Galectin-3 is associated with a poor prognosis in primary hepatocellular carcinoma

This study was approved by the Ethics Committee of the Cancer Center of Sun Yat-sen University (Guangdong, China). Written informed consent was obtained from each patient.

The human HCC cell lines, HepG2 (well differentiated, low metastatic potential), Hep3B (well differentiated, low metastatic potential), and Human liver adenocarc -inoma Endothelial cell line, Sk-Hep1 were obtained from the American Type Culture Collection (Manassas, VA, USA). Huh7 cells (well differentiated, low metastatic potential) were obtained from the Riken Cell Bank (Ibaraki, Japan). Bel-7402 cells (moderate differentiated, low metastatic potential) and the normal liver cell line LO2 were obtained from the Committee of Type Culture Collection of the Chinese Academy of Sciences (Shanghai, China). All cells were cultured in RPMI 1640 supplemented with 10% fetal bovine serum (FBS) in 5% CO₂ at 37°C.

Tissue samples, including HCC tumor tissues and adjacent non-cancerous tissues (n = 44), were obtained from patients who had resection of primary HCC in the Cancer Center of Sun Yat-sen University between 2011 and 2012. None of these patients had received preoperative chemotherapy or radiotherapy. After resection, matched fresh tissues were immersed immediately in RNAlater® (Ambion, Austin, TX, USA), kept overnight at 4°C, then stored at 80°C until RNA isolation. A total of 165 paraffin-embedded HCC samples were obtained from patients who underwent hepatectomy at the Cancer Center of Sun Yat-sen University between 2001 and 2004. Follow-up was in our outpatient department, and involved clinical and laboratory examinations every 3 months for the first 2 years, every 6 months during the third to fifth years, and annually for an additional 5 years or until death, whichever occurred first. Overall survival (i.e., time from surgery to death or final follow-up) was used as a measure of prognosis. Histological types were assigned according to classification criteria set by the World Health Organization.

Total RNA was extracted by using Trizol solution (Invitrogen, Shanghai, China) according to manufacturer’s instructions. Total protein was extracted with RIPA buffer (Beyotime, Shanghai, China) according to the manufacturer’s protocol. RNA and protein samples were stored at 80°C until use.

Real-time PCR amplification was undertaken with an ABI 7900HT Real-time PCR system (Life Technologies, Carlsbad, CA, USA). The primers used for amplifying galectin-3, caspase3, caspase9, PARP, BAX, Bcl-2, GAPDH were selected. That is, for galectin-3, they were: forward, 5’-ACGAGCGGAAAATGGCAGA-3’; and reverse, 5’-GATAGGAAGCCCCTGGGTAGC-3’. For glyceraldehyde-3-phosphatedehydroge nase (GAPDH), forward 5’-CTCCTCCTGTTCGACAGTCAGC-3’, and reverse, 5’-CCCAATACGACCAAATCCGTT-3; for caspase3, forward 5’-ATCTCGG TCTG GTACAGATGTCGAT-3’, and reverse 5’-TGAATTTCGCCAAGAATAATACCA-3’; for caspase9, forward 5’-GCCATGGACGAAGCGGATCGGC-3’, reverse 5’-GGC CTGGATGAAGAAGAGCTTGGG-3’; for PARP, forward primer 5’-CGGAGTCTT CGGATAAGCTCT-3’, reverse primer 5’-TTTCCATCAAACATGG GCGAC-3’; for BAX, forward primer 5’-CCCGAGAGGTCTTTTTCCGAG-3’, reverse primer 5’-CCAGCCCATGATGGTTCTGAT-3’; for Bcl-2, forward primer 5’-GGTGGGGTCA TGTGTGTGG-3’, reverse primer 5’-CGGTTCAGGTACTCAGTCATCC-3’. The PCR was carried out in a final volume of 15 μL, consisting of 7.5 μL of 2× SYBR Green master mix (Invitrogen), 2 μL of each 5’- and 3’- primer (1.5 pmol/?L), 0.5 μL of the sample cDNA, and 5 μL water. The PCR conditions were 95°C for 10 min, one cycle, followed by 95°C for 30 s and 60°C for 60 s, 45°Cycles. The relative expression levels of galectin-3, caspase3, caspase9, PARP, BAX, Bcl-2 were normalized to that of the internal control gene, GAPDH. Data were analyzed using the comparative threshold cycle (2– –ΔΔCCT) method.

Isolated tumors were fixed in 10% neutral buffered formalin for 48 h and embedded in paraffin according to standard protocols. Sections (thickness, 4 μm) were deparaffinized and rehydrated in a graded series of alcohol solutions. For antigen retrieval, slides were immersed in ethylenediamine tetra-acetic acid (EDTA; 1 mmol/L, pH8.0) and boiled for 15 min in a microwave oven. Endogenous peroxidase acti- vity was blocked in 3% H₂O₂ at room temperature for 15 min, and non-specific binding was abolished by 5% bovine serum albumin (BSA) for 30 min. Sections were then stained with anti-galectin-3 (rabbit anti-galectin-3 polyclonal antibody; 1:250 dilution; Abcam, Cambridge, UK) antibody, anti-CD34 (rabbit anti-CD34 polyclonal antibody; 1:400 dilution; Gene Tech, Shanghai, China) antibody at 4°C overnight. After washing with phosphate-buffered saline (PBS), sections were incubated with horseradish peroxidase (HRP)-conjugated secondary antibody (Envision Detection kit, GK500705, Gene Tech, Shanghai, China) at room temperature for 30 min. After washing thrice with PBS, antibody complexes were colored with 3, 3’-diamino benzidine and then counterstained with hematoxylin. Slides were dehydrated and evaluated.

The total galectin-3 immunostaining score was calculated as the sum of the positively stained tumor cells and staining intensity. Briefly, the percentage of positive staining was scored as “0” (<5%, negative), “1” (5–25%, sporadic), “2” (25–50%, focal), or “3” (>50%, diffuse). Staining intensity was scored as “0” (no staining), “1” (weak staining), “2” (moderate staining), or “3” (strong staining). Both the percentage of positive cells and the staining intensity were evaluated under double-blind conditions. The total immunostaining score was calculated as the value of percent positivity score × staining intensity score, and ranged from 0 to 9. We defined galectin-3 expression levels as: “–” (score 0–1), “+” (2–3), “++” (4–6) and “+++” (>6). Based on their levels of galectin-3 expression, patients were divided into two groups: low galectin-3 (“–” and “+”) and high galectin-3 (“++” and “+++”). For intratumoral MVD assessment, micro-vessels were recorded by counting CD34-positive stained endothelial cells according to the international consensus on the methodology and criteria of evaluation of angiogenesis quantification in solid tumors. After scanning the whole section at low magnifications (100×), ten tumor areas with the greatest number of distinctly highlighted micro-vessels (hot spot) were selected. The value of MVD was evaluated by the average of ten 200 × field micro-vessel counts. Data were shown as mean ± SD. The score assessment was performed independently by two pathologists blinded to the clinical parameters.

HCC samples (tumor and adjacent non-tumor tissues) and cell lines were lysed in RIPA lysis buffer. Lysates were harvested by centrifugation (12,000 rpm for 20 min at 4°C. Protein samples (≈30 μg) were resolved in 12% sodium dodecyl sulfate polyacrylamide gel for electrophoresis and transferred to a polyvinylidene difluoride (PVDF) membrane. After blocking non-specific binding sites for 60 min with 8% non-fat milk, membranes were incubated overnight at 4°C with a rabbit polyclonal antibody against galectin-3 (1:1,000 dilution; Abcam), caspase3 (1:500 dilution, cell signaling technology), caspase9 (1:500 dilution, cell signaling technology), PARP (1:500 dilution, cell signaling technology), BAX (1:500 dilution; Proteintech Group), Bcl-2 (1:1000 dilution; Proteintech Group) or GAPDH (1:10,000 dilution; Proteintech Group). Membranes were washed four times with TRIS-buffered saline with Tween-20 for 10 min. After washing, membranes were probed with HRP-conjugated secondary antibody and visualized using a chemiluminecent system (Cell Signaling Technology, Danvers, MA, USA). Band intensity was measured by Quantity One software (BioRad, Hercules, CA, USA).

For transient transfection experiments, certain small interfering ribonucleic acid (siRNA) molecules were used. That is, for galectin-3-siRNA: sense, 5’-GUACA AUCAUCGGGUUAAATT-3’ and antisense, 5’-UUUAACCCGAUGAUUGUACTT-3’. For the negative control: sense, 5’-UUCUCCGAACGUGUCACGUTT-3’ and anti- sense, 5’-ACGUGACACGUUCGGAGAATT-3’. The siRNAs were synthesized by GenePharma (Shanghai, China). Cells at around 70% confluence were transfected with the indicated siRNA using Lipofectamine RNAiMax reagent (Invitrogen) according to manufacturer’s instruction. 72 hours after transfection, cells were detached with trypsin/EDTA, suspension, and allowed to grow overnight before treatment. Knockdown efficiency was evaluated by western blotting.

The galectin-3-overexpressing recombined Lentivirus vector and the control vector were constructed by GenePharma (Shanghai, China). Lentiviral infection was performed by adding virus solution to Hep3B cells in the presence of 5 μg/ml polybrene (Sigma-Aldrich, St. Louis, MO, USA). After infection for 48 h, the cells were selected in the presence of 2 μg/ml puromycin, and puromycin-resistant cells were collected and cultured. The stable cell lines were specified as Hep3B-galectin-3 and Hep3B-mock, respectively.

Cell growth rates were measured with a (3-(4, 5-dimethylthiazol-2-yl) -5-(3-carboxy methoxyphenyl)-2-(4-sulfophenyl)-2H-tetr-azolium) (MTS) cell proliferation assay. Cells were plated in triplicate in 96-well plates at 2500 cells per well. At each time point, the medium was removed and cells incubated with 20 μL of MTS (5 mg/mL; Sigma-Aldrich, St Louis, MO, USA) for 4 hours in 5% CO₂ at 37°C. Finally, the optical density of formazan was measured using a microplate reader at 490 nm. Three independent experiments were performed to analyze the cell growth. Statistical analyses were carried out using the two-tailed unpaired Student’s t-test.

HCC cells transfected with gal-siRNA, NC, galectin-3-overexpression were collected after 36 h, washed twice in PBS, and fixed in 75% ethanol overnight at ?20°C. Fixed cells were washed with ice-cold PBS once, resuspended in 500 μL PBS and 20 μL RNaseA, and then incubated in a 37°C water-bath for 30 min. Cells were stained with propidium iodide (PI; Bestbio, Shanghai, China) at 4°C in the dark for 30–60 min. Flow cytometry data were analyzed using Beckman Coulter (Fullerton, CA, USA) software.

Invasion assays were carried out using transwell membrane filter inserts (diameter, 6.5 mm; pore size, 8 μm) in a 24-well tissue culture plate. Briefly, transfected Bel-7402, HepG2, Huh7 and Hep3B cells were harvested at 24 h and resuspended in serum-free RIPM 1640. Cells (1 × 10⁵/well) in 200 μL of growth medium without FBS were added to the upper chamber, and the bottom chamber was filled with 500 μL of growth medium containing 10% FBS. After 48 h, non-migrating cells were removed from the top of the filter with a cotton swab. Invading cells on the bottom of the filter were fixed with methanol, stained with 0.5% crystal violet, and counted. Stained cells at 10 random fields were counted using an inverted microscope. Each experiment was conducted in triplicate. Statistical analyses were carried out using the two-tailed unpaired Student’s t-test.

We assessed the migration of control- and galectin-3-siRNA, galectin-3-overex -pression transfected cells using an in vitro wound-healing assay. HepG2, Bel-7402, Hep3B cells (1 × 10⁵cells/well) transfected with gal-siRNA, galectin-3-overexpression and negative control were plated in six-well plates, wounded by scratching with a pipette tip, then incubated with RIPM 1640 without 10% FBS for 24 h in 5% CO₂ at 37°C. All experiments were carried out in triplicate.

Statistical analyses were conducted using SPSS version 17.0 (SPSS, Chicago, IL, USA). The paired-samples t-test was used to investigate the differences in expression of mRNA and protein in HCC tumors and non-tumor tissue samples. The correlation between galectin-3 expression and clinicopathological features was evaluated by the ?² test. Overall survival was calculated using the Kaplan–Meier method and the log-rank test was used for comparison. The Cox proportional hazards regression model was used for univariate and multivariate analyses. The results were expressed as mean × SD and analyzed using the Students’ t-test. Differences were considered significant at p?<?0.05.

Firstly, HCC cell lines and primary HCC tissue samples were used to examine galectin-3 expression by quantitative real-time PCR and Western blot analyses. Western blot analyses confirmed that the protein transcript levels of galectin-3 in all four HCC cell lines were higher than the normal liver cell line LO2 (Figure 1A). Additionally, in 44 paired primary HCC tissue samples, an increased mRNA level of galectin-3 was observed in most tumor tissues (28/44) compared with the corresponding adjacent non-tumor tissues, as evidenced by quantitative real-time PCR analyses (P?=?0.039; Figure 1B). This tendency was also verified at the protein level with western blot analyses (P?=?0.002; Figure 1C).

To confirm the prognostic significance of galectin-3 for the early diagnosis and prediction of outcome in HCC, we analyzed the correlation between expression of galectin-3 and clinicopathological and survival parameters. An immunohistochemistry analysis revealed that galectin-3 was abundantly accumulated in the cytoplasm of Hepatocellular cancer cells. We found 135 samples with a high expression of galectin-3 (immunostaining levels of “++” and “+++”) (Figure 2A). The relationship between the clinicopathological features of HCC and galectin-3 expression is summarized in Table 1. We observed that galectin-3 expression was positively correlated with serum levels of AFP (P?<?0.01). However, there was no apparent relationship between galectin-3 expression and other clinicopathological parameters, including differentiation status (both P?>?0.05).

However, high galectin-3 expression was significantly associated with a poor prognosis (Figure 2B). Overall survival was significantly lower in the group with high galectin-3 expression than the group with low galectin-3 expression (135 vs. 30 P?<?0.01). Further univariate and multivariate analyses were employed to compare the associations of galectin-3 expression with other clinicopathological parameters. Overall survival was significantly correlated with galectin-3 expression (P?<?0.01), CD34 (P?=?0.049), tumor size (P?=?0.005), and histological differentiation (P?=?0.032), but not with sex, age, cirrhosis of the liver, serum level of alpha fetoprotein (AFP), serum level of the surface antigen of the hepatitis B virus (HBSAg), metastasis, or recurrence (Table 2). Thus, galectin-3 may be a useful maker for predicting the overall survival of HCC patients.

Intratumoral MVD was quantified by counting CD34-positive endothelial cells in the same series of HCC tissues shown in Figure 2A and the staining intensity of MVD ranged broadly from 0 to 77 micro-vessels/200? μmagnification fields. Although a statistically significant difference was not detected, high MVD tended to be associated with histological differentiation (P?=?0.217) and tumor size (P?=?0.103), and there was no statistically significant correlation between MVD and any other clinicopathologic factors (Table 1). However, MVD showed a significant difference in the low and high galectin-3 group (24.9 × 18.1 vs. 38.2 × 20.76, P =0.043). There was a positive correlation between galectin-3 protein and MVD (P?=?0.001, R?=?0.265).

To explore the function of galectin-3 in HCC progression, we transfected gal-siRNA and negative control siRNA into four HCC cell lines (HepG2, Bel-7402, Hep3B, Huh7). Silencing efficiency in galectin-3-siRNA transfected cells were verified by Western blotting (Figure 3A). Meanwhile, the Hep3B cells were infected with galectin-3 -vector and control-vector. Galectin-3 expression in the infected cells was confirmed by western blot (Figure 3A).

To investigate the effect of galectin-3 expression on cell proliferation, we assessed the proliferation rates of control- and gal-siRNA-HepG2, Bel-7402, Huh7 as well as Hep3B cells with a MTS assay. We detected a significant decrease in the proliferation rate of cells transfected with gal-siRNA compared with the rate of the control cells at days 3–7 (P?<?0.05) (Figure 3B). In contrast, Hep3B cells transfected with overexpression vector significantly increased cell proliferation compared to the control vector (Figure 3C). Furthermore, to ascertain the reason for the restraint in cell growth, the cell-cycle distribution was determined by flow cytometry. The cells were collected for cell cycle distribution analysis after 36 hours, DNA content of gal-siRNA or negative control- transfected cells were detected by flow cytometry. As shown in Figure 3D, silenced galectin-3 expression did not significantly change the G1, S, or G2 levels in HCC cells, including in HepG2, Bel7402, Hep3B and Huh7 cells. This tendency was also found in the Hep3B cells with galectin-3 overexpression vector (Figure 3E).

The next question was whether the effect of galectin-3 knockdown on the inhibition of cell growth was associated with an induction of apoptosis. We then used the Annexin V-FITC binding assay to explore the effects of galectin-3 on apoptosis in HCC cells. After 72 hours, the cells were collected for apoptosis analysis. The percentages of Annexin-V or propidium iodide-positive cells were detected by flow cytometry. Analysis of the proportion of apoptotic cells revealed that a statistical difference between gal-siRNA cells and negative control cells were observed (Figure 4A), Silence of galectin-3 caused an increase in the percentage of apoptotic HCC cells. Furthermore, galectin-3 overexpression resulted in a decrease of apoptosis (Figure 4B). These results suggest that galectin-3 may play an important anti-apoptotic role in HCC development. Caspase activation is an important event in the apoptosis signaling pathway. Thus, to confirm the mechanism of galectin-3 knockdown on apoptosis, we detected the mRNA expression of certain pro-apoptotic proteins and anti-apoptotic proteins, PARP, caspase-3/9, BAX, and Bcl-2 by Real-time PCR. As shown in Figure 5C, galectin-3 knockdown markedly induced the activation of PARP and caspase-3/9, resulting in an increase of the mRNA levels in PARP and caspase-3/9. Galectin-3 knockdown also increased the expression of the pro-apoptotic protein BAX and suppressed the expression of the anti-apoptotic protein Bcl-2. To verify the results observed in real-time PCR, we further examined the protein expression levels by western blotting. In accordance with previous findings, knockdown of galectin-3 markedly induced the activation of caspase-3/9, resulting in an increase in the levels of PARP, caspase-3/9, BAX proteins (Figure 4C).

Previous studies have shown that galectin-3 expression levels are correlated with tumor proliferation in cancer. Hence, we examined the impact of galectin-3 knockdown on the migration and invasion of HepG2, Bel-7402, Hep3B, Huh7 cells. We employed a transwell assay to evaluate the effects of galectin-3 expression on cell migration. HepG2, Bel7402, Hep3B and Huh7 cells infected with gal-siRNA migrated into the lower compartment of the migration chamber significantly less frequently than did cells infected with the negative control (Figure 5A). Wound-healing assays confirmed the inhibitory effect of galectin-3 silence on cell migration. They revealed that the time required for wound closure of galectin-3-silenced HCC cells was significantly longer than that required for the corresponding control cells (Figure 5C).Consistent with the results of the migration assay, galectin-3-siRNA also significantly inhibited cell invasion through a Matrigel-coated membrane (Figure 6A). In addition, we investigated the overexpression of galectin-3 on the migration and invasion of Hep3B. The wound-healing assay and cell migration assay revealed that the ability of cell migration was increased when galectin-3 was overexpressed in Hep3B cells (Figure 5B and D). The invasion results were consistent with the results of the migration assay (Figure 6B).