| Objective: Pancreatic duct adenocarcinoma(PDAC)is a highly malignant type of cancer with a low five-year survival rate.Gene alterations are crucial to the molecular pathogenesis of PDAC.Therefore,the present study analyzed gene expression profiles to reveal genes involved in the tumorigenesis of PDAC.Methods: Firstly,a total of 8 gene expression profiles(GSE15471,GSE16515,GSE41368,GSE62165,GSE62452,GSE71729,GSE71989 and GSE91035)from two platforms(Affymetrix and Agilent)were retrieved from the Gene Expression Omnibus(GEO)database,included 452 PDAC samples and 204 normal pancreatic tissue samples.Log2 conversion and quantile normalization was performed on each individual GEO dataset.Differential expression genes(DEGs)of each dataset were then screened using the limma package with R/Bioconductor 3.9 software.The RobustRankAggreg(RRA)package was used for gene integration analysis of the DEGs in the eight datasets.The functions of common DEGs were further analyzed using the clusterProfiler package with R/Bioconductor software for functional enrichment analysis.This included the following gene ontology(GO)categories: Molecular function(MF),biological process(BP)and cellular component(CC),as well as enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways.Then the STRING database was applied to identify the protein-protein interaction(PPI)network of the prognostic DEGs.This network was reconstructed via the Cytoscape software and the Cytoscape MCODE plug-in was used to find clusters,based on topology,to locate densely connected regions.As the GSE62452 dataset and The Cancer Genome Atlas(TCGA)data contain patient survival information,they can be used as training and validation datasets,respectively.Univariate Cox proportional hazard analysis was applied to identify the prognosis-associated genes in GEO datasets(training set),using survival analysis in R.Multivariate Cox regression analysis was then applied to further screen for factors associated with patient survival.Subsequently,a prediction system was constructed consisting of five signature prognostic genes.Finally,The five aforementioned prognostic signature genes were applied to TCGA dataset(validation set)to verify whether they could effectively predict the prognosis of PDAC.Results: A total of 136 DEGs(67 up-and 69 downregulated genes)were identified between PDAC tissues and normal tissues.The ‘extracellular matrix-related' genes were the most enriched in the GO term analysis.‘Pancreatic secretion',‘phosphoinositide-3-kinase–protein kinase B/Akt(PI3K-Akt)signaling pathway',‘protein digestion and absorption' and ‘ECM-receptor interaction' were the most enriched categories in KEGG pathway analysis.Following PPI network construction,the 10 most significant genes(ALB,EGF,MMP9,EGFR,FN1,MMP1,SERPINE1,TIMP1,PLAU and PLAUR)exhibiting a high degree of connectivity,were identified as the hub genes likely to be associated with the pathogenesis of PDAC.In addition,a prognostic predictive system for PDAC,composed of five genes(LAMC2,LAMB3,SERPINB5,AREG and SFRP4),was constructed.This was validated in the GSE62452 dataset and TCGA PDAC dataset.Conclusion: 1.High enrichment of ECM GO term/pathway and PI3K-Akt pathway related genes suggests that ECM regulation and PI3K-Akt pathway is closely associated with PDAC progression.It plays a fundamental role in facilitating cell differentiation,apoptosis,proliferation and migration.2.the present study confirmed certain DEGs: ALB,EGF,MMP9,EGFR,FN1,MMP1,SERPINE1,TIMP1,PLAU and PLAUR,some of these were not discovered in individual analyses.These genes are closely related to the growth,metastasis,invasion and prognosis of PDAC,which may be of great significance for the early diagnosis of PDAC or a potential target for treatment.3.A prognostic predictive system for PDAC,composed of five genes(LAMC2,LAMB3,SERPINB5,AREG and SFRP4),was constructed.It may help to better evaluate patient prognosis,provide a basis for clinical decision-making and indicate new therapeutic targets. |
PDF Full Text Request