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OBJECTIVE@#To investigate the correlation between the human epidermal growth factor receptor-2-related genes (HRGs) and survival prognosis of bladder cancer and to construct a predictive model for survival prognosis of bladder cancer patients based on HRGs.@*METHODS@#HRGs in bladder cancer were found by downloading bladder tumor tissue mRNA sequencing data and clinical data from the cancer genome atlas (TCGA), downloading HER-2 related genes from the molecular signatures database (MsigDB), and crossing the two databases. Further identifying HRGs associated with bladder cancer survival (P < 0.05) by using single and multi-factor Cox regression analysis and constructing HRGs risk score model (HRSM), the bladder cancer patients were categorized into high-risk and low-risk groups accor-ding to the median risk score. Survival analysis of the patients in high- and low-risk groups was conducted using R language and correlation of HRGs with clinical characteristics. A multi-factor Cox regression analysis was used to verify the independent factors affecting the prognosis of the patients with bladder cancer. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of HRSM was calculated, and a nomogram was constructed for survival prediction of the bladder cancer patients. Analysis of HRSM and patient immune cell infiltration correlation was made using the TIMER database.@*RESULTS@#A total of 13 HRGs associated with patient survival were identified in this study. Five genes (BTC, CDC37, EGF, PTPRR and EREG) were selected for HRSM by multi-factor Cox regression analysis. The 5-year survival rate of the bladder cancer patients in the high-risk group was significantly lower than that of the patients in the low-risk group. High expression of PTPRR was found to be significantly and negatively correlated with tumor grade and stage by clinical correlation analysis, while EREG was found to be the opposite; Increased expression of EGF was associated with high grade, however, the high expression ofCDC37showed the opposite result. And no significant correlation was found between BTC expression and clinical features. Correlation analysis of HRSM with immune cells revealed a positive correlation between risk score and infiltration of dendritic cells, CD8+T cells, CD4+T cells, neutrophils and macrophages.@*CONCLUSION@#HRGs have an important role in the prognosis of bladder cancer patients and may serve as new predictive biomarkers and potential targets for treatment.
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Humanos , Fator de Crescimento Epidérmico , Prognóstico , Neoplasias da Bexiga Urinária/genética , Nomogramas , Bexiga UrináriaRESUMO
Objective:To explore the prognostic biomarkers of glioblastoma (GBM) in the tumor microenvironment (TME) and its function.Methods:A total of 169 GBM samples of 161 GBM patients were collected from the Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm in R4.1.0 software was used to calculate the proportion of immune components and stromal components in TME, which were expressed as immune score and stromal score, respectively. According to the median value of the two scores, 169 GBM samples were divided into the high score group and the low score group, respectively, 84 each in each group (those whose scores were equal to the median were not involved in the grouping). The differentially expressed genes (DEG) [false discovery rate (FDR) < 0.05] between the high score group and the low score group of the two scores were obtained by using limma package, and the co-up-regulated and co-down-regulated DEG of the two scores were obtained by using Venn program. Based on the STRING database, the protein interaction (PPI) network of co-up-regulated and down-regulated DEG of immune score and stromal score was constructed, and the top 30 genes with connectivity were selected. Univariate Cox proportional hazard model analysis of overall survival (OS) of 161 GBM patients in the TCGA database was performed on co-up-regulated and down-regulated DEG between immune score and stromal score by using R4.1.0 software to obtain the DEG affecting OS. The intersection of the DEG obtained from PPI analysis and Cox analysis was taken as the prognostic core genes. According to the median expression value of prognostic core genes in GBM samples from the TCGA database, 161 patients were divided into prognostic core genes high expression group and low expression group (patients whose scores were equal to the median were not involved in the grouping), with 80 cases in each group. Kaplan-Meier survival analysis of OS was performed by using R4.1.0 software. GSEA 4.2.1 software was used to perform gene set enrichment analysis (GSEA) on all genes with transcriptome data of GBM patients in the two groups of the TCGA databases, and the main enriched functions of the two groups of genes were obtained. The CIBERSORT algorithm was used to test the accuracy of the proportion of tumor infiltrating immune cell (TIC) subsets in 169 GBM samples from the TCGA database, and 57 GBM samples were finally obtained. Immune cells with differential expression levels and immune cells related to the expression of prognostic core genes among the samples with different expression levels of prognostic core genes were analyzed; Venn program was used to obtain the intersection of immune cells with differential levels and related immune cells, and differentially expressed TIC related to expressions of prognostic core genes in GBM were obtained.Results:Based on the immune score and stromal score of GBM samples in the TCGA database, a total of 693 co-up-regulated and co-down-regulated DEG of both scores were screened out. After the intersection of 78 DEG related to OS obtained by univariate Cox regression analysis and 30 DEG obtained by PPI network results, CC motif chemokine receptor 2 (CCR2) was identified as the prognostic core gene ( HR = 1.294, 95% CI 1.060-1.579, P = 0.011). GBM patients with CCR2 high expression had worse OS compared with those with CCR2 low expression ( P = 0.009). GSEA analysis showed that genes in the CCR2 high expression group were mainly enriched in immune-related pathways, while genes in the CCR2 low expression group were mainly enriched in metabolism-related pathways. Among 57 screened GBM samples, there were differences in the levels of 3 immune cells between the CCR2 high expression group and the CCR2 low expression group ( P < 0.05). CCR2 expression was correlated with the levels of 9 immune cells (all P < 0.05). Venn program analysis showed that differentially expressed 3 TIC in GBM related to CCR2 gene expression were obtained; among them, M2 macrophages were positively correlated with CCR2 expression, while T follicular helper cell and activated NK cells were negatively correlated with CCR2 expression. Conclusions:CCR2 may be the core gene related to the prognosis in the TME of GBM. As reference, the level of CCR2 can help to predict the status of TME and prognosis in GBM patients, which is expected to provide a new direction for the treatment of GBM.
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【Objective】 To investigate the expression of Kinesin family member 14 (KIF14), and its correlation with clinical prognosis and immune cell infiltration of clear cell renal cell carcinoma (ccRCC). 【Methods】 The correlation between KIF14 expression in ccRCC and different clinicopathological features were analyzed with TCGA, GEO and Ualcan databases. The correlation between KIF14 expression and prognosis was analzyed with Kaplan-Meier method. The correlation between KIF14 expression and immune cell infiltration was analzyed with TIMER. The protein-protein interaction network of KIF14 was conducted with Genemania. The co-expression genes of KIF14 in TCGA-KIRC were picked out in Linkedomics database and were used to perform GO annotations and KEGG pathway enrichment analysis with R software. The biological functions of KIF14 were verified with in vitro functional assay. 【Results】 KIF14 was highly expressed in ccRCC tissue and was positively correlated with clinical stage, pathological grade, and lymphatic metastasis, but negatively correlated with clinical prognosis. KIF14 expression was an independent risk factor for overall survival of ccRCC patients. GO annotations showed that KIF14 was involved in DNA replication, nuclear division, organelle fission, and cell adhesion. KEGG pathway enrichment analysis showed that KIF14 participated in cell cycle and p53 signaling pathway. Genemania analysis indicated KIF14 interacted with CENPE, CIT, KIF23, and other proteins. Timer showed that KIF14 was positively correlated with immune cell infiltration. Knockdown of KIF14 expression suppressed cell proliferation, migration, and invasion of ccRCC. 【Conclusions】 KIF14 may serve as a novel prognostic marker and a potential therapeutic target of clear cell renal cell carcinoma.
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Long non-coding RNA KCNQ1OT1 is highly expressed in a variety of tumors, but there are few studies in gastric cancer and the results are inconsistent. The relevant research of its specific mechanism in gastric cancer is also scarce. Through the analysis of several TCGA public databases, we found that KCNQ1OT1 was generally highly expressed in gastric cancer, and the prognosis of gastric cancer patients with a high expression of KCNQ1OT1 was poor. The expression of KCNQ1OT1 is closely related to many clinical factors of gastric cancer, especially the mutation of TP53, and its expression is significantly related to immune cell infiltration. KCNQ1OT1 is generally highly expressed in gastric cancer cell lines. Knockdown of KCNQ1OT1 can inhibit the proliferation of gastric cancer cell lines. Co- expression network analysis showed that its expression was closely related to tumor metabolism. Glutaminase 1 (GLS1) is generally highly expressed in gastric cancer, which is closely related to a poor prognosis. There is a significant correlation between the expression of KCNQ1OT1 and GLS1. Knockdown of KCNQ1OT1 can inhibit the expression of GLS1 mRNA, and overexpression of GLS1 can partially rescue the proliferation of gastric cancer cells caused by knockdown of KCNQ1OT1. Therefore, we speculate that KCNQ1OT1 may regulate the growth of gastric cancer cells through GLS1. Our study explored the role of KCNQ1OT1 in gastric cancer through bioinformatics database and experiments, suggesting that KCNQ1OT1 may promote the development of gastric cancer by regulating glutamine metabolism, which provides a new target for the clinical research on targeted treatment in gastric cancer.
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Immune cell infiltration is of great significance for the diagnosis and prognosis of cancer. In this study, we collected gene expression data of non-small cell lung cancer (NSCLC) and normal tissues included in TCGA database, obtained the proportion of 22 immune cells by CIBERSORT tool, and then evaluated the infiltration of immune cells. Subsequently, based on the proportion of 22 immune cells, a classification model of NSCLC tissues and normal tissues was constructed using machine learning methods. The AUC, sensitivity and specificity of classification model built by random forest algorithm reached 0.987, 0.98 and 0.84, respectively. In addition, the AUC, sensitivity and specificity of classification model of lung adenocarcinoma and lung squamous carcinoma tissues constructed by random forest method 0.827, 0.75 and 0.77, respectively. Finally, we constructed a prognosis model of NSCLC by combining the immunocyte score composed of 8 strongly correlated features of 22 immunocyte features screened by LASSO regression with clinical features. After evaluation and verification, C-index reached 0.71 and the calibration curves of three years and five years were well fitted in the prognosis model, which could accurately predict the degree of prognostic risk. This study aims to provide a new strategy for the diagnosis and prognosis of NSCLC based on the classification model and prognosis model established by immune cell infiltration.