ObjectiveTo analyze the expression and clinical significance of cyclin-dependent kinase 1 (CDK1) in lung adenocarcinoma by bioinformatics.MethodsBased on the gene expression data of lung adenocarcinoma patients in The Cancer Genome Atlas (TCGA), the differential expression of CDK1 in lung adenocarcinoma tissues and normal lung tissues was analyzed. The expression of CDK1 gene in lung adenocarcinoma was analyzed by UALCAN at different angles. Survival analysis of different levels of CDK1 gene expression in lung adenocarcinoma was performed using Kaplan-Meier Plotter. Correlation Cox analysis of CDK1 expression and overall survival was based on clinical data of lung adenocarcinoma in TCGA. Gene set enrichment analysis was performed on gene sequences related to CDK1 expression in clinical cases. The protein interaction network of CDK1 from Homo sapiens was obtained by STRING. CDK1-related gene proteins were obtained and analyzed by the web server Gene Expression Profiling Interactive Analysis (GEPIA).ResultsBased on the analysis of TCGA gene expression data, CDK1 expression in lung adenocarcinoma was higher than that in normal lung tissues. UALCAN analysis showed that high CDK1 expression may be associated with smoking. Survival analysis indicated that when CDK1 gene was highly expressed, patients with lung adenocarcinoma had a poor prognosis. Univariate and multivariate Cox regression analysis of CDK1 expression and overall survival showed that high CDK1 expression was an independent risk factor for survival of patients with lung adenocarcinoma. Gene set enrichment analysis revealed that high CDK1 expression was closely related to DNA replication, cell cycle, cancer pathway and p53 signaling pathway.ConclusionCDK1 may be a potential molecular marker for prognosis of lung adenocarcinoma. In addition, CDK1 regulation may play an important role in DNA replication, cell cycle, cancer pathway and p53 signaling pathway in lung adenocarcinoma.
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.
ObjectiveTo explore the significance of mesenchymal epithelial transition factor (MET) as a clinical prognostic evaluation index for patients with pancreatic cancer based on bioinformatics analysis.MethodsThe GSE28735 and GSE62452 gene chips from GEO database were downloaded and the difference of MET gene expression between cancer and adjacent cancerous tissues were analyzed by bioinformatics. We downloaded pancreatic cancer gene chip from TCGA database to analyze the correlation between MET gene expression and clinicopathological features of pancreatic cancer patients and prognosis risk. Finally, the possible molecular mechanism of MET involved in pancreatic carcinogenesis was analyzed by GO and KEGG enrichment analysis.ResultsThe expression level of MET gene in pancreatic cancer tissues was significantly higher than that in adjacent cancerous tissues (P<0.001). The overall survival and disease-free survival of pancreatic cancer patients in the high MET gene expression group were lower than those in the low expression group (P<0.001). The expression level of MET gene was related to the age of pancreatic cancer patients, T stage, and histological grading of tumors (P<0.05), and high MET gene expression, age >65 years, and N1 stage were independent risk factors affecting the prognosis of pancreatic cancer patients. KEGG enrichment analysis showed that MET was mainly related to PI3K/AKT signaling pathway, FAK signaling pathway, and cancer transcription dysregulation and so on.ConclusionMET may be a valuable tumor marker for pancreatic cancer and can predict the poor prognosis of patients with pancreatic cancer.
Objective To analyze the relationship between the expression of carbonic anhydrase 3 (CA3) in breast cancer tissues, its prognostic potential and the number of immune cells by a variety of online databases. Methods GEPIA2.0 and TIMER databases were used to analyze the difference of CA3 mRNA expression in breast cancer tissues. Bc-GenExMinerv4.7 database was used to analyze the difference of CA3 mRNA expression in breast cancer subcategories. Kaplan-Meier plotter, Bc-GenExMinerv4.7 and PrognoScan databases were used to analyze the effect of CA3 mRNA expression levels on prognosis of patient. LinkedOmics database was used to analyze of the biological behavior involved in CA3 co-expressed genes. TIMER database was used to analyze the relationship between CA3 mRNA expression and immune cells infiltration in breast cancer tissues. Results The expression of CA3 mRNA in breast cancer tissues was lower than that in normal breast tissues (P<0.05), and the expression levels of CA3 mRNA were higher in ER negative (P<0.05), PR negative (P<0.05), HER2 negative (P<0.05) and no lymphatic metastasis (P<0.05). In addition, the expression level of CA3 in breast cancer patients with high Ki67 expression was lower (P<0.05) and closely related to SBR and NPI grade (P<0.05). Breast cancer patients with low expression of CA3 mRNA had lower overall survivall, recurrence free survival, and disease free survival ( P<0.05). Ten of the top 50 positively correlated co-expressed genes screened out had low risk ratio (P<0.05), and 11 of the top 50 negatively correlated co-expressed genes screened out had high risk ratio (P<0.05). The expression of CA3 mRNA was positively correlated with CD4+ T cells and CD8+ T cells in breast cancer tissues (rs=0.175, P<0.001; rs=0.137, P<0.001), and negatively correlated with T cell failure markers LAG3, TIM-3 and PVRL2 (rs=–0.100, P<0.01; rs=–0.143, P<0.001; rs=–0.082, P<0.05). Conclusions The low expression of CA3 mRNA in breast cancer tissues is correlated with the occurrence, development and prognosis of breast cancer. CA3 can be used as a potential independent prognostic marker for breast cancer and may be related to immune infiltration.
Objective To explore the correlation and mechanism of ferroptosis with pulmonary fibrosis. Methods Pulmonary fibrosis tissue sequencing data were obtained from Gene Expression Omnibus and FerrDb databases from January 2019 to December 2023. Differentially expressed genes (DEGs) between the normal control group and the pulmonary fibrosis group were analyzed by bioinformatic method, and DEGs related to pulmonary iron addiction were extracted. The hub genes were screened by enrichment analysis, protein-protein interaction (PPI) analysis and random forest algorithm. The mouse model of pulmonary fibrosis was made for exercise intervention, and the expression of hub genes was verified by real-time quantitative reverse transcription polymerase chain reaction. Results A comparison of 103 patients with idiopathic pulmonary fibrosis and 103 normal lung tissues showed that 13 up-regulated genes and 7 down-regulated genes were identified as ferroptosis-related DEGs. PPI results showed that there was an interaction between these ferroptosis-related genes. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment and Genome Ontology enrichment analysis showed that ferroptosis-related genes were involved in organic anion transport, hypoxia response, oxygen level reduction response, hypoxia-inducible factor-1 signaling pathway, renal cell carcinoma, and arachidonic acid metabolic signaling pathway. Genes identified by PPI analysis and random forest algorithm included CAV1, NOS2, GDF15, HNF4A, and CDKN2A. Real-time fluorescence quantitative polymerase chain reaction results of mouse fibrotic lung tissue showed that compared with the exercise group, the mRNA levels of NOS2, PTGS2 and GDF15 were up-regulated and the mRNA levels of CAV1 and CDKN2A were down-regulated in the bleomycin group (P<0.05); compared with the bleomycin group, the expression of CAV1 and CDKN2A increased and the expression of NOS2, PTGS2 and GDF15 decreased in the bleomycin + exercise group (P<0.05). Conclusions Bioinformatic analysis identifies 20 potential genes associating with ferroptosis in pulmonary fibrosis. CAV1, NOS2, GDF15, and CDKN2A influence the development of pulmonary fibrosis by modulating ferroptosis. Treadmill training can reduce ferroptosis in fibrotic tissues, thereby reducing lung inflammation.
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 detect the expression and clinical significance of POLD1 gene in non-small cell lung cancer (NSCLC) via bioinformatics method. Methods The expression difference of POLD1 in NSCLC tissue and normal lung tissue was investigated by TIMER database. UALCAN database was used to further verify different expression of POLD1 as well as the relationship between POLD1 expression and clinicopathological characteristics of NSCLC. The correlation between POLD1 gene and prognosis of NSCLC patients was detected by GEPIA and TIMER database. cBioPortal database was used to analyze frequencies of POLD1 gene mutation. POLD1-related protein-protein interaction network was constructed by STRING database. The relationship between POLD1 and immune infiltration was based on TISIDB database. Results The expression of POLD1 gene in lung adenocarcinoma and lung squamous cell carcinoma was significantly higher than that in normal lung tissue. In lung adenocarcinoma, patients with lower POLD1 level showed better prognosis. 1.2% of lung adenocarcinoma patients and 1.8% of lung squamous cell carcinoma patients carried mutated POLD1 gene, mainly missense mutations. POLD1 may interact with POLD2, POLD3, POLD4, POLE, RPA1, PCNA, MSH6, MSH2 and FEN1. The biological processes include DNA replication, mismatch repair, etc. Besides, the expression of POLD1 in NSCLC was correlated with the number of different immune cells. Conclusions The POLD1 gene is highly expressed in NSCLC patients, and negatively related with survival prognosis in patients of lung adenocarcinoma. POLD1 gene may be a potential diagnostic target and prognostic marker in NSCLC.
ObjectiveTo explore the functional heterogeneity of T lymphocytes in various organs after SARS-CoV-2 infection. Methods Using the public database GEO data (GSE171668, GSE159812, GSE159556, GSE167747) and the analysis method of single-cell technology, the functional differences of T lymphocytes in various organs of patients after infection with SARS-CoV-2 were analyzed. Results Through single-cell data extraction of 16 livers, 19 hearts,2 spleens, 6 brains, 58 lungs, 21 kidneys and 5 pancreases from SARS-CoV-2 infected patients, invasion genes were relatively highly expressed in T lymphocytes of the lung and pancreas. The lung had a special ability to express the interferon signaling pathway, while the expression of other organs was relatively low; at the same time, the T lymphocytes of the lung also highly expressed fatty acid binding sites. Conclusion After SARS-CoV-2 infection, compared with other organs, the lung has a special interferon-activated signaling pathway and fatty acid binding site.
Objective To explore key genes and mechanisms of depression aggravating Crohn disease. Methods In March 2023, the Public Health Genomics and Precision Health Knowledge Base and Gene Expression Omnibus database were used to identify the overlapping differentially expressed genes between Crohn disease and depression and the key genes were screened by Metascape, STRING, Cytoscape, and protein interaction network analysis. The Gene Expression Omnibus database was used to analyze the correlations between key genes and clinical pathologies such as Crohn Disease Endoscopic Index of Severity and intestinal microvilli length. Results There were 137 overlapping differentially expressed genes between Crohn disease and depression, and 25 key genes were further screened out. Among them, CREB1, FKBP5, MAPT, NTSR1, OXTR, PROK2, POMC, HTR2B, and PPARGC1A genes were significantly correlated with multiple clinical parameters. The functions of PROK2 and PROK2-related genes were mainly enriched in neutrophil and granulocyte migration, neutrophil and granulocyte chemotaxis, etc. Conclusions There are 25 key genes, especially CREB1, FKBP5, MAPT, NTSR1, OXTR, PROK2, POMC, HTR2B, and PPARGC1A, that possibly contribute to the establishment and deterioration of Crohn disease caused by depressive disorder. Among these genes, PROK2 showes the possibility of regulating immune cell (neutrophils and CD8+ T cells) infiltration.
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.