ObjectiveTo analyze the expression of cold-induced RNA-binding protein (CIRBP) in lung adenocarcinoma and its clinical significance based on bioinformatics, in order to provide a new direction for the study of therapeutic targets for lung adenocarcinoma.MethodsThe CIRBP gene expression data and patient clinical information data in lung adenocarcinoma tissues and adjacent tissues were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The expression of CIRBP in lung adenocarcinoma was analyzed. Furthermore, its relationship with clinicopathological features and prognosis in patients with lung adenocarcinoma was analyzed. GO and KEGG enrichment analysis were carried out for the screened genes. The CIRBP protein interaction network was constructed by STRING, and the correlation analysis was carried out using the GEPIA online website.ResultsThe expression level of CIRBP gene in lung adenocarcinoma tissues was significantly lower than that in adjacent tissues (P<0.01), and its expression level was correlated with T stage and N stage in clinicopathological features. The prognosis of patients with high CIRBP expression in lung adenocarcinoma was significantly better than that with low CIRBP expression. Univariate and multivariate Cox regression analysis showed that CIRBP was an independent prognostic factor in patients with lung adenocarcinoma. GO functional annotation showed its enrichment in organelle fission, nuclear fission, chromosome separation, and DNA replication, etc. KEGG analysis showed that it was mainly involved in cell cycle and DNA replication. Protein interaction network and GEPIA online analysis showed that the expression level of CIRBP was negatively correlated with the expression level of cyclin B2.ConclusionCIRBP gene is down-regulated in lung adenocarcinoma tissues, and its expression level is closely related to patient prognosis. CIRBP gene may be a potential therapeutic target and prognostic marker for lung adenocarcinoma.
Objective To investigate the relationship between miR-3187-5p in peripheral blood and pericardial drainage after coronary artery bypass grafting (CABG) and postoperative atrial fibrillation (POAF). Methods Patients who underwent CABG in the Heart Center of Beijing Chao-Yang Hospital from March to May 2022 were enrolled. Peripheral blood and pericardial drainage were collected at 0 h after surgery (immediate time for patients to return to ICU from operating room) to detect miR-3187-5p, and perioperative confounding factors were also collected. The miR-3187-5p was measured by quantitative real-time PCR and its regulated target genes were analyzed by bioinformatics. Results A total of 15 patients were enrolled, including 9 males and 6 females with an average age of 65.6±8.2 years. The incidence rate of POAF was 40.0%. miR-3187-5p in pericardial drainage at 0 h after surgery was an independent predictor for POAF. A total of 1 642 target genes of miR-3187-5p were predicted. GO function enrichment analysis and KEGG signal pathway enrichment analysis showed that target genes of miR-3187-5p were enriched in TGF-β, MAPK, Wnt and other classical collagen metabolic signal pathways, which might activate collagen metabolism by negatively regulating SMAD6 and other inhibitors of the pathways. Conclusion This study is the first to find that miR-3187-5p in pericardial drainage at 0 h after surgery is a potential, novel, and predictive factor for POAF, which may be related to the regulation of myocardial fibrosis signal pathways like TGF-β, MAPK and Wnt pathways, promoting the early collagen metabolism imbalance after CABG, increasing the collagen deposition in the atrium, and then promoting the early structural reconstruction after CABG and leading to the occurrence of POAF. The result provides a research basis for the accurate prediction and prevention of clinical POAF.
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 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 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 identify the core genes involved in the great saphenous varicose veins (GSVVs) through bioinformatics method. MethodsThe transcriptional data of GSVVs and normal great saphenous vein tissues (control tissues) were downloaded from the gene expression omnibus database. The single sample gene set enrichment analysis (ssGSEA) was used to calculate the Hallmark score. The weighted gene co-expression network analysis (WGCNA) combined with machine learning algorithms was used to screen the key genes relevant GSVVs. The protein-protein interaction (PPI) analysis was performed using the String database, and the receiver operating characteristic (ROC) curve was used to reflect the discrimination ability of the target genes for GSVVs. ResultsCompared with the control tissues, there were 548 up-regulated genes and 706 down-regulated genes in the GSVVs tissues, the Hallmark points of KRAS signaling and apical junction were down-regulated, while which of peroxisomes, coagulation, reactive oxygen species pathways, etc. were up-regulated in the GSVVs tissues. A total of 639 differentially expressed genes relevant GSVVs were obtained and 165 interaction relations between proteins encoded by 372 genes, and the top 10 genes with the highest betweeness values, ADAM10, APP, NCBP2, SP1, ASB6, ADCY4, HP, UBE2C, QSOX1, and CXCL1, were located at the center of the interaction relation. And the core genes were mainly related to copper ion homeostasis, neutrophil degranulation G protein coupled receptor signaling, response to oxidative stress, and regulation of amide metabolism processes. The SP1 and QSOX1 were both Hub genes. The expressions of the SP1 and QSOX1 in the GSVVs tissues were significantly up-regulated as compared with the control tissues. The areas under the ROC curves of SP1 and QSOX1 in distinguishing GSVVs tissues from normal tissues were 0.972 and 1.000, respectively. ConclusionsSP1 and QSOX1 are core genes in the occurrence and development of GSVVs. Regulation of SP1 or QSOX1 gene is expected to achieve precise treatment of GSVVs.
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 investigate the relationship between the expression of mast cell expressed membrane protein 1 (MCEMP1) in gastric cancer and its relationship with prognosis and tumor immune infiltration. Methods Transcriptome expression profile data and clinical data information of gastric cancer and normal samples were downloaded from TCGA database, and differentially expressed genes in gastric cancer tumor microenvironment were extracted using R 4.0.5 software. Protein-protein interaction network of differentially expressed genes was constructed by using STRING online website, protein-protein interaction network and univariate Cox proportional hazards regression analysis were used for cross-tabulation analysis to obtain key genes. Kruskal-Wallis rank sum test was used to investigate the correlation between key genes and clinicopathological features. The possible signaling pathways involved in key genes were predicted by gene set enrichment analysis. We further analyzed the relationship between expression of key gene and the level of immune infiltration and immune molecules in gastric cancer by TISIDB online database and CIBERSORT algorithm. Results A total of 760 differentially expressed genes in gastric cancer were found and a key gene of MCEMP1 was derived from cross-tabulation analysis based on the results of protein-protein interaction network and univariate Cox proportional hazards regression analysis. Expression of MCEMP1 was significantly upregulated in gastric cancer tissues (P<0.001), and survival analysis showed that the overall survival rate of the group with high expression level of MCEMP1 was lower than that of low expression [HR=1.176, 95%CI (1.066, 1.297）, P=0.046]. Expression of MCEMP1 also correlated with age, T-stage, and clinical stage of gastric cancer (P<0.05) , and expression of MCEMP1 was significantly associated with a variety kinds of immune cells and expression of immune molecules (P<0.05). Conclusion MCEMP1 is a potential prognostic marker for gastric cancer and is associated with immune infiltration in gastric cancer.
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.
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.