Identification And The Correlation Of Prognosis Of Key Genes And Relationship In HBV-Related Hepatocellular Carcinoma

Part 1: Bioinformatics Analysis of Key Node Genes in HBV-related Hepatocellular CarcinomaObjectivesThis study is to conduct bioinformatics analysis of hepatitis B-related hepatocellular carcinoma in the GEO database,aiming to explore the key genes that play a role in HBV-HCC.In order to find tumor molecular markers and drug targets hepatocellular carcinoma,provide a theoretical basis for early diagnosis and early treatment of liver cancer.Methods1.Search for related data sets with "liver cancer" or "HCC" or "hepatocellular carcinoma" and "HBV" or "hepatitis B virus" from the GEO database,screening HBV-related hepatocellular carcinoma and download the related datasets.2.Use R language to standardize the expression profile dataset and extract the differentially expressed genes（DEGs）of each data set separately.Then use RRA method to integrate genes sorting,screening finally DEGs expressed for all.Screening criteria: P adjust <0.05 and | log2 FC |> 1.3.Use the DAVID online analysis platform to perform GO gene function annotation and KEGG pathway enrichment analysis on the selected differentially expressed genes.4、Use the STRING online analysis website to construct a protein-protein interaction network of differentially expressed genes.5 、 Discover the main disease function modules in HBV-related hepatocellular carcinoma through the Cytoscape software Mcode plug-in.Select the top two disease modules for visualization.And analyze the KEGG enrichment pathway for the differentially expressed genes in the first two disease modules.6、The gene screening of key nodes in the protein interaction network was carried out through the Hubba plug-in of Cytoscape software.The key node proteins were identified based on four different centrality parameters degree centrality（DC）,betweenness centrality（BC）,and closeness centrality（CC）,Maximal Clique Centrality（MCC）.7、Verify the relationship between the level of expression of key node genes and its relationship with cancer stage and prognosis by GEPIA2 database.Result1.Four eligible datasets including GSE121248,GSE19665,GSE47197 and GSE55092 were selected from GEO dataset for the subsequent analysis.There was a total of 185 cancer tissue samples and 196 adjacent normal tissue samples in these four datasets,2.Through the RRA integration analysis of four data sets,a total of 320 DEGs were identified from the four datasets,including 104 upregulated genes and216 downregulated genes.3.The GO functional analysis of the upregulated DEGs in HBV-HCC significantly regulating the mainly terms including: cell cycle and catabolism,chromosome and spindle,protein binding and histone binding.The GO functional analysis of the downregulated DEGs in HBV-HCC significantly regulating the mainly terms including inflammatory response and immune response,extracellular exosome and membrane attack complex,heme binding and iron ion binding.4.The identified DEGs associated with HBV-related HCC were mainly enriched in the signaling pathway of metabolic pathways,p53 signaling pathway,cell cycle and other signaling pathways.5.The genes in module 1 were mainly enriched in cell cycle,oocyte meiosis,p53 signaling pathway,small cell lung cancer;and the genes in module 2 were enriched in complement and coagulation cascades,prion diseases,systemic lupus erythematosus.6.Use the Hubba plug-in of Cytoscape software to identified six key node genes based on four different centrality parameters that are CDK1,CDC20,CDKN3,HMMR,MKI67,and CCNB1.7.The m RNA expression of six key node genes（i.e.CDK1,CDC20,CDKN3,HMMR,MKI67 and CCNB1）and study the relationship between cancer staging in the GEPIA2 database were evaluated.The results showed that the expression levels of CDK1,CDC20,CDKN3,HMMR,MKI67 and CCNB1 were higher in HCC cancer tissue than in normal tissue（P<0.05）;we compared the expression levels of these key genes between different stages of HCC.There were significant differences of expression in CDK1（P<0.001）、CDC20（P<0.001）、CCNB1（P<0.001）、CDKN3（P= 0.004）、HMMR（P= 0.001）和 MKI67（P<0.001）.8.The associations of the key genes（i.e.CDK1,CDC20,CDKN3,HMMR,MKI67 and CCNB1）with the survival of HCC patients in GEPIA were explored.The results were shown that CDK1（HR = 1.7,Log Rank P= 0.001）,CDC20（HR = 1.6,Log Rank P= 0.003）,HMMR（HR = 1.6,Log Rank P= 0.003）,CDKN3（HR = 1.5,Log Rank P= 0.007）,MKI67（HR = 1.9,Log Rank P<0.001）and CCNB1（HR = 2,Log Rank P<0.001）m RNA level were associated with the worse DFS in patients with HCC.The result shown that the high expression of CDK1（HR = 2,Log Rank P<0.001）,CDC20（HR = 2.3,Log Rank P<0.001）,HMMR（HR = 1.7,Log Rank P= 0.003）,MKI67（HR =1.9,Log Rank P<0.001）and CCNB1（HR = 2,Log Rank P<0.001）were significant difference on the survival curve in HCC.Still can not believe the level of expression CDKN3 overall survival in patients with hepatocellular carcinoma associated（HR = 1.4,Log Rank P= 0.058）.Conclusion1.Cell mitosis and cell cycle regulation are closely related to the occurrence and evolution of HBV-related hepatocellular carcinoma.The key node genes CDK1,CCNB1,CDC20,CDKN3,HMMR and MKI67 play an important role in the occurrence and development of HBV-related hepatocellular carcinoma.2.The key node genes CDK1,CCNB1,CDC20,CDKN3,HMMR and MKI67 m RNA expression levels are upregulated in hepatocellular carcinoma and are associated with disease-free survival.The expression levels of CDK1,CCNB1,CDC20,HMMR and MKI67 are related to the overall survival of HCC patients.Part 2: Association Analysis between the Key Genes Expression such as CCNB1 and Prognosis in HBV-related Hepatocellular CarcinomaObjectives To detect the expression levels of CCNB1,CDK1,CDC20,CDKN3,HMMR,MKI67,HBX,CDC25 c,P53 proteins in HBV-related Hepatocellular Carcinoma（HBV-HCC）tissues and explore their relationship with the patient’s clinicopathology and prognosis of HBV-HCC patients.Methods 1.We collected and paraffin-embedded liver cancer tissues and their corresponding adjacent cancerous tissue from 69 cases of HBV-HCC patients and 6 cases of non-HBV-related HCC patients,who underwent hepatectomy from November 2013 to November 2014 in Guangxi Medical University cancer hospital.A tissue chip with 159 sites was constructed.At the same time,we collected the clinicopathological parameters and follow-up data of these patients.2.Immunohistochemistry was used to detect the protein expression encoded by nine genes,including CCNB1,CDK1,CDC20,CDKN3,HMMR,MKI67,HBX,CDC25 c,and P53.We analyzed the relationship between the expression of the proteins mentioned above and the clinicopathological parameters of HBVHCC patients.3.We analyzed the expression of key genes in liver cancer tissues,adjacent tissues,and normal liver tissues with HBV infected or not.4.We used the Kaplan-Meier method to evaluate the correlation between the expression level of nine key genes and the overall survival time of HBV-HCC patients.5.We used Spearman’s rank correlation analysis to measure the correlation of HBX and the other 8 key genes.6.Univariate and multivariate survival analyses based on Cox proportional hazard model were conducted to identify the independent prognostic factors of survival lifetime of HBV-HCC patients.And build a predictive scoring model based on those factors.7.The Survival ROC software package in R language was used to draw the time-dependent ROC（t ROC）curve of key genes on its 1-year,3-year,and 5-year accuracy,which can help us assess the prognostic prediction level of 9 key genes.Results 1.The results of immunohistochemical staining showed that nine key genes were all expressed in HBV-HCC,including CCNB1,CDK1,CDC20,CDKN3,HMMR,MKI67,HBX,CDC25 c,and P53.The expression levels of CCNB1 and CDK1 in liver cancer tissues were significantly higher than those in adjacent tissues.The expression levels of HMMR,CDC20,and CDC25 c in liver cancer tissues were significantly lower than those in adjacent tissues,and the difference was statistically significant（P<0.05）.2.In HBV-HCC patients,the expression of CDK1 is related to CA199（P=0.003）and lymph node metastasis（P=0.046）;the expression of CCNB1 is related to B cells（P=0.046）;the expression of CDC20 is related to gender（P=0.042）and the degree of differentiation of liver cancer（P=0.005）;the expression of CDKN3 is related to B cells（P=0.020）;the expression of HMMR is related to AFP（P=0.040）),CEA（P=0.005）,tumor number（P=0.018）;the expression of MKI67 is related to the degree of differentiation（P=0.034）;the expression of CDC25 c is related to age（P=0.004）,AFP（P=0.045）;The expression of HBX is related to CEA（P=0.010）and microvascular thrombosis（P=0.007）.3.In different HBV infection situations,the expression of HMMR（P=0.041）,CDKN3（P=0.028）,and HBX（P<0.001）in the liver cancer tissues is different;HMMR（P=0.050）,HBX（P<0.001）The expression in the adjacent cancerous tissues is different;the expression of HBX（P<0.001）in the normal live tissues is different,and the difference was statistically significant.4.The Kaplan-Meier analysis indicated that P53 expression correlated with higher overall survival（P=0.015）.5.CDK1,CCNB1,CDC20,CDKN3,HMMR,MKI67,P53,and CDC25 c are significantly positively correlated with HBX,and all were statistically significant（P<0.05）.6.BCLC staging and P53 are the prognostic survival factors of HBV-HCC patients;P53,BCLC staging,HBV-DNA,tumor envelope,and total bilirubin levels are independent risk factors affecting the prognosis of HBV-HCC patients.Prognostic Index（PI）scoring system for the prediction of prognosis were then established and evaluated by ROC curve analysis for its predictive power.7.The ROC survival prediction models of PI scoring model can predict the prognosis of HBV-HCC patients in the 3^st、5^st years accuratelyConclusion 1.CCNB1,CDK1,HMMR,CDC20,CDC25 c,might contribute to the occurrence and development of HBV-HCC,which can be used as potential targets for molecular-based therapy of liver cancer;2.The expression of P53 is related to the overall survival of HBV-HCC patients;3.HBX is positively correlated with the expression of 8 key genes.HBV infection may affect the expression of key genes through specific pathways,mediating the regulation of cell cycle pathways（especially G2/M phase）,DNA damage repair,and P53 conduction pathways,thereby participating in the evolution of HBV-HCC;4.BCLC staging and P53 are the prognostic survival factors of HBV-HCC;P53,BCLC staging,HBV-DNA,tumor envelope,and total bilirubin levels are independent prognosis factors HBV-HCC affecting the survival time of patients;5.The PI scoring model based on P53,BCLC staging,HBV-DNA,tumor envelope and total bilirubin can predict the prognosis of HBV-HCC patients in the 3^st、5^st years accurately.This model may provide new indicators,new ideas,and new strategies for the diagnosis,efficacy evaluation,treatment monitoring,and prognosis judgment of HBV-HCC patients.