Thyroid cancer tissues, confirmed by pathological diagnosis, were obtained from 196 patients who underwent thyroidectomy between 2003 and 2012 at the Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center. The corresponding non-tumor thyroid tissues were obtained at least 1 cm away from the tumor. All tissue samples were formalin-fixed and paraffin-embedded. TNM staging was classified based on the criteria of the American Joint Committee on Cancer (AJCC, 7th edition) for thyroid cancer. The study was approved by the Human Ethics Committee/Institutional Review Board of Fudan University Shanghai Cancer Center. Written informed consent was obtained from all 196 patients. IHC staining was performed by using a highly sensitive streptavidin-biotin-peroxidase detection system with thyroid cancer tissue microarrays. Rabbit polyclonal anti-Slit-2 (working dilution 1:50) was purchased from Proteintech Group Inc. (Chicago, IL, USA) and rabbit anti-HIF-1-α (working dilution 1:50) was purchased from Abcam (Cambridge, MA, USA). Immunolabeling was conducted using Dako EnVision + Rabbit Polymer (cat. no. K4003) from Dako (Carpinteria, CA, USA). The slides were counterstained with hematoxylin and coverslipped.

The immunohistochemically stained tissue sections were scored separately by two pathologists blinded to the clinical parameters. The staining intensity was scored as 0 (negative), 1 (weak), 2 (medium) or 3 (strong). Extent of staining was scored as (0, <5%; 1, 5–25%; 2, 26–50%; 3, 51–75%; and 4, >75%) according to the percentages of the positive staining areas in relation to the whole carcinoma area. Scores for staining intensity and percentage positivity of cells were then multiplied to generate the immunoreactivity score (IS) for each case. Tissues having a final staining score of <4, 4, 6 and ≥8 were considered to be −, +, ++ and +++, respectively.

Thyroid cancer tissues, confirmed by pathological diagnosis, were obtained from 130 patients who underwent thyroidectomy between 2012 and 2015 at the Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center. Total RNA was extracted from tissues using TRIzol reagent (Invitrogen) according to the manufacturer's instructions. A total of 1 μg RNA was reverse-transcribed using a PrimeScript RT reagent kit (Takara Bio, Dalian, China). For quantitative real-time PCR (qPCR), cDNA was amplified using SYBR-Green Premix Ex Taq (Takara Bio) following the manufacturer's instructions. Slit-2 expression was normalized against β-actin mRNA expression in three independent experiments. The primers used for amplifying Slit2 were 5′-AACTGCCTTCGGGTAGATGC-3′ (forward) and 5′-GAATGGCCCGAAGAGGTGAA-3′ (reverse). The primers used for amplifying β-actin were 5′-CTACGTCGCCCTGGACTTCGAGC-3′ (forward) and 5′-GATGGAGCCGCCGATCCACACGG-3′ (reverse). The results were analyzed and calculated relative to cycle threshold values and then converted into fold changes.

PTC cell line K1, was purchased from the Institute of Biochemistry and Cell Biology at the Chinese Academy of Sciences (Shanghai, China). The cells were cultured in RPMI-1640 supplemented with 10% fetal bovine serum, 2% streptomycin/penicillin and 1% amphotericin B. K1 cells were cultured as monolayer in 5% CO₂ at 37°C.

pLKO.1 TRC cloning vector (Addgene plasmid 10878) was used to generate shRNA-expression constructs. Targets (21 bp) against Slit2 were CCTCACCTTAATTCTTAGTTA and CCTGGAGCTTTCTCACCATAT, respectively. Scramble RNA (Addgene plasmid 1864) was used as control vector. HRE-luciferase construct (Addgene plasmid 26731), containing three hypoxia response elements from Pgk-1 gene, was used to assess HIF1α transcriptional activity.

HIF1α transcriptional activity was evaluated by using by using the Promega Dual-Luciferase^® reporter (DLR™) assay system. pRL-TK plasmid was used as control vector for expression of Renilla luciferase.

Cell proliferation was examined by using Cell Counting kit-8 (CCK-8; Dojindo Molecular Technologies). In brief, cells were seeded in 96-well plates (3×10³ cells/well), 10 μl CCK-8 solution were added to each well at 0, 24, 48 and 72 h, and the plates were incubated at 37°C in 5% CO₂ for 1 h. The absorbance of each sample was measured at a wavelength of 450 nm using a microplate reader.

K1 cells were digested and 200 cells were seeded and incubated in a fresh 6-well plate for 14 days to allow colonies to form. Colonies were fixed in methanol, stained with 0.1% crystal violet solution and counted.

Glucose uptake colorimetric assay kit (BioVision Inc., Milpitas, CA, USA) and Lactate colorimetric assay kit (BioVision) were purchased to examine the glycolysis process in K1 cells according to the manufacturer's protocol. To test expression of glycolytic enzymes and HIF1α transcriptional activity, real-time PCR was performed. In brief, total RNA was prepared using TRIzol reagent (Invitrogen, Carlsbad, CA, USA), followed by reverse transcription using Takara PrimeScript RT reagent to obtain cDNA. The expression status was assessed by quantitative real-time PCR (qRT-PCR) using ABI 7900HT Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). All reactions were run in triplicate. Primer sets Glut1 F, 5′-CAGTTTGGCTACAACACTGGAG-3′ and R, 5′-GCCCCCAACAGAAAAGATGG-3′; HK2: F, 5′-CAAAGTGACAGTGGGTGTGG-3′ and R, 5′-GCCAGGTCCTTCACTGTCTC-3′; LDHA F, 5′-CCCAGTTTCCACCATGATTAAGG-3′ and R, 5′-TTCTGTCCCAAAATGCAAGGAA-3′; PKM2 F, 5′-CAAAGGACCTCAGCAGCCATGTC-3′ and R, 5′-GGGAAGCTGGGCCAATGGTACAGA-3′; β-actin F, 5′-AGAGCTACGAGCTGCCTGAC-3′ and R, 5′-AGCACTGTGTTGGCGTACAG-3′.

Protein extracts were prepared and resolved on 10% SDS-PAGE gels, transferred to nitrocellulose membranes (0.45 mm) and immunoblotted with primary antibodies. Slit2, c-myc and HIF1α antibodies were purchased from Abcam. β-actin antibody was used as loading control antibody. Images were developed with ECL (GE Healthcare, Pittsburgh, PA, USA).

All statistical analyses were performed using SPSS 16.0 software. Results were expressed as means ± standard deviation. Two-tailed unpaired Student's t-tests and one-way analysis of variance were used to evaluate the data. P-values <0.05 were considered as statistically significant.

One hundred pairs of specimens were investigated by qPCR to investigate the expression of Slit2. The average mRNA expression levels of Slit2 were significantly lower in PTC tissues compared with adjacent non-tumor thyroid tissues (Fig. 1A). Next, we examined Slit2 mRNA expression in PTC samples with lymph node metastasis (LNM). The results demonstrated that the lower Slit2 mRNA levels were associated with lymph node metastasis at diagnosis (Fig. 1B).

The protein levels of Slit2 in PTC samples were analyzed by IHC staining. Slit2 level was lower in PTC samples than that in non-tumorous samples (Fig. 2). Furthermore, to test the role of Slit2 in LNM of PTC, we examined the level of Slit2 in PTC tissue micro-array containing 196 patients samples. The clinicopathological features of the patients are listed (Table I). In addition, further analysis demonstrated that Slit2 is a negative indicator for LNM at diagnosis (Table II).

In order to examine the role of Slit2 in thyroid cancer proliferation in vitro, we used shRNA mediated silencing of Slit2 in K1 cells. Two shRNAs against Slit2 effectively decreased Slit2 expression (Fig. 3A). Then we examined the effect of silencing Slit2 on cell viability by CCK-8 assay. A significant increase in cell viability was observed upon Slit2 silencing (Fig. 3B). Moreover, clone formation assay also suggested that silencing Slit2 increased cloning capacity of K1 cells, indicating that Slit2 is a negative regulator of cell proliferation (Fig. 3C and D).

It is well accepted that many cancer cells exhibit elevated glucose uptake and lactate production, regardless of oxygen availability, known as aerobic glycolysis or Warburg effect. Enhanced glycolysis facilitates uncontrolled proliferation of cancer cells by providing building blocks for macromolecule synthesis and energy source. We next asked whether the effect of Slit2 on cell proliferation was associated with glycolysis. In K1 cells, decreased Slit2 expression enhanced glucose uptake, which is the first step for glucose metabolism (Fig. 4A). Lactate, the key product of the Warburg effect, was not only used as a source for metabolism, but also created an acidic environment that leads to destabilization of extracellular matrix that facilitates metastasis of cancer cells. Our results also demonstrated that decreased Slit2 expression enhanced lactate production of K1 cells (Fig. 4B).

HIF1α is a transcription factor that mediated the primary transcriptional response to hypoxic stress of transformed cancer cells. In conjugation with c-myc, they are considered to be key regulators of the Warburg effect. We speculated that Slit2 might regulate glycolysis in part through HIF1α and c-myc. Consistent with this assumption, we observed an increase in HIF1α and c-myc protein levels in Slit2 knock-down cells (Fig. 5A). In order to assess the role of Slit2 on HIF1α transcriptional activity, we performed HRE luciferase reporter assay. Silencing Slit2 increased HRE-luciferase activity, supporting the result that Slit2 regulated HIF1α protein level (Fig. 5B). As a transcription factor, HIF1α regulates series of genes in glycolysis process. GLUT1 (glucose transporter 1) is a membrane protein that facilitates glucose transport across the cell membrane, which is subsequently catalyzed to glucose 6-phosphate by HK2 (hexokinase 2). In the last step of glycolysis, L-lactate and NAD are converted to pyruvate and NAHD by LDHA (lactate dehydrogenase A). PKM2 (pyruvate kinase isozyme M2) catalyzes the dephosphorylation of phosphoenolpyruvate to pyruvate, and is responsible for net ATP production. PKM2 is also a HIF1α target gene. To further confirm the function of Slit2 on HIF1α transcriptional activity control, we examined the expression of HIF1α targeted glycolysis genes upon Slit2 downregulation, and observed that GLUT1, HK2 and LDHA were significantly upregulated. However, the expression of PKM2 was changed slightly, indicating that there may be other mechanisms for PKM2 regulation in thyroid cancer (Fig. 5C).

Correlation between Slit2 and HIF1α was analyzed by IHC staining. HIF1α level was low in Slit2 positive PTC samples (Fig. 6). Statistical analysis demonstrated that there is a negative association between Slit2 and HIF1α in PTC tissue microarray (Table III).

In conclusion, we found that decreased Slit2 expression in PTC sample renders a proliferation advantage to PTC cells, the potential mechanism may involve the participation of Slit2 in HIFα control (Fig. 7).