3.1 | High expression of SGOL2 in HCC
We identified that SGOL2 mRNA was upregulated in various types of cancers in the Oncomine database, including hepatocellular carcinoma, colorectal cancer, lung cancer, and breast cancer (Fig. 1A). In addition, we further searched the GEPIA database to systematically assess the expression profile of SGOL2 in a variety of carcinomas (Fig. 1B). To better understand its expression levels in different diseases, including HCC, cirrhosis and dysplasia, data from the Wurmbach liver dataset were selected and further analyzed (Fig. 1C). The results showed that SGOL2 was significantly upregulated in HCC tissues compared with normal tissues (P < 0.05), whereas there was no significant difference between the cirrhosis, dysplasia and normal liver tissue groups. Moreover, SGOL2 protein expression was analyzed using the HPA database. As shown in Fig. 1D, SGOL2 was not detected in the normal liver (Patient ID: 3402) and showed weak staining in the HCC liver (Patient IDs: 2429/2279). Furthermore, we analyzed the protein levels of SGOL2 in HCC and matched adjacent normal tissues of patients in cohort 2 by Western blot and found that SGOL2 was significantly upregulated in HCC (Fig. 1E), and immunohistochemistry (IHC) staining from the two independent HCC cohorts confirmed the results (Fig. 1F-H). In addition, we divided the patients in cohort 2 into 2 groups according to differentiation levels: a well-differentiated group and a poorly differentiated group. Interestingly, the expression levels of SGOL2 in the poorly differentiated group were significantly higher than those in the well-differentiated group, indicating that the expression level of SGOL2 is directly proportional to tumor progression (Fig. 1H). Similarly, we found the same result: the expression level of SGOL2 in the Grade 3 group was significantly higher than that in the Grade 1/2 group in cohort 1 (supplement Table 1, p = 0.001).
To expand the number of patients included in the analysis, we further confirmed the overexpression of SGOL2 in HCC data from TCGA. We found that the expression level of SGOL2 showed a positive association with grade levels (Fig. 2G), consistent with our previous results in cohorts 1 and 2. Moreover, SGOL2 was found to be roughly proportional to stage levels (Fig. 2B). As shown in Fig. 2A, SGOL2 mRNA levels were higher in HCC samples (n = 371) than in normal samples (n = 50). We also conducted subgroup analysis in various subgroups (race, sex, age, weight), which showed significantly elevated SGOL2 expression levels (Fig. 2C-F). In addition, SGOL2 was significantly elevated in TP53-mutant patients (Fig. 2I). Interestingly, there was no significant difference between HCC patients with and without lymph node metastasis (Fig. 2H). Thus, these results indicated that the SGOL2 overexpression was related the development of HCC.
3.2 | Upregulation of SGOL2 indicated poor prognosis in HCC patients
To identify whether SGOL2 could be a novel prognostic marker in HCC, we used the Kaplan-Meier Plotter database to analyze its prognostic significance in HCC patients. SGOL2 overexpression was closely related to poor overall survival (OS, HR = 2.29 (1.6–3.28), P = 3.5e-06), relapse-free survival (RFS, HR = 1.96 (1.38–2.78), P = 0.00013), progression-free survival (PFS, HR = 2.1 (1.55–2.84), P = 9.2e-07) and disease-specific survival (DSS, HR = 2.84 (1.81–4.47), P = 2.3e-06) in HCC patients (Fig. 3A-D). To better identify the negative correlation between the expression level of SGOL2 and the prognosis of liver cancer, we performed survival analysis in verification cohort 1. According to the immunohistochemical score, we divided the patients (n = 97) in cohort 1 into a low expression group (n = 44) and a high expression group (n = 53) and then performed survival analysis. As shown in Fig. 3E, we found that the overall survival (OS) of the high expression group was significantly lower than that of the low expression group (p = 0.001). Subsequently, we conducted a SGOL2-based prognostic model. All HCC patients in cohort 1 were randomly divided into two groups: training group (n = 67), validation group (n = 30). Next, we identified four variables which were closely related to survival: expression, grade, AJCC TNM and AFP based on lasso regression model (Fig. 3F, G). We also established a nomogram to predict the 3-year survival of HCC patients based on the multivariable Cox proportional hazards model (Fig. 3H), whose discrimination power was evaluated by receiver operating characteristic (ROC) curves. The areas under the ROC curve (AUCs) of the 3-year survival probability in the training and validation groups were 0.898 and 0.687, respectively (Fig. 3I-K). The calibration curves of the nomogram showed good probability consistencies between the prediction and observation (Fig. 3L,M). In conclusion, high SGOL2 expression was associated with poor prognosis in HCC patients.
3.3 | The association between SGOL2 expression and immune infiltration, mutations in HCC
Next, we assessed the mutation frequency of SGOL2 in HCC. The somatic mutation frequency of SGOL2 in HCC was 0.7% (Fig. 4A). Thus, we further evaluated the mutation types of SGOL2 in COSMIC. As shown in Fig. 4B, 45.80% of the samples showed missense substitutions, and 13.19% of the samples showed synonymous substitutions. As shown in Fig. 4C, the substitution mutations mainly occurred at C > T (74 samples, 17.96%). Then, we used the TIMER database to identify the association between SGOL2 expression and immune infiltration. We evaluated the relationship between SGOL2 expression and immune cells abundances. As shown in Fig. 4D, SGOL2 expression had a slightly positive correlation with tumor purity (Cor = 0.148, P = 5.68E-03). Moreover, SGOL2 expression had dramatically positive correlations with all immune infiltrates, especially macrophages (Cor = 0.483, P = 2.69E-21) and dendritic cells (Cor = 0.47, P = 3.99E-20).
3.4 | SGOL2 promoted malignancy of HCC cells in vitro and in vivo
SK-HEP-1 and HEP3B cells were chosen to evaluate the function of SGOL2 according to the mRNA levels of SGOL2 in different HCC cell lines based on the data from CCLE (Fig. 5A). After transfection with lentivirus, we confirmed that SGOL2 was significantly decreased at both the mRNA and protein levels by RT-PCR and Western blot, respectively (Fig. 5B, C). Transwell assays indicated that low SGOL2 expression suppressed the migration and invasion of HCC cells (Fig. 5D, E). Moreover, we also tested the expression alterations of key EMT-related proteins responding to the downregulation of SGOL2 (Fig. 5K). Interestingly, the results showed that shSGOL2 resulted in increased expression of E-cadherin and reduced expression of N-cadherin, fibronectin, vimentin, β-catenin, and MMP9. Thus, downregulation of SGOL2 could inhibit cell metastasis by repressing migration, invasion and EMT in HCC. Next, a sphere formation assay was performed to determine whether stemness could be influenced by downregulating SGOL2. Consistently, the spheres in the shSGOL2 group were dramatically fewer and smaller than those in the shNC group (Fig. 5F). Furthermore, the shSGOL2 group developed fewer cell colonies than the NC group (Fig. 5G), and we also observed that low SGOL2 expression suppressed the proliferation of HCC cells by CCK-8 assay (Fig. 5J). In addition, flow cytometry-based assays demonstrated that apoptotic indices in the shSGOL2 group were dramatically higher than those in the NC group (Fig. 5H), and the cell cycle was strongly influenced by the downregulation of SGOL2 (Fig. 5I). To further verify the role of SGOL2 in HCC in vivo, xenograft tumor models were constructed by SK-HEP-1 shNC and SK-HEP-1 shSGOL2. The mice were sacrificed on day 21 after inoculation, and the formed tumors, lung and liver were statistically analyzed (Fig. 6A, B). Both the volumes and weights of the formed tumors were dramatically decreased in the shSGOL2 group compared with the shNC group (Fig. 6B). We further analyzed angiogenesis markers (CD34), proliferation markers (Ki-67 and proliferating cell nuclear antigen [PCNA]), and EMT-related markers (E-cadherin, N-cadherin, vimentin, Snail, and Slug) in xenograft specimens by IHC. Downregulation of SGOL2 led to a reduction in proliferation and metastasis (Fig. 6C, D), which was consistent with the above in vitro results. We also found that the apoptotic area in the shSGOL2 group was much larger than that in the shNC group (Fig. 6E). Thus, these data indicated that SGOL2 promoted tumor growth and metastasis in vitro and in vivo.
3.5 | MAD2 overexpression was closely related to SGOL2 and indicated poor prognosis in HCC patients
Next, we tried to clarify the signal transduction pathway of SGOL2 in HCC cells. SGOL2 and MAD2 were reported to be involved in the separation of eukaryotic sister chromatids during the cell cycle. Thus, we hypothesized that SGOL2 promotes tumors by influencing the expression of MAD2. To explore the role of MAD2 in liver cancer, a factor closely related to SGOL2, we evaluated its expression and prognostic significance by the UALCAN database. As shown in Fig. 7A, MAD2 was also markedly upregulated in HCC. Moreover, we found that the expression of MAD2 in HCC was positively correlated with SGOL2 in the TCGA database (R = 0.78, P = 0) (Fig. 7B). Interestingly, we found that high MAD2 expression was also related to poor OS, RFS and PFS in HCC patients (Fig. 7C-E).
3.6 | Differentially expressed genes associated with SGOL2 and MAD2 in HCC
We used the data from the LinkedOmics database for analysis to identify differential genes related to both SGOL2 and MAD2 in HCC by Spearman’s test (Fig. 8A, D). The top 50 positively and top 50 negatively correlated markers were represented in Fig. 8B-F. Then, the positively correlated genes with coefficients > 0.8 were selected for further analysis. In total, we identified 85 genes positively associated with SGOL2 and 51 genes positively related to MAD2. Among these, we identified 47 genes positively related to both SGOL2 and MAD2 (Fig. 9A). Then, we constructed a PPI network based on the 47 differentially expressed genes using STRING and Cytoscape (Fig. 9B) and used it for GO and KEGG enrichment analysis (Fig. 9C-F).
3.7 | Identify hub genes and their prognostic significance in HCC
The top 15 hub genes of the network were chosen for further analysis using cytoHubba based on the clusters identified in the PPI network using MCODE (Fig. 10A). Biological processes, such as cell division, cell proliferation and apoptotic process, were significantly affected and enriched based on GO analysis results (Fig. 10B). The coexpressed genes were mainly involved in the cell cycle, progesterone-mediated oocyte maturation, oocyte meiosis and p53 signaling pathway based on KEGG results (Fig. 10C). Then, we tried to assess whether these identified hub genes were related to prognosis or not. All 15 genes were significantly related to poor OS (BUB1B, NUSAP1, TTK, CCNB2, TOP2A, KIF2C, CCNB1, KIF23, TPX2, KIF11, KIF4A, CDK1, BUB1, CENPE, CDCA8) (Fig. 10D).
3.8 | SGOL2 dysregulated the cell cycle process by activating the MAD2 protein
To validate these data through the bioinformatics analysis above, we further demonstrated the role of SGOL2 in HCC cells, especially in cell cycle process based on above results. First, we found that the protein level of MAD2 was extremely decreased in HCC cells through downregulating SGOL2 (Fig. 11A), while overexpression of SGOL2 increased the expression of MAD2 (Fig. 11B). After knockdown of SGOL2, the protein levels of PCNA, cyclin D1 and cyclin E1 were significantly decreased (Fig. 11A), whereas upregulation of SGOL2 extremely increased the expression of PCNA, cyclin D1 and cyclin E1 (Fig. 11B). Furthermore, when MAD2 was blocked by its specific inhibitor M2I-1, highly aggressive malignant behaviors of HCC cells caused by overexpression of SGOL2 were significantly reversed (Fig. 11C, D). Altogether, these data indicated that SGOL2 dysregulated the cell cycle and promoted the development of HCC by activating the MAD2 protein.
3.9 | SGOL2 exerted its effect by directly binding with MAD2
Next, we were interested in defining whether SGOL2 could directly interact with MAD2. Immunofluorescence (IF) staining showed that SGOL2 colocalized with MAD2 in both SK-HEP-1 and HEP3B cell lines (Fig. 12A), and the coimmunoprecipitation (Co-IP) assay further verified that SGOL2 could bind with MAD2 (Fig. 12B). Altogether, these data collectively verified that SGOL2, binding with MAD2 and forming a SGOL2-MAD2 complex, activated MAD2 and then fueled tumor cell growth by dysregulating the cell cycle process, which finally promoted the malignant behaviors of HCC cells, including proliferation, migration, invasion, stemness and EMT (Fig. 12C).