell types in peripheral blood mononuclear cells (PBMC) in rheumatoid arthritis (RA) patients and to analyze T cell subsets to obtain key genes that may lead to RA.

Materials and methods: The sequencing data of 10,483 cells were obtained from the GEO data platform. The data were filtered and normalized initially and, then, principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (TSNE) cluster analysis were performed using the Seurat package in R language to group the cells, thereby obtaining the T cells. The T cells were subjected to subcluster analysis. The differentially expressed genes (DEGs) in T cell subclusters were obtained, and the hub genes were determined by Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) network construction. Finally, the hub genes were validated using other datasets in the GEO data platform.

Results: The PBMC of RA patients were mainly divided into T cells, natural killer (NK) cells, B cells, and monocyte cells. The number of T cells was 4,483, which were further divided into seven clusters. The pseudotime trajectory analysis showed that the differentiation of T cells developed from cluster 0 and cluster 1 to cluster 5 and cluster 6. Through GO, KEGG and PPI analysis, the hub genes were identified. After validation by external data sets, nine genes were identified as candidate genes highly associated with the occurrence of RA, including CD8A, CCL5, GZMB, NKG7, PRF1, GZMH, CCR7, GZMK, and GZMA.

Conclusion: Based on single-cell sequencing analysis, we identified nine candidate genes for diagnosing RA, and validated their diagnostic value for RA patients. Our findings may provide new sights for the diagnosis and treatment of RA.

Identification of key genes and pathways associated with sex differences in rheumatoid arthritis based on bioinformatics analysis

INTRODUCTION

Women are more likely than men to develop the chronic, progressive autoimmune disease known as rheumatoid arthritis (RA). Although there may be a complex interplay between sex-based differences and autoimmune dysfunction. Their function in RA is largely unknown, though. The purpose of this study was to pinpoint the crucial genes and metabolic pathways that control biological variations in RA between men and women.

METHODS

First, the Gene Expression Omnibus database's gene expression information for GSE39340 and GSE55457 was downloaded (GEO). R software was used to find each of the individually identified differentially expressed genes (DEGs) between the sexes. DEGs that overlapped were found. The interactions between the overlapping DEGs were then further examined using a protein-protein interaction (PPI) network. The Kyoto Encyclopedia of Genes and Genomes and Gene Ontology tools, respectively, were used to perform enrichment analyses.

RESULTS

According to our findings, there were 1169 DEGs that overlapped between RA males and females, comprising 845 up-regulated genes and 324 down-regulated genes. Ten hub genes, including PIK3R1, RAC1, HRAS, PTPN11, UQCRB, NDUFV1, EGF, UBA1, UBE2G1, and UBE2E1, were discovered in the PPI network. According to a functional enrichment analysis, these genes were primarily enriched in neurodegenerative illnesses, including various disease pathways, MAPK signaling, insulin signaling, and autophagy.

CONCLUSION

The current data point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease. Key Points • Our research point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. • PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease. • These findings may aid in the development of novel diagnostic and treatment techniques for RA in men and women.

Novel diagnostic biomarkers related to immune infiltration in Parkinson's disease by bioinformatics analysis

Background

Parkinson's disease (PD) is Pengfei Zhang Liwen Zhao Pengfei Zhang Liwen Zhao a common neurological disorder involving a complex relationship with immune infiltration. Therefore, we aimed to explore PD immune infiltration patterns and identify novel immune-related diagnostic biomarkers.

Materials and methods

Three substantia nigra expression microarray datasets were integrated with elimination of batch effects. Differentially expressed genes (DEGs) were screened using the "limma" package, and functional enrichment was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to explore the key module most significantly associated with PD; the intersection of DEGs and the key module in WGCNA were considered common genes (CGs). The CG protein-protein interaction (PPI) network was constructed to identify candidate hub genes by cytoscape. Candidate hub genes were verified by another two datasets. Receiver operating characteristic curve analysis was used to evaluate the hub gene diagnostic ability, with further gene set enrichment analysis (GSEA). The immune infiltration level was evaluated by ssGSEA and CIBERSORT methods. Spearman correlation analysis was used to evaluate the hub genes association with immune cells. Finally, a nomogram model and microRNA-TF-mRNA network were constructed based on immune-related biomarkers.

Results

A total of 263 CGs were identified by the intersection of 319 DEGs and 1539 genes in the key turquoise module. Eleven candidate hub genes were screened by the R package "UpSet." We verified the candidate hub genes based on two validation sets and identified six (SYT1, NEFM, NEFL, SNAP25, GAP43, and GRIA1) that distinguish the PD group from healthy controls. Both CIBERSORT and ssGSEA revealed a significantly increased proportion of neutrophils in the PD group. Correlation between immune cells and hub genes showed SYT1, NEFM, GAP43, and GRIA1 to be significantly related to immune cells. Moreover, the microRNA-TFs-mRNA network revealed that the microRNA-92a family targets all four immune-related genes in PD pathogenesis. Finally, a nomogram exhibited a reliable capability of predicting PD based on the four immune-related genes (AUC = 0.905).

Conclusion

By affecting immune infiltration, SYT1, NEFM, GAP43, and GRIA1, which are regulated by the microRNA-92a family, were identified as diagnostic biomarkers of PD. The correlation of these four genes with neutrophils and the microRNA-92a family in PD needs further investigation.

Identification and validation of immune cells and hub genes alterations in recurrent implantation failure: A GEO data mining study

Introduction: Recurrent implantation failure (RIF) is a distressing problem in assisted reproductive technology (ART). Immunity plays a vital role in recurrent implantation failure (RIF) occurrence and development, but its underlying mechanism still needs to be fully elucidated. Through bioinformatics analysis, this study aims to identify the RIF-associated immune cell types and immune-related genes. Methods: The differentially expressed genes (DEGs) were screened based on RIF-associated Gene Expression Omnibus (GEO) datasets. Then, the enrichment analysis and protein-protein interaction (PPI) analysis were conducted with the DEGs. The RIF-associated immune cell types were clarified by combining single sample gene set enrichment analysis (ssGSEA) and CIBERSORT. Differentially expressed immune cell types-related modules were identified by weighted gene co-expression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. The overlapping genes between DEGs and genes contained by modules mentioned above were delineated as candidate hub genes and validated in another two external datasets. Finally, the microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) that interacted with hub genes were predicted, and the competing endogenous RNA (ceRNA) regulatory network was structured. Results: In the present study, we collected 324 DEGs between RIF and the control group, which functions were mainly enriched in immune-related signaling pathways. Regarding differential cell types, the RIF group had a higher proportion of activated memory CD4 T cells and a lower proportion of γδ T cells in the endometrial tissue. Finally, three immune-related hub genes (ALOX5AP, SLC7A7, and PTGS2) were identified and verified to effectively discriminate RIF from control individuals with a specificity rate of 90.8% and a sensitivity rate of 90.8%. In addition, we constructed a key ceRNA network that is expected to mediate molecular mechanisms in RIF. Conclusion: Our study identified the intricate correlation between immune cell types and RIF and provided new immune-related hub genes that offer promising diagnostic and therapeutic targets for RIF.

CD8A is a Promising Biomarker Associated with Immunocytes Infiltration in Hyperoxia-Induced Bronchopulmonary Dysplasia

Background

Bronchopulmonary dysplasia (BPD) refers to a chronic lung disease which is commonly observed in preterm infants. It can usually be caused by several pathological processes that endanger the long-term lung development, such as inflammation and immune dysfunction.

Methods

In this study, a bioinformatics approach was applied to identify the differentially expressed immune-related genes (DEIRGs). We downloaded the transcriptional profiles (GSE32472 dataset) from the Gene Expression Omnibus (GEO) database and performed gene set enrichment analysis (GSEA). Cell type Identification By Estimating Relative Subsets of RNA Transcripts (CIBERSORT), microenvironment cell populations counter (MCPcounter), and Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) were used for the analysis of the immune cell infiltration landscape of BPD. A weighted co-expression network was subsequently constructed using weighted gene co-expression network analysis (WGCNA) to screen candidate differentially expressed immune related genes (DEIRGs).

Results

GSEA results indicated that immune-related pathways were mainly involved in BPD. Ten significantly different immune cell types were observed between BPD and normal groups. A total of 228 DEGs in the turquoise module were identified, and 31 DEIRGs were further identified. Cluster of the differentiation 8 alpha (CD8A) expression was down-regulated in BPD, and its expression was validated by the GSE25286, GSE25293, GSE99633 datasets and qRT-PCR. In addition, CD8A expression was closely associated with immune cells infiltration, especially T cells CD8 and neutrophil.

Conclusion

A distinct immune cell infiltration landscape was found between BPD and normal group. CD8A can be a novel candidate biomarker for BPD, which plays an essential role in the onset and progress of hyperoxia-related BPD via the disruption of immune cell functions.

Identification of candidate aberrant differentially methylated/expressed genes in asthma

BACKGROUND

Asthma is an important non-communicable disease worldwide. DNA methylation is associated with the occurrence and development of asthma. We are aimed at assuring differential expressed genes (DEGs) modified by aberrantly methylated genes (DMGs) and pathways related to asthma by integrating bioinformatics analysis.

METHODS

One mRNA dataset (GSE64913) and one gene methylation dataset (GSE137716) were selected from the Gene Expression Omnibus (GEO) database. Functional enrichment analysis was performed using GeneCodies 4.0 database. All gene expression matrices were analyzed by Gene set enrichment analysis (GSEA) software. STRING was applied to construct a protein-protein interaction (PPI) network to find the hub genes. Then, electronic validation was performed to verify the hub genes, followed by the evaluation of diagnostic value. Eventually, quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to detect the expression of hub genes.

RESULTS

In total, 14 hypomethylated/high-expression genes and 10 hypermethylated/low-expression genes were obtained in asthma. Among them, 10 hub genes were identified in the PPI network. Functional analysis demonstrated that the differentially methylated/expressed genes were primarily associated with the lung development, cytosol and protein binding. Notably, HLA-DOA was enriched in asthma. FKBP5, WNT5A, TM4SF1, PDK4, EPAS1 and GMPR had potential diagnostic value for asthma.

CONCLUSION

The project explored the pathogenesis of asthma, which may provide a research basis for the prediction and the drug development of asthma.

Role of m6A modification and novel circ_0066715/ miR-486-5p/ ETS1 axis in rheumatoid arthritis macrophage polarization progression

Rheumatoid arthritis (RA) is a systemic disease dominated by inflammatory synovitis. RA synovial macrophages tend undergo M1-type macrophage polarization. Then, polarized M1-type macrophages secrete abundant pro-inflammatory cytokines, causing joint and cartilage destruction. N6-methyladenosine (m6A) methylation modification, circular RNA (circRNA), microRNA (miRNA), messenger RNA (mRNA), etc. are involved in the inflammatory response of RA. We found that there is an imbalance of inflammatory polarization in RA, which is manifested by a sharp increase in inflammatory markers and a high inflammatory response. Here, we show that RA was closely associated with low expression of circ_0066715. The overexpression of circ_0066715 significantly increased the ETS1 levels in RA-FLS cells, decreased cytokine secretion by M1-type macrophages, elevated M2-type cytokines, and inhibited FLS proliferation. Interestingly, the overexpression of miR-486-5p significantly suppressed the attenuation of the cell function and the effect on M1 macrophage polarization caused by circ_0066715 positive expression. WTAP may be involved in the methylation process of ETS1 in RA. ETS1 m6A methylation levels were altered upon WTAP intervention. The overexpression or interference of circ_0066715 decreased or increased WTAP expression. Our findings provide a novel circRNA/miRNA/mRNA regulatory axis and m6A regulatory mechanism involved in the process of RA macrophage polarization, thereby providing a powerful diagnostic and therapeutic strategy for RA treatment.

Identification of key genes in late-onset major depressive disorder through a co-expression network module

Late-onset major depressive disorder (LOD) increases the risk of disability and suicide in elderly patients. However, the complex pathological mechanism of LOD still remains unclear. We selected 10 LOD patients and 12 healthy control samples from the GSE76826 dataset for statistical analysis. Under the screening criteria, 811 differentially expressed genes (DEGs) were screened. We obtained a total of two most clinically significant modules through the weighted gene co-expression network analysis (WGCNA). Functional analysis of the genes in the most clinically significant modules was performed to explore the potential mechanism of LOD, followed by protein-protein interaction (PPI) analysis and hub gene identification in the core area of the PPI network. Furthermore, we identified immune infiltrating cells using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm between healthy subjects and LOD patients with the GSE98793 dataset. Next, six hub genes (CD27, IL7R, CXCL1, CCR7, IGLL5, and CD79A) were obtained by intersecting hub genes with DEGs, followed by verifying the diagnostic accuracy with the receiver operating characteristic curve (ROC). In addition, we constructed the least absolute shrinkage and selection operator (LASSO) regression model for hub gene cross-validation. Finally, we found that CD27 and IGLL5 were good diagnostic indicators of LOD, and CD27 may be the key gene of immune function change in LOD. In conclusion, our research shows that the changes in the immune function may be an important mechanism in the development of LOD, which can provide some guidance for the related research of LOD in the future.

Identification of biomarkers associated with CD8+ T cells in rheumatoid arthritis and their pan-cancer analysis

Introduction

Rheumatoid arthritis (RA), a prevailing chronic progressive autoimmune disease, seriously affects the patient's quality of life. However, there is still a lack of precise treatment and management methods in clinical practice. Previous studies showed that CD8+ T cells take a lead in the progression of RA.

Methods

Genes closely related to CD8+T cells in RA were identified through multiple RA datasets, CIBERSORT, and WGCNA algorithms. Further machine learning analysis were performed to identify CD8+T cell-related genes most closely related to RA. In addition, the relationship between these three key genes and 33 cancer species was also explored in this study.

Results

In this study, 10 genes were identified to be closely related to CD8+T cells in RA. Machine learning analysis identified 3 CD8+T cell-related genes most closely related to RA: CD8A, GZMA, and PRF1.

Discussion

Our research aims to provide new ideas for the clinical treatment of RA.

Machine learning-based identification of a novel prognosis-related long noncoding RNA signature for gastric cancer

Gastric cancer (GC) is one of the most common malignancies with a poor prognosis. Immunotherapy has attracted much attention as a treatment for a wide range of cancers, including GC. However, not all patients respond to immunotherapy. New models are urgently needed to accurately predict the prognosis and the efficacy of immunotherapy in patients with GC. Long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and progression of cancers. Recent studies have identified a variety of prognosis-related lncRNA signatures in multiple cancers. However, these studies have some limitations. In the present study, we developed an integrative analysis to screen risk prediction models using various feature selection methods, such as univariate and multivariate Cox regression, least absolute shrinkage and selection operator (LASSO), stepwise selection techniques, subset selection, and a combination of the aforementioned methods. We constructed a 9-lncRNA signature for predicting the prognosis of GC patients in The Cancer Genome Atlas (TCGA) cohort using a machine learning algorithm. After obtaining a risk model from the training cohort, we further validated the model for predicting the prognosis in the test cohort, the entire dataset and two external GEO datasets. Then we explored the roles of the risk model in predicting immune cell infiltration, immunotherapeutic responses and genomic mutations. The results revealed that this risk model held promise for predicting the prognostic outcomes and immunotherapeutic responses of GC patients. Our findings provide ideas for integrating multiple screening methods for risk modeling through machine learning algorithms.

Machine learning and bioinformatics-based insights into the potential targets of saponins in Paris polyphylla smith against non-small cell lung cancer

Background: Lung cancer has the highest mortality rate among cancers worldwide, and non-small cell lung cancer (NSCLC) is the major lethal factor. Saponins in Paris polyphylla smith exhibit antitumor activity against non-small cell lung cancer, but their targets are not fully understood.

Methods: In this study, we used differential gene analysis, lasso regression analysis and support vector machine recursive feature elimination (SVM-RFE) to screen potential key genes for NSCLC by using relevant datasets from the GEO database. The accuracy of the signature genes was verified by using ROC curves and gene expression values. Screening of potential active ingredients for the treatment of NSCLC by molecular docking of the reported active ingredients of saponins in Paris polyphylla Smith with the screened signature genes. The activity of the screened components and their effects on key genes expression were further validated by CCK-8, flow cytometry (apoptosis and cycling) and qPCR.

Results: 204 differential genes and two key genes (RHEBL1, RNPC3) stood out in the bioinformatics analysis. Overall survival (OS), First-progression survival (FP) and post-progression survival (PPS) analysis revealed that low expression of RHEBL1 and high expression of RNPC3 indicated good prognosis. In addition, Polyphyllin VI(PPVI) and Protodioscin (Prot) effectively inhibited the proliferation of non-small cell lung cancer cell line with IC50 of 4.46 μM ± 0.69 μM and 8.09 μM ± 0.67μM, respectively. The number of apoptotic cells increased significantly with increasing concentrations of PPVI and Prot. Prot induces G1/G0 phase cell cycle arrest and PPVI induces G2/M phase cell cycle arrest. After PPVI and Prot acted on this cell line for 48 h, the expression of RHEBL1 and RNPC3 was found to be consistent with the results of bioinformatics analysis.

Conclusion: This study identified two potential key genes (RHEBL1 and RNPC3) in NSCLC. Additionally, PPVI and Prot may act on RHEBL1 and RNPC3 to affect NSCLC. Our findings provide a reference for clinical treatment of NSCLC.

Identification and Validation of Hub Genes for Predicting Treatment Targets and Immune Landscape in Rheumatoid Arthritis

Background

Rheumatoid arthritis (RA) is recognized as a chronic inflammatory disease featured by pathological synovial inflammation. Currently, the underlying pathophysiological mechanisms of RA remain unclear. In the study, we attempted to explore the underlying mechanisms of RA and provide potential targets for the therapy of RA via bioinformatics analysis.

Methods

We downloaded four microarray datasets (GSE77298, GSE55235, GSE12021, and GSE55457) from the GEO database. Firstly, GSE77298 and GSE55457 were identified DEGs by the “limma” and “sva” packages of R software. Then, we performed GO, KEGG, and GSEA enrichment analyses to further analyze the function of DEGs. Hub genes were screened using LASSO analysis and SVM-RFE analysis. To further explore the differences of the expression of hub genes in healthy control and RA patient synovial tissues, we calculated the ROC curves and AUC. The expression levels of hub genes were verified in synovial tissues of normal and RA rats by qRT-PCR and western blot. Furthermore, the CIBERSORTx was implemented to assess the differences of infiltration in 22 immune cells between normal and RA synovial tissues. We explored the association between hub genes and infiltrating immune cells.

Results

CRTAM, CXCL13, and LRRC15 were identified as RA's potential hub genes by machine learning and LASSO algorithms. In addition, we verified the expression levels of three hub genes in the synovial tissue of normal and RA rats by PCR and western blot. Moreover, immune cell infiltration analysis showed that plasma cells, T follicular helper cells, M0 macrophages, M1 macrophages, and gamma delta T cells may be engaged in the development and progression of RA.

Conclusions

In brief, our study identified and validated that three hub genes CRTAM, CXCL13, and LRRC15 might involve in the pathological development of RA, which could provide novel perspectives for the diagnosis and treatment with RA.

Construction and analysis of a lncRNA–miRNA–mRNA competing endogenous RNA network from inflamed and normal synovial tissues after anterior cruciate ligament and/or meniscus injuries

Background: Despite ample evidence demonstrating that anterior cruciate ligament (ACL) and meniscus tears are associated with posttraumatic osteoarthritis (PTOA) development, the contributing factors remain unknown. Synovial inflammation has recently been recognized as a pivotal factor in the pathogenesis of OA. However, there is a lack of data on synovial profiles after ACL or meniscus injuries, which may contribute to PTOA.

Methods: Twelve patients with ACL tears and/or meniscus injuries were recruited. During surgery, synovial tissues were obtained from the injured knees. The inflammation status of the synovium was characterized according to macroscopic criteria and histological synovitis grades. Then the synovial tissues were classified as control group or inflamed group. High-throughput RNA sequencing of the synovial samples (3 vs. 3) was conducted to identify differentially expressed (DE) RNAs. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein–protein interaction (PPI) analyses were performed to investigate DE mRNAs. Next, competing endogenous RNA (ceRNA) networks were constructed based on bioinformatics analyses. Associations of the identified DE genes (DEGs) with infiltrating immune cells were explored using Pearson correlation analysis.

Results: The results showed that 2793 mRNAs, 3392 lncRNAs and 211 miRNAs were significantly DE between two groups. The top 3 significantly upregulated GO terms and KEGG pathways were immune response, adaptive immune response and immune system process, systemic lupus erythematosus, haematopoietic cell lineage and cytokine–cytokine receptor interaction, respectively. In PPI networks, the top 10 hub genes were IL6, CCR7, C3, CCR5, CXCR3, CXCL8, IL2, CCR3, CCR2 and CXCL1. Seven mRNAs (EPHA5, GSN, ORC1, TLN2, SOX6, NKD2 and ADAMTS19), 4 lncRNAs (MIR4435-2HG, TNXA, CEROX1 and TMEM92-AS1) and 3 miRNAs (miR-486-5p, miR-199a-3p and miR-21-3p) were validated by quantitative real-time polymerase chain reaction and sub-networks were constructed. In correlation analysis, MMP9 correlated positively with M0 macrophages and plasma cells, NKD2 positively with CD8 T cells, and CCR7 and IL2RB positively with naive B cells.

Conclusion: Our study provides foundational synovial inflammation profiles following knee trauma. The ceRNA and PPI networks provide new insight into the biological processes and underlying mechanisms of PTOA. The differential infiltration profiles of immune cells in synovium may contribute to PTOA development. This study also highlights immune-related DEGs as potential PTOA treatment biomarkers.

Comprehensive Analysis of GDF10 Methylation Site-Associated Genes as Prognostic Markers for Endometrial Cancer

Growth differentiation factor-10 (GDF10) with its methylation trait has recently been found to play a crucial regulatory and communication role in cancers. This investigation aims to identify GDF10 methylation site-associated genes that are closely associated with endometrial cancer (EC) patients' survival based on normal and UCEC samples from the UCSC Xena database. Our study revealed for the first time that EC exhibited significantly higher levels of GDF10 promoter methylation in comparison with normal tissues. Multiple differentiated methylation sites, which have prognostic value due to their apparent survival differences, were found in the GDF10 promoter region. We performed weighted gene coexpression network analysis (WGCNA) on EC tissues and paraneoplastic tissues while using these differentially methylated sites as phenotypes for selecting the most correlated key modules and their internal genes. To obtain a gene set, the key module genes and differentially expressed genes (DEGs) of EC were intersected. The least absolute shrinkage and selection operator (LASSO) regression along with multivariate Cox regression were performed from the gene set and we screened out the key genes B4GALNT3, DNAJC22, and GREB1. Finally, a prognostic model was validated for effectiveness based on these genes. Additionally, Kaplan-Meier analysis and time-dependent receiver operating characteristics (ROC) were applied to assess and verify the model, and they showed good prognosis prediction. Moreover, the differences in risk scores were statistically significant with age, tumor stage, and grade. They may be related to the immune infiltration of tumors as well. In conclusion, based on the methylation-related genes associated with GDF10, we developed a prognosis model for EC patients. It might provide a fresh view for further research and treatment of EC.

AKAP12 and RNF11 as Diagnostic Markers of Fibromyalgia and Their Correlation with Immune Infiltration

Fibromyalgia (FM) is a chronic nonarticular rheumatic disease mainly characterized by diffuse disseminated skeletal muscle pain, with varied symptoms including anxiety, sleep disturbance, and fatigue. Due to its unknown etiology and pathogenesis, FM is easily ignored in clinical practice, resulting in unclear diagnosis and difficult treatment. This study is aimed at investigating whether AKAP12 and RNF11 can be used as biomarkers for the diagnosis of FM and at determining their correlation with immune infiltration. The FM dataset in Gene Expression Omnibus (GEO) database was downloaded and was randomly divided into the training and test sets. Differentially expressed genes (DEGs) were screened, and functional correlation analysis was performed. Diagnostic markers of FM were screened and validated by random forest (RF). The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm was then used to evaluate immune cell infiltration in the FM patients' peripheral blood. Finally, Spearman's rank correlation analysis was used to identify correlation between the diagnostic indexes and immune cell infiltration. A total of 69 DEGs were selected. Results indicated that AKAP12 and RNF11 can be used as diagnostic markers of FM, and CD8 + T cells might contribute in the pathogenesis of FM. In addition, AKAP12 was positively correlated with CD8 + T cells, while RNF11 was negatively correlated with CD8 + T cells. In conclusion, AKAP12 and RNF11 can be used as diagnostic indicators of FM, and CD8 + T cells may be involved in the occurrence and development of FM.

Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease with symptoms characterized by typical circadian rhythmic changes. This study aimed to identify the hub circadian rhythm genes (CRGs) in RA and explore their association with immune cell infiltration and pathogenesis of RA.

Methods

The differentially expressed CRGs (DECRGs) between RA and normal control samples were screened from Datasets GSE12021 and GSE55235. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to explore the potential functional mechanisms of DECRGs in RA. Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator regression analysis were performed to identify hub CRGs of RA. CIBERSORT was conducted to compare the infiltration level of immune cells in RA and control synovial tissue and their relationship with hub genes. In addition, the diagnostic value of hub biomarkers was evaluated by the area under the receiver operator characteristic curve. Further, a nomogram prediction model was constructed and its significance for clinical decision-making was evaluated.

Results

The green module was identified as the hub module associated with RA. Four hub CRGs (EGR1, FOSL2, GADD45B, and NFIL3) were identified and showed that they had the highest specificity and sensitivity for RA diagnosis, respectively. The expression levels and diagnostic values of these genes were externally validated in the dataset GSE55457. A nomogram prediction model based on the four hub CRGs was constructed and proved to have a certain clinical decision value. Additionally, the correlation analysis of immune cells with hub genes showed that all hub genes were significantly positively correlated with activated mast cells, resting memory CD4+ T cells, and monocytes. Whereas, all hub genes were negatively correlated with plasma cells, CD8+ T cells, and activated memory CD4+ T cells. Meanwhile, FOSL2 and GADD45B were negatively correlated with Tfh cells.

Conclusion

Four hub CRGs were identified and showed excellent diagnostic value for RA. These genes may be involved in the pathological process of RA by disrupting the rhythmic oscillations of cytokines through immune-related pathways and could be considered molecular targets for future chronotherapy against RA.

Bioinformatics analyses of potential ACLF biological mechanisms and identification of immune-related hub genes and vital miRNAs

Acute-on-chronic liver failure (ACLF) is a critical and refractory disease and a hepatic disorder accompanied by immune dysfunction. Thus, it is essential to explore key immune-related genes of ACLF and investigate its mechanisms. We used two public datasets (GSE142255 and GSE168048) to perform various bioinformatics analyses, including WGCNA, CIBERSORT, and GSEA. We also constructed an ACLF immune-related protein-protein interaction (PPI) network to obtain hub differentially expressed genes (DEGs) and predict corresponding miRNAs. Finally, an ACLF rat model was established to verify the results. A total of 388 DEGs were identified in ACLF, including 162 upregulated and 226 downregulated genes. The enrichment analyses revealed that these DEGs were mainly involved in inflammatory-immune responses and biosynthetic metabolic pathways. Twenty-eight gene modules were obtained using WGCNA and the coral1 and darkseagreen4 modules were highly correlated with M1 macrophage polarization. As a result, 10 hub genes and 2 miRNAs were identified to be significantly altered in ACLF. The bioinformatics analyses of the two datasets presented valuable insights into the pathogenesis and screening of hub genes of ACLF. These results might contribute to a better understanding of the potential molecular mechanisms of ACLF. Finally, further studies are required to validate our current findings.

Identification of Effective Diagnostic Biomarkers and Immune Cell Infiltration in Atopic Dermatitis by Comprehensive Bioinformatics Analysis

Background: Atopic dermatitis (AD) is a dermatological disorder characterized by symptoms such as chronically inflamed skin and frequently intolerable itching. The mechanism underlying AD development is still unclear. Our study aims to identify the diagnostic and therapeutic biomarkers for AD and provide insight into immune mechanisms at the molecular level through bioinformatics analysis.

Methods: The GSE6012, GSE32924, and GSE36842 gene expression profiles were obtained for analysis from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were segregated using the “Batch correction” and “RobustRankAggreg” methods. Weighted gene co-expression network analysis (WGCNA) was performed to screen for module genes with AD traits. Then, common DEGs (co-DEGs) were screened out via combined differential expression analysis and WGCNA. Functional enrichment analysis was performed for these co-DEGs using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), followed by protein-protein interaction network analysis. Candidate hub genes were identified using the “cytoHubba” plugin in Cytoscape, and their value for AD diagnosis was validated using receiver operating characteristic curve analysis in the external database GSE120721. Immunohistochemical staining was performed for further validation. The CIBERSORT algorithm was used to evaluate skin samples obtained from healthy controls (HCs) and lesions of AD patients, to determine the extent of immune cell infiltration. The association between the identified hub genes and significant differential immune cells was analyzed using Pearson correlation analysis.

Results: A total of 259 DEGs were acquired from the intersection of DEGs obtained by the two independent procedures, and 331 AD-trait module genes were separated out from the blue module via WGCNA analysis. Then, 169 co-DEGs arising from the intersection of the 259 DEGs and the 331 AD-trait module genes were obtained. We found that co-DEGs were significantly enhanced in the type I interferon and IL-17 signal transduction pathways. Thirteen potential hub genes were identified using Cytoscape. Five hub genes (CCR7, CXCL10, IRF7, MMP1, and RRM2) were identified after screening via external dataset validation and immunohistochemical analysis. We also identified four significant differential immune cells, i.e., activated dendritic cells, plasma cells, resting mast cells, and CD4+ naïve T cells, between AD patients and HCs. Moreover, the relationship between the identified hub genes and significant differential immune cells was analyzed. The results showed that the CCR7 expression level was positively correlated with the number of CD4+ naïve T cells (R = 0.42, p = 0.011).

Conclusion: CCR7, CXCL10, IRF7, MMP1, and RRM2 could be potential diagnostic and therapeutic biomarkers for AD. CCR7 expression level was positively correlated with the number of CD4+ naïve T cells in AD. These findings need to be corroborated in future studies.

Predicted Immune-Related Genes and Subtypes in Systemic Lupus Erythematosus Based on Immune Infiltration Analysis

Objective

The present investigation is aimed at identifying key immune-related genes linked with SLE and their roles using integrative analysis of publically available gene expression datasets.

Methods

Four gene expression datasets pertaining to SLE, 2 from whole blood and 2 experimental PMBC, were sourced from GEO. Shared differentially expressed genes (DEG) were determined as SLE-related genes. Immune cell infiltration analysis was performed using CIBERSORT, and case samples were subjected to k-means cluster analysis using high-abundance immune cells. Key immune-related SLE genes were identified using correlation analysis with high-abundance immune cells and subjected to functional enrichment analysis for enriched Gene Ontology Biological Processes and KEGG pathways. A PPI network of genes interacting with the key immune-related SLE genes was constructed. LASSO regression analysis was performed to identify the most significant key immune-related SLE genes, and correlation with clinicopathological features was examined.

Results

309 SLE-related genes were identified and found functionally enriched in several pathways related to regulation of viral defenses and T cell functions. k-means cluster analysis identified 2 sample clusters which significantly differed in monocytes, dendritic cell resting, and neutrophil abundance. 65 immune-related SLE genes were identified, functionally enriched in immune response-related signaling, antigen receptor-mediated signaling, and T cell receptor signaling, along with Th17, Th1, and Th2 cell differentiation, IL-17, NF-kappa B, and VEGF signaling pathways. LASSO regression identified 9 key immune-related genes: DUSP7, DYSF, KCNA3, P2RY10, S100A12, SLC38A1, TLR2, TSR2, and TXN. Imputed neutrophil percentage was consistent with their expression pattern, whereas anti-Ro showed the inverse pattern as gene expression.

Conclusions

Comprehensive bioinformatics analyses revealed 9 key immune-related genes and their associated functions highly pertinent to SLE pathogenesis, subtypes, and identified valuable candidates for experimental research.

EA, Wang M, Yan H, Huang X, Zeng S, Zha D. Analysis and Experimental Validation of Rheumatoid Arthritis Innate Immunity Gene CYFIP2 and Pan-Cancer

Rheumatoid arthritis (RA) is a chronic, heterogeneous autoimmune disease. Its high disability rate has a serious impact on society and individuals, but there is still a lack of effective and reliable diagnostic markers and therapeutic targets for RA. In this study, we integrated RA patient information from three GEO databases for differential gene expression analysis. Additionally, we also obtained pan-cancer-related genes from the TCGA and GTEx databases. For RA-related differential genes, we performed functional enrichment analysis and constructed a weighted gene co-expression network (WGCNA). Then, we obtained 490 key genes by intersecting the significant module genes selected by WGCNA and the differential genes. After using the RanddomForest, SVM-REF, and LASSO three algorithms to analyze these key genes and take the intersection, based on the four core genes (BTN3A2, CYFIP2, ST8SIA1, and TYMS) that we found, we constructed an RA diagnosis. The nomogram model showed good reliability and validity after evaluation, and the ROC curves of the four genes showed that these four genes played an important role in the pathogenesis of RA. After further gene correlation analysis, immune infiltration analysis, and mouse gene expression validation, we finally selected CYFIP2 as the cut-in gene for pan-cancer analysis. The results of the pan-cancer analysis showed that CYFIP2 was closely related to the prognosis of patients with various tumors, the degree of immune cell infiltration, as well as TMB, MSI, and other indicators, suggesting that this gene may be a potential intervention target for human diseases including RA and tumors.

G-Protein-Coupled Receptors in Rheumatoid Arthritis: Recent Insights into Mechanisms and Functional Roles

Rheumatoid arthritis (RA) is a chronic inflammatory disease that leads to joint damage and even disability. Although there are various clinical therapies for RA, some patients still have poor or no response. Thus, the development of new drug targets remains a high priority. In this review, we discuss the role of G-protein-coupled receptors (GPCRs), including chemokine receptors, melanocortin receptors, lipid metabolism-related receptors, adenosine receptors, and other inflammation-related receptors, on mechanisms of RA, such as inflammation, lipid metabolism, angiogenesis, and bone destruction. Additionally, we summarize the latest clinical trials on GPCR targeting to provide a theoretical basis and guidance for the development of innovative GPCR-based clinical drugs for RA.

Identification of CXCR4 Upregulation in Diffuse Large B-Cell Lymphoma Associated with Prognostic Significance and Clinicopathological Characteristics

Background

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous malignant lymphoma with distinct characteristics. Patients with treatment failure after the standard immunochemotherapy have worse prognosis, which implies the necessity to uncover novel targets. The C-X-C chemokine receptor 4 (CXCR4) overexpression has been identified in several hematopoietic malignancies. However, the expression signatures and prognostic significance of CXCR4 in DLBCL associated with clinicopathological features remain unclear.

Methods

Gene expression profiles of DLBCL were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then, a meta-analysis with an integrated bioinformatic analysis was performed to assess the relationship between CXCR4 expression and clinicopathological features of DLBCL. Finally, experimental verification including immunohistochemical (IHC) staining and real-time quantitative PCR (qPCR) was carried out using patient samples. In vitro cell line viability tests were conducted using CXCR4 inhibitor WZ811.

Results

DLBCL patients with activated B-cell-like (ABC) subtype have higher expression level of CXCR4 with worse survival. Differential expressed genes in the CXCR4-upregulation group were enriched in canonical pathways associated with oncogenesis. DLBCL with CXCR4 upregulation had lower degree of CD8⁺ T cell infiltration. TIMER analysis demonstrated that the CXCR4 expression was positively correlated with the expression of CD5, MYC, NOTCH1, PDCD1, CD274, mTOR, FOXO1, and hnRNPA2B1 in DLBCL. IHC study in patient samples showed the positive correlation between CXCR4 and nongerminal center B-cell (non-GCB) subtype and mTOR expression. Meanwhile, quantitative polymerase chain reaction results revealed that high CXCR4 mRNA level was correlated to double-hit DLBCL. Finally, cell viability test showed that WZ811 exerted antiproliferation effect in DLBCL cell lines in a dose-dependent manner.

Conclusion

CXCR4 was upregulated in ABC-DLBCL associated with worse prognosis. Our analysis predicted CXCR4 as a potential target for DLBCL treatment, which may serve as an inhibitor both on BCR signaling and nuclear export warranting further investigation in clinical trials.

B Cells on the Stage of Inflammation in Juvenile Idiopathic Arthritis: Leading or Supporting Actors in Disease Pathogenesis?

Juvenile idiopathic arthritis (JIA) is a term that collectively refers to a group of chronic childhood arthritides, which together constitute the most common rheumatic condition in children. The International League of Associations for Rheumatology (ILAR) criteria define seven categories of JIA: oligoarticular, polyarticular rheumatoid factor (RF) negative (RF-), polyarticular RF positive (RF+), systemic, enthesitis-related arthritis, psoriatic arthritis, and undifferentiated arthritis. The ILAR classification includes persistent and extended oligoarthritis as subcategories of oligoarticular JIA, but not as distinct categories. JIA is characterized by a chronic inflammatory process affecting the synovia that begins before the age of 16 and persists at least 6 weeks. If not treated, JIA can cause significant disability and loss of quality of life. Treatment of JIA is adjusted according to the severity of the disease as combinations of non-steroidal anti-inflammatory drugs (NSAIDs), synthetic and/ or biological disease modifying anti-rheumatic drugs (DMARDs). Although the disease etiology is unknown, disturbances in innate and adaptive immune responses have been implicated in JIA development. B cells may have important roles in JIA pathogenesis through autoantibody production, antigen presentation, cytokine release and/ or T cell activation. The study of B cells has not been extensively explored in JIA, but evidence from the literature suggests that B cells might have indeed a relevant role in JIA pathophysiology. The detection of autoantibodies such as antinuclear antibodies (ANA), RF and anti-citrullinated protein antibodies (ACPA) in JIA patients supports a breakdown in B cell tolerance. Furthermore, alterations in B cell subpopulations have been documented in peripheral blood and synovial fluid from JIA patients. In fact, altered B cell homeostasis, B cell differentiation and B cell hyperactivity have been described in JIA. Of note, B cell depletion therapy with rituximab has been shown to be an effective and well-tolerated treatment in children with JIA, which further supports B cell intervention in disease development.