ObjectiveTo observe the expressions of miR-143-3p in gastric cancer cells and gastric carcinoma tissues with its clinical significance, and to analyze the target genes with enriched pathway by using bioinformatics methods.MethodsThe expressions of miR-143-3p in different differentiation gastric cancer cells and normal gastric mucosa cell line, and the expressions in gastric cancer tissues and adjacent tissues were detected by real-time fluorescent quantitative PCR. In addition, OncomiR and YM500 databases were used to analyze the expression of miR-143-3p in gastric cancer tissues compared with adjacent tissues. Furthermore, the targets of miR-143-3p were predicted by using the software of miRecords website database, and at least three software-supported target genes were chosen to analyze the enriched the signal pathways in which the target gene was involved with DAVID 6.7 software.ResultsThe expressions of miR-143-3p in the different differentiation degree of gastric cancer cells compared with normal gastric mucosa cell line were downregulated (P<0.001), and the expression of miR-143-3p in gastric cancer tissues compared with adjacent tissues was also downregulated (downregulated in 36 cases, upregulated in 18 cases, and no alteration in 4 cases). The expression of miR-143-3p in gastric cancer tissues was associated with lymph node metastasis and invasion depth (P<0.05). Bioinformatics analysis results showed that the target genes of miR-143-3p were enriched in 38 signaling pathways associated with cancer.ConclusionMiR-143-3p is a down-regulated molecular marker in gastric cancer and a potentially clinically related tumor suppressor gene, which may be involved in the cancerous phenotype in carcinogenesis and development of gastric cancer.
Objective To explore depression-related biomarkers and potential therapeutic drugs in order to alleviate depression symptoms and improve patients’ quality of life. Methods From November 2022 to January 2024, gene expression profiles of depression patients and healthy volunteers were downloaded from the Gene Expression Omnibus database. Differential expression analysis was performed to identify differentially expressed genes. Enrichment analysis of these genes was conducted, followed by the construction of a protein-protein interaction network. Finally, Cytoscape software with the Cytohubba plugin was used to identify potential key genes, and drug prediction was performed. Results Through differential expression analysis, a total of 110 differentially expressed genes (74 upregulated and 36 downregulated) were identified. Protein-protein interaction network identified 10 key genes, and differential expression analysis showed that 8 of these genes (CPA3, HDC, IL3RA, ENPP3, PTGDR2, VTN, SPP1, and SERPINE1) exhibited significant differences in expression levels between healthy volunteers and patients with depression (P<0.05). Enrichment analysis revealed that the upregulated genes were significantly enriched in pathways related to circadian rhythm, niacin and nicotinamide metabolism, and pyrimidine metabolism, while the downregulated genes were primarily enriched in extracellular matrix-receptor interaction and interleukin-17 signaling pathways. Six overlapping verification genes (SALL2, AKAP12, GCSAML, CPA3, FCRL3, and MS4A3) were obtained across two datasets using the Wayn diagram. Single-cell sequencing analysis indicated that these genes were significantly expressed in astrocytes and neurons. Mendelian randomization analysis suggested that the FCRL3 gene might play a critical role in the development of depression. Drug prediction analysis revealed several potential antidepressant agents, such as cefotiam, harmol, lincomycin, and ribavirin. Conclusions Circadian rhythm, nicotinate and nicotinamide metabolism, and pyrimidine metabolism pathways may represent potential pathogenic mechanisms in depression. Harmol may be a potential therapeutic drug for the treatment of depression.
ObjectiveTo explore the mechanism of DDX46 regulation of esophageal squamous cell carcinoma.MethodsPicture signals of fluorescence in gene array were scanned and differential expression of gene in two groups (a DDX46-shRNA-LV group and a control-LV group) were compared by GCOSvL.4 software. These differential expressed genes were analyzed by bioinformatics methods finally, and validated by quantitative real time polymerase chain reaction (qRT-PCR) analysis.ResultsAccording to the screening criteria of fold change ≥2 and P<0.05, 1 006 genes were differentially expressed after DDX46 knockdown, including 362 up-regulated and 644 down-regulated genes. Bioinformatics analysis and gene co-expression network building identified that these differentially expressed genes were mainly involved in cell cycle, proliferation, apoptosis, adhesion, energy metabolism, immune response, etc. Phosphatidylinositol 3-kinase (PI3K) was the key molecule in the network. The results of RT-qPCR were completely consistent with the results of gene microarra.ConclusionBioinformatics can effectively exploit the microarray data of esophageal squamous cell carcinoma after DDX46 knockdown, which provides a valuable clue for further exploration of DDX46 tumorigenesis mechanism and helps to find potential drug therapy.
ObjectiveTo investigate differentially expressed genes (DEGs) and potential molecular mechanisms between hepatitis C-related hepatocellular carcinoma (HCV-HCC) and hepatitis B-related HCC (HBV-HCC). MethodsThe data of HCV-HCC and HBV-HCC gene expressions were downloaded and integrated from the public gene expression database, and the limma package was used to investigate the DEGs between the HCV-HCC and HBV-HCC samples. The gene set enrichment analysis (GSEA) was used to explore the differences in suppressed or activated gene sets between the HCV-HCC and HBV-HCC samples, and the MCODE was used to explore the key molecular modules, and then the potential biological processes and molecular pathways of the key molecular modules were analyzed. The effect of key genes on survival of the HCC patients was analyzed by the Kaplan-Meier-Plotter database.ResultsIn this study, 119 HBV-HCC samples and 163 HCV-HCC samples were obtained, and the 199 DEGs were screened out. Compared with HBV-HCC, the activated gene sets of HCV-HCC were mainly enriched in the gene sets of inflammation, complement, up-regulation of genes in response to interferon, up-regulation of genes in response to KRAS, genes regulated by the nuclear factor- κB-tumor necrosis factor pathway, and apoptosis. However, the cell cycle-related gene sets were obviously suppressed. Eight key molecular modules enriched by DEGs were found, which included 18 key genes (IFI27, DDX60, MX1, IRF9, OAS3, OAS1, RSAD2, GBP4, HERC6, ISG15, IFIT1, CMPK2, EPSTI1, IFI44, IFI44L, HERC5, IFITM1, CXCL10). GO analysis showed that the biological process was mainly concentrated in the body response related to virus infection, the molecular component was mainly in the host cells, and the molecular function was mainly enriched in the biological combination. KEGG analysis showed that the key genes were mainly involved in the molecular signaling pathway related to virus infection. The survival analysis showed that the 9 key genes (CXCL10, HERC6, DDX60, IFITM1, IFI27, GBP4, IFI44L, IFI44, MX1) were closely related to better prognosis of patients with HCC (HR<1, P<0.05). ConclusionsThere is an essential difference between HBV-HCC and HCV-HCC. Occurrence of HCV-HCC is mainly related to virus infection and immune response induced by the virus. Therefore, for HCV infection, active antiviral treatment is necessary for avoiding hepatitis turning into chronic viral infection and preventing or blocking HCV infection converting to HCC.
ObjectiveTo investigate the relation between disulfidptosis-related genes (DRGs) and prognosis or immunotherapy response of patients with pancreatic cancer (PC). MethodsThe transcriptome data, somatic mutation data, and corresponding clinical information of the patients with PC in The Cancer Genome Atlas (TCGA) were downloaded. The DRGs mutated in the PC were screened out from the 15 known DRGs. The DRGs subtypes were identified by consensus clustering algorithm, and then the relation between the identified DRGs subtypes and the prognosis of patients with PC, immune cell infiltration or functional enrichment pathway was analyzed. Further, a risk score was calculated according to the DRGs gene expression level, and the patients were categorized into high-risk and low-risk groups based on the mean value of the risk score. The risk score and overall survival of the patients with high-risk and low-risk were compared. Finally, the relation between the risk score and (or) tumor mutation burden (TMB) and the prognosis of patients with PC was assessed. ResultsThe transcriptome data and corresponding clinical information of the 177 patients with PC were downloaded from TCGA, including 161 patients with somatic mutation data. A total of 10 mutated DRGs were screened out. Two DRGs subtypes were identified, namely subtype A and subtype B. The overall survival of PC patients with subtype A was better than that of patients with subtype B (χ2=8.316, P=0.003). The abundance of immune cell infiltration in the PC patients with subtype A was higher and mainly enriched in the metabolic and conduction related pathways as compaired with the patients with subtype B. The mean risk score of 177 patients with PC was 1.921, including 157 cases in the high-risk group and 20 cases in the low-risk group. The risk score of patients with subtype B was higher than that of patients with subtype A (t=14.031, P<0.001). The overall survival of the low-risk group was better than that of the high-risk group (χ2=17.058, P<0.001), and the TMB value of the PC patients with high-risk was higher than that of the PC patients with low-risk (t=5.642, P=0.014). The mean TMB of 161 patients with somatic mutation data was 2.767, including 128 cases in the high-TMB group and 33 cases in the low-TMB group. The overall survival of patients in the high-TMB group was worse than that of patients in the low-TMB group (χ2=7.425, P=0.006). ConclusionDRGs are closely related to the prognosis and immunotherapy response of patients with PC, and targeted treatment of DRGs might potentially provide a new idea for the diagnosis and treatment of PC.
Objective A series of bioinformatics methods were used to identify ferroptosis related biomarkers in lupus nephritis (LN). Methods We retrieved sequencing data of GSE112943 from the GEO (Gene Expression Omnibus) database and screened LN differentially expressed genes. We searched for ferroptosis-related gene (FRG) through FerrDb database, and screened LN-FRG. We conducted enrichment analysis on the LN-FRGs using David online bioinformatics database and screened the core LN-FRG using cytoHubba. We used external data sets to verify the core LN-FRGs, constructed competing endogenous RNA networks, and conducted molecular docking analysis. Results A total of 37 LN-FRGs were selected through screening. These genes are mainly enriched in inflammation, immune regulation and ferroptosis related signaling pathways. Through the cytoHubba and external dataset validation, the key core LN-FRG of ATF3 (activating transcription factor 3) was ultimately identified, and its expression was significantly increased in LN (P<0.05). Molecular docking analysis showed that ATF3 was closely bound to SLC7A11 and NRF2, and may participate in the occurrence and development of LN through the microRNA-27-ATF3 regulation axis. Conclusion The pivotal gene ATF3 may participate in the inflammation and immune injury of LN through ferroptosis.
Objective To identify potential hub genes and key pathways in the early period of septic shock via bioinformatics analysis. MethodsThe gene expression profile GSE110487 dataset was downloaded from the Gene Expression Omnibus database. Differentially expressed genes were identified by using DESeq2 package of R project. Then Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were constructed to investigated pathways and biological processes using clusterProfiler package. Subsequently, protein-protein interaction (PPI) network was mapped using ggnetwork package and the molecular complex detection (MCODE) analysis was implemented to further investigate the interactions of differentially expressed genes using Cytoscape software. Results A total of 468 differentially expressed genes were identified in septic shock patients with different responses who accepted early supportive hemodynamic therapy, including 255 upregulated genes and 213 downregulated genes. The results of GO and the KEGG pathway enrichment analysis indicated that these up-regulated genes were highly associated with the immune-related biological processes, and the down-regulated genes are involved in biological processes related to organonitrogen compound, multicellular organismal process, ion transport. Finally, a total of 23 hub genes were identified based on PPI and the subcluster analysis through MCODE software plugin in Cytoscape, which included 19 upregulated hub genes, such as CD28, CD3D, CD8B, CD8A, CD160, CXCR6, CCR3, CCR8, CCR9, TLR3, EOMES, GZMB, PTGDR2, CXCL8, GZMA, FASLG, GPR18, PRF1, IDO1, and additional 4 downregulated hub genes, such as CNR1, GPER1, TMIGD3, GRM2. KEGG pathway enrichment analysis and GO functional annotation showed that differentially expressed genes were primarily associated with the items related to cytokine-cytokine receptor interaction, natural killer cell mediated cytotoxicity, hematopoietic cell lineage, T cell receptor signaling pathway, phospholipase D signaling pathway, cell adhesion molecules, viral protein interaction with cytokine and cytokine receptor, primary immunodeficiency, graft-versus-host disease, type 1 diabetes mellitus. Conclusions Some lymphocytes such as T cells and natural killer cells, cytokines and chemokines participate in the immune process, which plays an important role in the early treatment of septic shock, and CD160, CNR1, GPER1, and GRM2 may be considered as new biomarkers.
Objective To explore the key genes, pathways and immune cell infiltration of bicuspid aortic valve (BAV) with ascending aortic dilation by bioinformatics analysis. Methods The data set GSE83675 was downloaded from the Gene Expression Omnibus database (up to May 12th, 2022). Differentially expressed genes (DEGs) were analyzed and gene set enrichment analysis (GSEA) was conducted using R language. STRING database and Cytoscape software were used to construct protein-protein interaction (PPI) network and identify hub genes. The proportion of immune cells infiltration was calculated by CIBERSORT deconvolution algorithm. Results There were 199 DEGs identified, including 19 up-regulated DEGs and 180 down-regulated DEGs. GSEA showed that the main enrichment pathways were cytokine-cytokine receptor interaction, pathways in cancer, regulation of actin cytoskeleton, chemokine signaling pathway and mitogen-activated protein kinase signaling pathway. Ten hub genes (EGFR, RIMS3, DLGAP2, RAPH1, CCNB3, CD3E, PIK3R5, TP73, PAK3, and AGAP2) were identified in PPI network. CIBERSORT analysis showed that activated natural killer cells were significantly higher in dilated aorta with BAV. Conclusions These identified key genes and pathways provide new insights into BAV aortopathy. Activated natural killer cells may participate in the dilation of ascending aorta with BAV.
Objective To screen the key genes in childhood therapy-resistant asthma by bioinformatic method, and to verify its expression and diagnostic value in peripheral blood of children with therapy-resistant asthma. Methods The transcriptome dataset GSE27011 of peripheral blood mononuclear cells from healthy children (healthy control group), mild asthma (MA) children (MA group) and severe asthma (SA) children (SA group) was downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information of the United States. Key genes were obtained by using R software for gene differential expression analysis, weighted gene co-expression network analysis (WGCNA) and clinical phenotypic correlation analysis. The differential expression levels of key genes were verified in children with asthma and immune cell transcriptome datasets. Seventy-eight children with asthma and 30 healthy children who were diagnosed in the Department of Pediatrics of Tangshan People’s Hospital between September 2020 and September 2021 were selected and divided into control group, MA group and SA group. Peripheral blood samples from children with asthma and healthy children who underwent physical examination were collected to detect the expression levels of key genes and inflammatory factors interleukin (IL)-4 and IL-17 in peripheral blood of children. Receiver operating characteristic curve was used to evaluate the sensitivity, specificity and accuracy of key genes in predicting childhood therapy-resistant asthma. Results The key gene GNA15 was obtained by bioinformatic analysis. Analysis of asthma validation dataset showed that GNA15 was up-regulated in asthma groups, and was specifically expressed in eosinophils. Clinical results showed that the expression levels of IL-4, IL-17 and GNA15 among the three groups were significantly different (P<0.05). The expression levels of IL-4 and IL-17 in the MA group and the SA group were higher than those in the control group (P<0.05). Compared with the control group and the MA group, the expression level of GNA15 in the SA group was up-regulated (P<0.05). Neither the difference in the expression level of IL-4 or IL-17 between the MA group and the SA group, nor the difference in the expression level of GNA15 between the control group and the MA group was statistically significant (P>0.05). The specificity, sensitivity and accuracy of GNA15 in predicting SA were 92.90%, 80.00% and 86.10%, respectively. Conclusion GNA15 has a significant clinical value in predicting the childhood therapy-resistant asthma, and may become a potential diagnostic marker for predicting the severity of asthma in children.
Objective To analyze the pathways, biomarkers and diagnostic genes of systemic sclerosis associated interstitial lung disease (SSc-ILD) using bioinformatics. Methods SSc-ILD related gene data sets from April to June 2023 were downloaded from the Gene Expression Omnibus database for differential analysis and enrichment analyses including gene ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, disease ontology analysis, and gene set enrichment analysis. Least absolute shrinkage and selection operator regression and support vector machine algorithms were applied to screen and take the intersection to get the diagnostic genes and validate the results. Disease-related data were analyzed by immune cell infiltration. Results A total of 178 differential genes were obtained, and enrichment analyses showed that they were related to 5 signaling pathways and associated with 3 diseases. The diagnostic genes screened were TNFAIP3, ID3, and NT5DC2, and immune cell infiltration showed that the diagnostic genes were associated with plasma cells, resting mast cells, activated natural killer cells, macrophage M1 and M2, resting dendritic cells, and activated dendritic cells. Conclusion The screened diagnostic genes and immune cells may be involved in the development of SSc-ILD.