Objective To validate the different expressions of human fxyd6 gene between normal bile duct tissues and malignant tumor tissues, and to observe the subcellular localization of human fxyd6 gene in human cholangiocarcinoma cells. MethodsThe different expressions between normal bile duct tissues and malignant tumor tissues were identified by RT-PCR. In situ polymerase chain reaction （IS-RT-PCR） was applied to detect the subcellular localization of fxyd6 gene in paraffin sections of human cholangiocarcinoma cells. Image analysis software was used to semiquantitatively determine the difference between normal and malignant tissues. ResultsHuman fxyd6 gene was highly expressed in cholangiocarcinoma tissues and lowly expressed in normal ones. There was a significant difference between the expressions of carcinoma cells and normal cells （P＜0.05）. IS-RT-PCR showed that fxyd6 gene localized in the kytoplasma of epithelial cells of human cholangiocarcinoma. ConclusionHuman fxyd6 gene may act as an essential component of the malignant transformation process in human cholangiocarcinoma.
ObjectiveTo investigate the molecular pathogenesis of pulmonary fibrosis induced by bleomycin in a murine model,and provide novel insights for clinical diagnosis and treatment. MethodsFrom Gene Expression Omnibus,we downloaded microarray data extracted from experiments of bleomycin induced pulmonary fibrosis in wild-type mice. With BRB-Array Tools,differentially expressed genes at different time points during disease development were screened,selected and analyzed by DAVID software. ResultsBRB array analysis identified 45101 differentially expressed genes. After induction by bleomycin on 7th day,1164 genes and 735 genes were significantly up-regulated and down-regulated (P<0.05,fold change>2),respectively. On 14th day,731 genes and 390 genes were significantly up-regulated and down-regulated (P<0.05,fold change>2),respectively. DAVID analysis revealed that the up-regulated genes were significantly enriched in cell cycle,p53 signaling and chemokine signaling pathway,damaging reaction and collagen metabolism gene sets. While the down-regulated genes were enriched in the drug metabolism pathway gene set. ConclusionsBioinformatics methodologies are able to efficiently analyze microarray data and extract its underlying information,provide novel insights for major molecular events and shift of cell signaling pathway during pulmonary fibrosis progression,and furthermore,finding molecular markers for early diagnosis and therapeutic targets.
Objective To investigate the expression levels of fatty acid metabolism-related genes in acute myeloid leukemia (AML) and construct a prognostic risk regression model for AML. Methods Gene expression data from control groups and AML patients were downloaded from the GTEx database and The Cancer Genome Atlas (TCGA) database, followed by screening for differentially expressed genes (DEGs) between AML patients and controls. Fatty acid metabolism-related genes were obtained from the MSigDB database. The intersection of DEGs and fatty acid metabolism-related genes yielded fatty acid metabolism-associated DEGs. A protein-protein interaction network was constructed using the STRING database. Hub genes were analyzed via random forest, Kaplan-Meier survival, and Cox proportional hazards regression based on TCGA clinical data to establish a prognostic model and evaluate their diagnostic and prognostic significance. Immune cell infiltration differences between high- and low-risk groups were assessed using CIBERSORT algorithms to explore immune microenvironment variations and correlations with risk scores. Results A total of 60 fatty acid metabolism-related DEGs were identified. Further screening revealed 15 hub genes, among which four genes (HPGDS, CYP4F2, ACSL1, and EHHADH) were selected via integrated random forest, Cox regression, and Kaplan-Meier analyses to construct an AML prognostic lipid metabolism gene signature. Heatmaps demonstrated statistically significant differences in tumor-infiltrating immune cell proportions between risk groups (P<0.05). Conclusion The constructed lipid metabolism gene prognostic model may serve as a biomarker for overall survival in AML patients and provide new insights for immunotherapy drug development.
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.
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.
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.
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.
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.
ObjectiveTo analyze the expression profile changes of osteogenic-related genes during spontaneous calcification of rat bone marrow mesenchymal stem cells (BMSCs). MethodsBMSCs were isolated from 3-day-old healthy Sprague Dawley rats;cells at the 4th generation were used to establish the spontaneous calcification model in vitro. Spontaneous calcification process was recorded by inverted phase contrast microscope observation and alizarin red staining after 7 and 14 days of culture. For gene microarray analysis, cell samples were collected at 0, 7, and 14 days after culture; the differentially expressed genes were analyzed by bioinformatics methods and validated by real-time quantitative PCR (RT-qPCR) assay. ResultsRat BMSCs calcified spontaneously in vitro. When cultured for 7 days, the cells began to aggregate and were weakly positive for alizarin red staining. After 14 days of culture, obvious cellular aggregation and typical mineralized nodules were observed, the mineralized nodules were brightly positive for alizarin red staining. A total of 576 gene probe-sets expressed differentially during spontaneous calcification, corresponding 378 rat genes. Among them, 359 gene probe-sets expressed differentially between at 0 and 7 days, while only 13 gene probe-sets expressed differentially between at 7 and 14 days. The 378 differentially expressed genes were divided into 6 modes according to their expression profiles. Moreover, according to their biological functions, differentially expressed genes related to bone cell biology could be classified into 7 major groups:angiogenesis, apoptosis, bone-related genes, cell cycle, development, cell communication, and signal pathways related to osteogenic differentiation. In cell cycle group, 12 down-regulated genes were linked with each other functionally. Matrix metalloproteinase 13 (Mmp13), secreted phosphoprotein 1 (Spp1), Cxcl12, Mmp2, Mmp3, Apoe, and Itga7 had more functional connections with other genes. The results of genes Spp1, Mgp, Mmp13, Wnt inhibitory factor 1, Cxcl12, and cyclin A2 by RT-qPCR were consistent with that of gene microarray. ConclusionThe first 7 days after rat BMSCs were seeded are a key phase determining the fate of spontaneous calcification. Multiple genes related with cell communication, bone-related genes, cell cycle, transforming growth factor-β signaling pathway, mitogen-activated protein kinase signaling pathway, and Wnt signaling pathway are involved during spontaneous calcification.
Lung cancer is one of the malignant tumors with the greatest threat to human health, and studies have shown that some genes play an important regulatory role in the occurrence and development of lung cancer. In this paper, a LightGBM ensemble learning method is proposed to construct a prognostic model based on immune relate gene (IRG) profile data and clinical data to predict the prognostic survival rate of lung adenocarcinoma patients. First, this method used the Limma package for differential gene expression, used CoxPH regression analysis to screen the IRG to prognosis, and then used XGBoost algorithm to score the importance of the IRG features. Finally, the LASSO regression analysis was used to select IRG that could be used to construct a prognostic model, and a total of 17 IRG features were obtained that could be used to construct model. LightGBM was trained according to the IRG screened. The K-means algorithm was used to divide the patients into three groups, and the area under curve (AUC) of receiver operating characteristic (ROC) of the model output showed that the accuracy of the model in predicting the survival rates of the three groups of patients was 96%, 98% and 96%, respectively. The experimental results show that the model proposed in this paper can divide patients with lung adenocarcinoma into three groups [5-year survival rate higher than 65% (group 1), lower than 65% but higher than 30% (group 2) and lower than 30% (group 3)] and can accurately predict the 5-year survival rate of lung adenocarcinoma patients.