MAD2L1 Functions As a Novel Diagnostic and Predictive Biomarker in Cholangiocarcinoma

Abstract

Background:

Most cholangiocarcinoma (CCA) patients are diagnosed at an advanced stage of disease, and the postoperational recurrence rates are high in those undergoing resection. The lack of satisfying biomarkers for early diagnoses and effective targeting of driver pathways is the leading reason for therapeutic failures. The goal of this study was to find a biomarker for making improved diagnoses with enhanced prognostic capabilities for CCA.

Materials and Methods:

Our study used bioinformatic analyses of microarray data from the Gene Expression Omnibus (GEO) database and investigated mitotic arrest deficient 2-like protein 1 (MAD2L1) expression in tumor and adjacent non-neoplastic biliary ducts through immunocytochemistry in 42 surgically removed primary CCAs from a single institute. In vitro and in vivo models were used to explore the function of MAD2L1.

Results:

In total, 297 high probability differentially expressed genes (DEGs) were obtained from overlapping the DEGs from the three individual data sets. Through enrichment assays and protein-protein interaction networks analyses, seven hub genes were identified. MAD2L1 was picked up as a novel biomarker based on hierarchical cluster analyses and Kaplan-Meier survival analyses. MAD2L1 was expressed in cancer tissues but not in the surrounding normal tissue, with 31 (73.81%) of 42 CCAs MAD2L1 positive by immunohistochemistry (IHC). MAD2L1 expression levels were significantly correlated with tumor size, pathological grade, and clinical stage. A Kaplan-Meier survival analysis demonstrated an inverse correlation with MAD2L1 expression. Real-time polymerase chain reaction and immunoblotting results further confirmed the results of IHC and bioinformatic analyses. In vitro and in vivo models demonstrated decreasing MAD2L1 could significantly suppress tumor growth, whereas increasing MAD2L1 could promote tumor growth.

Conclusion:

MAD2LI could be used as a predictive biomarker and potentially serve as a therapeutic target in CCA.

Clinical Trial Registration Number: [2020]KY157-01

Introduction

Cholangiocarcinoma (CCA) is the second most prevalent type of liver cancer and accounts for ∼15% of all liver cancer cases in adults (Khan et al., 2019). Depending on the anatomical location of tumor initiation, CCAs are classically subtyped into three groups: intrahepatic CCA, perihilar CCA, and distal CCA (Khan et al., 2005). All the subtypes originate from malignant bile duct epithelial cells (Parkin et al., 1993). Owing to improvements in the diagnosis of CCA, the incidence and mortality rates are both increasing worldwide, particularly in east Asia (Cardinale et al., 2010; Albert et al., 2015; Mukkamalla et al., 2018; Bertuccio et al., 2019).

However, to the best of our knowledge, a successful therapeutic strategy other than surgical resection and liver transplantation has yet to be identified (Mukkamalla et al., 2018). Data from the past 30 years suggest that the 5-year overall survival rate has shown no improvement (Bergquist and von Seth, 2015). This is primarily due to the fact that ∼65% of patients are diagnosed with unresectable cancer, and >90% of patients experience recurrence soon after receiving surgical treatment (Rizvi and Gores, 2013; Tariq et al., 2019). It is, therefore, essential to identify novel biomarkers to improve early diagnosis, monitor recurrence, and investigate the mechanisms underlying CCA progression.

Over the past two decades, microarray technology and bioinformatics have been widely applied to analyze gene expression in CCA (Qian et al., 2018; Cheng et al., 2019). An increasing amount of microarray data have been uploaded to the Gene Expression Omnibus (GEO) database for sharing. Screening genetic alterations from GEO and identifying the differentially expressed genes (DEGs) and pathways involved in carcinogenesis is an emerging strategy for cancer research (Liang et al., 2016; Yang et al., 2018).

In this study, the GEO database was searched, and three mRNA microarray data sets were obtained. After standardization, the data were analyzed to obtain DEGs within carcinoma tissues and noncarcinoma tissues. Thereafter, online bioinformatics analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein interaction (PPI) network analyses, were performed to investigate the molecular mechanisms underlying tumorigenesis and progression.

Mitotic arrest deficient 2-like protein 1 (MAD2L1), one of the DEGs identified, was confirmed to be highly expressed in tumors, and was also demonstrated to be associated with poor survival in CCA from The Cancer Genome Atlas (TCGA) database. Next, this study investigated the expression levels of MAD2L1 in tumor tissues and adjacent non-neoplastic biliary duct from patients with CCA through immunohistochemistry (IHC) in 42 surgically resected primary CCAs from a single institute.

This study also investigated the correlation between MAD2L1 expression and histological tumor grade, progression, metastasis, clinical stage, and postoperative survival rate. In the functional investigation, this study altered MAD2L1 in CCA cell lines and assessed cell proliferation, and the results revealed that MAD2L1 could promote cell proliferation. The in vivo study results were also confirmed in the in vitro study.

In conclusion, the results of this study demonstrated that MAD2L1 could be a novel biomarker in CCA and targeting MAD2L1 may be a potential therapeutic strategy for CCA.

Materials and Methods

Microarray data

A total of three gene expression data sets (GSE26566, GSE32225, and GSE77984) were downloaded from the GEO. The GSE26566 data set contained 104 human CCA tissues and 6 noncancerous samples (Andersen et al., 2012). The GSE32225 data set contained 150 tumor samples and 5 normal human cholangiocytes (Sia et al., 2013).

The GSE77984 data set contained 10 CCA cells and 12 normal human cholangiocytes (Merino-Azpitarte et al., 2017). GEO2R (www.ncbi.nlm.nih.gov/geo/geo2r) was performed to analyze and obtain the DEGs between CCA and noncancerous samples from the three data sets. A total of 297 DEGs were extracted that overlapped between the three data sets. The adjusted p value <0.01 and logFC (fold change) >1 were considered to indicate a statistically significant difference.

Bioinformatics analysis

The database for annotation, visualization, and integrated Discovery (DAVID; http://david.ncifcrf.gov; version 6.7) was used to perform the bioinformatics analyses (Huang et al., 2007). p < 0.05 was considered to indicate a statistically significant difference. The search tool for the retrieval of interacting genes (STRING; http://string-db.org; version 11.0) was performed to predict the functional interactions between proteins, and a combined score >0.4 was considered statistically significant (Franceschini et al., 2013).

The plug-in molecular complex detection (MCODE; version 1.4.2) of Cytoscape was used to identify densely connected regions. The following criteria were used for the selection process: MCODE scores >5, degree cutoff = 2, node score cutoff = 0.2, max depth = 100, and k-score = 2, and the hub genes were selected with degrees ≥15 (Kutmon et al., 2018). Hierarchical clustering and the overall survival analyses of hub genes were constructed using the UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) (Kent et al., 2002).

Patient selection

A total of 42 surgically resected primary CCAs were collected between January 2004 and January 2014 at No. 2 People's Hospital of Changzhou, Nanjing Medical University, Changzhou, China. This study was approved by the ethics committee of No. 2 People's Hospital of Changzhou and performed following the statements of the Declaration of Helsinki. All patients provided written informed consent for the use of their CCA samples and clinical information. This study was also performed according to the ethical guidelines of the Declaration of Helsinki 2008. The specimens were anonymized and analyzed in a blinded manner. Follow-up time for surviving patients ranged from 1 to 36 months (median, 15.3 months). All 42 patients were followed up for at least 1 year after surgery.

Histology and IHC

Formalin-fixed and paraffin-embedded 5-μm tissue sections were deparaffinized and rehydrated. Antigen retrieval was performed by incubating the tissue slides in 0.01 M citric acid buffer at 100°C for 10 min. After blocking with 3% H₂O₂ and 5% fetal bovine serum (Thermo Fisher Scientific, Inc.), the slides were incubated with a monoclonal antibody against MAD2L1 (1:100, cat. no. ab10691; Abcam) at 4°C overnight, then rinsed with phosphate buffered saline (PBS), and incubated with secondary antibody.

The slides were then incubated with polymer-horseradish peroxidase reagent. The peroxidase activity was visualized with diaminobenzidine tetrahydroxychloride solution. The sections were counterstained with hematoxylin. Dark brown cytoplasmic staining of at least 1% tumor cells was defined as positive, and no staining or <1% cells stained was defined as negative. As a negative control (NC), the primary antibody was replaced with 5% fetal bovine serum.

Cell culture

Of the CCA cell lines, QBC939 was kindly donated by Dr Li's laboratory (Wang et al., 2020), HuCCT1 cells were purchased from the Japanese Collection of Research Bioresources Cell Bank, and the 293-cell line was obtained from the American Type Culture Collection. Cells were maintained in Dulbecco's modified Eagle's medium (DMEM; Invitrogen, Thermo Fisher Scientific, Inc.) supplemented with 10% fetal bovine serum (Thermo Fisher Scientific, Inc.), 1% glutamine, and 1% penicillin/streptomycin (Invitrogen, Thermo Fisher Scientific, Inc.). Cells were humidified in a 5% (v/v) CO₂ environment at 37°C. Direct polymerase chain reaction (PCR) tests for mycoplasma contamination yielded negative results.

Cell count

A Trypan blue dye exclusion test was performed by staining cells with 0.2% Trypan blue solution (Sigma Aldrich; Merck KGaA) to reveal necrotic cells after each experimental condition, according to the manufacturer's instructions. Furthermore, unstained viable cells were manually counted using a hemocytometer. Counts were performed in triplicate, according to standard methodology. All measurements were performed in triplicate.

Colony formation assay

QBC939 cells were treated with shMAD2L1, and HuCCT1 cells were treated with oeMAD2L1, and were subsequently seeded for colony formation in six-well plates at a density of 500 cells per well. After 14 days, colonies were washed twice with PBS, fixed with 4% paraformaldehyde for 30 min at room temperature, and then stained with crystal violet for 30 min for visualization and counting. Each assay was performed in triplicate on two independent occasions.

Animal experiments

A total of eight 6-week-old nude mice, four male and four female with mean weight 20 ± 3.12 g, were purchased from Animal Core Facility of Nanjing Medical University (Nanjing, China) and divided into two groups for injection of QBC939 cells that had been transfected/engineered with (1) short hairpin (sh) NC; (2) shMAD2L1.

The prepared stable QBC939 cells (medium mixed with Matrigel, 1:1) were injected at 1 × 10⁶ into the right flank subcutaneous tissue of the mice. The mice were sacrificed after 8 weeks with anesthesia and euthanasia. The tumors were removed for study. Animal protocols were approved by the Animal Care and Use Committee of Nanjing Medical University (Nanjing, China). All animal experiments followed the guidelines for ethical review of laboratory animal welfare of Nanjing Medical University (http://iacuc.njmu.edu.cn).

Lentiviral expression plasmids and virus infection

The lentivirus system with the standard calcium chloride transfection method was applied to generate the virus. pWPI vectors overexpressing MAD2L1 and pLKO.1 vectors expressing shRNA were used to generate a lentivirus for transfection into CCA cells. The pWPI/pWPI-MAD2L1/pLKO.1/pLKO.1-shMAD2L1#1/pLKO.1-shMAD2L1#2 plasmids (EMD Millipore), pMD2G (cat. no. 12259; Addgene, Inc.) envelope plasmid, and psPAX2 (cat. no. 12260; Addgene, Inc.) packaging plasmid were cotransfected into the 293 cells.

After 8 h, the medium was changed with a warm fresh DMEM. The 293 cells were then placed in the virus room incubator for the generation of the virus. After 48 h, the supernatants, including the viruses, were harvested and used immediately or frozen at −80°C for later use. The sequences were as follows: shMAD2L1#1 targeting sequence, 5′-CCTATTGAATCAGTTTCCAAT-3′; and shMAD2L1#2 targeting sequence, 5′-CGAGTTCTTCTCATTCGGCAT-3′.

Western blotting

Freshly frozen tissues were isolated and lysed in RIPA buffer (Wuhan Boster Biological Technology, Ltd.,). The protein concentration was measured using a BCA protein assay kit (ab102536; Abcam) and ultraviolet spectrophotometry. Proteins (30 μg per lane) were separated through SDS/PAGE (6-10% gel) and then transferred onto PVDF membranes (EMD Millipore).

After blocking with 5% bovine serum albumin (Thermo Fisher Scientific, Inc.) at room temperature for 1.5 h, the PVDF membranes were incubated with primary antibodies at 4°C overnight, and subsequently with HRP-conjugated secondary antibodies (1:5000, Product # 31430, Invitrogen) at room temperature for 2 h. Subsequently, the ECL system (Thermo Fisher Scientific, Inc.) was used to visualize the protein bands. The primary antibodies used for the Western blotting were anti-MAD2L1 (1:1000; Abcam) and anti-GAPDH (1:5000; Santa Cruz Biotechnology, Inc.).

Reverse transcription PCR analysis

Twenty fresh samples, which contained 10 T₁ and 10 T_2-4 and were saved well in our tissue bank, from patients' tumor or paracarcinoma tissue were used to analyze the mRNA level of MAD2L1 using the ABI StepOnePlus PCR System (Applied Biosystems, Thermo Fisher Scientific, Inc.). Total RNA was extracted using TRIzol^® reagent (Invitrogen, Thermo Fisher Scientific, Inc.), and all primers were purchased from Invitrogen, Thermo Fisher Scientific, Inc., MAD2L1 mRNA was normalized to GAPDH, and the results are presented as the ratio of MAD2L1 to GAPDH. The primers used were as follows: MAD2L1: forward, 5′-ACGGTGACATTTCTGCCACT-3′, and reverse, 5′-TGGTCCCGACTCTTCCCATT-3′; GAPDH: forward, 5′-GGAGCGAGATCCCTCCAAAAT-3′, and reverse, 5′-GGCTGTTGTCATACTTCTCATGG-3′.

Statistical analysis

Statistical analysis was performed using SPSS software (version 23.0; IBM Corp.). Mann-Whitney U test was applied to test the two groups with continuous data. The association between MAD2L1 expression and clinicopathological parameters was evaluated using the χ² test and Fisher's exact test. Survival rates were calculated using the unadjusted Kaplan-Meier method, and the log-rank analysis analyzed the difference in survival curves. All cell experiments were performed at least three times. The quantification results are presented as the mean ± standard deviation. Statistical significance for the cell experiments was determined using the independent sample t‑test. p < 0.05 was considered to indicate a statistically significant difference.

Results

Expression profiles and integrated screening of DEGs in human CCA tissues from the bioinformatics analysis

The three microarray data sets containing cancerous and noncancerous tissues from GEO were analyzed to obtain the DEGs. First, the microarray results were standardized, and the DEGs were identified (4702 in GSE26566, 1092 in GSE32225, and 447 in GSE77984). A total of 297 genes overlapped between the three data sets, as shown in the Venn diagram (Fig. 1A). Functional and pathway enrichment analyses of 297 DEGs were performed using DAVID, and the top 20 GO pathways are presented in Figure 1B.

FIG. 1.

Bioinformatics analysis revealed seven hub genes expression in CCA. (A) Selected DEGs had a log (fold change) >1 and p-value <0.01 among the mRNA expression profiling sets GSE26566, GSE32225, and GSE77984. (B) Bubble chart showing the top 20 significant pathways (p < 0.05) of GO functional enrichment analysis for the DEGs. (C) Bubble chart showing all the differential pathways of KEGG enrichment of the DEGs (p < 0.05). (D) The PPI network of DEGs was constructed using Cytoscape. The two most significant modules were obtained from the PPI network. (E) Hierarchical clustering of seven hub genes was created using UCSC. The upregulation of genes is marked in red; downregulation of genes is marked in blue. CCA, cholangiocarcinoma; DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction.

The results of the GO analysis demonstrated that changes in biological processes of DEGs were significantly enriched in “protein binding” and “negative regulation of the apoptotic process.” Changes in molecular function of DEGs were mainly enriched in “protein binding,” “poly(A) RNA binding,” and “protein homodimerization activity.” Changes in cell component of DEGs were primarily enriched in the “cytoplasm,” “nucleus,” and “perinuclear region of cytoplasm” (Fig. 1B). KEGG pathway analysis revealed that the 297 DEGs were primarily enriched in pathways in cancer (Fig. 1C).

To further extract the hub genes from the DEGs, STRING and Cytoscape were used to predict the PPI network and determine the most significant modules (Fig. 1D). A total of seven genes were identified as hub genes with degrees ≥15. Hierarchical clustering showed that the hub genes could differentiate the CCA samples from the noncancerous samples (Fig. 1E). Subsequently, the Kaplan-Meier survival analysis was performed to explore the association between expression levels of hub genes and overall survival. MAD2L1 revealed a significant difference in survival between high and low expression levels (Fig. 2A). However, no difference in overall survival was observed among the other six hub genes (Fig. 2B-G).

FIG. 2.

Overall survival analyses of seven hub genes were performed using the UCSC Xena online platform. p < 0.05 was considered to indicate a statistically significant difference. MAD2L1, mitotic arrest deficient 2-like protein 1.

Overall, the results from Figures 1 and 2 suggest that MAD2L1 may play important roles in the carcinogenesis or progression of CCA.

Clinical data indicate that MAD2L1 expression is associated with poor outcome in CCA

To evaluate MAD2L1 expression in CCA, IHC was applied to detect MAD2L1 in 42 cases of CCA. Of the 42 cases, 31 (73.81%) were MAD2L1 positive, whereas 11 (26.19%) were negative. All paracarcinoma tissues were negative when compared with tumor tissues, and for 30 cases (71.43%) with lymphovascular invasion, only 8 (26.67%) expressed MAD2L1 in the metastatic lymph nodes. Representative images of positive versus negative staining for MAD2L1 in CCA tissue are presented in Figure 3A. This study next analyzed the association between MAD2L1 expression and a variety of clinicopathological characteristics.

FIG. 3.

MAD2L1 expression and clinical outcome. (A) MAD2L1 was exclusively expressed in primary CCA, paracarcinoma tissues, and metastatic lymph nodes, as demonstrated by IHC (original magnifications, × 200). (a) Representative images of MAD2L1-negative tumor and (b) MAD2L1-positive tumor tissue sections. MAD2L1-positive tumor surrounded by (c) negatively stained normal tissue and (d) MAD2L1 weakly positive staining in a representative lymph node metastasis. (B) Postoperative survival analysis of 42 patients with at least 12 months of follow-up. (C) MAD2L1 mRNA expression in 10 fresh cancer samples with T₁ versus 10 with T_2-4 size. The Mann-Whitney U test was used to test MAD2L1 expression in the two groups. (D) MAD2L1 protein expression in paired tumor and adjacent tissues. p < 0.05 was considered to indicate a statistically significant difference. *p < 0.05. IHC, immunohistochemistry.

Notably, MAD2L1 expression was more frequently associated with larger tumor size [odds ratio (OR), 6.231; p = 0.023], higher pathological grade (OR, 5.6; p = 0.048), and higher stage (OR, 6.000; p = 0.035). However, there was no association between MAD2L1 and metastasis (p = 1.000) (Table 1). In the 42 patients who were followed up for at least 12 months, Kaplan-Meier analysis showed that patients with positive MAD2L1 expression had a shorter overall postoperative survival time than the patients with no MAD2L1 expression (Fig. 3B; p = 0.0424).

Table 1.

Univariate Analysis of Mitotic Arrest Deficient 2-Like Protein 1 Expression and Clinicopathological Risk Factors in Cholangiocarcinoma Patients

		MAD2L1 protein expression
		Total	Positive, n (%)	OR (95% CI)	p
Age (years)	>60	25	19, (76%)	11.319 (0.329-5.295)	0.973
	≤60	17	12, (70.59%)	1	0.973
Gender	Female	12	9, (75%)	1.091 (0.235-5.073)	1.000
	Male	30	22, (73.33%)	1	1.000
Sizes (cm)	T₁	22	13, (59.09%)	1	0.023^a
	T_2-4	20	18, (90.00%)	6.231 (1.150-33.771)	0.023^a
Pathological grade	Low	18	10, (55.56%)	1	0.048^a
	High	24	21, (87.5%)	5.600 (1.218-25.751)
Tumor stage	Ⅰ-Ⅱ	14	7, (50.00%)	1	0.035^a
	Ⅲ-Ⅳ	28	24, (85.71%)	6.000 (1.353-26.604)
Metastasis	No	12	9, (75%)	1.091 (0.235-5.073)	1.000
	Yes	30	22, (73.33%)	1

Statistically significant.

CI, confidence interval; MAD2L1, mitotic arrest deficient 2-like protein 1; OR, odds ratio.

To confirm the expression of MAD2L1 in CCAs, fresh tumor tissues, adjacent, and non-neoplastic biliary ducts were isolated from 20 CCA cases. Real-time PCR analysis and immunoblotting were applied to detect MAD2L1 mRNA and protein expression levels, respectively. As shown in Figure 3C and D, higher MAD2L1 mRNA levels were detected in larger tumors (T_2-4) than in small tumors (T₁). Compared with the paracarcinoma tissues, MAD2L1 protein was detected exclusively in tumors.

Overall, the results shown in Figure 3A-D indicate that MAD2L1 expression may serve as a biomarker to predict prognosis in CCA.

Preclinical study using in vitro and in vivo models to test the role of MAD2L1 in CCA

Previous clinical data demonstrated that positive MAD2L1 expression exhibited a significant association with tumor size, but not metastasis (Table 1). To further validate the role of MAD2L1 in CCA progression, gain- or loss-of-function assays were performed in this study using a lentiviral system in CCA cells. We applied the immunoblot to examine the MAD2L1 expression in QBC939 and HuCCT-1 cell lines, and the result revealed higher MAD2L1 expression in QBC939 cells and lower expression in HuCCT-1 cells (not shown).

MAD2L1 was knocked down in QBC939 cells with MAD2L1-shRNA (Fig. 4A) and overexpressed in HuCCT1 cells with MAD2L1-cDNA (Fig. 4B). After altering MAD2L1 in QBC939 and HuCCT1 cell lines, cell count and colony formation assays were performed to assess cell proliferation. The results revealed that knocking down MAD2L1 with shMAD2L1 significantly suppressed cell proliferation (Fig. 4C). Consistently, in HuCCT1 cells, overexpression of MAD2L1 increased cell proliferation (Fig. 4D).

FIG. 4.

MAD2L1 promotes tumor progression through increasing cell proliferation. (A) Western blotting verification of MAD2L1 knockdown in QBC939 cells. (B) Western blotting verification of MAD2L1 overexpression in HuCCT1 cells. (C) Cell counting (left) and colony formation (right) assays were performed to assess cell proliferation after knocking down MAD2L1 with the shMAD2L1 (#1 and #2). (D) Cell counting (left) and colony formation (right) were performed to assess cell proliferation after overexpressing MAD2L1 with MAD2L1-cDNA. (E) Representative nude mice from two groups of in vivo models. (F) Gross CCA tumor samples of nude mice after 4 weeks of injections of QBC939 cells. (G) MAD2L1 expression of the representative samples from two groups was detected through Western blotting. **p < 0.01.

This study performed subcutaneous tumor formation in a total of two groups (1: QBC939-shNC; 2: QBC939-shMAD2L1; four nude mice for each group) as an in vivo model. After 4 weeks, the tumor volume of the shMAD2L1 group was significantly decreased when compared with the control group (Fig. 4E, F) due to the low MAD2L1 protein levels (Fig. 3G).

In summary, the results shown in Figure 4A-G suggest that MAD2L1 may promote tumor progression by increasing cell proliferation.

Discussion

This study applied bioinformatics analyses to identify hub genes that were aberrantly expressed between CCA tissues and paracarcinoma tissues from GEO data sets. First, 294 DEGs were obtained, and the DAVID was utilized to perform the GO and KEGG analyses. The GO analysis demonstrated that these DEGs were enriched in “protein binding,” “cytoplasm,” and “signal transduction,” whereas the results of the KEGG analysis revealed that DEGs were enriched in pathways in cancer.

Using a degree ≥15, this study obtained seven hub genes. Among these hub genes, it has been reported that abnormal expression of enhancer of zeste homolog 2 (EZH2) is associated with tumorigenesis and progression in a number of different malignancies, including cholangiocarcinogenesis, prostate cancer, and nonsmall cell lung carcinoma (Sasaki et al., 2008; Ren et al., 2012; Behrens et al., 2013). As a lysine methyltransferase, EZH2 catalyzes methylation of histone H3 at lysine 27, and establishes and maintains H3K27 trimethylation repressive marks (Shen et al., 2008; Sneeringer et al., 2010).

Under physiological conditions, EZH2 plays an important role in cellular differentiation; however, under pathological conditions, it contributes to cancer cell proliferation (Sarma et al., 2008). A study into the mechanism of hepatocellular carcinoma (HCC) revealed that EZH2 suppressed miR-30d, which may degrade karyopherin subunit β 1 (KPNB1) mRNA, and eventually increased KPNB1 protein expression. Ectopic expression of KPNB1 promoted HCC cell proliferation (Zhang et al., 2014).

In this study, EZH2 also exhibited higher expression levels in tumor tissues compared with those in adjacent tissues; however, the overall survival assay revealed no difference between positive and negative EZH2 expression. A bioinformatics study on HCC revealed that overexpression of the cell-division cycle protein 20 (CDC20), MAD2L1, and DNA replication licensing factor (MCM3) could predict poor prognosis (Zhuang et al., 2018; Yang et al., 2019). In this study, partially due to limited clinical samples, CDC20 and MCM3 exhibited no significant difference in the overall survival analysis. According to a previously published functional study, these seven hub genes were correlated with cell cycle progression of cancer cells and may be involved in cell proliferation (Zhang et al., 2014; Wei et al., 2016; Park et al., 2019).

TCGA database suggests that although all seven hub genes exhibited high expression levels in tumor tissues, only MAD2L1 was significantly associated with overall survival (Fig. 1F). The clinical data in this study confirmed that positive MAD2L1 expression was associated with poor survival in CCA. MAD2L1 positivity was associated with larger tumor size, higher stage, and higher grade; however, no association was observed between metastasis and positive MAD2L1 expression (Table 1). The IHC results revealed weak positive expression of MAD2L1 in the metastatic lymph nodes and confirmed that MAD2L1 promotes tumor cell proliferation.

To test this hypothesis, this study artificially altered MAD2L1 expression in CCA cell lines and investigated the resultant cell proliferation. The results of the cell proliferation analysis demonstrated that MAD2L1 promoted CCA cell proliferation. However, knocking down MAD2L1 was able to suppress cell proliferation. It has been previously demonstrated that MAD2L1 plays very important roles, under either physiological or pathological conditions. Physiologically, MAD2L1 acts synergistically with SCF and GM-CSF to promote the proliferation of immature hematopoietic progenitor cells (Ito et al., 2006). Under pathological conditions, numerous upstream genes or miRNAs function through MAD2L1 to promote cell proliferation.

For example, downregulation of miR-200c-5p increased MAD2L1 expression in HCC and enhanced cell proliferation (Shi et al., 2016; Li et al., 2017). However, to the best of our knowledge, there are currently no studies demonstrating MAD2L1 expression in CCA. Therefore, this study has been the first to investigate MAD2L1 expression in CCA, both in vitro and in vivo. The results demonstrated that targeting MAD2L1 may significantly suppress CCA growth, and MAD2L1 may be a valid therapeutic target for developing more effective novel strategies to treat CCA.

Conclusion

In conclusion, MAD2L1 acts as an oncogene. Although its function is not yet fully understood, this study demonstrated that MAD2L1 is highly expressed in cancer cells and can increase cell proliferation. MAD2L1 may be involved in multiple signaling pathways in cancer development. Therefore, manipulating MAD2L1 expression in CCA therapy may be promising as a new therapeutic target worthy of further study.

Ethics Approval and Consent to Participate

This study was approved by the clinical research ethics committee of the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, and written informed consent was obtained from all individual participants included in the study. The clinical trial registration number is [2020]KY157-01.

Patient Consent for Publication

All included patients provided informed consent.

Availability of Data and Materials

All data generated and/or analyzed during this study are included in this published article.

Footnotes

Authors' Contributions

Y.G. and X.Q. conceived and designed the study. Y.G. and X.O. performed all the experiments. Y.L., L.S., X.O., and C.Z. made substantial contributions to the design of this study, acquisition of data, interpretation of data, and revising the article. All authors read and approved the final article.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This study was partially funded by the endowment from the Division of General Surgery of Changzhou No. 2 People's Hospital, Changzhou Sci&Tech Program (Grant No. CJ20200058), and the Social Development Foundation of Science and Technology of Jiangsu (Grant No. BE2016658 to XQ).

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