Screening And Validation Of Biomarkers Related To The Diagnosis And Prognosis Of Prostate Cancer

The clinical characteristics of PCa are not obvious.The clinical control is complex.And the detection has many limitations for PCa,which makes the diagnosis and prognosis face huge challenges.At the same time,the treatment of advanced PCa is also completely tricky.Therefore,the using of scientific technology and tools to obtain biomarkers with good differentiation and guidance is a matter of great urgency for PCa.High throughput sequencing technology has been improved by leaps and bounds.It has been made immeasurable contributions in the field of biomedical science.It provides a lot of biomedical big data for our research.Researchers used bioinformatics analysis software and R language to conduct in-depth analysis of biological big data.It provide serviceable scientific materials for cancer research.In this study,the data related PCa was from Gene Expression Omnibus and The Cancer Genome Atlas.For in-depth excavation,bioinformatics related software and R language were used.The purpose of this study is to screen out the potential biomarkers.Subsequently.For screening potential biomarkers for PCa from the above big data,Real-time quantitative PCR(qPCR)was used to verify these expression level.The results are as follows:1.Based on the gene expression profiles GSE69223,GSE3325,GSE55945 of the GEO database and the mRNA-seq data about PCa of the TCGA database,the R language was used to identify DEGs.A series of processes including raw data download,chip quality assessment,background correction,standardized processing and data analysis was completed.the number of common up-regulated DEGs and down-regulated DEGs were 312 and 85.The enrichment and analysis of Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)were completed for the DEGs.The STRING online database was used in conjunction with Cytospace software for protein-protein interaction(PPI)network analysis to define hub genes.Verification of gene transcription level and translation level in GEPIA and HPA databases,Five hub genes were identified as potential diagnostic biomarkers,including FLNA,FLNC,VCL,ACTC1 and MYLK,They were not suitable as prognostic markers.2.We used univariate and multivariate Cox regression analyses in R language.Seven prognostic genes(BCO1,BAIAP2L2,C7,AP000844.2,ASB9,MKI67P1 and TMEM272)were screened to construct a prognostic gene signature.The ability of prognostic gene characteristics to predict clinical outcomes was controlsed the area of Area Under Curve(AUC).The AUC was 0.995,0.886,0.812 and 0.606 for 1,3,5 and 10 year,the results show that the prognosis gene signature shows good survival prediction ability.3.The dataset GSE112264 and the miRNA-seq data of PCa were source of data.Through data download,background correction,standardization and data analysis,the common up-regulated DEMs(hsa-miR-5706,hsa-miR-92a-3p,hsa-miR-592,hsa-miR-32-3p,hsa-miR-519a-3p,hsa-miR-106a-5p,hsa-miR-522-3 p,hsa-miR-5586-5p)and the common down-regulated DEMs(hsa-miR-760,hsa-miR-204-5p,hsa-miR-133b,hsa-miR-326,hsa-miR-542-5p)was identified.PPI network combined with Cytoscape visualization software was used to identify the top 10 hub target genes corresponding to up-regulated miRNA and down regulated miRNA respectively.Through the miRNA-mRNA interaction network,We found that hsa-miR-92a-3p and hsa-miR-204-5p are the main regulatory miRNAs of the central target genes.These miRNAs give scope to significant roles based on Go analysis and KEGG pathway enrichment analysis in tumorigenesis related processes and pathways.Hsa-miR-92a-3p and hsa-miR-204-5p were identified as potential biomarkers,but not suitable for prognosis.4.A large number of peripheral blood and paraffin tissue sections were obtained for pretreatment.The source were from PCa patients and controls.And Total RNA and miRNA were extracted.Real-time quantitative PCR was used to verify these expression level for screening potential biomarkers of PCa.The expression levels of hsa-miR-92a-3p were significantly up-regulated in PCa.The expression levels of FLNA,FLNC,VCL,ACTC1,MYLK and hsa-miR-204-5p were significantly down-regulated in PCa.