Objective To explore the pathogenesis of acute respiratory disease syndrome (ARDS) by bioinformatics analysis of neutrophil gene expression profile in order to find new therapeutic targets. Methods The gene expression chips include ARDS patients and healthy volunteers were screened from the Gene Expression Omnibus (GEO) database. The differentially expressed genes were carried out through GEO2R, OmicsBean, STRING, and Cytoscape, then enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathways was conducted to investigate the biological processes involved in ARDS via DAVID website. Results Bioinformatics analysis showed 86 differential genes achieved through the GEO2R website. Eighty-one genes were included in the STRING website for protein interaction analysis. The results of the interaction were further analyzed by Cytoscape software to obtain 11 hub genes: AHSP, ALAS2, CD177, CLEC4D, EPB42, GPR84, HBD, HVCN1, KLF1, SLC4A1, and STOM. GO analysis showed that the differential gene was enriched in the cellular component, especially the integrity of the plasma membrane. KEGG analysis showed that multiple pathways especially the cytokine receptor pathway involved in the pathogenesis of ARDS. Conclusions A variety of genes and pathways have been involved in the pathogenesis of ARDS. Eleven hub genes are screened, which may be involved in the pathogenesis of ARDS and can be used in subsequent studies.
ObjectiveThe role of ferroptosis-related genes in the occurrence and development of lung injury caused by sepsis was investigated by bioinformatics methods, and the closely related genes were predicted. MethodsThe Dataset GSE154653 was downloaded from the gene expression database (GEO), and a total of 8 cases of microarray gene set were included in normal group and lipopolysaccharide (LPS)-induced sepsis lung tissue. The differential expression genes (DEGs) were screened out under conditions of |log2 FC|>1 and P.adj<0.05. Meanwhile, the selected DEGs were combined with the driver and suppressor genes of ferroptosis downloaded from the ferroptosis database (FerrDb) to obtain the differential genes associated with ferroptosis in sepsis (Fe-DEGs). These Fe-DEGs were further analyzed using R language, DAVID, and STRING online tools to identify GO-KEGG functions and pathways, and the construction of PPI network. Results The Bioinformatics approach screened out 3533 DEGs and intersected 53 key genes related to ferroptosis. The further biological process (BP) of GO enrichment analysis mainly involves the positive regulation of transcription, the positive regulation of RNA polymerase II promoter transcription, the cytokine mediated signaling pathway, and the positive regulation of angiogenesis. The molecular function (MF) mainly involves the same protein binding, transcriptional activation activity and REDOX enzyme activity. The pathways are enriched in iron death, HIF-1 signaling pathway and AGE-RAGE signaling pathway. Five key Fe-DEGs genes were screened by constructing PPI network, including CYBB, LCN2, HMOX1, TIMP1 and CDKN1A. Conclusion CYBB、LCN2、HMOX1、TIMP1 and CDKNIA genes may be key genes involved in ferroptosis of lung tissue caused by sepsis.
Objective To screen the differentially expressed genes and pathways involved in rosacea using bioinformatics analysis. Methods The GSE65914 gene chipset was collected from the Gene Expression Omnibus (up to July 12th, 2021). It was searched according to the keyword “rosacea”. The data was analyzed by GEO2R platform. The common differential genes of three subtypes of rosacea were screened out. The online DAVID analysis tool was used to perform the gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction networks of differentially expressed genes were made by String and Cytoscape. The key modules and genes were screened by Mcode and Cytohubba. Results A total of 957 common differential genes were identified, including 533 up-regulated genes and 424 down-regulated genes. GO enrichment analysis showed that these genes were mainly involved in immune response, inflammatory response, intercellular signal transduction, positive regulation of T cell proliferation, chemokine signaling pathways, cell surface receptor signaling pathways, cellular response to interferon-γ, and other biological processes. KEGG pathway enrichment analysis mainly included cytokine-cytokine receptor interaction, rheumatoid arthritis, chemokine signaling pathway, PPAR signaling pathway, Toll-like receptor signaling pathway, nuclear transcription factor-κB signaling pathway, tumor necrosis factor signaling pathway and other signaling pathways. Cytohubba analysis revealed 10 key genes, including PTPRC, MMP9, CCR5, IL1B, TLR2, STAT1, CXCR4, CXCL10, CCL5 and VCAM1. Conclusion The key genes and related pathways may play an important role in the pathogenesis of rosacea.
Chronic cerebral hypoperfusion (CCH) plays an important role in the occurrence and development of vascular dementia (VD). Recent studies have indicated that multiple stages of immune-inflammatory response are involved in the process of cerebral ischemia, drawing increasing attention to immune therapies for cerebral ischemia. This study aims to identify potential immune therapeutic targets for CCH using bioinformatics methods from an immunological perspective. We identified a total of 823 differentially expressed genes associated with CCH, and further screened for 9 core immune-related genes, namely RASGRP1, FGF12, SEMA7A, PAK6, EDN3, BPHL, FCGRT, HSPA1B and MLNR. Gene enrichment analysis showed that core genes were mainly involved in biological functions such as cell growth, neural projection extension, and mesenchymal stem cell migration. Biological signaling pathway analysis indicated that core genes were mainly involved in the regulation of T cell receptor, Ras and MAPK signaling pathways. Through LASSO regression, we identified RASGRP1 and BPHL as key immune-related core genes. Additionally, by integrating differential miRNAs and the miRwalk database, we identified miR-216b-5p as a key immune-related miRNA that regulates RASGRP1. In summary, the predicted miR-216b-5p/RASGRP1 signaling pathway plays a significant role in immune regulation during CCH, which may provide new targets for immune therapy in CCH.
Objective To study the expression of 4 circular RNA (circRNA) in peripheral blood mononuclear cells (PBMC) of patients with epilepsy and to predict its function by bioinformatics, so as to provide basis for exploring the pathogenesis of epilepsy. Methods From May 2020 to May 2021, 22 epilepsy patients were treated in the Department of Neurology of the First Affiliated Hospital of Baotou Medical College of Inner Mongolia University of Science and Technology, and 22 control group were selected. There were 13 males and 8 females in the epilepsy group, with an average age of (36.41±8.39)years. There were 11 males and 11 females in the control group, with an average age of (34.41±8.68) years. The expression levels of circRNA EFCAB2, C14orf159, PARG and TMEM39 in PBMC were detected by real-time fluorescence quantitative PCR, and their functions were predicted by bioinformatics. Results Compared with the control group, the relative expression of EFCAB2 and C14orf159 in PBMC of epileptic patients was 1.42±0.06 (t=29.41) and 1.31±0.03 (t=25.27), PARG and TMEM39 were not detected in peripheral blood PBMC. Bioinformatics analysis showed that three mirnas obtained by EFCAB2 were miR-6873-3p, miR-6739-3p and miR-7110-3p. Three mirnas were obtained by C14orf159: miR-1180-3p, miR-6501-3p, and miR-3622b-5p. The seizure-related genes were predicted by TargetScan database. EFCAB2: miR-6873-3p met the requirements of 11 downstream genes. A total of 7 downstream genes of miR-6739-3p met the requirements.A total of 14 downstream genes were eligible for miR-7110-3p and a total of 9 downstream genes were eligible for miR-6501-3p. A total of 14 downstream genes were eligible for miR-3622B-5p.miR-1180-3p has a total of 1 downstream genes that meet the requirements. Conclusions Studies have shown that two circrnas, EFCAB2 and C14orf159, may be important biological markers of epilepsy. Through bioinformatics analysis, these two circrnas may act as "molecular sponges" to regulate epilepsy. EFCAB2 has the potential to act as a "molecular sponge" for miR-6873-3p and miR-7110-3p, and it was found that miR-6873-3p and miR-7110-3 share a common downstream target gene MAP1B-which plays a role in epilepsy by regulating voltage-gated sodium channels. C14orf159 can act as a molecular sponge for miR-6501-3p to regulate the expression of CCL3 and play a role in epilepsy.
Objective To explore the mode and role of differential expression of circular RNAs (circRNAs) in myelodysplastic syndrome (MDS). Methods We preprocessed and analyzed the circRNA expression profile datasets GSE163386, GSE94591, and GSE81173 in the GEO (Gene Expression Omnibus) database. By using the circBank database and the ENCORI, miRDB, and miRWalk databases to predict microRNAs (miRNAs) that interacted with differentially expressed circRNAs and messenger RNAs (mRNAs), the circRNA-miRNA-mRNA axis was constructed. We retrieved miRNAs related to MDS in PubMed and further obtained competing endogenous RNA (ceRNA) networks related to MDS by taking intersections. Results Through analysis, 128 differentially expressed circRNAs were identified, 48 highly expressed, and 80 low expressed. Among differentially expressed circRNAs with multiple differences>10, 3 were upregulated and 11 were downregulated. Through analysis, 101 differentially expressed mRNA were identified, with 9 upregulated and 92 downregulated. Intersecting with the MDS related miRNAs retrieved by PubMed, we further obtained the MDS related ceRNA network, namely circRNA (has_circ_0061137)-miRNA (has-miR-16-5p)-mRNA (RUBCNL, TBC1D9, SLC16A6) and circRNA (has_circ_0061137)-miRNA (has-miR-125b-5p)-mRNA (CCR5, SLC16A6, IRF4), all of which were downregulated. Conclusion The ceRNA networks revealed in this study may help elucidate the circRNA mechanism in MDS.
ObjectiveTo bioinformatically analyze the gene chip data of chondrocytes from osteoarthritis patients from the Gene Expression Omnibus (GEO) database, and explore the molecular mechanisms of osteoarthritis.MethodsWe searched the GEO database (up to April 23rd, 2021) for data of chondrocytes and gene expression profiling in human knee osteoarthritis via the key words of “osteoarthritis OR cartilage OR chondrocyte*”. Then, we selected the samples by our inclusion criteria. The data were normalized before analysis. After differentially expressed genes were identified, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Search Tool for the Retrival of Interacting Genes/Proteinsm, R language, Perl language, Cytoscape software, and DAVID database were used to perform differentially expressed gene analysis, functional annotation, and enrichment analysis.ResultsThe differentially expressed genes were mostly enriched in cell components and some extracellular regions, which participated in cell division, mitosis, cell proliferation and inflammatory response mainly via the regulation of protein kinase activity. The differentially expressed genes were mainly involved in the cell proliferation signaling pathway, mitogen-activated protein kinase signaling pathway, oocyte meiosis, cell cycle and so on.ConclusionsMultiple signaling pathways are involved in the changes of chondrocytes in human knee osteoarthritis, mainly about cell cycle and protein metabolism genes/pathways. Inflammatory factors and cytokines may be the most important links in the pathogenesis of osteoarthritis.
ObjectiveTo establish and validate the diagnostic model of ferroptosis genes for acute myocardial infarction (AMI) based on bioinformatics. MethodsFive AMI gene expression data were obtained from Gene Expression Omnibus (GEO), namely GSE66360, GSE48060, GSE60993, GSE83500, GSE34198. Among them, GSE66360 was used as the training set to perform differential analysis, and intersection of differential genes and ferroptosis genes was taken to obtain differentially expressed ferroptosis genes in AMI. GO and KEGG enrichment analysis was performed using Metascape website. Subsequently, random forest (RF) algorithm was used to screen out key genes with high classification performance according to the Keeny coefficient score, and artificial neural network (ANN) diagnostic model of AMI ferroptosis feature gene was constructed by model group GSE83500. The area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation was used to evaluate the performance and generalization ability of the model, and 3 external independent datasets were used to verify the diagnostic performance of this model. The single sample gene setenrichment analysis was used to explore the difference in immune cell infiltration between infarcted myocardium and normal myocardium after AMI. In addition, correlation analysis between immune cells and key genes was also conducted. Finally, potential drugs that would prevent and treat AMI by regulating ferroptosis were screened out from the Coremin Medical platform. ResultsA total of 16 differentially expressed ferroptosis genes were obtained in the training set, GO enrichment analysis showed that they mainly participated in biological functions such as cellular response to biological stimuli and chemical stress, regulation of interleukin 17, etc. KEGG enrichment analysis showed that these genes were significantly enriched in NOD-like receptor signaling pathway, programmed cell necrosis, Leishmaniasis and other pathways. Four genes with good classification performance were screened out using RF algorithm, namely EPAS1, SLC7A5, FTH1, and ZFP36. The results of 10-fold cross-validation showed that the minimum AUC value was 0.746, the maximum value was 0.906, and the average value was 0.805. The AUC of the ANN model was 0.859, and the AUC values of the three independent validation sets were 0.763 (GSE48060), 0.673 (GSE60993), 0.698 (GSE34198). Immune cell infiltration found that macrophages, mast cells and monocytes were significantly active after AMI. Correlation analysis found that there were positive correlations between 4 key genes and activated dendritic cells, eosinophils and γδT cells. A total of 20 potential western medicines were predicted which could prevent and treat AMI by regulating ferroptosis, and the predicted potential Chinese medicine was mainly heat-clearing and detoxifying and blood-activating and removing blood stasis drugs. ConclusionThe identified AMI ferroptosis genes by bioinformatics method have certain diagnostic significance, which provides a reference for disease diagnosis and treatment.
The rapid development of high-throughput chromatin conformation capture (Hi-C) technology provides rich genomic interaction data between chromosomal loci for chromatin structure analysis. However, existing methods for identifying topologically associated domains (TADs) based on Hi-C data suffer from low accuracy and sensitivity to parameters. In this context, a TAD identification method based on spatial density clustering was designed and implemented in this paper. The method preprocessed the raw Hi-C data to obtain normalized Hi-C contact matrix data. Then, it computed the distance matrix between loci, generated a reachability graph based on the core distance and reachability distance of loci, and extracted clustering clusters. Finally, it extracted TAD boundaries based on clustering results. This method could identify TAD structures with higher coherence, and TAD boundaries were enriched with more ChIP-seq factors. Experimental results demonstrate that our method has advantages such as higher accuracy and practical significance in TAD identification.
ObjectiveA competing endogenous RNA (ceRNA) regulatory network associated with long non-coding RNA (lncRNA) specific for lung adenocarcinoma (LUAD) was constructed based on bioinformatics methods, and the functional mechanism of actinfilament-associated protein 1-antisense RNA1 (AFAP1-AS1) in LUAD was analyzed, in order to provide a new direction for the study of LUAD therapeutic targets. MethodsThe gene chip of LUAD was downloaded from the Gene Expression Omnibus (GEO), and lncRNA and mRNA with differential expression between LUAD and normal tissues were screened using GEO2R online software, and their target genes were predicted by online databases to construct ceRNA networks and perform enrichment analysis. In cell experiments, AFAP1-AS1 was genetically knocked down and siRNA was constructed and transfected into LUAD cells A549 by cell transfection. CCK8, transwell, scratch assay and flow cytometry were used to detect the ability of cells to proliferate, invade, migrate and apoptosis. ResultsA total of 6 differentially expressed lncRNA and 494 differentially expressed mRNA were identified in the microarray of LUAD. The ceRNA network involved a total of 6 lncRNA, 22 miRNA, and 55 mRNA. Enrichment analysis revealed that mRNA was associated with cancer-related pathways. In cell assays, knockdown of AFAP1-AS1 inhibited cell proliferation, invasion, and migration, and AFAP1-AS1 promoted apoptosis. ConclusionIn this study, we construct a lncRNA-mediated ceRNA network, which may help to further investigate the mechanism of action of LUAD. In addition, through cellular experiments, AFAP1-AS1 is found to have potential as a therapeutic target for LUAD.