| Objective:The mortality and morbidity rates associated with pancreatic cancer(PACA)are extremely high.Various studies have demonstrated that pancreatic cancer will be the fourth cancer-related death by 20230,raising more concern for scholars to find effective methods to prevent and treat in order to improve the pancreatic cancer outcome.Since studies have shown that key genes have a significant impact on the development of many cancers through molecular mechanisms,research has been conducted to elucidate every single step possible in their interactions.Concerning pancreatic cancer,many molecular mechanisms of key genes are still to be elucidated,making it imperative to pinpoint key genes that may provide insight into these processes and affect patients’ outcomes.Using bioinformatic analysis,this study aims to pinpoint key genes that could impact PACA patients’ prognosis and could be used as therapeutic targets.Methods:The TCGA and GEO datasets were integratively analyzed to identify prognosis-related differentially expressed genes.Next,the STRING database was used to develop PPI networks,and the MCODE and Cyto NCA Cytoscape in Cytoscape was used to screen for critical genes.Through Cyto NCA,three kinds of topology analysis were considered(degree,betweenness,and eigenvector);these were used as the three inputs for conducting the centrality analysis.Essential genes were confirmed as potential target treatment through Go function and pathways enrichment analysis,a developed predictive risk model based on multivariate analysis,and the establishment of nomograms using the clinical information.Results:1-Overall,the GSE183795 and TCGA datasets associated 1311 and 2244 genes to pancreatic cancer prognosis,respectively.We identified 132 genes that were present in both datasets and we considered them to be prognosis-related(DEGs).2-The PPI network analysis using,the centrality analysis approach with the Cyto NCA plug-in,showed that,CDK2,PLK1,CCNB1,and TOP2 A ranked in the top 5% across all three metrics and were all present in Module 1 with the highest centrality,based on this,they were chosen as key genes.3-The independent analysis of a risk model,as shown by univariate and multivariate analysis,revealed that crucial genes(CDK2,PLK1,CCNB1 and TOP2A)had a Hazard Ratio(HR)> 1 which were 1.75,1.34,1.43,1.50 Respectively(p < 0.05).This implies that these genes can independently influence pancreatic cancer patients prognosis.4-The monogram showed the predictive risk model and individual patient survival predictions were accurate,which estimated survival at one year,three years,and five years.The results indicate that the effect of the selected vital genes was significant and that they could be used as biomarkers to predict a patient’s outcome and as possible target therapy in patients with pancreatic cancer.5-GO function and pathway analysis demonstrated that crucial genes might affect the P53 signaling pathway and Fox O signaling pathway,through which Meiotic nuclear division and cell cycle may have a significant function in essential genes affecting the outcome of patients who have pancreatic cancer.Conclusions:1-This study suggests that CDK2,CCNB1,PLK1 and TOP2 A are four key genes having a significant influence on PACA migration and proliferation.2-CDK2,CCNB1,PLK1,and TOP2 A can be used as potential PACA prognostic biomarkers and therapeutic targets.3-However,experimental validation is necessary to confirm these predictions.Ours study comes into contributions to the development of personalized target therapy for pancreatic cancer patients... |
PDF Full Text Request