| Objective: Differentially expressed genes(DEGs)in pancreatic adenocarcinoma(PAAD)were screened by GEO database,and TCGA database was used to analyze the prognosis of DEGs and build a prognostic risk model,and ICGC database was used for verification that an effective model can evaluate the prognostic outcome of postoperative surgery for PAAD.Pan-cancer analysis was performed on the prognostic model genes of 33 cancers in the TCGA database to explore the functions and pathways of model genes in pan-cancer.Finally,the prognosticrelated DEGs and differential methylated genes(DMGs)were determined as therapeutic targets,to predict potential therapeutic medicine for PAAD,and further to explore the content of PAAD's drug therapy.Method:Part I: Construction and validation of a prognostic risk model differentially expressed genes(DEGs)in PAADThe PAAD's gene expression microarray datasets were selected in the GEO database.Differential analysis on the eligible data sets were performed,and the DEGs of PAAD was obtained after intersecting.At the same time,the expression and survival data of DEGs in PAAD's samples in TCGA were extracted,and the prognostic DEGs of PAAD were screened.The STRING online website was used to build a PPI network and Cytoscape was used to build a sub-network.The prognostic DEGs in the sub-network were elected by the highest score,and GO and KEGG enrichment analysis were performed.Lasso regression was used to construct the prognostic model of PAAD based on the highest rated subnetwork,and it was verified in the PACA-AU and PACA-CA data sets of the ICGC database.Part II: Pan-cancer analysis of model genesModel genes in 33 cancers in the TCGA database were analyzed for differences,and survival analysis by K-M method and univariate COX regression was performed on the prognostic model genes in 33 tumors,based on the 4 outcome indicators in the survival data,including OS,DSS,DFS and PFS.The expression levels of model genes in different stages of pancreatic cancer were compared and the correlation between the expression of prognostic model genes and tumor mutation burden(TMB),microsatellite instability(MSI),RNAss and DNAss scores of tumor stem cells,and stromal and immune cell scores of the tumor microenvironment were analyzed,and the expression levels of model genes were analyzed by immune subtypes.The c Bio Portal database was used to analyze the mutations and polymorphisms of model genes,the common pathways of model genes were explored in pancancer and GSEA enrichment analysis of MET was performed.Finally,the ENCORI database was used to predict mi RNA and ce RNA targeting prognostic model genes.Part III: Prediction of PAAD's Treatment DrugsIntersection of DEGs and DMGs was used to obtain differentially expressed DMGs,and prognostic analysis was performed.The DGIdb website was used to find potential targeted drugs related to the prognostic DEGs and differentially expressed methylated genes.Result:Part I: Construction and verification of a prognostic model for pancreatic adenocarcinoma1.Filtering from 8 different datasets of GSE62452,GSE28735,GSE16515,GSE15471,GSE32676,GSE71989,GSE41368,GSE19650,there were a total of 82 down-regulated differential genes and 197 up-regulated differential genes in 6 or more data sets.2.In the TCGA-PAAD dataset,16 down-regulated differential genes and 153 up-regulated genes related to prognosis were screened.3.The prognosis of PAAD was closely related to DEGs.A PPI network with 139 edges and 169 nodes was built,which contained 4 sub-networks with scores of 7.571,6.667,4,and 3,respectively.4.The risk prognosis model of PAAD was constructed,composed of MET and LAMA3.Risk value = MET* 0.390219297898873 + LAMA3* 0.0664262145025541.In the TCGAPAAD experimental group,the difference was statistically significant(P<0.05)between the high and low risk groups of this prognostic model.In the ICGC-AU validation group,the difference was not statistically significant(P>0.05).In the ICGC-CA validation group,the difference was statistically significant(P<0.05).The AUC values of 1-year,2-year,and 3-year survival rates in the TCGA-PAAD experimental group were 0.731,0.703,and 0.690,respectively,while the AUC values of 1-year,2-year,and 3-year survival rates in the ICGC-AU verification group were 0.644,0.607,0.628,and the AUC values of 1-year,2-year,and 3-year survival rates in the ICGC-CA verification group were 0.381,0.530,and 0.419,respectively.Part II: Pan-cancer analysis of model genes1.The high and low expressions of MET and LAMA3 in different tumors were inconsistent.In the K-M survival analysis,there were statistical differences in OS,DDS,DFS and PFS in PAAD between the two groups with high and low expression of MET and LAMA3(P<0.05).The HR of MET in PAAD was 1.823,95%CI: 1.464?2.270,P<0.001;DSS in PAAD,HR was1.773,95% CI: 1.393?2.257,P<0.001;DFS in PAAD,HR was 2.436,95%CI: 1.524?3.894,P<0.001;PFS in PAAD,HR was 1.718,95%CI: 1.410?2.093,P<0.001.The HR of LAMA3 in PAAD was 1.513,95%CI: 1.268?1.805,P<0.001;the HR of DSS in PAAD was 1.444,95%CI:1.192?1.749,P<0.001;the HR of DFS in PAAD was 1.580,95%CI: 1.160?2.154,P<0.05;PFS in PAAD,HR was 1.365,95%CI: 1.169?1.595,P<0.001.2.In the clinical correlation analysis,the expression levels of PAAD's MET and LAMA3 were statistically different between the early stage I and II(P<0.05),but no difference was seen in other clinical stages(P>0.05).In the correlation analysis of TMB,MET and LAMA3 were correlated with the TMB of PAAD(P<0.05),while there was no significant correlation(P>0.05)in the correlation analysis of MSI.The DNAss score of PAAD was negatively related to the expression of MET(P<0.05)in the correlation analysis of cancer stem cells.In the correlation analysis of tumor microenvironment,the expression of MET in PAAD was negatively related to immune cell content(r=-0.21,P<0.05),and negatively related to stromal cell content(r=-0.17,P<0.05);Correlation between the expression of LAMA3 and the content of immune cells and stromal cells in PAAD was no statistical significance(P>0.05).3.In the analysis of immune subtypes,the expressions of MET and LAMA3 were statistically different in different immune subtypes of pan-cancer(P<0.001).4.In the prediction of gene interaction,53 mi RNAs and 19 ce RNAs correspond to the target gene MET,and 21 mi RNAs and 29 ce RNAs correspond to the target gene LAMA3.5.0.6% missense mutations in MET,while 14% gene amplification in LAMA3.MET might activate KRAS in downstream pathways to play a role in pan-cancer.Part III: Prediction of PAAD's Treatment Drugs1.728 down-regulated genes were obtained from GSE71989,and 490 down-regulated genes were obtained from GSE41368,and 112 hypermethylated genes were obtained from GSE40097.Finally,3 PAAD's genes with high methylation and low expression were obtained by intersection,namely CEL,CTRC and AZGP1,but failed to correlate with the prognosis.3227 up-regulated genes were obtained in GSE71989,and 1251 up-regulated genes were obtained in GSE41368,and 2 hypomethylated genes were obtained in GSE40097.Finally,Effective hypomethylation and high expression of PAAD's genes were not taken by intersection.2.For 169 prognostic DEGs of PAAD,41 potential targeted drugs for PAAD were screened.Conclusion:1.The model constructed by MET and LAMA3 had certain value in evaluating postoperative prognosis of PAAD;2.The model genes MET and LAMA3 were not suitable for the diagnosis of PAAD,and are suitable for prognostic evaluation and treatment targets,and they might be suitable for evaluating the effect of PAAD immunotherapy.Among them,MET might evaluate the degree of differentiation of PAAD's cells and predict their invasion and metastasis ability;3.The survival and prognosis of PAAD might be mainly determined by the activation of KRAS-related signaling pathways by MET,and non-coding RNA targeting MET might play a role in this signaling pathway;4.Among the potential therapeutic drugs for PAAD,14 drugs targeting MET were expected to be applied in the clinic. |
PDF Full Text Request