OBJECTIVE: To explore the key target molecules and potential mechanisms of oridonin against non-small cell lung cancer (NSCLC). METHODS: The target molecules of oridonin were retrieved from SEA, STITCH, SuperPred and TargetPred databases; target genes associated with the treatment of NSCLC were retrieved from GeneCards, DisGeNET and TTD databases. Then, the overlapping target molecules between the drug and the disease were identified. The protein-protein interaction (PPI) was constructed using the STRING database according to overlapping targets, and Cytoscape was used to screen for key targets. Molecular docking verification were performed using AutoDockTools and PyMOL software. Using the DAVID database, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted. The impact of oridonin on the proliferation and apoptosis of NSCLC cells was assessed using cell counting kit-8, cell proliferation EdU image kit, and Annexin V-FITC/PI apoptosis kit respectively. Moreover, real-time quantitative PCR and Western blot were used to verify the potential mechanisms. RESULTS: Fifty-six target molecules and 12 key target molecules of oridonin involved in NSCLC treatment were identified, including tumor protein 53 (TP53), Caspase-3, signal transducer and activator of transcription 3 (STAT3), mitogen-activated protein kinase kinase 8 (MAPK8), and mammalian target of rapamycin (mTOR). Molecular docking showed that oridonin and its key target molecules bind spontaneously. GO and KEGG enrichment analyses revealed cancer, apoptosis, phosphoinositide-3 kinase/protein kinase B (PI3K/Akt), and other signaling pathways. In vitro experiments showed that oridonin inhibited the proliferation, induced apoptosis, downregulated the expression of Bcl-2 and Akt, and upregulated the expression of Caspase-3. CONCLUSION Oridonin can act on multiple targets and pathways to exert its inhibitory effects on NSCLC, and its mechanism may be related to upregulating the expression of Caspase-3 and downregulating the expressions of Akt and Bcl-2.
Diterpenes, Kaurane/chemistry* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Humans ; Network Pharmacology ; Lung Neoplasms/pathology* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Cell Line, Tumor ; Signal Transduction/drug effects* ; Gene Expression Regulation, Neoplastic/drug effects* ; Reproducibility of Results ; Gene Ontology

OBJECTIVE: To explore the mechanism of action of asperosaponin VI (AVI) in the treatment of rheumatoid arthritis (RA) and validate it in ex vivo experiments using network pharmacology and molecular docking methods. METHODS: The predicted targets of AVI were obtained from PharmMaper, UniProt and SwissTarget Prediction platforms, the disease targets were collected from Online Mendelian Inheritance in Man, Therapeutic Target Database and GeneCards databases, the intersection targets of AVI and RA were obtained from Venny 2.1.0, and the protein-protein interaction (PPI) network was obtained from STRING database, which was analyzed by Cytoscape software and screened to obtain the core targets. Cytoscape software was used to analyze PPI network and screen the core targets. Based on the Database for Annotation, Visualization and Integrated Discovery database, Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed, and Cytoscape software was used to construct the "Disease-Pathway-Target-Drug" network, which was finally verified by molecular docking and animal experiments. RESULTS: Network pharmacological studies showed that AVI was able to modulate 289 targets, with 102 targets for the potential treatment of RA, with the core pathway being the AKT/PI3K signaling pathway, and the core targets being the epidermal growth factor receptor (EGFR) and matrix metalloproteinase 9 (MMP9). Molecular docking results showed that AVI could produce strong binding with both of the 2 core targets. In vitro cellular experiments showed that AVI reduced nitric oxide, prostaglandin E₂, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and IL-1 β levels (P<0.05) and inhibited cyclooxygenase-2, nitric oxide synthase, EGFR, MMP9, phosphorylated phosphoinositide 3-kinase (p-PI3K), and phosphorylated serine-threonine kinase (p-AKT) proteins (P<0.05). The results of in vivo studies showed that AVI improved RA score and foot swelling thickness and decreased TNF-α, IL-6, p-PI3K and p-AKT levels in RA rats (P<0.05). CONCLUSION AVI exerts anti-inflammatory and anti-RA effects which might be related to the EGFR/MMP9/AKT/PI3K pathway.
Saponins/chemistry* ; ErbB Receptors/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Signal Transduction/drug effects* ; Molecular Docking Simulation ; Animals ; Arthritis, Rheumatoid/drug therapy* ; Proto-Oncogene Proteins c-akt/metabolism* ; Matrix Metalloproteinase 9/metabolism* ; Protein Interaction Maps/drug effects* ; Humans ; Network Pharmacology ; Male ; Rats

OBJECTIVES: To explore the underlying pharmacological mechanisms and its potential effects of Chinese medicine herbal formula Sini Powder (SNP) on hepatocellular carcinoma (HCC). METHODS: The active components of SNP and their in vivo distribution were identified using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Construction of component-target-disease networks, protein-protein interaction network, Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and molecular docking were employed to analyze the active components and anti-HCC mechanisms of SNP. Cell viability assay and wound healing assay were utilized to confirm the effect of SNP-containing serum (2.5%, 5.0%, 10%, 20%, and 40%), isoprenaline or propranolol (both 10, 100, and 1,000 µ mol/L) on proliferation and migration of HepG 2 or Huh7 cells. Meanwhile, the effect of isoprenaline or propranolol on the β 2 adrenergic receptor (ADRB2) mRNA expression on HepG2 cells were measured by real-time quantitative reverse transcription (RT-qPCR). Mice with subcutaneous tumors were either subjected to chronic restraint stress (CRS) followed by SNP administration (364 mg/mL) or directly treated with SNP (364 mg/mL). These two parallel experiments were performed to validate the effects of SNP on stress responses. Stress-related proteins and hormones were quantified using RT-qPCR, enzyme-linked immunosorbent assay, and immunohistochemistry. Metagenomic sequencing was performed to confirm the influence of SNP on the gut microbiota in the tumor-bearing CRS mice. RESULTS: The distribution of the 12 active components of SNP was confirmed in various tissues and feces. Network pharmacology analysis confirmed the anti-HCC effects of the 5 active components. The potential anti-HCC mechanisms of SNP may involve the epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein kinase Src (SRC) and signal transducer and activator of transcription 3 (STAT3) pathways. SNP-containing serum inhibited the proliferation of HepG2 and Huh7 cells at concentrations of 2.5% and 5.0%, respectively, after 24 h of treatment. Furthermore, SNP suppressed tumor progression in tumor-bearing mice exposed to CRS. SNP treatment also downregulated the expressions of stress-related proteins and pro-inflammatory cytokines, primarily by modulating the gut microbiota. Specifically, the abundance of Alistipes and Prevotella, which belong to the phylum Bacteroidetes, increased in the SNP-treated group, whereas Lachnospira, in the phylum Firmicutes, decreased. CONCLUSION SNP can combat HCC by alleviating stress responses through the regulation of gut microbiota.
Animals ; Gastrointestinal Microbiome/drug effects* ; Liver Neoplasms/microbiology* ; Carcinoma, Hepatocellular/microbiology* ; Humans ; Drugs, Chinese Herbal/therapeutic use* ; Powders ; Cell Proliferation/drug effects* ; Mice ; Molecular Docking Simulation ; Cell Line, Tumor ; Hep G2 Cells ; Receptors, Adrenergic, beta-2/genetics* ; Stress, Physiological/drug effects* ; Cell Movement/drug effects* ; Male ; Protein Interaction Maps/drug effects* ; Cell Survival/drug effects* ; Proto-Oncogene Mas

OBJECTIVE: To explore the main components and potential mechanisms of Sangqi Qingxuan Liquid in the treatment of arterial vascular endothelial cells (AVECs) injury in hypertension through network pharmacology. METHODS: Traditional Chinese Medicine Systems Pharmacology and Analysis Platform (TCMSP) and Traditional Chinese Medicine Integrated Database (TCMID) were used to screen the active components of Sangqi Qingxuan Liquid (SQQX), which met the oral utilization rate and drug similarity criteria. An active component-target network was constructed using Cytoscape 3.6 software. A protein-protein interaction (PPI) network of targets associated with SQQX treatment for hypertension was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The Metascape database was used to perform enrichment analysis of gene ontology biological functions and MSigDB pathway enrichment analysis of proteins in the PPI network. Further analysis of the main components of SQQX was performed using UPLC-MS. Based on the results of network pharmacology, the mechanism of SQQX to improve the injury of AVECs in hypertension was verified through lentiviral transfection by Wnt/ β -catenin signaling pathway. AVECs induced by angiotensin II (Ang II ) was used to establish a model of endothelial function injury in hypertension. Cell viability, intracellular nitric oxide content, malonaldehyde content, and superoxide dismutase activity were measured to determine the optimal induction conditions. The optimal intervention conditions for SQQX were determined based on cell viability, cellular DNA activity, and the gradient method. The cells were further divided into blank, model, overexpression lentivirus negative control, overexpression lentivirus, overexpression lentivirus + SQQX intervention (2.47 mg/mL, 12 h), inhibition lentivirus negative control, inhibition lentivirus, and inhibition lentivirus + SQQX intervention (2.47 mg/mL, 12 h) groups. Finally, quantitative real-time PCR and Western blotting were performed to analyze the molecular mechanisms of SQQX in the Wnt/ β -catenin signaling pathway. RESULTS: The main SQQX components were betaine, buddleoside, and chlorogenic acid, in descending order. Network pharmacology analysis screened 12 pathways associated with the hypertensive vascular endothelium. The results showed that 1 µ mol/L for 12 h was the optimal condition for Ang II to induce AVECs injury, and 2.47 mg/mL SQQX intervention for 12 h was the optimal condition for treating AVECs injury. In the experimental validation based on the interaction network of the Wnt/ β -catenin signaling pathway, SQQX significantly decreased the expressions of β -catenin, Smad2, peroxisome proliferator-activated receptors (PPARs), endothelial nitric oxide synthase (eNOS), and endothelin-1 (ET-1) caused by the β -catenin overexpression lentivirus (P<0.05 or P<0.01). The function of vascular endothelial cells can be improved by the β -catenin inhibition lentivirus, and no obvious changes were observed after further intervention with SQQX. CONCLUSION SQQX may protect against AVECs injury by regulating the Wnt/β -catenin signaling pathway.
Drugs, Chinese Herbal/therapeutic use* ; beta Catenin/metabolism* ; Hypertension/metabolism* ; Endothelial Cells/metabolism* ; Protein Interaction Maps/drug effects* ; Humans ; Wnt Signaling Pathway/drug effects* ; Network Pharmacology ; Endothelium, Vascular/injuries* ; Cell Survival/drug effects* ; Angiotensin II/pharmacology* ; Nitric Oxide/metabolism*

OBJECTIVES: Hepatocellular carcinoma is one of the most common primary malignant tumors with the third highest mortality rate worldwide. This study aims to identify key genes associated with hepatocellular carcinoma prognosis using the Gene Expression Omnibus (GEO) database and provide a theoretical basis for discovering novel prognostic biomarkers for hepatocellular carcinoma. METHODS: Hepatocellular carcinoma-related datasets were retrieved from the GEO database. Differentially expressed genes (DEGs) were identified using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and key genes were identified using Cytoscape software. The University of Alabama at Birmingham Cancer Data Analysis Resource (UALCAN) was used to analyze the expression levels of key genes in normal and hepatocellular carcinoma tissues, as well as their associations with pathological grade, clinical stage, and patient survival. The Human Protein Atlas (THPA) was used to further validate the impact of key genes on overall survival. Expression levels of key genes in the blood of hepatocellular carcinoma patients were evaluated using the expression atlas of blood-based biomarkers in the early diagnosis of cancers (BBCancer). RESULTS: A total of 78 DEGs were identified from the GEO database. GO and KEGG analyses indicated that these genes may contribute to hepatocellular carcinoma progression by promoting cell division and regulating protein kinase activity. Sixteen key genes were screened via Cytoscape and validated using UALCAN and THPA. These genes were overexpressed in hepatocellular carcinoma tissues and were associated with disease progression and poor prognosis. Finally, BBCancer analysis showed that ASPM and NCAPG were also elevated in the blood of hepatocellular carcinoma patients. CONCLUSIONS This study identified 16 key genes as potential prognostic biomarkers for hepatocellular carcinoma, among which ASPM and NCAPG may serve as promising blood-based markers for hepatocellular carcinoma.
Humans ; Carcinoma, Hepatocellular/mortality* ; Liver Neoplasms/pathology* ; Prognosis ; Computational Biology/methods* ; Protein Interaction Maps/genetics* ; Biomarkers, Tumor/genetics* ; Gene Expression Regulation, Neoplastic ; Gene Expression Profiling ; Gene Ontology ; Databases, Genetic

OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks

BACKGROUND: PANoptosis has the features of pyroptosis, apoptosis, and necroptosis. Numerous studies have confirmed the diverse roles of various types of cell death in acute liver failure (ALF), but limited attention has been given to the crosstalk among them. In this study, we aimed to explore the role of PANoptosis in ALF and uncover new targets for its prevention or treatment. METHODS: Three ALF-related datasets (GSE14668, GSE62029, and GSE74000) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Hub genes were identified through intersecting DEGs, genes obtained from weighted gene co-expression network analysis (WGCNA), and genes related to PANoptosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein‍‒‍protein interaction (PPI) analyses and gene set enrichment analysis (GSEA) were performed to determine functional roles. Verification was performed using an ALF mouse model. RESULTS: Our results showed that expression of seven hub genes (B-cell lymphoma-2-modifying factor (BMF), B-cell lymphoma-2-interacting protein 3-like (BNIP3L), Caspase-1 (CASP1), receptor-interacting protein kinase 3 (RIPK3), uveal autoantigen with coiled-coil domains and ankyrin repeats protein (UACA), uncoordinated-5 homolog B receptor (UNC5B), and Z-DNA-binding protein 1 (ZBP1)) was up-regulated in liver samples of patients. However, in the ALF mouse model, the expression of BNIP3L, RIPK3, phosphorylated RIPK3 (P-RIPK3), UACA, and cleaved caspase-1 was up-regulated, while the expression of CASP1 and UNC5B was down-regulated. The expression of ZBP1 and BMF increased only during the development of ALF, and there was no significant change in the end stage. Immunofluorescence of mouse liver tissue showed that macrophages expressed all seven markers. Western blot results showed that pyroptosis, apoptosis, and necroptosis were always involved in lipopolysaccharide (LPS)/ d-galactosamine (d-gal)‍-induced ALF mice. The ALF cell model showed that bone marrow-derived macrophages (BMDMs) form PANoptosomes after LPS stimulation. CONCLUSIONS: Our results suggest that PANoptosis of macrophages promotes the development of ALF. The seven new ALF biomarkers identified and validated in this study may contribute to further investigation of diagnostic markers or novel therapeutic targets of ALF.
Animals ; Liver Failure, Acute/genetics* ; Computational Biology ; Mice ; Pyroptosis/genetics* ; Humans ; Protein Interaction Maps ; Apoptosis/genetics* ; Necroptosis/genetics* ; Gene Regulatory Networks ; Gene Ontology ; Gene Expression Profiling ; Disease Models, Animal

OBJECTIVES: To identify the key genes and immunological pathways shared by type 2 diabetes mellitus (T2DM) and chronic obstructive pulmonary disease (COPD) and explore the potential therapeutic targets of T2DM complicated by COPD. METHODS: GEO database was used for analyzing the gene expression profiles in T2DM and COPD to identify the common differentially expressed genes (DEGs) in the two diseases. A protein-protein interaction network was constructed to identify the candidate hub genes, which were validated in datasets and disease sets to obtain the target genes. The diagnostic accuracy of these target genes was assessed with ROC analysis, and their expression levels and association with pulmonary functions were investigated using clinical data and blood samples of patients with T2DM and COPD. The abundance of 22 immune cells was analyzed with CIBERSORT algorithm, and their relationship with the target genes was examined using correlation analysis. DGIdb database was used for analyzing the drug-gene interactions and the druggable genes followed by gene set enrichment analysis. RESULTS: We identified a total of 175 common DEGs in T2DM and COPD, mainly enriched in immune- and inflammation-related pathways. Among these genes, CXCL12 was identified as the final target gene, whose expression was elevated in both T2DM and COPD (P<0.05) and showed good diagnostic efficacy. Immune cell infiltration correlation analysis showed significant correlations of CXCL12 with various immune cells (P<0.01). GESA analysis showed that high CXCL12 expression was significantly correlated with "cytokine-cytokine receptor interaction". Drug-gene analysis showed that most of CXCL12-related drugs were not targeted drugs with significant cytotoxicity. CONCLUSIONS CXCL12 is a potential common key pathogenic gene of COPD and T2DM, and small-molecule targeted drugs against CXCL12 can provide a new strategy for treatment T2DM complicated by COPD.
Humans ; Pulmonary Disease, Chronic Obstructive/complications* ; Diabetes Mellitus, Type 2/genetics* ; Chemokine CXCL12/metabolism* ; Protein Interaction Maps ; Gene Expression Profiling

OBJECTIVES: To explore the mechanism by which Pulsatilla saponin D (PSD) inhibits invasion and metastasis of triple-negative breast cancer (TNBC). METHODS: The public databases were used to identify the potential targets of PSD and the invasion and metastasis targets of TNBC to obtain the intersection targets between PSD and TNBC. The "PSD-target-disease" interaction network was constructed and protein-protein interaction (PPI) analysis was performed to obtain the core targets, which were analyzed for KEGG pathway and GO functional enrichment. Molecular docking study of the core targets and PSD was performed, and the therapeutic effect and mechanism of PSD were verified using Transwell assay and Western blotting in cultured TNBC cells. RESULTS: Network pharmacology analysis identified a total of 285 potential PSD targets and 26 drug-disease intersection core targets. GO analysis yielded 175 entries related to the binding of biomolecules (protein, DNA and RNA), enzyme activities, and regulation of gene transcription. KEGG analysis yielded 46 entries involving pathways in cancer, chemical carcinogenesis-receptor activation, microRNAs in cancer, chemical carcinogenesis-reactive oxygen species, PD-L1 expression and PD-1 checkpoint pathway in cancer. Molecular docking showed high binding affinities of PSD to MTOR, HDAC2, ABL1, CDK1, TLR4, TERT, PIK3R1, NFE2L2 and PTPN1. In cultured TNBC cells, treatment with PSD significantly inhibited cell invasion and migration and lowered the expressions of MMP2, MMP9, N-cadherin and the core proteins p-mTOR, ABL1, TERT, PTPN1, HDAC2, PIK3R1, CDK1, TLR4 as well as NFE2L2 expressionin the cell nuclei. CONCLUSIONS The inhibitory effects of PSD on TNBC invasion and metastasis are mediated by multiple targets and pathways.
Humans ; Triple Negative Breast Neoplasms/metabolism* ; Saponins/pharmacology* ; Pulsatilla/chemistry* ; Female ; Molecular Docking Simulation ; Cell Line, Tumor ; Neoplasm Invasiveness ; Protein Interaction Maps ; Neoplasm Metastasis ; Signal Transduction/drug effects* ; Cell Movement/drug effects*

OBJECTIVES: To explore the role of oxidative stress and immune infiltration in rheumatoid arthritis (RA). METHODS: RA datasets GSE55235 (10 RA vs 10 normal samples) and GSE55457 (13 RA vs 10 normal samples) from the GEO database were merged as the test set to identify the differentially expressed genes (DEGs) in RA using R. The DEGs were intersected with oxidative stress-related genes to obtain oxidative stress-associated DEGs. KEGG and GO enrichment analyses of the DEGs were performed, and the RA-related pathways and biological processes were analyzed using GSEA. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, and the top 10 key genes were obtained using the Degree algorithm. The validation dataset GSE1919 from GEO database was used for ROC analysis of the key genes to obtain the core genes, and their correlations with infiltrating immune cells were analyzed using CIBERSORT. The results were verified by RT-qPCR for detecting expression levels of the core genes in RA and normal joint samples. RESULTS: We identified 89 oxidative stress-associated DEGs. Enrichment analysis suggested that these DEGs were involved in the biological processes including oxidative stress, chemical stress response, reactive oxygen species response, and lipopolysaccharide response. ROC analysis showed that the 5 core genes (STAT1, MMP9, MYC, CCL5, and JUN) all had AUC values >0.7, indicating their high diagnostic sensitivity and specificity for RA. These genes were closely correlated with immune cells, particularly T cells. RT-qPCR confirmed significant differential expressions of the core genes between RA and normal samples. CONCLUSIONS Oxidative stress and diverse immune responses are features of RA, and the immune responses contribute to activation of oxidative stress. The identified core genes can potential serve as new diagnostic markers for RA.
Arthritis, Rheumatoid/genetics* ; Oxidative Stress/genetics* ; Humans ; Computational Biology ; Protein Interaction Maps ; Gene Expression Profiling ; Gene Regulatory Networks