Objective: Using bioinformatics methods to analyze the core pathogenic genes and related pathways in elderly osteoporosis. Methods: From November 2020 and August 2021, eight elderly osteoporosis patients who received treatment and five healthy participants who underwent physical examinations in Beijing Jishuitan Hospital were selected as subjects. The expression level of RNA in the peripheral blood of eight elderly osteoporosis patients and five healthy participants was collected for high-throughput transcriptome sequencing and analysis. The gene ontology (GO) analysis Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed for the differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was constructed using the STRING website and Cytoscape software, and the most significant modules and hub genes were screened out. Results: Among the eight elderly osteoporosis patients, there were seven females and one male, with an average age of 72.4 years (SD=4.2). Among the five healthy participants, there were four females and one male, with an average age of 68.2 years (SD=5.7). A total of 1 635 DEGs (847 up-regulated and 788 down-regulated) were identified. GO analysis revealed that the molecular functions of DEGs were mainly enriched in structural constituents of the ribosome, protein dimerization activity, and cellular components were mainly enriched in the nucleosome, DNA packaging complex, cytosolic part, protein-DNA complex and the cytosolic ribosome. KEGG pathway analysis showed that DEGs were mainly enriched in systemic lupus erythematosus and ribosome. Gene UBA52, UBB, RPS27A, RPS15, RPS12, RPL13A, RPL23A, RPL10A, RPS25 and RPS6 were selected and seven of them could encode ribosome proteins. Conclusion: The pathogenesis of elderly osteoporosis may be associated with ribosome-related genes and pathways.
Female ; Humans ; Male ; Aged ; Gene Expression Profiling/methods* ; Transcriptome ; Protein Interaction Maps/genetics* ; Computational Biology/methods* ; Osteoporosis/genetics*

OBJECTIVES: To investigate possible cross-talk genes, associated pathways, and transcription factors between chronic periodontitis (CP) and chronic obstructive pulmonary disease (COPD). METHODS: The gene expression profiles of CP (GSE10334 and GSE16134) and COPD (GSE76925) were downloaded from the GEO database. Differential expression and functional clustering analyses were performed. The protein‑protein interaction (PPI) network was constructed. The core cross-talk genes were filtered using four topological analysis algorithms and modular segmentation. Then, functional clustering analysis was performed again. RESULTS: GSE10334 detected 164 differentially expressed genes (DEGs) (119 upregulated and 45 downregulated). GSE16134 identified 208 DEGs (154 upregulated and 54 downregulated). GSE76925 identified 1 408 DEGs (557 upregulated and 851 downregulated). The PPI network included 21 nodes and 20 edges. The final screening included seven cross-talk genes: CD79A, FCRLA, CD19, IRF4, CD27, SELL, and CXCL13. Relevant pathways included primary immunodeficiency, the B-cell receptor signaling pathway, and cytokine-cytokine receptor interaction. CONCLUSIONS This study indicates the probability of shared pathophysiology between CP and COPD, and their cross-talk genes, associated pathways, and transcription factors may offer novel concepts for future mechanistic investigations.
Humans ; Chronic Periodontitis/genetics* ; Gene Regulatory Networks ; Gene Expression Profiling ; Protein Interaction Maps/genetics* ; Pulmonary Disease, Chronic Obstructive/genetics*

OBJECTIVES: The common differentially expressed mRNAs in brain, heart and liver tissues of deceased sudden infant death syndrome (SIDS) and infectious sudden death in infancy (ISDI) confirmed by autopsy was screened by bioinformatics to explore the common molecular markers and pathogenesis of SIDS and ISDI. METHODS: The datasets of GSE70422 and GSE136992 were downloaded, the limma of R software was used to screen differentially expressed mRNA in different tissue samples of SIDS and ISDI decedents for overlapping analysis. The clusterProfiler of R software was used to conduct gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The protein-protein interaction (PPI) network was constructed by STRING database, while the hub gene was screened by cytoHubba plug-in. RESULTS: Compared with the control group, there were 19 significant differentially expressed genes in the tissue samples of SIDS and ISDI decedents, among which 16 in the heart tissue and 3 in the liver tissue, and the astrotactin 1 (ASTN1) gene expression difference in the heart tissue was most significant. The PPI network identified Ras homolog family member A (RHOA), integrin subunit alpha 1 (ITGA1), and H2B clustered histone 5 (H2BC5) were hub genes. The analysis of GO and KEGG showed that differentially expressed genes were enriched in the molecular pathways of actin cytoskeleton regulation, focal adhesion and response to mycophenolic acid. CONCLUSIONS ASTN1, RHOA and ITGA1 may participate in the development of SIDS and ISDI. The enrichment of differentially expressed genes in immune and inflammatory pathways suggests a common molecular regulatory mechanism between SIDS and ISDI. These findings are expected to provide new biomarkers for molecular anatomy and forensic identification of SIDS and ISDI.
Humans ; Infant ; Gene Expression Profiling ; Sudden Infant Death/genetics* ; Gene Regulatory Networks ; Protein Interaction Maps/genetics* ; Computational Biology

To explore the mechanism of ovarian toxicity of Hook. F. (TwHF) by network pharmacology and molecular docking. The candidate toxic compounds and targets of TwHF were collected by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Comparative Toxicogenomics Database (CTD). Then, the potential ovarian toxic targets were obtained from CTD, and the target genes of ovarian toxicity of TwHF were analyzed using the STRING database. The protein-protein interaction (PPI) network was established by Cytoscape and analyzed by the cytoHubba plug-in to identify hub genes. Additionally, the target genes of ovarian toxicity of TwHF were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses by using the R software. Finally, Discovery Studio software was used for molecular docking verification of the core toxic compounds and the hub genes. Nine candidate toxic compounds of TwHF and 56 potential ovarian toxic targets were identified in this study. Further network analysis showed that the core ovarian toxic compounds of TwHF were triptolide, kaempferol and tripterine, and the hub ovarian toxic genes included , , , , , , , , and . Besides, the GO and KEGG analysis indicated that TwHF caused ovarian toxicity through oxidative stress, reproductive system development and function, regulation of cell cycle, response to endogenous hormones and exogenous stimuli, apoptosis regulation and aging. The docking studies suggested that 3 core ovarian toxic compounds of TwHF were able to fit in the binding pocket of the 10 hub genes. TwHF may cause ovarian toxicity by acting on 10 hub genes and 140 signaling pathways.
Drugs, Chinese Herbal/toxicity* ; Medicine, Chinese Traditional ; Molecular Docking Simulation ; Network Pharmacology ; Protein Interaction Maps

OBJECTIVE: To identify the key genes and explore mechanisms in the development of myelodysplastic syndrome (MDS) by bioinformatics analysis. METHODS: Two cohorts profile datasets of MDS were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed gene (DEG) was screened by GEO2R, functional annotation of DEG was gained from GO database, gene ontology (GO) enrichment analysis was performed via Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and key genes were screened by Matthews correlation coefficient (MCC) based on STRING database. RESULTS: There were 112 DEGs identified, including 85 up-regulated genes and 27 down-regulated genes. GO enrichment analysis showed that biological processes were mainly enriched in immune response, etc, cellular component in cell membrane, etc, and molecular function in protein binding, etc. KEGG signaling pathway analysis showed that main gene enrichment pathways were primary immunodeficiency, hematopoietic cell lineage, B cell receptor signaling pathway, Hippo signaling pathway, and asthma. Three significant modules were screened by Cytoscape software MCODE plug-in, while 10 key node genes (CD19, CD79A, CD79B, EBF1, VPREB1, IRF4, BLNK, RAG1, POU2AF1, IRF8) in protein-protein interaction (PPI) network were screened based on STRING database. CONCLUSION These screened key genes and signaling pathways are helpful to better understand molecular mechanism of MDS, and provide theoretical basis for clinical targeted therapy.
Computational Biology ; Gene Expression ; Gene Expression Profiling ; Humans ; Microarray Analysis ; Myelodysplastic Syndromes/genetics* ; Protein Interaction Maps

Objective: To screen and analyze the key differentially expressed genes characteristics in nonalcoholic fatty liver disease (NAFLD) with bioinformatics method. Methods: NAFLD-related expression matrix GSE89632 was downloaded from the GEO database. Limma package was used to screen differentially expressed genes (DEGs) in healthy, steatosis (SS), and nonalcoholic steatohepatitis (NASH) samples. WGCNA was used to analyze the output gene module. The intersection of module genes and differential genes was used to determine the differential genes characteristic, and then GO function and KEGG signaling pathway enrichment analysis were performed. The protein-protein interaction network (PPI) was constructed using the online website STRING and Cytoscape software, and the key (Hub) genes were screened. Finally, R software was used to analyze the receiver operating characteristic curve (ROC) of the Hub gene. Results: 92 differentially expressed genes characteristic were obtained through screening, which were mainly enriched in inflammatory response-related functions of "lipopolysaccharide response and molecular response of bacterial origin", as well as cancer signaling pathways of "proteoglycan in cancer" and "T-cell leukemia virus infection-related". 10 hub genes (FOS, CXCL8, SERPINE1, CYR61, THBS1, FOSL1, CCL2, MYC, SOCS3 and ATF3) had good diagnostic value. Conclusion: The differentially expressed hub genes among the 10 NAFLD disease-related characteristics obtained with bioinformatics analysis may become a diagnostic and prognostic marker and potential therapeutic target for NAFLD. However, further basic and clinical studies are needed to validate.
Computational Biology/methods* ; Gene Expression Profiling/methods* ; Gene Regulatory Networks ; Humans ; Non-alcoholic Fatty Liver Disease/genetics* ; Protein Interaction Maps/genetics*

OBJECTIVE: To study the therapeutic mechanism of Longqi Fang (LQF) for diabetic kidney disease (DKD) based on GEO database and network pharmacology. METHODS: LQF and DKD targets were obtained using the databases including GEO, TCMSP, CNKI, ChemDraw, and SwissTarget Prediction, and LQF-DKD intersection targets were obtained with VENNY. String was used for protein-protein interaction (PPI) analysis, and R package for KEGG and GO enrichment analysis. Cytoscape 3.7.2 software Network graphs were constructed. The results of network pharmacology analysis were verified in SD rat models of DKD by daily treatment of the rats with LQF at low (1 g/kg), medium (2 g/kg), and high (2 g/kg) doses, and kidney pathology was observed with HE staining and the changes in renal function were assessed. Western blotting was used to detect the expression levels of NF-κB and p-NF-κB proteins. RESULTS: We identified 760 main targets of LQF, and obtained 1026 differential genes using GEO database and 61 LQF-DKD intersection targets using Venny database. The core targets obtained through PPI network analysis included Myc, EGF, CASP3, VEGFA, CCL2, SPP1, VCAM1 and ICAM1. Go analysis showed that LQF affects mainly nuclear receptor activity and ligand activated transcription factor activity. KEGG analysis showed that LQF affects inflammatory signaling pathways by interfering with NF-κB, TNF, and PI3K-AKT. In rat models of DKD, treatment with LQF resulted in significant improvements of the renal functions (P < 0.05) and glomerular and tubular structure and arrangement in a dose-dependent manner. Western blotting results showed that LQF dose-dependently downregulated NF-κB and p-NF-κB expressions in the rat models. CONCLUSION The therapeutic mechanism of LQF for DKD involves multiple components, targets and signal pathways that mediate an inhibitory effect on NF-κB signaling pathway to protect the renal function.
Animals ; Diabetes Mellitus ; Diabetic Nephropathies/metabolism* ; Network Pharmacology ; Phosphatidylinositol 3-Kinases/metabolism* ; Protein Interaction Maps ; Rats ; Rats, Sprague-Dawley

Objective To screen out the key genes leading to diabetic cardiomyopathy by analyzing the mRNA array associated with diabetic cardiomyopathy in the GEO database. Methods The online tool GEO2R of GEO was used to mine the differentially expressed genes (DEG) in the datasets GSE4745 and GSE5606.R was used to draw the volcano map of the DEG,and the Venn diagram was established online to identify the common DEG shared by the two datasets.The clusterProfile package in R was used for gene ontology annotation and Kyoto encyclopedia of genes and genomes pathway enrichment of the DEG.GSEA was used for gene set enrichment analysis,and STRING for the construction of a protein-protein interaction network.The maximal clique centrality algorithm in the plug-in Cytohubba of Cytoscape was used to determine the top 10 key genes. The expression of key genes was studied in the primary cardiomyocytes of rats and compared between the normal control group and high glucose group. Results The expression of Pdk4,Ucp3,Hmgcs2,Asl6,and Slc2a4 was consistent with the array analysis results.The expression of Pdk4,Ucp3,and Hmgcs2 was up-regulated while that of Acsl6 and Slc2a4 was down-regulated in the cardiomyocytes stimulated by high glucose (25 mmol/L) for 72 h. Conclusion Pdk4,Ucp3,Hmgcs2,Asl6,and Slc2a4 may be associated with the occurrence and development of diabetic cardiomyopathy,and may serve as the potential biomarkers of diabetic cardiomyopathy.
Animals ; Computational Biology/methods* ; Diabetes Mellitus ; Diabetic Cardiomyopathies/genetics* ; Gene Expression Profiling ; Glucose ; Protein Interaction Maps/genetics* ; Rats

OBJECTIVES: The biomarkers targeting colorectal cancer (CRC) prognosis are short of high accuracy and sensitivity in clinic. Through bioinformatics analysis, we aim to identify and confirm a series of key genes referred to the diagnosis and prognosis of CRC. METHODS: GSE31905, GSE35279, and GSE41657 were selected as complete RNA sequencing data sets of CRC and colorectal mucosa (CRM) tissues from the NCBI-GEO database, and the differentially expressed genes (DEGs) were analyzed. The common DEGs in these 3 data sets were obtained by Venn map, and enriched by STRING network system and Cytoscape software. The Kaplan-Meier plotter website was used to verify the correlation between the enriched genes and the prognosis of CRC. RESULTS: For the whole RNA sequencing data sets of CRC and normal intestinal mucosa samples, the DEGs of CRC and CRM in the 3 data sets (|log CONCLUSIONS The above 11 genes verified by bioinformatics retrieval and analysis can predict the poor prognosis of CRC to a certain extent, and they provide a possible target for the diagnosis and treatment of CRC.
Biomarkers, Tumor/metabolism* ; Colorectal Neoplasms/genetics* ; Computational Biology ; Formins ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Glycoproteins ; Humans ; Intercellular Signaling Peptides and Proteins ; Oncogenes ; Prognosis ; Protein Interaction Maps

OBJECTIVES: To identify the differentially expressed genes (DEGs) during the pathogenesis of periodontitis by bioinformatics analysis. METHODS: GEO2R was used to screen DEGs in GSE10334 and GSE16134. Then, the overlapped DEGs were used for further analysis. g:Profiler was used to perform Gene Ontology analysis and pathway analysis for upregulated and downregulated DEGs. The STRING database was used to construct the protein-protein interaction (PPI) network, which was further visua-lized and analyzed by Cytoscape software. Hub genes and key modules were identified by cytoHubba and MCODE plug-ins, respectively. Finally, transcription factors were predicted via iRegulon plug-in. RESULTS: A total of 196 DEGs were identified, including 139 upregulated and 57 downregulated DEGs. Functional enrichment analysis showed that the upregulated DEGs were mainly enriched in immune-related pathways including immune system, viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, leukocyte transendothelial migration, and chemokine receptors bind chemokines. On the contrary, the downregulated DEGs were mainly related to the formation of the cornified envelope and keratinization. The identified hub genes in the PPI network were CXCL8, CXCL1, CXCR4, SEL, CD19, and IKZF1. The top three modules were involved in chemokine response, B cell receptor signaling pathway, and interleukin response, respectively. iRegulon analysis revealed that IRF4 scored the highest. CONCLUSIONS The pathogenesis of periodontitis was closely associated with the expression levels of the identified hub genes including CXCL8, CXCL1, CXCR4, SELL, CD19, and IKZF1. IRF4, the predicted transcription factor, might serve as a dominant upstream regulator.
Computational Biology ; Gene Expression Profiling ; Humans ; Microarray Analysis ; Periodontitis ; Protein Interaction Maps