Screening Of Differentially Expressed LncRNA In Plasma Of Patients With HBV-related Hepatocellular Carcinoma And Its Clinical Application

Objective: In this study,the bioinformatics method was used to mine the microarray data of HBV-related hepatocellular carcinoma(HCC)expression profile in public database,and the significant differential expression lncRNA profiles of HBV-related HCC were screened out.qRT-PCR and ROC curve analysis were used for clinical verification and application value evaluation,to find the molecular markers with high sensitivity and good specificity for early diagnosis of HBV-related HCC.Methods:1.The HBV-related HCC microarray datasets GSE55092,GSE19665,and GSE84402 were sought and downloaded from the GEO database as a screening dataset for differential genes,which including 109 cases of liver cancer tissues and 67 controls.The limma package in R was used to filter out the differentially expressed lncRNAs(DElncRNAs)and mRNAs(DEmRNAs),using log|(FC)| > 1,P < 0.05 as the threshold.Using feature selection and constructing a classification model,the optimal diagnostic lncRNA biomarkers for HBV-related HCC were determined.2.The lncRNA-mRNA co-expression regulatory network was constructed by calculating the Pearson correlation coefficients of lncRNAs and mRNAs,GO functional annotation and KEGG pathway enrichment analysis of mRNAs in the regulatory network were performed using the David online analysis tool,the STRING database was applied to draw the protein interaction network(PPI)of mRNAs in the regulatory network and the hub genes were visualized by Cytoscape software and screened by MCODE app.3.RT-q PCR was performed and delta CT analysis to validate the expression trends of the candidate DElncRNAs screened from the microarray in 10 surgically resected-HBV HCC tissues and adjacent noncancerous tissues,as well as in the plasma of 45 HBV-related HCC patients,19 HBV negative HCC patients,57 HBV carriers and 32 healthy controls.The correlation of the finally validated differential lncRNAs with the main clinical indexes was explored.All data were analyzed using R software and Graph Pad Prism 8 software.4.The value of individual DElncRNAs in plasma as diagnostic markers for HBV-related HCC was evaluated by calculating the area under the curve through ROC curve analysis.Stepwise logistic regression analysis was applied to determine the variables to be included in the joint analysis.The joint discrimination model for predicting the risk of HBV-related HCC was constructed by combining the lncRNAs with AFP or PIVKA-II,and ROC curve analysis was used to evaluate its joint diagnostic value.Results:1.The datasets GSE55092,GSE19665,and GSE84402 were analyzed,and a total of 38 DElncRNAs and 543 DEmRNAs were screened.Through feature variable selecting by random forest,nine lncRNAs differentially expressed in HBV-related HCC tissues were selected: including 5 upregulated(AC093642.1,AL445524.1,TRIM52-AS1,EHMT2-AS1 and AL356056.2)and 4 downregulated(AC003991.1,LINC00844,LINC01018 and AC008040.1)genes to serve as candidate diagnostic lncRNA biomarkers for HBV-related HCC.2.The co-expression network of candidate lncRNAs and 126 mRNAs was consist of 199 co-expression relationship pairs.GO functional annotation of the co-expressed differential mRNAs showed that the differential genes were mainly enriched in 181 GO functional annotation results including monocarboxylic acid metabolic process(GO: 0032787,P = 9.59E-06),cell basal membrane(GO: 009925,P = 1.48E-03),cofactor binding(GO: 0048037,P = 2.88E-05),and so on.Signal pathway analysis showed that the co-expressed DEmRNA was enriched in the p53 signal pathway(P =2.66E-03),retinol metabolism(P = 1.80E-03),PI3K-Akt signal pathway(P =2.95E-03),chemical carcinogenesis(P = 3.23E-03),etc.The PPI network contains 87 nodes,and two key modules were selected using the MCODE app,the analysis of signaling pathways of genes in the module was performed;the genes in module 1 were mainly enriched in the p53 signaling pathways,cell cycle,and DNA replication,and the genes in module 2 were mainly enriched in Salmonella infection,NF-κB signaling pathway,Toll-like receptor signaling pathway,phagosomes,and tuberculosis.3.As shown by qRT-PCR,there was no significant difference in AC093642.1,AL445524.1,TRIM52-AS1,EHMT2-AS1 and AL356056.2 between HBV related HCC groups and corresponding carcinomas(P > 0.05),but the expression was higher in the tumor group;AC003991.1,LINC01018,and AC008040.1 were not significant(P > 0.05),but lower in the tumor group,while LINC00844 was significantly downregulated in the tumor group(P < 0.05).The verification results show that the overall expression trend is consistent with the results of the microarray.In plasma,the expression of EHMT2-AS1 was significantly downregulated in HBV(+)controls compared with HBV(-)controls(P<0.001).AC003991.1 was down-regulated in the HBV(+)control group compared to the HBV(-)control and HBV(+)tumor group(P< 0.001).The expression of AL445524.1 was up-regulated in the tumor group,and the difference was statistically significant compared with the control group(P<0.001).The expression of LINC00844 was significantly downregulated in HBV(+)controls compared with HBV(+)tumors and HBV(-)controls(P< 0.001).The expression of TRIM52-AS1 was significantly upregulated in the tumor group and significantly different in comparison with the control group(P < 0.01),and TRIM52-AS1 was statistically different in the tumor group and downregulated in HBV(+)tumor group(P = 0.36).LINC01018 was significantly up-regulated in the tumor group,and the difference was statistically significant compared with the control group(P < 0.001).AC008040.1 was significantly up-regulated in the tumor group compared with the control group(P < 0.01).AC093642.1,P > 0.05 between HBV positive tumors and their controls,P = 1.6E-06 between HBV negative tumors and controls,while P =7.1E-10 between controls.The expression of AL56056.2 in the tumor group was significantly up-regulated compared with the control group(P < 0.05).Besides,the expression level of LINC00844 in plasma was significantly correlated with single or multiple intrahepatic lesions,and the expression level of TRIM52-AS1 was correlated with hepatitis B virus infection.4.By stepwise logistic regression,we selected the minimum AIC information statistics and determined that AC003991.1,AL445524.1,LINC00844,and LINC01018 were included in the joint analysis,and then combined with AFP or PIVKA-II for ROC curve analysis.The AUC of the logistic regression model constructed by combining 4-lncRNA was 0.910,and the sensitivity and specificity were 0.828,0.911,respectively;the AUC of the logistic regression model constructed by combining 4-lncRNA and AFP was 0.986,and the sensitivity and specificity were0.969,0.964,respectively;the AUC of the logistic regression model constructed by combining 4-lncRNA and PIVKA-II was 0.989,and the sensitivity and specificity were 0.922,0.982,respectively.Conclusions:1.LncRNAs AL356056.2,AL445524.1,TRIM52-AS1,AC008040.1,AC003991.1,LINC00844 and LINC01018 were up-regulated in the plasma of HBV-related HCC patients;2.The plasma lncRNA AL445524.1,TRIM52-AS1,AC003991.1,LINC00844,and LINC01018 could serve as potential circulating markers for the clinical diagnosis of HBV-related HCC,and the combination of AL445524.1,AC003991.1,LINC00844,LINC01018,and AFP or PIVKA-II could be considered as a potential early diagnostic marker for HBV-related HCC.