MicroRNA Signatures for circulating CD133-positive cells in hepatocellular carcinoma with HCV infection.

AIM: Molecular characterization of the CD133+ stem cells associated with hepatocarinogensis through identifying the expression patterns of specific microRNAs (miRNAs). METHODS: We investigated the expression pattern of 13 miRNAs in purified CD133+ cells separated from the peripheral blood of healthy volunteers, chronic hepatitis C (CHC), liver cirrhosis (LC) and hepatocellular carcinoma (HCC) patients a long with bone marrow samples from the healthy volunteers and the LC patients using custom miScript miRNA PCR array. RESULTS: The differential expression of the 13 studied miRNAs in CD133+ cells separated from the HCC patients' peripheral blood compared to the controls revealed that miR-602, miR-181b, miR-101, miR-122, miR-192, miR-125a-5p, and miR-221 were significantly up regulated (fold change = 1.8, 1.7, 2, 5.4, 1.6, 2.9 & 1.5 P value = 0.039, 0.0019, 0.0013, 0.0370, 00024, 0.000044 &0.000007 respectively). As for the HCC group compared to the CHC group; miR-602, miR-122, miR-181b, miR-125a-5p, and miR-192 were significantly up regulated (fold change = 13, 3.1, 2.8, 1.6 & 1.56, P value = 0.01, 0.001, 0.000004, 0.002 & 0.007 respectively). Upon comparing the HCC group to the LC group; miR-199a-3p, miR-192, miR-122, miR-181b, miR-224, miR-125a-5p, and miR-885-5p were significantly up regulated (fold change = 5, 6.7, 2.3, 3, 2.5, 4.2 & 39.5 P value = 0.001025, 0.000024, 0.000472, 0.000278, 0.000004, 0.000075 & 0.0000001 respectively) whereas miR-22 was significantly down regulated (fold change = 0.57 P value = 0.00002). Only, miR-192, miR-122, miR-181b and miR-125a-5p were significant common miRNAs in CD133+ cells of the HCC group compared to the other non-malignant groups. CONCLUSION: We identified a miRNA panel comprised of four miRNAs (miR-192, miR-122, miR-181b and miR-125a-5p) that may serve as a molecular tool for characterization of the CD133+ cells associated with different stages of hepatocarinogensis. This panel may aid in developing a new target therapy specific for those CD133+ cells.

Serum microRNA panels as potential biomarkers for early detection of hepatocellular carcinoma on top of HCV infection.

The identification of new high-sensitivity and high-specificity markers for hepatocellular carcinoma (HCC) is essential. We aimed at identifying serum microRNAs (miRNAs) as potential biomarkers for early detection of HCC on top hepatitis C virus (HCV) infection. We investigated serum expression of 13 miRNAs in 384 patients with HCV-related chronic liver disease (192 with HCC, 96 with liver cirrhosis (LC), and 96 with chronic hepatitis C (CHC)) in addition to 96 healthy participants enrolled as a control group. The miRNA expression was performed using real-time quantitative PCR-based SYBR Green custom miRNA arrays. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic performance of miRNA panels for early detection of HCC. Using miRNA panel of miR-122, miR-885-5p, and miR-29b with alpha fetoprotein (AFP) provided high diagnostic accuracy (AUC = 1) for early detection of HCC in normal population while using miRNA panel of miR-122, miR-885-5p, miR-221, and miR-22 with AFP provided high diagnostic accuracy (AUC = 0.982) for early detection of HCC in LC patients. However, using miRNA panel of miR-22 and miR-199a-3p with AFP provided high diagnostic accuracy (AUC = 0.988) for early detection of HCC in CHC patients. We identified serum miRNA panels that could have a considerable clinical use in early detection of HCC in both normal population and high-risk patients.

Early detection of hepatocellular carcinoma co-occurring with hepatitis C virus infection: A mathematical model.

AIM: To develop a mathematical model for the early detection of hepatocellular carcinoma (HCC) with a panel of serum proteins in combination with α-fetoprotein (AFP). METHODS: Serum levels of interleukin (IL)-8, soluble intercellular adhesion molecule-1 (sICAM-1), soluble tumor necrosis factor receptor II (sTNF-RII), proteasome, and ß-catenin were measured in 479 subjects categorized into four groups: (1) HCC concurrent with hepatitis C virus (HCV) infection (n = 192); (2) HCV related liver cirrhosis (LC) (n = 96); (3) Chronic hepatitis C (CHC) (n = 96); and (4) Healthy controls (n = 95). The R package and different modules for binary and multi-class classifiers based on generalized linear models were used to model the data. Predictive power was used to evaluate the performance of the model. Receiver operating characteristic curve analysis over pairs of groups was used to identify the best cutoffs differentiating the different groups. RESULTS: We revealed mathematical models, based on a binary classifier, made up of a unique panel of serum proteins that improved the individual performance of AFP in discriminating HCC patients from patients with chronic liver disease either with or without cirrhosis. We discriminated the HCC group from the cirrhotic liver group using a mathematical model (-11.3 + 7.38 × Prot + 0.00108 × sICAM + 0.2574 × ß-catenin + 0.01597 × AFP) with a cutoff of 0.6552, which achieved 98.8% specificity and 89.1% sensitivity. For the discrimination of the HCC group from the CHC group, we used a mathematical model [-10.40 + 1.416 × proteasome + 0.002024 × IL + 0.004096 × sICAM-1 + (4.251 × 10(-4)) × sTNF + 0.02567 × ß-catenin + 0.02442 × AFP] with a cutoff 0.744 and achieved 96.8% specificity and 89.7% sensitivity. Additionally, we derived an algorithm, based on a binary classifier, for resolving the multi-class classification problem by using three successive mathematical model predictions of liver disease status. CONCLUSION: Our proposed mathematical model may be a useful method for the early detection of different statuses of liver disease co-occurring with HCV infection.

Disease progression from chronic hepatitis C to cirrhosis and hepatocellular carcinoma is associated with increasing DNA promoter methylation.

BACKGROUND: Changes in DNA methylation patterns are believed to be early events in hepatocarcinogenesis. A better understanding of methylation states and how they correlate with disease progression will aid in finding potential strategies for early detection of HCC. The aim of our study was to analyze the methylation frequency of tumor suppressor genes, P14, P15, and P73, and a mismatch repair gene (O6MGMT) in HCV related chronic liver disease and HCC to identify candidate epigenetic biomarkers for HCC prediction. MATERIALS AND METHODS: 516 Egyptian patients with HCV-related liver disease were recruited from Kasr Alaini multidisciplinary HCC clinic from April 2010 to January 2012. Subjects were divided into 4 different clinically defined groups - HCC group (n=208), liver cirrhosis group (n=108), chronic hepatitis C group (n=100), and control group (n=100) - to analyze the methylation status of the target genes in patient plasma using EpiTect Methyl qPCR Array technology. Methylation was considered to be hypermethylated if >10% and/or intermediately methylated if >60%. RESULTS: In our series, a significant difference in the hypermethylation status of all studied genes was noted within the different stages of chronic liver disease and ultimately HCC. Hypermethylation of the P14 gene was detected in 100/208 (48.1%), 52/108 (48.1%), 16/100 (16%) and 8/100 (8%) among HCC, liver cirrhosis, chronic hepatitis and control groups, respectively, with a statistically significant difference between the studied groups (p-value 0.008). We also detected P15 hypermethylation in 92/208 (44.2%), 36/108 (33.3%), 20/100 (20%) and 4/100 (4%) , respectively (p-value 0.006). In addition, hypermethylation of P73 was detected in 136/208 (65.4%), 72/108 (66.7%), 32/100 (32%) and 4/100 (4%) (p-value <0.001). Also, we detected O6MGMT hypermethylation in 84/208 (40.4%), 60/108 (55.3%), 20/100 (20%) and 4/100 (4%), respectively (p value <0.001. CONCLUSIONS: The epigenetic changes observed in this study indicate that HCC tumors exhibit specific DNA methylation signatures with potential clinical applications in diagnosis and prognosis. In addition, methylation frequency could be used to monitor whether a patient with chronic hepatitis C is likely to progress to liver cirrhosis or even HCC. We can conclude that methylation processes are not just early events in hepatocarcinogenesis but accumulate with progression to cancer.