Tripartite motif 5 (TRIM5) plays a significant function in autophagy and involves in immune and tumor processes. While the function of TRIM5 remains poorly understood in glioma. We purpose to evaluate the possible prognostic role of TRIM5 in glioma via bioinformatics analyses. The database clinical samples of glioma in this study included low grade glioma (LGG) and glioblastoma multiforme (GBM). TRIM5 expression in glioma tissues were explored in Oncomine, GEPIA and The Cancer Genome Atlas (TCGA) databases. Survival analysis and the multivariate Cox regression analysis of TRIM5 based on TCGA were used to evaluate the prognostic role of TRIM5. The protein networks of TRIM5 was detected by STRING database. KEGG enrichment analyses were performed to predict the potential molecular pathways of TRIM5 in glioma. In addition, immune infiltration analysis was conducted by CIBERSORT and TIMER databases. We found that TRIM5 was strongly increased in glioma samples compared with normal samples in Oncomine, GEPIA and TCGA databases. Higher TRIM5 was significantly contributed to worse overall survival (OS) in LGG+GBM patients and LGG patients, while was no correlated with OS of GBM patients. Interaction networks analysis identified that IRF3, IRF7, OAS1, OAS2, OAS3, OASL, GBP1, PML, BTBD1 and BTBD2 proteins were contacted with TRIM5. Moreover, KEGG revealed that apoptosis and cancer- and immune-related pathways were enriched with elevated TRIM5. Specifically, TRIM5 could influence the immune infiltration levels, such as activated NK cells, monocytes, activated mast cells and macrophages in glioma. In conclusion, our data indicated that TRIM5 was upregulated in glioma tissues and associated with poor prognosis and immune infiltration. TRIM5 may be acted as a biomarker in prognosis and immunotherapy guidance of glioma.
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 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 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.
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.