MicroRNA profiles in various hepatocellular carcinoma cell lines

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-associated mortality worldwide. Although surgery is considered the most effective treatment for patients with HCC, its indication is restricted by limited criteria and a high relapse rate following surgery; therefore, systemic chemotherapy is required for patients with advanced-stage HCC to prolong their survival. MicroRNAs (miRNAs) are endogenous non-coding RNAs of 18–22 nucleotides in length. It has been reported that aberrant expression of miRNAs is a feature shared by various types of human cancer. Previous studies have indicated that the modulation of non-coding RNAs, particularly miRNAs, may be a valuable therapeutic target for HCC. The aim of the present study was to elucidate the miRNA profiles associated with differentiation and hepatitis B virus (HBV) infection observed in HCC cell lines. The human Alex, Hep3B, HepG2, HuH1, HuH7, JHH1, JHH2, JHH5, JHH6, HLE, HLF and Li-7 HCC cell lines were used for an miRNA array. Replicate data were analyzed following their classification into: i) Poorly- and well-differentiated human HCC cells and ii) HBV-positive and -negative human HCC cells. Out of the 1,719 miRNAs, 4 were found to be significantly upregulated and 52 significantly downregulated in the poorly-differentiated cells, as compared with the well-differentiated cells. Conversely, in the HBV-positive cells 125 miRNAs were found to be upregulated and 2 downregulated, as compared with the HBV-negative cells. Unsupervised hierarchical clustering analysis with Pearson's correlation revealed that the miRNA expression levels were clustered both together and separately in each group. In conclusion, miRNA profile characterization based on various parameters may be a novel approach to determine the etiology of HCC.


Introduction
Liver cancer is the third most common cause of cancer-associated mortality worldwide, accounting for an estimated 9.2% of total cancer-associated mortalities in 2008 (1). Surgery is considered the most effective treatment for patients with hepatocellular carcinoma (HCC) (2); however, the indications for surgery are restricted by the size and total number of tumors (2,3). Although the 5-year survival rate of patients with HCC has improved by >30% over the past decade, the recurrence rate following surgery is estimated to be nearly 50% (4); therefore, systemic chemotherapy is required for patients with advanced stages of HCC, in order to prolong their survival.
MicroRNAs (miRNAs) are endogenous non-coding RNAs of 18-22 nucleotides in length (3,5). The effect of miRNAs on the regulation of the expression of various genes is so broad that one miRNA controls >200 genes (6). Aberrant expression of miRNAs is a common feature among various types of human cancer, and has been reportedly associated with patient survival (7)(8)(9)(10). Regarding the correlation between miRNAs and HCC, several studies have detected the aberrant expression of specific miRNAs in HCC tissues when compared with normal tissues (11)(12)(13)(14). These studies indicated that the modulation of non-coding RNAs, particularly miRNAs, may be a valuable therapeutic target in HCC.
The aim of the present study was to elucidate the miRNA profiles that are associated with differentiation and hepatitis B virus (HBV) infection observed in HCC cell lines. The characterization of miRNA expression patterns using various parameters may be a novel approach for the treatment of patients with HCC.
Analysis of microRNA array. Total RNA was extracted from the cancer cell lines using a miRNeasy Mini kit (Qiagen, Hilden, Germany), according to the manufacturer's instructions. RNA samples typically showed A 260 / 280 ratios between 1.9 and 2.1 on an Agilent 2,100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA).
Following the measurement of the RNA using an RNA 6,000 Nano kit (Agilent Technologies, Tokyo, Japan), the samples were labeled using a miRCURY Hy3/Hy5 Power Labeling kit (Takara Bio Inc., Tokyo, Japan) and hybridized onto a human miRNA Oligo chip (version 19.0; Toray Industries, Inc., Tokyo, Japan). Scanning was conducted with the 3D-Gene Scanner 3,000 (Toray Industries, Inc., Kusatsu, Japan). 3D-Gene extraction software version 1.2 (Toray Industries, Inc.) was used to read the raw intensity of the image. To determine the change in miRNA expression between poorly-and well-differentiated HCC cell lines or HBV-positive and HBV-negative HCC cell lines, the raw data were analyzed using GeneSpringGX version 10.0 (Agilent Technologies). Samples were first normalized to the 28S RNA and the baseline was then corrected to the median of all samples.
Replicate data were analyzed following their classification into: i) Poorly-and well-differentiated human HCC cells, and ii) HBV-positive and -negative human HCC cells, which were organized by the hierarchical clustering in the GeneSpring software. For the log 2 ratios of the miRNA expression intensity between two groups, hierarchical clustering was performed using the furthest neighbor method with the absolute Pearson's correlation coefficient as a metric. The log 2 ratios were median-centered across each miRNA in a color-coding of the heat map. The P-value cutoff was set to 0.05. Only changes of >50% in at least one of the time points for each sample were considered significant. All of the analyzed data were scaled by global normalization.
Statistical analysis. All analyses were conducted using the JMP 8.0 software (SAS Institute, Inc., Cary, NC, USA). A paired analysis between the groups was conducted using a Student's t test. P<0.05 was used to indicate statistically significant differences between the groups.

Results
Differences in miRNA expression between poorly-and well-differentiated human HCC cell lines. Using a custom microarray platform, the expression levels of 1,719 miRNAs were analyzed in various human HCC cell lines. As shown in Fig. 1 and Tables I and II, of the 1,719 miRNAs, 4 were found to be significantly upregulated and 52 were significantly downregulated in the poorly-differentiated cells, as compared with the well-differentiated cells. Unsupervised hierarchical clustering analysis with Pearson's correlation showed that the poorly-differentiated HCC cell lines clustered both together and separately from the well-differentiated HCC cells (Fig. 1).

Differences in miRNA expression between HBV-positive and HBV-negative HCC lines.
To examine the effect of HBV infection on alterations in miRNAs, miRNA profiles were analyzed in HBV-positive and -negative human HCC cell lines. As shown in Fig. 2 and Tables III and IV, of the 1,719 miRNAs, 125 miRNAs were found to be significantly upregulated and 2 were significantly downregulated in the HBV-positive HCC cells, as compared with the HBV-negative HCC cells. Unsupervised hierarchical clustering analysis with Pearson's correlation showed that the HBV-positive HCC cell lines clustered both together and separately from the HBV-negative HCC cells (Fig. 2).

Discussion
The aim of the present study was to elucidate the targetable miRNAs associated with the etiology, diagnosis and treatment of HCC. Certain miRNAs, such as miR-26b and miR-132, were found to be downregulated in poorly-differentiated HCC. It has recently been reported that dedifferentiation is involved in the epithelial-mesenchymal transition (EMT), and particularly in the EMT of cancer (15). In order to invade and metastasize to different organs, cancer cells shed their differentiated epithelial phenotype through EMT (15), which suggests that miR-26b or miR-132 may be associated with cancer invasion and metastasis via EMT. In addition, miR-26b has been shown to directly suppress the expression of CDK6 and cyclin E1, resulting in reduced retinoblastoma-associated protein phosphorylation and inhibited cell proliferation (16). miR-132 also inhibits tumor cell proliferation, invasion and migration by targeting Sox5 (17). These studies also indicated that miR-26b and miR-132 may directly inhibit cancer invasion and metastasis.
In the present study, miR-4476 was upregulated in poorly-differentiated carcinoma. Recently, it has been demonstrated that miR-4476 is one of the top 10 validated miRNA markers differentiating pancreatobiliary cancer from other clinical conditions, including other types of cancer and healthy controls (18). Therefore, this result suggests that advanced stages of HCC, which includes poorly-differentiated cells, induce cholestasis in a similar fashion to pancreatobiliary cancers and may increase the miR-4476 upregulation.
Regarding the effect of HBV, miR-99b was found to be upregulated in HBV-infected HCC cells in the present study. It has been reported that the expression of miR-99b is     associated with the presence of lymph node metastasis (19). In addition, certain miRNAs are associated with the oncogenic processes of HBV-related HCC (3). This data indicates that miRNAs play an important role in the etiology of HBV-related HCC. In addition, Wang et al (20) demonstrated that 10 upregulated miRNAs (miR-217, miR-518b, miR-517c, miR-520g, miR-519a, miR-522, miR-518e, miR-525-3p, miR-512-3p, and miR-518a-3p) and 11 downregulated miRNAs (miR-138, miR-214, miR-214, miR-199a-5p, miR-433, miR-511, miR-592, miR-483-5p, miR-483-3p, miRNA-708 and miRNA-1275) were identified in HBV-associated HCC tissues. In the present study, the same microRNAs were not detected in HBV-positive HCC cells; therefore, adjacent normal tissues may be included in the human HCC tissues. These results indicate that the microRNA expression patterns are different from cancer cell lines and cancer tissues. Cell-cell interaction may affect microRNA expression in the microenvironment of cancer tissues.
In conclusion, changes in the regulation of key miRNAs due to differentiation and HBV infection were observed in human HCC cell lines. The present findings suggested that differences in miRNA expression may serve as a novel marker that can aid in elucidating the etiology of human HCC and assist in designing treatments.