| Objective:Pancreatic ductal adenocarcinoma(PDAC)is one of the most lethal malignancies without effective screening strategies during the early stage.Therefore,a novel screening panel was identified based on potential biomarkers associated with PDAC using the gene expression profile.In this study,bioinformatics methods were used to explore PDAC development related genes from the molecular level and look for key genes and important signaling pathways.Meanwhile,the function of the key genes and related signal pathways were verified by biological experiments,provide a partial theoretical basis for better study of PDAC.Methods:1.The PDAC expression profile dataset GSE15471 was downloaded from Gene Expression Omnibus(GEO)database.After splitting data into tumor and normal groups,the online plugin GEO2 R was used to compare differentially expressed genes(DEGs)between the two groups.Then DEGs were selected because it is statistically significant(p-value < 0.01 and ∣log Fold Change∣>2).Based on the Database for Annotation,Visualization,and Integrated Discovery(DAVID),Gene ontology(GO)analysis was used to annotate and classify the DEGs.The Kyoto Encyclopedia of Genes and Genomes(KEGG)website was applied to understand high-level functions and biological systems from molecular-level information.We excavated the detailed tissue expression,GO terms and KEGG pathways with p<0.05 as the cut-off value.2.The protein-protein interaction(PPI)network of DEGs was constructed on STRING database,and visualized by open-source bioinformatics software platform Cytoscape.Next,we ranked DEGs in the PPI network from highest to lowest according to the Betweenness(BC)scores with Cyto Hubba plugin,and downloaded the top 50 hub genes.A network of those hub genes and their co-expression genes was constructed using the c Bio Portal online platform.3.The website tool Gene Expression Profiling Interactive Analysis(GEPIA)is a resource originated from The Cancer Genome Atlas(TCGA)database.It was used in this study to verify the expression and determine the overall survival(OS)outcomes.Furthermore,Oncomine was applied to analyzing the relationship between expression levels of the hub genes and tumor grades.4.Altogether 117 pathological samples were collected from PDAC patients who underwent surgical resection at the First Affiliated Hospital of Zhejiang University from 2015 to 2019.Paraffin-embedded tissues and adjacent normal tissues of these patients were compared to verify the correlation between KRT7 and the clinicopathological characteristics by using immunohistochemistry.Meanwhile,we analyzed the influence of the KRT7 expression on PDAC overall survival,metastasis and recurrence.Result:The dataset GSE15471,which was downloaded from the GEO database,included matching pairs of normal and tumor tissue samples from the resected pancreas of 39 pancreatic cancer patients.The online tool GEO2 R was used to screen and focused on the DEGs.Then functional and pathway enrichment was performed and a DEG-associated PPI network was constructed by searching interacting genes in STRING.By using the visualization software Cytoscape,the modules were sorted in the PPI network and hub genes of DEGs through the Cyto Hubba plugins.In total,326 DEGs,including 306 upregulated genes and 20 downregulated genes,were targeted in PDAC.KEGG signaling pathways and GO function enrichment analysis,based on the DAVID,revealed that the DEGs are mainly involved in ‘Focal adhesion’,‘ECM-receptor interaction’ and ‘PI3K-Akt signaling pathway’.In addition,top 50 core genes were identified from the PPI network by Cyto Hubba.GEPIA survival analysis showed that high expressions of KRT7,KRT19,SEMA3 C,ITGA2,MYOF and ANXA1 may predict poor survival outcome in PDAC.Oncomine confirmed the high expressions of these genes with cancer grade.the expression of KRT7 was verified by immunohistochemistry.KRT7 was highly expressed in PDAC tumor tissue.High KRT7 was associated with macrovascular invasion,poor T staging and poor TNM staging,and predicted poor survival of PDAC patients.These hub genes and regulators straightened out the molecular pathways and recurrence mechanisms in PDAC and could be used as targets for PDAC’s diagnosis,treatment,and prognostic prediction.Conclusion:1.‘Focal adhesion’,‘ECM-receptor interaction’ and ‘PI3K-Akt signaling pathway’ are important signaling pathways related to PDAC.2.KRT7,KRT19,ANXA1,MYOF,SEMA3 C and ITGA2 are the hub genes screened in this study.3.The expression level of KRT7 in PDAC samples was up-regulated,and the expression level of KRT7 was correlated with neural invasion,poor AJCC staging and predicted poor survival of PDAC patients. |
