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

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

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 explore the function and mechanism of related genes in the occurrence and development of liver cancer, and the possibility of key genes as potential biomarkers and prognostic indicators for the treatment of liver cancer.Methods We selected 4 datasets(GSE57957, GSE121248, GSE36376 and GSE14520)from the GEO database.With
Biomarkers, Tumor/genetics* ; Carrier Proteins ; Computational Biology ; Cytochrome P-450 CYP2E1 ; Cytochrome P-450 CYP3A ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Glycoproteins ; Humans ; Liver Neoplasms/genetics* ; Prognosis ; Protein Interaction Maps

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

Objective Many physiological and pathological conditions, including cyanotic congenital heart diseases (CCHD), are accompanied by chronic hypoxia, which might interfere with the transcription process. However, the transcriptome profile in peripheral blood under hypoxia is still unidentified. The present work aimed to explore the transcriptional profile alteration of peripheral blood in chronic hypoxia. Methods The present study used a chronic hypoxia rat model to simulate the hypoxic state of CCHD patients. Two groups of Sprague-Dawley rats (n=6 per group) were either exposed to hypoxia (10% O₂) or normoxia (21% O₂) for 3 weeks. Body weight was measured weekly. Peripheral blood was collected and total RNA was extracted for RNA-Seq at the end of the hypoxia treatment. After quality assessment, the library was sequenced by the Illumina Hiseq platform. The differentially expressed genes were screened (false discovery rate<0.05 and fold change>2). The functional annotation analysis and cluster analysis of differentially expressed genes were performed based on the adjusted P-value (padj<0.05). Results Compared with the control group, the body weight of the rats in the hypoxia group was significantly lowered (P<0.01). RNA-Seq results showed that the transcriptome patterns of the two groups had significant differences. In total, 872 genes were identified as differentially expressed. Among all, 803 genes were down-regulated, while only 69 genes were up-regulated in the hypoxia group. The functional enrichment analysis of the 872 genes showed that multiple biological processes involved, such as porphyrin-containing compound metabolic process, hemoglobin complex and oxygen transporter activity. Conclusions Our study demonstrated the transcriptional profile alteration in peripheral blood of chronic hypoxia rat model. This study provided basic data and directions to further understand the physiological and pathological changes in patients with CCHD.
Animals ; Chronic Disease ; Cluster Analysis ; Disease Models, Animal ; Gene Expression Profiling/methods* ; Gene Ontology ; Gene Regulatory Networks ; Heart Defects, Congenital/genetics* ; Humans ; Hypoxia/genetics* ; Protein Interaction Maps/genetics* ; Rats, Sprague-Dawley

To identify novel genes in castration-resistant prostate cancer (CRPC), we downloaded three microarray datasets containing CRPC and primary prostate cancer in Gene Expression Omnibus (GEO). R packages affy and limma were performed to identify differentially expressed genes (DEGs) between primary prostate cancer and CRPC. After that, we performed functional enrichment analysis including gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway. In addition, protein-protein interaction (PPI) analysis was used to search for hub genes. Finally, to validate the significance of these genes, we performed survival analysis. As a result, we identified 53 upregulated genes and 58 downregulated genes that changed in at least two datasets. Functional enrichment analysis showed significant changes in the positive regulation of osteoblast differentiation pathway and aldosterone-regulated sodium reabsorption pathway. PPI network identified hub genes like cortactin-binding protein 2 (CTTNBP2), Rho family guanosine triphosphatase (GTPase) 3 (RND3), protein tyrosine phosphatase receptor-type R (PTPRR), Jagged1 (JAG1), and lumican (LUM). Based on PPI network analysis and functional enrichment analysis, we identified two genes (PTPRR and JAG1) as key genes. Further survival analysis indicated a relationship between high expression of the two genes and poor prognosis of prostate cancer. In conclusion, PTPRR and JAG1 are key genes in the CRPC, which may serve as promising biomarkers of diagnosis and prognosis of CRPC.
Computational Biology/methods* ; Gene Ontology ; Humans ; Jagged-1 Protein/genetics* ; Male ; Prognosis ; Prostatic Neoplasms, Castration-Resistant/mortality* ; Protein Interaction Maps ; Receptor-Like Protein Tyrosine Phosphatases, Class 7/genetics*