Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis

Apigenin exerts anti-cancer effects in colon cancer by targeting HSP90AA1

Apigenin, a flavonoid, has shown early promise in colon cancer (CC); thus, exploring potential mechanisms of Apigenin is obligatory. In this study, shared targets of Apigenin and CC were identified through online tools, which were then subjected to functional enrichment analyses, Gene Ontology and KEGG. Further, the protein-protein interaction network of the shared targets was developed (via STRING). The top targets of Apigenin in CC were identified by molecular docking; further investigated for differential gene and protein expression in CC and their influence on CC patient survival (using TCGA data). Out of 13 hub genes, the top 3 targets (HSP90AA1, MMP9, PTGS2) were selected based on docking score. Their expression was significantly elevated and related to poor overall survival in CC (except PTGS2). Molecular dynamics simulation further validated protein-ligand interactions and divulged HSP90AA1 as the best target of Apigenin in CC. Finally, the anti-cancer effects of Apigenin and its major metabolite, luteolin, were investigated in CC, which is involved in the cytotoxicity of CC cells (COLO-205) by reducing HSP90AA1 expression revealed by real-time PCR. Thus, HSP90AA1 was identified as one of the prime targets of Apigenin in CC, and Apigenin could be effective against CC.Communicated by Ramaswamy H. Sarma.

Comprehending interaction mechanism of natural actives of Colchicum autumnale L. for rheumatoid arthritis using integrative chemoinformatic approaches

This research delves into the realm of therapeutic potential within natural compounds derived from Colchicum autumnale L., emphasizing a holistic perspective on medications used in human therapy. Rather than confining the study to their primary actions, the research endeavors to unveil molecular targets for these natural compounds, with a specific focus on their potential applicability in the treatment of rheumatoid arthritis (RA). The study focuses on understanding interactions between specific natural actives that target RA. Fifteen RA target proteins were identified from OMIM, GeneScan and PharmaGKB. Their structures were downloaded from RCSB PDB. Two active components of C. autumnale L. were chosen for mass spectrometry investigation. Ligand characteristics were determined using the ADMETlab and SwissADME software tools. Molecular docking was performed, and the top three complexes were simulated for 200 ns, along with identification of free binding energies. The compounds β-sitosterol-IL-10 (-6.50 kcal/mol), colchicine-IL-10 (-6.01 kcal/mol), linoleic acid-IL-10 (-7.22 kcal/mol) and linoleic acid-IL-10 (-7.22 kcal/mol) exhibited best binding energies. β-Sitosterol and colchicine showed the highest stability in simulations, confirmed by molecular mechanics free energy binding calculations. This work provides insights into the molecular interaction of natural compounds against RA targets, offering potential therapeutic anti-RA medications.Communicated by Ramaswamy H. Sarma.

Characterization of lncRNA-associated ceRNA network to uncover novel potential biomarkers in coronary artery disease

The purpose of this study was to construct a competitive endogenous RNA (ceRNA) network related to long non-coding RNA (lncRNAs) via the bioinformatics analysis, reveal the pathogenesis of coronary heart disease (CAD) and develop new biomarkers for CAD. The gene expression datasets of peripheral blood of CAD were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed mRNAs, miRNAs and lncRNAs (DEmRNAs, DEmiRNAs and DElncRNAs) were identified. Subsequently, a ceRNA network involving lncRNAs, miRNAs, and mRNAs was built. Moreover, DElncRNAs in the cytoplasm were screened and a DElncRNA-associated ceRNA network was established. In total, 1860 DEmRNAs, 393 DElncRNAs and 20 DEmiRNAs were filtrated in patients with CAD compared with normal controls. Functional analysis suggested that DEmRNAs significantly enriched in CAD-related pathways, such as PI3K-Akt signaling pathways and MAPK signaling pathway. The ceRNA network contained 12 DEmiRNAs, 30 DElncRNAs and 537 DEmRNAs. Afterwards, the cytoplasm ceRNA network was consisted of 537 DEmRNAs, 12 DEmiRNAs and 12 DElncRNAs. Such as, up-regulated LncRNA-HOX transcript antisense RNA (HOTAIR) was interacted with down-regulated has-miR-326 and has-miR-1. The successful construction of lncRNA-associated ceRNA network is helpful to better clarify the pathogenesis of CAD and provide potential peripheral blood biomarkers for CAD.

TTC4 inhibits NLRP3 inflammation in rheumatoid arthritis by HSP70

OBJECTIVE

This experiment explored the function of TTC4 in rheumatoid arthritis inflammation and its possible mechanism.

METHODS

C57BL/6 mice were immunized intradermally with bovine type II collagen. Lipopolysaccharide induction was performed on RAW264.7 cells.

RESULTS

The mRNA expression of TTC4 in articular tissue of mice with rheumatoid arthritis was downregulated. Sh-TTC4 virus increased arthritis score, morphological change score, paw edema, and spleen index, as well as alkaline phosphatase level in mice with rheumatoid arthritis. Sh-TTC4 virus increased the levels of inflammatory factors and MDA, and decreased anti-oxidant factors in articular tissue of mice with rheumatoid arthritis. TTC4 reduced inflammation and oxidative stress in an in vitro model. TTC4 regulated HSP70 in a rheumatoid arthritis model. The inhibition of HSP70 reduced the effects of sh-TTC4 gene in mice with rheumatoid arthritis. METTL3 reduced the stability of the TTC4 gene.

CONCLUSION

In this study, the TTC4 gene reduced oxidative response and inflammation in the rheumatoid arthritis model through the HSP70/NLRP3 pathway. Therefore, it can be concluded that TTC4 can be used as diagnosis and prognosis evaluation of rheumatoid arthritis.

Identification and validation of immune-related hub genes based on machine learning in prostate cancer and AOX1 is an oxidative stress-related biomarker

To investigate potential diagnostic and prognostic biomarkers associated with prostate cancer (PCa), we obtained gene expression data from six datasets in the Gene Expression Omnibus (GEO) database. The datasets included 127 PCa cases and 52 normal controls. We filtered for differentially expressed genes (DEGs) and identified candidate PCa biomarkers using a least absolute shrinkage and selector operation (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. A difference analysis was conducted on these genes in the test group. The discriminating ability of the train group was determined using the area under the receiver operating characteristic curve (AUC) value, with hub genes defined as those having an AUC greater than 85%. The expression levels and diagnostic utility of the biomarkers in PCa were further confirmed in the GSE69223 and GSE71016 datasets. Finally, the invasion of cells per sample was assessed using the CIBERSORT algorithm and the ESTIMATE technique. The possible prostate cancer (PCa) diagnostic biomarkers AOX1, APOC1, ARMCX1, FLRT3, GSTM2, and HPN were identified and validated using the GSE69223 and GSE71016 datasets. Among these biomarkers, AOX1 was found to be associated with oxidative stress and could potentially serve as a prognostic biomarker. Experimental validations showed that AOX1 expression was low in PCa cell lines. Overexpression of AOX1 significantly reduced the proliferation and migration of PCa cells, suggesting that the anti-tumor effect of AOX1 may be attributed to its impact on oxidative stress. Our study employed a comprehensive approach to identify PCa biomarkers and investigate the role of cell infiltration in PCa.

IFIH1 was predicted as a key biomarker in primary Sjögren's syndrome based on transcriptome analysis and experimental verification in patients and mice

OBJECTIVES

To investigate the novel key genes and biological processes that may lead to primary Sjögren' s syndrome (pSS).

METHODS

We downloaded datasets about peripheral blood samples of pSS patients and healthy controls (GSE51092, GSE84844, and GSE66795) from Gene Expression Omnibus database. The weighted co-expression network analysis and differential expression analysis first were implemented. After that, protein-protein network interaction and Support Vector Machines were applied in the meantime to take intersection for key genes. Moreover, we conducted immune cell infiltration analysis to explore the relationship between the gene expression and concentration of immune cells in peripheral blood. Lastly, the expression of key genes was verified in pSS patients and murine models by reverse-transcription polymerase chain reaction. Meanwhile, correlation analysis of gene expression and disease activity was also performed.

RESULTS

Only 1 key gene, interferon induced with helicase c domain 1 (IFIH1), was identified to be both significantly up-regulated and important for the diagnosis of pSS. The increased expression of IFIH1 in peripheral blood was confirmed in data sets, patients and non-obese diabetic (NOD) mice. Its expression was correlated with disease activity in patients as well. In addition, the IFIH1 expression was also increased in spleen and salivary glands infiltrated with lymphocytes in NOD mice. Furthermore, immune cell infiltration analysis showed that the expression of IFIH1 was positively correlated with the proportion of memory B cells and activated dendritic cells, and negatively correlated with the proportion of macrophage M0.

CONCLUSIONS

Here, bioinformatics analyses and experimental assays were performed to provide a new insight for understanding of pSS. IFIH1 may be a new diagnostic marker or therapeutic target for pSS.

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.

Exploration and validation of the influence of angiogenesis-related factors in aortic valve calcification

Over the years, bioinformatics tools have been used to identify functional genes. In the present study, bioinformatics analyses were conducted to explore the underlying molecular mechanisms of angiogenic factors in calcific aortic valve disease (CAVD). The raw gene expression profiles were from datasets GSE153555, GSE83453, and GSE51472, and the angiogenesis-related gene set was from the Gene Set Enrichment Analysis database (GSEA). In this study, R was used to screen for differentially expressed genes (DEGs) and co-expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) Pathway enrichment analysis were performed on DEGs and validated in clinical samples. DEGs in CAVD were significantly enriched in numerous immune response pathways, inflammatory response pathways and angiogenesis-related pathways. Nine highly expressed angiogenesis-related genes were identified, of which secretogranin II (SCG2) was the most critical gene. MiRNA and transcription factors (TFs) networks were established centered on five DEGs, and zinc finger E-box binding homeobox 1 (ZEB1) was the most important transcription factor, verified by PCR, immunohistochemical staining and western blotting experiments. Overall, this study identified key genes and TFs that may be involved in the pathogenesis of CAVD and may have promising applications in the treatment of CAVD.

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 MMP9 as a Novel Biomarker to Mitochondrial Metabolism Disorder and Oxidative Stress in Calcific Aortic Valve Stenosis

Calcific aortic valve stenosis (CAVS) is the most common heart valve disorder among humans. To date, no effective method has been identified to prevent this disease. Herein, we aimed to identify novel diagnostic and mitochondria-related biomarkers of CAVS, based on two machine learning algorithms. We further explored their association with infiltrating immune cells and studied their potential function in CAVS. The GSE12644, GSE51472, and GSE83453 expression profiles were downloaded from the Gene Expression Omnibus (GEO) repository. The GSE12644 and GSE51472 datasets were integrated to identify differentially expressed genes (DEGs). GSE12644 contains 10 normal and 10 CAVS samples, whereas GSE51472 contains 5 normal and 10 CAVS samples. GO and KEGG assays of DEGs were conducted, and the correlation between matrix metalloproteinase 9 (MMP9) expression and immune cell infiltration was explored, using CIBERSORT. The LASSO regression model and SVM-RFE analysis were used to identify diagnostic genes. The expression of MMP9 in CAVS and non-CAVS samples was measured using RT-PCR, western blotting and immunohistochemistry. A series of functional experiments were performed to explore the potential role of MMP9 in mitochondrial metabolism and oxidative stress during CAVS progression. Twenty-two DEGs were identified, of which six genes (SCG2, PPBP, TREM1, CCL19, WIF1, and MMP9) were ultimately distinguished as diagnostic genes in CAVS. Of these, MMP9 was indicated as a mitochondria-related gene, the expression and diagnostic value of which were further confirmed in the GSE83453 dataset. Correlation analysis revealed a positive correlation between MMP9 and infiltrating immune cells. In our cohort, MMP9 expression was distinctly increased in CAVS samples, and its inhibition attenuated the calcification of valve interstitial cells (VICs) by suppressing mitochondrial damage and oxidative stress. Taken together, our findings suggest MMP9 as a novel mitochondrial dysfunction biomarker and therapeutic target for CAVS.

Integrated bioinformatics analysis identifies the effects of Sema3A/NRP1 signaling in oligodendrocytes after spinal cord injury in rats

Objective

To investigate the effect of Sema3A/NRP1 signaling in oligodendrocytes (OLs) after spinal cord injury.

Methods

Three analysis strategies, namely differential expression gene analysis, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were applied. The protein-protein interaction (PPI) network was constructed using the STRING website to explore the correlation between Sema3A/NRP1 and oligodendrocytes. Then, the T10 spinal cord segment of rats was injured by the Allen method to establish a spinal cord injury (SCI) model. Real-time quantitative PCR, Western blotting, Nissl staining and immunofluorescence staining were used to detect the effect of Sema3A/NRP1 signaling on oligodendrocytes in vivo.

Results

After the SCI model was established, significantly fewer oligodendrocytes were observed. At the same time, R software was used to analyze the expression of related genes, and NRP1 expression was increased. PCR also demonstrated similar results, and NRP1 ligand Sema3A was also upregulated. KEGG and GO functional enrichment analysis indicated that the SCI model was mainly related to cytokine interaction, cell proliferation, differentiation and maturation. Interestingly, we found that NRP1 was involved in semaphorin-plexin signaling pathway neuronal projection guidance and axon guidance, mediating cell growth and migration. Moreover, Sema3A/NRP1 signaling was closely associated with platelet-derived growth factor receptor α (PDGFRα) in the PPI network. When Sema3A/NRP1 signaling was specifically blocked at early stages, PDGFRα expression was effectively inhibited, and the expression of OLs was promoted. Furthermore, inhibition of Sema3A/NRP1 signaling increased the Basso-Beattie-Bresnahan (BBB) score of lower limb motor function in SCI rats and promoted the survival of motor neurons in the ventral horn of the injured spinal cord.

Conclusion

Our data suggest that Sema3A/NRP1 signaling may regulate the development of OPCs and OLs after SCI, thereby affecting functional recovery.

RNA-seq and Network Analysis Reveal Unique Chemokine Activity Signatures in the Synovial Tissue of Patients With Rheumatoid Arthritis

Purpose

This study aimed to provide a comprehensive understanding of the genome-wide expression patterns in the synovial tissue samples of patients with rheumatoid arthritis (RA) to investigate the potential mechanisms regulating RA occurrence and development.

Methods

Transcription profiles of the synovial tissue samples from nine patients with RA and 15 patients with osteoarthritis (OA) (control) from the East Asian population were generated using RNA sequencing (RNA-seq). Gene set enrichment analysis (GSEA) was used to analyze all the detected genes and the differentially expressed genes (DEGs) were identified using DESeq. To further analyze the DEGs, the Gene Ontology (GO) functional enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. The protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the hub genes were identified by topology clustering with the Molecular Complex Detection (MCODE)-Cytoscape. The most important hub genes were validated using quantitative real-time PCR (qRT-PCR).

Results

Of the 17,736 genes detected, 851 genes were identified as the DEGs (474 upregulated and 377 downregulated genes) using the false discovery rate (FDR) approach. GSEA revealed that the significantly enriched gene sets that positively correlated with RA were CD40 signaling overactivation, Th1 cytotoxic module, overactivation of the immune response, adaptive immune response, effective vs. memory CD8+ T cells (upregulated), and naïve vs. effective CD8+ T cells (downregulated). Biological process enrichment analysis showed that the DEGs were significantly enriched for signal transduction (P = 3.01 × 10-6), immune response (P = 1.65 × 10-24), and inflammatory response (P = 5.76 × 10-10). Molecule function enrichment analysis revealed that the DEGs were enriched in calcium ion binding (P = 1.26 × 10-5), receptor binding (P = 1.26 × 10-5), and cytokine activity (P = 2.01 × 10-3). Cellular component enrichment analysis revealed that the DEGs were significantly enriched in the plasma membrane (P = 1.91 × 10-31), an integral component of the membrane (P = 7.39 × 10-13), and extracellular region (P = 7.63 × 10-11). The KEGG pathway analysis showed that the DEGs were enriched in the cytokine-cytokine receptor interaction (P = 3.05 × 10-17), chemokine signaling (P = 3.50 × 10-7), T-cell receptor signaling (P = 5.17 × 10-4), and RA (P = 5.17 × 10-4) pathways. We confirmed that RA was correlated with the upregulation of the PPI network hub genes, such as CXCL13, CXCL6, CCR5, CXCR5, CCR2, CXCL3, and CXCL10, and the downregulation of the PPI network hub gene such as SSTR1.

Conclusion

This study identified and validated the DEGs in the synovial tissue samples of patients with RA, which highlighted the activity of a subset of chemokine genes, thereby providing novel insights into the molecular mechanisms of RA pathogenesis and identifying potential diagnostic and therapeutic targets for RA.

Bioinformatics study of the potential therapeutic effects of ginsenoside Rf in reversing nonalcoholic fatty liver disease

OBJECTIVE

Ginsenoside Rf, a tetracyclic triterpenoid only present in Panax ginseng, has been proven to relieve lipid metabolism and inflammatory reactions, which can be a potential treatment for nonalcoholic fatty liver disease (NAFLD). Therefore, this study aimed to reveal the underlying mechanisms of ginsenoside Rf in the treatment of early-stage NAFLD (NAFL) by using a bioinformatics method and biological experiments.

METHODS

Target genes associated with NAFL were screened from the Gene Expression Omnibus (GEO) database, a database repository of high-throughput gene expression data and hybridization arrays, chips, and microarrays. Subsequently, gene set enrichment analysis was performed by using Gene Ontology enrichment analysis tool. Then, the binding capacity between ginsenoside Rf and NAFL-related targets was evaluated by molecular docking. Finally, the FFA-induced HepG2 cell model treated with ginsenoside Rf was adopted to verify the effect of ginsenoside Rf and the related mechanisms.

RESULTS

There were 41 common differentially expressed genes in the GEO dataset. Gene Ontology and Reactome pathway enrichment analysis of the differentially expressed genes showed that many pathways could be related to the pathogenesis of NAFL, including those participating in the cytokine-mediated signaling pathway, G protein-coupled receptor signaling pathway, and response to lipopolysaccharide. Finally, the qRT-PCR analysis results indicated that ginsenoside Rf therapy could ameliorate the transcription of ANXA2, BAZ1A, DNMT3L and MMP9.

CONCLUSION

Our research discovered the relevant mechanisms and basic pharmacological effects of ginsenoside Rf in the treatment of NAFL. These results might facilitate the development of ginsenoside Rf as an alternative medication for NAFL.

Identification of Diagnostic Biomarkers, Immune Infiltration Characteristics, and Potential Compounds in Rheumatoid Arthritis

Aims

This study is aimed at investigating the pathogenesis of rheumatoid arthritis (RA) by identifying key biomarkers, associated immune infiltration, and small-molecule compounds using bioinformatic analysis.

Methods

Six datasets were obtained from the Gene Expression Omnibus database, and the batch effect was adjusted. Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyse differentially expressed genes (DEGs). Furthermore, candidate small-molecule drugs associated with RA were selected from the Connectivity Map (CMap) database. The least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and multivariate logistic regression analyses were performed on DEGs to screen for RA diagnostic markers. The receiver operating characteristic curve, concordance index, and GiViTi calibration band were the metrics used to assess the diagnostic markers of RA identified in this analysis. The single-sample gene set enrichment analysis was performed to calculate the scores of infiltrating immune cells and evaluate the activities of immune-related pathways. Finally, the correlation between screening markers and RA diagnosis was determined.

Results

A total of 227 DEGs were identified. Functional enrichment analysis and KEGG revealed that DEGs were enriched by the immune response. CMap analysis identified 11 small-molecule compounds with therapeutic potential for RA. In gene expression, the activities of 13 immune cells and 12 immune-related pathways significantly differed between patients with RA and healthy controls. DPYSL3 and SPP1 had the potential to diagnose RA. SPP1 expression was positively correlated with DPYSL3 in 11 immune cells and 10 immune-related pathways.

Conclusion

This study comprehensively analysed DEGs and immune infiltration and screened for potential diagnostic markers and small-molecule compounds of RA.

Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy

Left ventricular hypertrophy (LVH) is a pivotal manifestation of hypertensive organ damage associated with an increased cardiovascular risk. However, early diagnostic biomarkers for assessing LVH in patients with hypertension (HT) remain indefinite. Here, multiple bioinformatics tools combined with an experimental verification strategy were used to identify blood biomarkers for hypertensive LVH. GSE74144 mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database to screen candidate biomarkers, which were used to perform weighted gene co-expression network analysis (WGCNA) and establish the least absolute shrinkage and selection operator (LASSO) regression model, combined with support vector machine-recursive feature elimination (SVM-RFE) algorithms. Finally, the potential blood biomarkers were verified in an animal model. A total of 142 hub genes in peripheral blood leukocytes were identified between HT with LVH and HT without LVH, which were mainly involved in the ATP metabolic process, oxidative phosphorylation, and mitochondrial structure and function. Notably, lysosomal associated transmembrane protein 5 (LAPTM5) was identified as the potential diagnostic marker of hypertensive LVH, which showed strong correlations with diverse marker sets of reactive oxygen species (ROS) and autophagy. RT-PCR validation of blood samples and cardiac magnetic resonance imaging (CMRI) showed that the expression of LAPTM5 was significantly higher in the HT with LVH model than in normal controls, LAPTM5 demonstrated a positive association with the left ventricle wall thickness as well as electrocardiogram (ECG) parameters widths of the QRS complex and QTc interval. In conclusion, LAPTM5 may be a potential biomarker for the diagnosis of LVH in patients with HT, and it can provide new insights for future studies on the occurrence and the molecular mechanisms of hypertensive LVH.

Identification of Hub Genes and Biological Pathways in Inclusion Body Myositis Using Bioinformatics Analysis

Background

Inclusion body myositis (IBM) is a unique idiopathic inflammatory myopathy with unclear pathogenesis and poor prognosis. Although previous publications have identified some molecular biomarkers, the value of these biomarkers is unknown.

Objective

To identify hub genes and signaling pathways related to IBM for understanding the IBM-related mechanisms and providing guidance for therapy development.

Methods

Two microarray datasets (GSE3112 and GSE128470) were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was used to detect differentially expressed genes (DEGs) between IBM and normal muscle tissues. The hub genes were determined using protein–protein interaction (PPI) network in Cytoscape. The specific signaling pathways and biological functions of IBM were identified using GO, KEGG, and GSEA enrichment analyses. Moreover, CIBERSORT was applied to estimate the expression level of 22 immune cell types in IBM and normal muscle tissue. The relationship between the immune cell types and hub genes was then explored.

Results

A total of 219 DEGs and 10 hub genes were identified. Enrichment analyses revealed that the chemokine signaling pathway, cellular response to interferon-gamma, and P53 pathway have crucial roles in IBM. Immune infiltration analyses showed that IBM was associated with high level of CD8 T cells, Tregs, and macrophages. Finally, five potential drugs were predicted for IBM patients through CMap (connectivity map) database.

Conclusion

In this study, the underlying molecular mechanisms and immunological landscape of IBM were investigated, and thus may provide new directions for future research on IBM pathogenesis.

Research Advances in Pharmacology, Safety, and Clinical Applications of Yunnan Baiyao, a Traditional Chinese Medicine Formula

Ethnopharmacology relevance: Yunnan Baiyao (YNBY), a traditional Chinese medicine formulae, has some significant properties including activating blood circulation to dissipate blood stasis (Huo-Xue-Hua-Yu), eliminating swelling and alleviating pain (Xiao-Zhong-Zhi-Tong), and eliminating necrotic tissues and promoting granulation (Qu-Fu-Sheng-Ji).

Aim of this study: This paper intends to provide a comprehensive and critical analysis of studies on YNBY, proposing new possible therapeutic directions of this formula.

Materials and methods: Relevant data on YNBY were retrieved from available databases and a hand-search by searching the keywords such as “Yunnan Baiyao,” “pharmacology,” “toxicity,” and “clinical applications.”

Results: Traditionally, YNBY has been used to cure hemorrhage, bruises, swelling, and pain caused by injuries in the Chinese folk. Modern pharmacological studies show that YNBY possesses pharmacological activities including hemostasis, invigorating the circulation of blood, wound healing, anti-inflammation, analgesia, antibiosis, infection prevention, and other effects. Toxicological studies demonstrate that YNBY has a certain toxicology, which is mainly caused by Aconitum alkaloids from Cao-wu (CW, Aconiti Kusnezoffii Radix). The developmental non-toxic reaction dose (NOAEL) of YNBY for embryos and fetuses is 0.5 g/kg in rats. In addition, the NOAEL for fertility and early embryo development toxicity is 4.0 g/kg in rats. Clinical trials have confirmed the safety of YNBY in a large number of patients, and adverse drug reactions (ADRs) such as abdominal pain, diarrhea, allergy, and others in very few people. YNBY is routinely used in clinic to cure bleeding, pain, swelling, upper digestive tract ulcer, postoperative wound, arthritis, mouth ulcers, ulcerative colitis, etc.

Conclusions: Hemostasis is a conspicuous effect of YNBY. Except for this effect, analgesia and anti-infection may be new research directions of this formula. In addition, the in vitro and in vivo pharmacology and mechanisms of action of YNBY are encouraged as well as the pharmacokinetics of this formulae. Furthermore, the material basis of the pharmacological effects of YNBY also needs clear identification.

Machine learning to identify immune-related biomarkers of rheumatoid arthritis based on WGCNA network

OBJECTIVES

This study was designed to identify the potential diagnostic biomarkers of rheumatoid arthritis (RA) and to explore the potential pathological relevance of immune cell infiltration in this disease.

METHODS

Three previously published datasets containing gene expression data from 35 RA patients and 29 controls (GSE55235, GSE55457, and GSE12021) were downloaded from the GEO database, after which a weighted correlation network analysis (WGCNA) approach was utilized to clarify differentially abundant genes. Candidate biomarkers of RA were then identified via the use of a LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. Data were validated based upon the area under the receiver operating characteristic curve (AUC) values, with hub genes being identified as those with an AUC > 85% and a P value < 0.05. Lastly, the CIBERSORT algorithm was used to assess immune cell infiltration of RA tissues, and correlations between immune cell infiltration and disease-related diagnostic biomarkers were assessed.

RESULTS

The green-yellow module containing 87 genes was found to be highly correlated with RA positivity. FADD, CXCL2, and CXCL8 were identified as potential RA diagnostic biomarkers (AUC > 0.85), and these results were validated using the GSE77298 dataset. Immune cell infiltration analyses revealed the expression of hub genes to be correlated with mast cells, monocytes, activated NK cells, CD8 T cells, resting dendritic cells, and plasma cells.

CONCLUSION

These data indicate that FADD, CXCL2, and CXCL8 are valuable diagnostic biomarkers of RA, offering new insight that can guide future studies of RA incidence and progression.

Dermatomyositis: immunological landscape, biomarkers, and potential candidate drugs

INTRODUCTION

Dermatomyositis (DM) is a rare inflammatory disease characterized by the invasion of the skin and muscles. Environmental, genetic, and immunological factors contribute to disease pathology. To date, no bioinformatics studies have been conducted on the potential pathogenic genes and immune cell infiltration in DM. Therefore, we aimed to identify differentially expressed genes (DEGs) and immune cells, as well as potential pathogenic genes and immune characteristics, which may be useful for the diagnosis and treatment of DM.

METHOD

GSE1551, GSE5370, GSE39454, and GSE48280 from Gene Expression Omnibus were included in our study. Limma, ClusterProfiler, and Kyoto Encyclopedia of Genes and Genomes were used to identify DEGs, Gene Ontology (GO), and perform pathway analyses, respectively. Cytoscape was used to construct the protein-protein interaction (PPI) network. Small-molecule drugs were identified using a connectivity map (CMap), and the TIMER database was used to identify infiltrating cells.

RESULTS

DEG analysis identified 12 downregulated and 163 upregulated genes. GO analysis showed that DEGs were enriched in immune-related pathways. Ten hub genes were identified from the PPI network. Additionally, CMap analysis showed that caffeic acid, sulfaphenazole, molindone, tiabendazole, and bacitracin were potential small-molecule drugs with therapeutic significance. We identified eight immune cells with differential infiltration in patients with DM and controls. Finally, we constructed a powerful diagnostic model based on memory B cells, M1, and M2 macrophages.

CONCLUSIONS

This study explored the potential molecular mechanism and immunological landscape of DM and may guide future research and treatment of DM.

KEY POINTS

• We explored the molecular mechanism and immunological landscape of dermatomyositis. • GO analysis showed that DEGs were enriched in immune-related pathways. • We predicted small-molecular drugs with potential therapeutic significance based on bioanalytical techniques. • We identified six immune cells with differential infiltration in patients with DM and controls.