| Objective: We aimed to demonstrate Illumina450K chips can beuseful and effective serum methylation profile screening tool and getsome methylation sites which was related to primary hepatocellularcarcinoma. We aimed to explore the primary hepatocellular carcinomadiagnostic model based on peripheral blood and multi-parameter jointdiagnosis. Based on the serum indicators, we aimed to identify thediagnostic value of healthy controls and disease groups, benigndisease and malignant disease groups which may provide acomplementary diagnostic method for clinical diagnosis.Methods: Illumina450K methylation chip was used to detect theserum methylation levels of hepatocellular carcinoma and normalcontrol group. Bisulfite methylation sequencing was used to validatethe methylation levels of DBX2and THY1sites. Affymetrix GeneChip was used to detect the peripheral blood mRNA expressionin hepatocellular carcinoma and normal control group, and then thescreened genes which were related to hepatocellular carcinoma wereused to establish GeXP detection system. Based on multi-parameterjoint diagnosis, peripheral blood mRNA and GeXP detection platform,a standardized diagnostic model which were used for clinicalcomplementary diagnosis was built. Biochemical indicators weredetected by Hitachi7600automatic biochemical analyzer and RocheModular automatic biochemical analyzer detection. Immunizationindicators were detected by Roche E170EE automatic immunoassayanalyzer and the Abbott i2000automatic immune luminescenceanalyzer.10cytokines indicators were detected by Luminex200liquid chip detector. The receiver operating curve was used to evaluatethe diagnostic value of the index. Multi-parameter joint analysis wasevaluated by the binary Logistic regression analysis, discriminant analysis, classification tree analysis and artificial neural networkanalysis.Results: In our study, we found that the serum level of wholegenome-wide methylation in primary liver cancer group wassignificantly lower than the healthy control group. Compared withhealthy control group, the number of differentially methylation siteswas7333in hepatocellular carcinoma group, accounting for1.5%of450K microarray. In the7333differentially methylated sites, thenumber of reduced methylation sites was6953, the number ofincreased methylation sites was380. Distribution and percentage ofdifferentially methylated loci on each chromosome was inconsistent, itmay be related to chromosome instability which leaded tohepatocellular carcinoma. Differentially methylated sites locatedmainly in the promoter region and CpG islands. Beta value greaterthan0.5was defined as hypermethylation. Beta value which was less than0.2is defined as hypomethylation. We had screened453hypermethylation sites in healthy controls group and37hypermethylation sites in hepatocellular carcinoma group. After geneontology and functional enrichment analysis, we screened28genefunction enrichment of genes in the healthy control group and3genefunction enrichment of genes in the hepatocellular carcinomagroup. After gene interaction analysis, we selected DBX2and THY1hypermethylation sites for validation. The diagnostic sensitivity andspecificity of DBX2for differentiating healthy control group and thethe hepatocellular carcinoma group were89%and87%, while THY1were81%and85%respectively. In our study, we detected the mRNAexpression in peripheral blood of primary hepatocellular carcinomaand healthy normal control group. After the quality of gene chip wasevaluated. We selected40up-regulated genes and40down-regulatedgenes in hepatocellular carcinoma group, and then15genes were selected and detected by GeXP detection system, after the adjustmentof the gene-specific detection and primer concentration. We built theGeXP detection methods for hepatocellular carcinoma diagnosis. Thebest diagnostic model was CALR, PFN1, SPAG9, ANXA1, HGF,FOS, GPC3and HPSA1B gene. The diagnostic accuracy rate ofdistinguishing the normal control group, hepatitis B group group, livercirrhosis, liver cancer group and other groups were80.57%,78.17%,84.48%,73.24%and85.85%, and then the model was validated, thediagnostic accuracy rate of distinguishing the normal control group,hepatitis B group group, liver cirrhosis, liver cancer group and othergroups were83.33%,73.33%,100%,75.00%and95.24%,respectively. We had built a standardized diagnostic model based onperipheral blood mRNA, GeXP detection platform andmulti-parameter for the clinical diagnosis of primary liver cancer. Inour study, we detected61indicators in serum of tumors to distinguish between healthy control group and disease group. We found the areaunder the receiver operating curve of CRP and IL-8were greater than0.9. When the threshold value of CRP detection was0.29mg/L, thediagnostic sensitivity and specificity were89.90%and97.00%. Whenthe threshold of IL-8detection was25.38pg/mL, the sensitivity andspecificity were83.90%and85.50%. CRP was the best indicator todistinguish the healthy controls and disease groups when the indicatorwere detected alone. The joint diagnostic analysis to distinguishbetween healthy controls and disease groups can increase diagnosticvalue when compared to the single indicator detection, however, thediagnostic sensitivity and specificity of CRP were high enough,therefore, the diagnostic value of joint detection showed no obviousadvantages. The largest area under the receiver operating curve fordistinguishing between benign disease and malignant disease groupwas CA724, but the value of area under curve was only0.589. When the diagnostic threshold was1.685, the diagnostic sensitivity andspecificity were56.80%and59.00%. The diagnostic value was verylimited. The artificial neural network diagnostic method divided70%of the samples used for training,30%used for testing. After areaunder curve analysis, we found that the area under the receiveroperating curve analyzed by artificial neural network analysis fordifferentiating the benign disease group and tumor group was0.941. The overall correct rate of the validation set was81.30%,88.70%in the tumor training group and85.40%in the healthy controlgroup.Conclusion: Our study demonstrated that the450K chip was a verypromising serum methylation screening tool. The methylated siteswhich were related to primary hepatocellular carcinoma may serve asa complementary diagnostic method for clinical primaryhepatocellular carcinoma diagnosis. When we combined the peripheral blood, GeXP technology and multi-parameter analysistechniques together, it can effectively improve the diagnostic value ofprimary hepatocellular carcinoma. CRP was an effectively indicatorfor distinguishing between healthy controls and diseasegroups. When CA724was used for distinguishing between thebenign disease group and tumor group, the diagnostic value waslimited. The artificial neural network which was used fordistinguishing the benign disease group and tumor group had the bestdiagnostic value. Multi-parameter joint analysis which combinedmultiple parameters can effectively improve the diagnostic sensitivityand specificity, it may be a very promising clinical complementarydiagnosis. |
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