| Background:Pancreatic cancer is an awfully aggressive digestive cancer.Patients always have no visible symptoms in the early stage,which makes the early diagnosis of pancreatic cancer difficult to some extent.It is reported that about 80%to 90%of patients lose the chance of surgery.The current 5-year survival rate for patients is less than 8%.Therefore,it is imminent to explore the pathological mechanism of pancreatic cancer and identify early detection markers,which are essential to improve the survival time and prognosis of pancreatic cancer patients.Objective:Through bioinformatics analysis of pancreatic cancer gene chip data,we screened susceptible biological target molecules related to pancreatic cancer and conducted meta-analysis to not only verify the expression of core gene MUC1 in pancreatic neoplasms but also its significance in connection with clinicopathological data.To probe the differential expression of PTPRR between neoplasms and normal tissues,and its relevance with clinical data and survival analysis in pancreatic cancer patients.Methods:1.Pancreatic cancer related gene chip GSE16515,which belongs to the platform GPL570,was searched by GEO database.A total of 36 pancreatic cancer tissue samples and 16 adjacent normal tissue samples were selected.2.Limma package was used for differential analysis of microarray data,the threshold was set to|Log FC|>2 and P<0.01,and the differential genes obtained were subjected to GO functional enrichment analysis and KEGG pathway enrichment analysis.3.The differential genes were used construct a protein-protein interaction network by the STRING website.The MCODE and Cytohubba plug-ins in Cytoscape software were used to conduct disease modular analysis of the protein network and screen core genes according to the degree of connectivity.4.Meta-analysis was performed on the obtained core genes,and all published literatures of CNKI,Wanfang,VIP,CBM,Pubmed,Embase,Web of Sciences and Cochrane were searched by keywords such as pancreatic cancer,mucin 1,and immunohistochemical staining.Standard screening literature was used to extract the data in the text using Review Manager 5.3 software for meta-analysis.Odds ratio(OR),95%confidence interval(CI)and P value were used for analysis.5.Depending on the magnitude of the heterogeneity of the results,a fixed or random effects model was picked to analysis effect sizes.The publication bias test was performed using stata 15.1.6.Bioinformatics methods were used to verify the expression and prognosis of PTPRR gene,enrichment analysis of the PTPRR co-expressed genes,and predict the interacting proteins of PTPRR gene.7.The immunohistochemistry was used to dig the expression PTPRR protein in different tissues.We also probe the relationship with clinical data and survival time of pancreatic cancer patients.Results:1.The gene chip GSE16515 data set was analyzed.With|Log FC|>2 and P<0.01as the criterion,322 significantly different genes in pancreatic cancer tissues were screened,including 259 up-regulated genes and 63 down-regulated genes.2.GO functional analysis showed that the biological process primarily included the organization of extracellular matrix,anti-microbial humoral response,digestion and other functions;KEGG pathway enrichment analysis manifested that the differential genes have a line with protein digestion and absorption,pancreatic juice secretion and ECM-receptor interaction.3.The obtained differential genes were imported into the STRING online website for protein-protein interaction network analysis,and 197 points were obtained.A total of 2 key protein modules were obtained and the top 20 core genes were selected by using related plug-ins.4.After the database was searched and screened,there are 23 literatures was included.Then we extracted corresponding data and used fixed effect model(I~2=29%<50%,P=0.15>0.1)to combine the effect size,the OR value was 19.93,95%CI=(8.07-16.86),revealing that the manifestation of MUC1 was vastly higher in pancreatic tumors compared with adjacent tissues,Z=13.02,P<0.05 and statistically significant.5.Bioinformatics analysis showed that PTPRR gene was highly expressed in pancreatic cancer,and who with high expression always had a poor prognosis(P=0.00054).Based on TCGA database,PTPRR gene expression level showed no significant difference in cancer tissues with different race,age,gender and TNM stage.6.Enrichment analysis of PTPRR and its co-expressed genes,GO enrichment analysis is mainly enriched in biological processes related to cancer such as unfolded protein response,glycoprotein metabolism,protein glycosylation;KEGG enrichment analysis results It was shown that co-expressed genes were mainly enriched in adhesion junctions,endoplasmic reticulum protein processing,N-glycan biosynthesis,endometrial cancer,thyroid cancer,estrogen signaling pathway and other cancer-related pathways.After protein-protein interaction network prediction,a total of 19 proteins interacting with PTPRR were obtained.7.Through experiments we prove that the positive rate of PTPRR was noticeably higher in pancreatic neoplasms compared with adjacent tissues(?2=7.55,P=0.006),The expression of PTPRR protein was associated with vascular invasion(?2=3.905,P=0.048)and lymph node metastasis(?2=4.384,P=0.036)in the clinicopathological parameters of pancreatic ductal adenocarcinoma.The expression of PTPRR protein was not related to age,gender,tumor size(T),distant metastasis,preoperative CEA,CA125,CA199 levels,and nerve bundle invasion in clinicopathological parameters,which were no statistical significance.Conclusion:1.The meta-analysis show that MUC1 protein is positively correlated with the occurrence of pancreatic cancer and lymph node metastasis,which can improve the probability of tumor occurrence and metastasis.2.Bioinformatics synthesis shows that PTPRR is highly expressed in pancreatic ductal adenocarcinoma,which may be related to pancreatic carcinogenesis and poor prognosis.3.Patients with high expression have a poor prognosis and PTPRR is expected to become a new diagnostic marker for pancreatic ductal adenocarcinoma. |
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