| Objective: In this study,we explored the hub genes,biomarkers and potential therapeutic small molecule drugs in the development of bladder cancer through bioinformatics.Methods: The bladder cancer m RNA expression profile data and clinical data were downloaded from the TCGA(The Cancer Genome Atlas)database,and the bladder cancer high-throughput sequencing expression profile dataset GSE133624 was downloaded from the GEO(Gene Expression Omnibus)database,and limma package of R software was used to perform differentially expressed genes(DEGs)analysis.The Venn Diagram package was used to intersect and visualize the DEGs of the two datasets,and the intersected DEGs were analyzed by functional enrichment(Gene Ontology,GO;Kyoto Encyclopedia of Genes and Genomes,KEGG)to explore the potential molecular mechanism of DEGs.Then the STRING database was used to construct a proteinprotein interaction network,and the hub genes screened through the cyto Hubba plugin of Cytoscape 3.8.0 software was performed survival analysis and correlation analysis.And their m RNA expression levels were validated using the Oncomine tumor database.Finally,the CMap database was used to explore small molecule drugs of potential treatment for bladder cancer,and their structure and molecular formula were displayed through the Pub Chem database.Results: A total of 777 DEGs were obtained by differential gene analysis,including 194 up-regulated genes and 583 down-regulated genes.GO analysis results showed that DEGs are involved in biological processes such as extracellular matrix organization,extracellular structure organization,organelle fission,nuclear division,regulation of muscle system process,axonogenesis,microtubule cytoskeleton organization involved in mitosis,regulation of chromosome segregation,spindle organization,etc.KEGG analysis results showed that DEGs mainly regulate the progression of bladder cancer through vascular smooth muscle contraction,Calcium signaling pathway,cell cycle,c GMP-PKG signaling pathway,cell adhesion molecules,ECM-receptor interaction,PI3K-Akt signaling pathway,Apelin signaling pathway,etc.In addition,7 hub genes(ANLN,CCNB1,CDC20,CTSV,OIP5,IGF1,and PLK1)significantly associated with prognosis were screened,with significant correlations between these genes.Finally,5 potential small molecule drugs for the treatment of bladder cancer were screened,including phenoxybenzamine,trichostatin A,apigenin,GW-8510,thioguanosine.Conclusion: The results of this study showed that ANLN,CCNB1,CDC20,CTSV,OIP5,IGF1 and PLK1 were significantly associated with the prognosis of bladder cancer patients,which can be used as markers for early diagnosis and improved prognosis of bladder cancer as well as molecular targets for therapy.The c GMP-PKG,PI3K-Akt,and Apelin,etc.signaling pathways play a key role in the occurrence and development of bladder cancer.In addition,phenoxybenzamine,trichostatin A,apigenin,GW-8510,thioguanosine may be potential drugs for bladder cancer therapy.Objective: This study aimed to explore the expression level and clinical significance of CTSV in bladder cancer,construct a prognostic nomogram model and evaluate its efficacy,and explore the guiding significance of this model in formulating clinical treatment plans.Methods: Based on the results of the first part,CTSV was selected as the target gene for this part.The expression level of CTSV in bladder cancer tissues and adjacent tissues was evaluated based on the TCGA database,and the GEPIA online database,GSE13507 dataset,Oncomine database,and bladder cancer tissues were used to verify the expression level of CTSV.The association of CTSV and clinicopathological features was analyzed using the Wilcoxon signed-rank test.According to the cutoff value of CTSV expression(based on the survminer package of R software),bladder cancer patients were divided into high expression group and low expression group,and logistic regression was used to analyze the correlation between CTSV expression level and clinicopathological variables.Kaplan–Meier survival analysis was used to compare the overall survival rate between the high CTSV group and the low CTSV group.The Person test was used to analyze the correlation between CTSV and other prognosticrelated hub genes.The STRING database was used to construct a CTSV related proteinprotein interaction(PPI)network,and GESA was performed to identify CTSV related signaling pathways.Then univariate and multivariate Cox regression were used to analyze the relationship between CTSV and clinical variables and survival.Finally,a prognostic nomogram was constructed based on the meaningful factors of multivariate Cox,which was validated and evaluated.Results: 408 bladder cancer tumor samples and 19 adjacent non-cancerous tissue samples were obtained from the TCGA database.The analysis results showed that the expression of CTSV in bladder cancer tissue was significantly higher than that in adjacent tissues(P < 0.05),and the survival time of bladder patients with high expression of CTSV was significantly shorter than that of patients with low expression of CTSV(P = 0.0062).The expression of CTSV was significantly correlated with gender(P = 0.046),histological grade(P < 0.001),race(P = 0.0039),and T stage(P = 0.043).Univariate logistic regression analysis showed that the high expression of CTSV was associated with race(OR = 2.519 for white vs.others),histological grade(OR = 1.662 for low vs.high),clinical stage(OR = 1.589 for I-II vs.III-IV),status(OR = 1.435 for normal vs.tumor),T stage(OR = 1.589 for T1-2 vs.T3-4)and M stage(OR = 4.499 for M0 vs.M1)(P < 0.05).Person correlation analysis showed that ANLN(R=0.56),CCNB1(R=0.46),CDC20(R=0.53),OIP5(R=0.5),and PLK1(R=0.53)were positively correlated with CTSV(P < 0.05),while ADCY5(R=-0.18)was negatively correlated with CTSV(P < 0.05).The PPI network related to CTSV showed that CTSV was closely related to CTSL,CTSA,MMP,HLA families,etc.GSEA analysis results showed that cell cycle,actin cytoskeleton regulation,tight junctions,cell matrix adhesion,Wnt signaling pathway,MAPK signaling pathway,cell adhesion molecules,JAK-STAT signaling Pathways,insulin signaling pathways,etc.were significantly enriched in the phenotype with high CTSV expression.Multivariate Cox regression analysis showed that age(HR: 1.033,95%CI: 1.016-1.050,P < 0.001),T stage(HR: 1.459,95%CI: 1.088-1.958,P = 0.012),N stage(HR: 1.433,95%CI: 1.075-1.909,P = 0.014)and CTSV(HR: 1.495,95%CI: 1.069-2.089,P = 0.019)were independent risk factors for the prognosis of patients with bladder cancer.The prognostic nomogram was constructed with meaningful variables based on multivariate Cox regression,the areas under the ROC curves at 1,3,and 5 years were 0.729,0.701,and 0.709,respectively,the C-index of the internal verification model was 0.669(95 % CI : 0.624-0.714),the calibration curve showed that the predicted survival rate and the actual survival rate had a good calibration,the decision curve analysis showed that the net benefit rate was useful between 35-80%,which had good discrimination,consistency and clinical applicability.Conclusion: The results of this study showed that the expression of CTSV in bladder cancer tissue was significantly higher than adjacent tissues,and the bladder cancer patients with high CTSV expression have a worse prognosis.CTSV was significantly associated with advanced clinicopathological features,such as pathological grade(high grade),clinical stage(III-IV),T stage(T3-T4),and M stage(M1).Age,T stage,N stage and CTSV were independent risk factors affecting the survival of bladder cancer patients.Prognostic predictive nomogram models had good discrimination,calibration,and clinical efficacy. |
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