Literature DB >> 26287798

Biological and Clinical Significance of MAD2L1 and BUB1, Genes Frequently Appearing in Expression Signatures for Breast Cancer Prognosis.

Zhanwei Wang1, Dionyssios Katsaros2, Yi Shen1, Yuanyuan Fu1, Emilie Marion Canuto2, Chiara Benedetto2, Lingeng Lu3, Wen-Ming Chu4, Harvey A Risch3, Herbert Yu1.   

Abstract

To investigate the biologic relevance and clinical implication of genes involved in multiple gene expression signatures for breast cancer prognosis, we identified 16 published gene expression signatures, and selected two genes, MAD2L1 and BUB1. These genes appeared in 5 signatures and were involved in cell-cycle regulation. We analyzed the expression of these genes in relation to tumor features and disease outcomes. In vitro experiments were also performed in two breast cancer cell lines, MDA-MB-231 and MDA-MB-468, to assess cell proliferation, migration and invasion after knocking down the expression of these genes. High expression of these genes was found to be associated with aggressive tumors and poor disease-free survival of 203 breast cancer patients in our study, and the association with survival was confirmed in an online database consisting of 914 patients. In vitro experiments demonstrated that lowering the expression of these genes by siRNAs reduced tumor cell growth and inhibited cell migration and invasion. Our investigation suggests that MAD2L1 and BUB1 may play important roles in breast cancer progression, and measuring the expression of these genes may assist the prediction of breast cancer prognosis.

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Year:  2015        PMID: 26287798      PMCID: PMC4546117          DOI: 10.1371/journal.pone.0136246

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

Accurately predicting the prognosis of breast cancer remains a significant challenge [1]. Many clinical, pathologic and molecular markers have been identified for breast cancer prognosis, including disease stage, tumor grade and histology, lymph node involvement, hormone/growth factor receptor status and recently molecular subtypes, but none of them provides ideal accuracy in predicting prognosis and treatment response. High-throughput analyses, based on microarray chips and other technologies, are used to determine gene expression “signatures” for improved accuracy of prognosis. The first such signature was reported in 2002, which was on based on the expression of 70 genes using a DNA-microarray technology [2]. Since then, more than a dozen of gene expression signatures have been published for breast cancer prognosis [3-15]. Interestingly, when comparing the genes in the signatures, only a small number are shared by these signatures, and most of the genes are not overlapping. Reasons for this disparity in gene lists are probably multiple, including diverse patient populations, disease characteristics, analytical technologies, tissue preservation and preparation methods, and some degree of false positivity. Because of the inconsistency of gene inclusion across signatures as well as the feasibility and cost of using such signatures in patient management, many of the expression signatures have not been confirmed in prospective studies. To date, only two prognostic signatures [2, 16] are being evaluated in clinical trials for utility [17, 18], but the results will not be available until 2020. While many studies are still aiming to develop new methods and techniques to improve the accuracy of prediction with the use of gene signatures, the biological relevance or pathologic involvement of the genes in signatures are often overlooked. Usually, the genes in a signature involve a number of biological processes and functions, making it difficult to identify key components that may drive tumor progression. For example, the first 70-gene signature [2] contains genes involved in multiple biological activities, including cell cycle regulation, cell invasion and metastasis, angiogenesis, and signal transduction. Which process play key roles in tumor progression is unclear. Furthermore, the effects on tumor cells of many genes in the signatures are unknown. Thus, in an attempt to address some of these issues, we identified and compared genes from 16 publications that have reported gene-expression signatures for breast cancer prognosis. From the genes that appeared in multiple expression signatures, we selected two, MAD2L1 (Mitotic Arrest Deficient 2-like 1) and BUB1 (Budding Uninhibited by Benzimidazoles 1), for further validation of their associations with patient survival in our own and other studies, as well as for in vitro evaluation of their biological involvements in breast cancer.

Material and Methods

Selection of gene expression signatures

We searched the PubMed database (US National Library of Medicine) using the phrases “breast cancer”, “gene expression”, “signature”, and “survival”. The search was updated in May 2015. The studies selected for our analysis were based on the following conditions: a) it was an initial report of a gene signature associated with breast cancer survival; b) an entire list of genes involved in the signature was reported; and c) gene expression data were generated from microarray analysis. Following the criteria, a total of 14 studies (or signatures) were selected [2–15, 18, 19]. Two additional studies by van Vliet et al. [20] and Dai et al. [21] were also included in our analysis. These studies analyzed the same data from Van’s Veer [2], but used very different strategies and generated different signatures with regard to the lists of genes. Therefore, they were considered as independent studies. The study conducted by Van’s Veer et al [2] identified a 231-gene signature in the initial stage. We used the genes in the initial signature for our analysis. A well-known EndoPredict signature based on RT-qPCR analysis was also included in our study [12].

Breast cancer patients

We recruited 203 female patients diagnosed with primary breast cancer from the University Hospital at University of Turin between January 1998 and July 1999. The patients were enrolled in the study before surgery and were followed through February 2007. The study was approved by the ethical review committee of University Hospital at University of Turin. All participants provided written informed consent. Fresh breast tumor samples removed during surgery were snap-frozen in liquid nitrogen immediately after resection and stored at -80°C until analysis. Clinical and pathological characteristics of the patients are shown in the result section.

RNA extraction and analysis

Protocols for RNA extraction and gene expression analysis have been described elsewhere [22]. Briefly, total RNA was extracted from fresh-frozen tumor samples according to the conventional phenol-chloroform method following manual pulverization of tissue specimens in liquid nitrogen. The RNA samples were purified and concentrated by RNeasy MinElute Cleanup Kit (Qiagen Inc., Valencia, CA). Concentrations and integrities of total RNA were analyzed using the RNA 6000 Nano LabChip Kit and Agilent 2100 Bioanalyzer. Microarray analyses were performed using the Illumina Expression BeadChip (HumanRef-8 v3) following the manufacturer’s protocol.

Antibodies

Antibodies used for western blot analysis were purchased from various companies. Anti-MAD2L1 (D8A7, #4636) antibody and horseradish peroxidase (HRP)-conjugated secondary antibody were purchased from Cell Signaling Technology (Danvers, MA), anti-BUB1 antibody (ab54893) was from Abcam (Cambridge, MA), and anti-β-actin antibody (A2228) was from Sigma-Aldrich (St. Louis, MO).

Cell lines and cultures

Human breast cancer cell lines MDA-MB-231 (MB231) and MDA-MB-468 (MB468) used for our in vitro experiments were supplied by Dr. Richard Yip at University of Hawaii Cancer Center, who obtained them from NCI as part of the NCI-60 DTP Human Tumor Cell Screening Panel. The cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 100 units/ml of penicillin/streptomycin (Pen/Strep) at 37°C in a humidified atmosphere of 5% CO2.

Western blots

In siRNA knockdown experiments, protein expression was analyzed by western blot. Cells were treated with a lysis buffer [1% Triton x-100, 150 mM NaCl, 50 mM Tris-Cl, pH 7.4, 0.5 mM EDTA, 1 mM sodium orthovanadate, 5 mM beta glycerol phosphate, and 1x protease inhibitor cocktail (Roche, Indianapolis, IN)] after transfection with indicated siRNAs for 36 h, and protein concentration was measured. Proteins (40–60 μg) from the cell lysates were separated by SDS-PAGE (10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis) under denaturing conditions, and then transferred to polyvinylidene difluoride (PVDF) membranes (Millipore, Billerica, MA). After transferring, the membranes were blocked with 5% non-fat milk for 45 minutes, and then incubated with a primary antibody followed by incubation with a secondary antibody. The signals were detected by the enhanced chemiluminescence system (ECL) as described by the manufacturer (Pierce, Rockford, IL).

siRNA transfection

The ON-TARGET plus SMARTpool siRNA of human MAD2L1 and BUB1 and negative control ON-TARGET plus Non-targeting Pool were purchased from Dharmacon (Lafayette, CO). The sequences of MAD2L1 and BUB1 siRNAs as well as control siRNAs are (a) ON-TARGET plus SMARTpool Human MAD2L1 siRNA: UUACUCGAGGCAGAAAUA (siMAD2L1-1), CUACUGAUCUUGAGCUCAU (siMAD2L1-2), GGUUGUAGUUAUCUCAAAU (siMAD2L1-3) and GAAAUCCGUUCAGUGAUCA (siMAD2L1-4); (b) ON-TARGET plus SMARTpool Human BUB1 siRNA: CGAAGAGUGAUCACGAUUU (siBUB1-1), CAAAGAAGGGUGUGAAACA (siBUB1-2), GAAUGUAAGCGUUCACGAA (siBUB1-3), and GCAACAAACCAUGGAACUA (siBUB1-4); and (c) ON-TARGET plus Non-targeting siRNA: UGGUUUACAUGUCGACUAA (siRNA-1), UGGUUUACAUGUUGUGUGA (siRNA-2), UGGUUUACAUGUUUUCUGA (siRNA-3), and UGGUUUACAUGUUUUCCAU (siRNA-4). Cells were seeded onto 6-well plates in antibiotic-free medium plus 10% FBS overnight. When 70% confluent, cells were transfected with Lipofectamine RNAiMAX (Invitrogen) according to the manufacturer’s protocol. The final concentration of siRNAs was 50 nM. After transfection, cells were cultured for 36 hours, and then analyzed by western blot for protein expression of MAD2L1 and BUB1, as well as cell proliferation, in vitro migration and invasion assays.

Cell proliferation assay

Cells were plated onto 96-well plates at a density of 3 x 103 cells per well, and cultured in the complete medium at 37°C under a humidified atmosphere containing 5% CO2. At 0, 24, 48, and 72 hours of incubation, cell proliferation reagent WST-1 was added into the wells in accordance with the manufacturer’s instructions (Roche, Penzberg, Germany). Cells were continuously incubated for 2 hours with WST-1, and then the color in each well of the plate was measured by a microplate spectrophotometer (Biotek Synergy 2, Winooski, VT) at the 450 nm wavelength. The light absorbance was proportional to the numbers of cells in each well. The results of measurement at different time points of incubation were referenced to the measurement at 0 hours of incubation. Each proliferation assay was performed in triplicate.

Transwell migration and invasion assays

In vitro cell migration and invasion assays were performed in 24-well plates with the Costar Transwell permeable membrane support with 8.0-μm pore size (Corning, NY). In the cell invasion assay, 200 μl control and siRNA knockdown cells in serum-free medium (1 x 104 cells per well) were seeded in the upper chambers coated with growth-factor-reduced Matrigel, 1 mg/ml (BD Pharmingen, San Diego, CA). The lower chambers were filled with 600 μl culture medium containing 10% FBS. Cell migration assays were performed similarly but without Matrigel coating. Cells invading or migrating to the underside of the filter membrane were stained with HEME 3 Solution (Fisher Diagnostics, Middletown, VA) after 36 hours of culture, and then counted using an Olympus CKX41 microscope with an Infinity 2 camera. All experiments were repeated three times with triplicate wells.

Statistical analysis

DAVID Bioinformatics Resources v6.7 (http://david.abcc.ncifcrf.gov) was used for the pathway enrichment analysis, with recommended standard parameters selected in the analysis [23, 24]. KEGG (Kyoto Encyclopedia of Genes and Genomes) database (http://www.genome.ad.jp/kegg/) was employed for the analysis of protein functions in different pathways. All the microarray data were pre-processed by the BeadStudio Software and analyzed using the R statistical software and Bioconductor [25]. The Lumi R package was used to normalize the data [26]. Wilcoxon rank-sum test was performed to assess the association of gene expression and clinicopathologic characteristics of breast cancer patients. Kaplan-Meier survival analysis and Cox proportional hazards regression models were used to analyze the association of gene expression with breast cancer survival. Log-rank test was performed for Kaplan-Meier survival curve comparisons. Hazards ratios (HRs) and 95% confidence intervals (CIs) were calculated in the Cox regression analysis, and both univariate and multivariate models were developed. In multivariate analysis, HRs were adjusted for patient age at surgery, tumor grade, disease stage, and estrogen receptor (ER) and progesterone receptor (PR) status. Two types of survival outcomes were considered in survival analysis, overall survival (OS) defined as the time between the date of surgery and date of death or last follow-up, and relapse-free survival (RFS) defined as the period from surgery to recurrence or last follow-up. MAD2L1 and BUB1 expression were categorized into low, medium and high based on their tertile distributions. To validate the associations of MAD2L1 and BUB1 expression with breast cancer survival, an online tool named Gene expression-based Outcome for Breast cancer Online (GOBO) (http://co.bmc.lu.se/gobo) was used [27]. All of the values presented in the figures of in vitro experiments are means and standard deviations. Two-tailed Student t test was performed to determine the differences in means between groups, and P<0.05 was considered statistically significant. All statistical analyses were performed using the Statistical Analysis System, version 9.2, (SAS Institute Inc., Cary, NC) and R software (version 3.0.2).

Results

Genes in 16 reported expression signatures

Studies reported gene expression signatures in association with breast cancer prognosis are listed in S1 Table. These signatures included a total of 1,399 distinct genes. Of them, 1,138 were found to have official names. One gene, BIRC5 (also known as Survivin), was included in 7 signature lists, another, MYBL2, was in 6 lists, 5 genes (BUB1, CENPF, MAD2L1, RRPM2, PRC1) were in 5 lists, and 12 genes were in 4 lists. The complete list of genes appearing in 3 or more signature lists is provided in S2 Table. Using the DAVID Bioinformatics Resources v6.7 to analyze the 1,138 genes for pathway enrichment, we found four significantly enriched pathways. Cell cycle was the most significant one (Benjamini-Hochberg corrected p = 10−9.5), followed by p53 signaling pathway (p = 10−4.2), pathways in cancer (p = 10−3.6), and DNA replication (p = 0.016). We found that three genes involved in multiple signatures were also associated with survival outcomes in our study, including MAD2 mitotic arrest deficient-like 1 (MAD2L1), pituitary tumor-transforming gene-1 (PTTG1), and budding uninhibited by benzimidazole 1 (BUB1). All of these genes were involved in the cell cycle pathway. Since the effects of PTTG1 on breast cancer cells have been studied previously by Yoon at el. [28], we focused our analyses on MAD2L1 and BUB1.

Associations of MAD2L1 and BUB1 expression with clinical and pathologic features

Using the gene expression data from our microarray analysis of breast cancer [22], we examined the associations of MAD2L1 and BUB1 expression with tumor features and disease outcomes. Table 1 shows the clinical and pathologic characteristics of breast cancer patients in association with MAD2L1 and BUB1 expression. Patients with PR negative, ER negative, and high grade tumors had higher expression of MAD2L1 and BUB1 compared to those with PR positive, ER positive, and low grade tumors, respectively. Patient age, disease stage and lymph node involvement were not associated with MAD2L1 or BUB1 expression.
Table 1

Associations of MAD2L1 and BUB1 expression with clinical and molecular characteristics of breast cancer.

Variables MAD2L1 BUB1
Low No. (%)Middle No. (%)High No. (%) P Low No. (%)Middle No. (%)High No. (%) P
Age at surgery 0.3750.113
≤57.11 35(34.31)30(29.42)37(36.27)37(36.27)27(26.4838(37.25)
>57.11 33(32.35)39(38.24)30(29.41)31(30.39)41(40.20)30(29.41)
Estrogen receptor 0.0270.00036
Positive47(37.01)47(37.01)33(25.98)52(40.94)45(35.43)30(23.62)
Negative21(27.27)22(28.57)34(44.16)16(20.78)23(29.87)38(49.35)
Progesterone receptor 0.0120.000002
Positive42(41.18)36(35.29)24(23.53)49(48.04)34(33.33)19(18.63)
Negative25(25.51)32(32.65)41(41.84)18(18.37)33(33.67)47(47.96)
Lymph node status 0.2540.662
Positive31(31.31)39(39.39)29(29.29)30(30.30)35(35.35)34(34.34)
Negative37(35.24)30(28.57)38(36.19)38(36.19)33(31.43)34(32.38)
Grade 0.000001< .000001
118(69.23)4(15.38)4(15.38)15(57.69)7(26.92)4(15.38)
229(37.18)33(42.31)16(20.51)38(48.72)25(32.05)15(19.23)
320(20.41)31(31.63)47(47.96)13(13.27)36(36.73)49(50.00)
Disease stage 0.2370.093
I28(42.42)22(33.33)16(24.24)26(39.39)26(39.39)14(21.21)
II34(30.63)36(32.43)41(36.94)36(32.43)34(30.63)41(36.94)
III, IV6(22.22)11(40.74)10(37.04)6(22.22)8(29.63)13(48.15)
To evaluate whether MAD2L1 and BUB1 are over-expressed in breast cancer, a dataset (GSE37751) was downloaded from the Gene Expression Omnibus (GEO) website (www.ncbi.nlm.nih.gov/geo). This dataset included gene expression data from 61 breast cancer and 47 adjacent non-tumor tissues analyzed by the GeneChip Human Gene 1.0 ST arrays (Affymetrix, Santa Clara, CA). The expression data were processed by the RMA algorithm using the Affymetrix Expression Console software [29]. Both MAD2L1 and BUB1 expression were found to be higher in tumor than in adjacent non-tumor tissues (5.97±0.66 versus 5.36±0.38 for MAD2L1, 6.80±1.14 versus 5.50±0.74 for BUB1, respectively, and both P<0.0001) in this database. Similar differences were also observed in another GEO dataset (GSE29044) [30].

Associations of MAD2L1 and BUB1 expression with overall and relapse-free survival

We further investigated the associations of MAD2L1 and BUB1 expression with breast cancer survival. Fig 1 shows that high expression of MAD2L1 was significantly associated with increased risk of disease recurrence and death. Patients with high tumor expression of BUB1 also had poorer relapse-free and overall survival compared to those with low tumor expression. The associations of MAD2L1 and BUB1 expression with relapse-free survival remained statistically significant after adjusting for age at surgery, ER and PR status, tumor grade, and disease stage (Table 2). The relationships of breast cancer survival with MAD2L1 and BUB1 expression were also analyzed in the GOBO database consisting of 914 tumor samples from 11 publically available microarray datasets. The results of the GOBO database analysis were similar to the findings of our study. High expression of MAD2L1 and BUB1 were significantly associated with poor relapse-free survival (Fig 2B and 2D). High MAD2L1 expression was also associated with poor overall survival (Fig 2A).
Fig 1

Kaplan-Meier survival curves by different levels of MAD2L1 and BUB1 expression in 203 breast cancer patients.

A) Overall survival (OS) by low, medium and high MAD2L1 expression; B) Relapse-free survival (RFS) by low, medium and high MAD2L1 expression; C) Overall survival (OS) by low, medium and high BUB1 expression; (D) Relapse-free survival (RFS) by low, medium and high BUB1 expression.

Table 2

Associations of MAD2 and BUB1 expression with overall and relapse-free survival.

VariablesUnadjustedAdjusted*
OverallRelapse-freeOverallRelapse-free
HR95% CIHR95% CIHR95% CIHR95% CI
MAD2L1
low1111
Mid0.7860.3261.8961.2090.5812.5140.5720.2311.4170.8770.411.876
High 2.163 1.042 4.487 3.177 1.671 6.042 1.5280.6733.472 2.08 1.027 4.214
P for trend 0.0257 0.00016 0.223 0.0183
BUB1
low1111
Mid1.2690.5013.2162.0670.9964.2891.2140.4393.3551.6520.7443.67
High 3.263 1.459 7.298 3.378 1.691 6.746 2.5430.966.734 2.329 1.037 5.231
P for trend 0.0019 0.00038 0.033 0.0054

* Adjusted for age at surgery, tumor grade, disease stage, ER status, and PR status.

Fig 2

Kaplan-Meier survival curves by different levels of MAD2L1 and BUB1 expression in the GOBO database.

A) Overall survival (OS) by low, medium and high MAD2L1 expression; B) Relapse-free survival (RFS) by low, medium and high MAD2L1 expression; C) Overall survival (OS) by low, medium and high BUB1 expression; (D) Relapse-free survival (RFS) by low, medium and high BUB1 expression.

Kaplan-Meier survival curves by different levels of MAD2L1 and BUB1 expression in 203 breast cancer patients.

A) Overall survival (OS) by low, medium and high MAD2L1 expression; B) Relapse-free survival (RFS) by low, medium and high MAD2L1 expression; C) Overall survival (OS) by low, medium and high BUB1 expression; (D) Relapse-free survival (RFS) by low, medium and high BUB1 expression.

Kaplan-Meier survival curves by different levels of MAD2L1 and BUB1 expression in the GOBO database.

A) Overall survival (OS) by low, medium and high MAD2L1 expression; B) Relapse-free survival (RFS) by low, medium and high MAD2L1 expression; C) Overall survival (OS) by low, medium and high BUB1 expression; (D) Relapse-free survival (RFS) by low, medium and high BUB1 expression. * Adjusted for age at surgery, tumor grade, disease stage, ER status, and PR status.

Suppression of MAD2L1 and BUB1 expression in breast cancer cell lines

To evaluate the biological effects of MAD2L1 and BUB1 on breast cancer, we designed two RNA interference (RNAi) assays to knockdown the expression of MAD2L1 or BUB1. The experiments were performed in two triple-negative breast cancer cell lines MDA-MB-231 and MDA-MB-468, which had high expression of endogenous MAD2L1 and BUB1. Cell growth, invasion and migration were evaluated in our in vitro experiments. Western blot analyses showed that MAD2L1 and BUB1 proteins were significantly declined after the cells were transfected with siRNAs specific for MAD2L1 or BUB1 (Figs 3 and 4). Compared with those transfected with scrambled siRNA, cells with MAD2L1 or BUB1 siRNA had lower cell proliferation. Cell invasion and migration were also reduced in those treated with MAD2L1 or BUB1 siRNA. Taken together, these results suggest that low MAD2L1 or BUB1 expression may inhibit breast cancer cell proliferation, invasion, and migration.
Fig 3

Effects of MAD2L1 or BUB1 depletion on the phenotype of MDA-MB-231 cancer cells in vitro.

A) MAD2L1 protein expression by western blot analysis, β-actin used as control. B) Knockdown of MAD2L1 expression inhibited cell proliferation. C) Knockdown of MAD2L1 expression inhibited cell migration. D) Knockdown of MAD2L1 expression inhibited cell invasion. E) Representative pictures of cell migration and invasion before and after knockdown. F) BUB1 protein expression by western blot analysis, β-actin used as control. G) Knockdown of BUB1 expression inhibited cell proliferation. H) Knockdown of BUB1 expression inhibited cell migration. I) Knockdown of BUB1 expression inhibited cell invasion. J) Representative pictures of cell migration and invasion before and after knockdown. * = P<0.001.

Fig 4

Effects of MAD2L1 or BUB1 depletion on the phenotype of MDA-MB-468 cancer cells in vitro.

A) MAD2L1 protein expression by western blot analysis, β-actin used as control. B) Knockdown of MAD2L1 expression inhibited cell proliferation. C) Knockdown of MAD2L1 expression inhibited cell migration. D) Knockdown of MAD2L1 expression inhibited cell invasion. E) Representative pictures of cell migration and invasion before and after knockdown. F) BUB1 protein expression by western blot analysis, β-actin used as control. G) Knockdown of BUB1 expression inhibited cell proliferation. H) Knockdown of BUB1 expression inhibited cell migration. I) Knockdown of BUB1 expression inhibited cell invasion. J) Representative pictures of cell migration and invasion before and after knockdown. * = P<0.001.

Effects of MAD2L1 or BUB1 depletion on the phenotype of MDA-MB-231 cancer cells in vitro.

A) MAD2L1 protein expression by western blot analysis, β-actin used as control. B) Knockdown of MAD2L1 expression inhibited cell proliferation. C) Knockdown of MAD2L1 expression inhibited cell migration. D) Knockdown of MAD2L1 expression inhibited cell invasion. E) Representative pictures of cell migration and invasion before and after knockdown. F) BUB1 protein expression by western blot analysis, β-actin used as control. G) Knockdown of BUB1 expression inhibited cell proliferation. H) Knockdown of BUB1 expression inhibited cell migration. I) Knockdown of BUB1 expression inhibited cell invasion. J) Representative pictures of cell migration and invasion before and after knockdown. * = P<0.001.

Effects of MAD2L1 or BUB1 depletion on the phenotype of MDA-MB-468 cancer cells in vitro.

A) MAD2L1 protein expression by western blot analysis, β-actin used as control. B) Knockdown of MAD2L1 expression inhibited cell proliferation. C) Knockdown of MAD2L1 expression inhibited cell migration. D) Knockdown of MAD2L1 expression inhibited cell invasion. E) Representative pictures of cell migration and invasion before and after knockdown. F) BUB1 protein expression by western blot analysis, β-actin used as control. G) Knockdown of BUB1 expression inhibited cell proliferation. H) Knockdown of BUB1 expression inhibited cell migration. I) Knockdown of BUB1 expression inhibited cell invasion. J) Representative pictures of cell migration and invasion before and after knockdown. * = P<0.001.

Discussion

More than a dozen gene expression signatures involving over 1,300 genes have been published for breast cancer prognosis. Among them, no single gene was included in all of the signatures, and only two genes (MYBL2, BIRC5) were shared by six or seven signatures. These genes were reported to be associated with breast cancer survival, and suppressing the expression of these genes inhibited tumor cell proliferation [31, 32]. Five genes appeared in five signatures, and twelve genes in four signatures. This low frequency of genes overlapping in the signatures suggests that the reported expression signatures are highly heterogeneous. This heterogeneity most likely reflects a diversity of patient populations or tumor specimens with regard to their clinical and pathologic characteristics. It is also possible that some of the results were observed by chance or from false-positive findings. To exclude this possibility, validation of the signature genes in independent studies or distinct patient populations is necessary. In our study of microarray expression data from 203 breast cancer patients, we analyzed the genes present in multiple signatures, and found three genes (MAD2L1, PTTG1 and BUB1) significantly associated with disease-free or overall survival. Of these genes, PTTG1 has been investigated by Yoon et al. who demonstrated the possible biological relevance of the gene to breast cancer [28]. No reports were found for MAD2L1 and BUB1. Based on the pathway enrichment analysis of the genes involved in multiple expression signatures for prognosis, the cell cycle pathway was suggested to be the most significantly enriched. All the three genes mentioned above are in this pathway. Genes included in the expression signatures can be involved in multiple biological functions and cellular activities. In the studies exploring gene expression profiles as signatures for survival outcomes, biological and pathologic relevance of the genes to cancer in the signatures are often not evaluated experimentally and validated independently [33]. Breast cancer is a heterogeneous disease characterized by distinct pathologic features, disparate treatment responses, and varied disease outcomes [34]. These differences may reflect specific gene expression signatures. Using the genes in signatures, our pathway enrichment analysis showed that cell cycle is the most significant pathway. As cell cycle involves the processes of cell growth and division, and uncontrolled cell proliferation is a hallmark of cancer, [35, 36], it is not difficult to imagine why these signatures are predictive of disease prognosis. Thus, in this study, we focused on MAD2L1 and BUB1, two critical mitotic checkpoint genes which play an important role in the mitotic process. Our investigation showed that high MAD2L1 or BUB1 expression was associated with poor prognosis of breast cancer, and moreover in vitro suppression of their expression resulted in reduced cell proliferation and less aggressive cell behavior. In addition, we found from publicly available data that expression of MAD2L1 and BUB1 was higher in tumor than in adjacent non-tumor tissues. Collectively, all of these observations suggest that MAD2L1 and BUB1, two genes present in multiple gene expression signatures for breast cancer prognosis, may be important molecules influencing tumor cell activity and patient survival. Under normal physiologic circumstances, cell cycle is tightly controlled by several mechanisms that regulate cell division and proliferation. One of the primary cell-cycle control mechanisms is the mitotic checkpoints in mitosis [37]. As part of the mitotic checkpoints, the MAD (mitotic arrest-deficient) and BUB (budding uninhibited by benzimidazole) gene families are essential components of the spindle checkpoint, and were first identified by genetic screening of budding yeast for mutants [38]. MAD2L1 is required in mitosis when chromosomes are unattached to the mitotic spindle that maintains chromosomal segregation, and is involved in the spindle checkpoint during mitosis [39]. Dysregulation of these genes are associated with chromosomal instability and substantial aneuploidy which occur often in cancer [40, 41]. One study revealed that mRNA levels of many spindle checkpoint genes (MAD1L1, MAD2L1, MAD2L2, BUB1, BUB1B, BUB3, CDC20 and TTK) were almost uniformly increased in breast cancer cell lines relative to the levels in normal breast cells (MCF10A and primary mammary gland); high expression was also observed in high-grade primary breast cancer [42]. In our study, both MAD2L1 and BUB1 were expressed substantially higher in high-grade than in low-grade tumors. Our analysis of MAD2L1 and BUB1 in two datasets (GSE37751 and GSE29044) also demonstrated higher expression in breast cancer than in adjacent non-tumor tissues. One review has pointed out that high levels of MAD2L1 or BUB1 could lead to the formation of aggressive tumors in multiple organs [43]. MAD2L1 has been found to be overexpressed in several types of tumor or cancer cell lines, including breast, lung, liver and stomach [44, 45]. Overexpression of BUB1 and other family members has been found to be associated with tumor cell proliferation [46]. Transgenic mice overexpressing BUB1 developed various spontaneous tumors, and showed accelerated myc-induced lymphomagenesis [47]. High expression of BUB1 was observed in a variety of human malignancies including gastric cancer [46, 48], colorectal cancer [49], and lymphomas [50]. We and others found high expression of MAD2L1 and BUB1 in breast cancer and their associations with unfavorable prognosis [2]. Although mounting evidence suggests that high MAD2L1 or BUB1 are associated with tumor progression, study findings have been inconsistent. Reduced BUB1 expression was observed in a subset of pancreatic cancer cells [51]. This inconsistency may arise from different expression in these checkpoint components between actively proliferating cells and quiescent or differentiated cells. Increased BUB1 expression accords with higher mitotic index in tumors compared to neighboring tissues [47]. Alternatively, variation in function may reflect compensation for other defects in the mitotic checkpoint. In our in vitro experiments, we also observed that down-regulation of MAD2L1 or BUB1 expression resulted in reduced cell migration and invasion. The underlying mechanism for these effects is unknown. One study showed that MAD2L1 was a direct transcriptional target of E2F, and their interaction could cause retinoblastoma pathway dysregulation [52], which may lead to the suppression of migration and invasion. Another recent study found that knockdown of BUB1 could significantly inhibit the TGF-β-mediated induction of cell migration and invasion [53]. In summary, our study confirmed the prognostic value of two key mitotic checkpoint genes MAD2L1 and BUB1, which have been included in multiple gene expression signatures for breast cancer prognosis. We also found that these genes are biologically relevant to breast cancer progression, as suppression of their expression was associated with reduced tumor cell growth, migration and invasion. Together, our investigation suggests that these genes may serve as tumor markers for breast cancer prognosis as well as potential therapeutic targets to suppress tumor growth.

Summary of published gene signature included in the current study.

(DOCX) Click here for additional data file.

List of genes appeared three or more gene signatures.

(DOCX) Click here for additional data file.
  53 in total

1.  A double missense variation of the BUB1 gene and a defective mitotic spindle checkpoint in the pancreatic cancer cell line Hs766T.

Authors:  Paula M Hempen; Harsha Kurpad; Eric S Calhoun; Susan Abraham; Scott E Kern
Journal:  Hum Mutat       Date:  2003-04       Impact factor: 4.878

2.  The cell cycle and cancer.

Authors:  K Collins; T Jacks; N P Pavletich
Journal:  Proc Natl Acad Sci U S A       Date:  1997-04-01       Impact factor: 11.205

3.  A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.

Authors:  Soonmyung Paik; Steven Shak; Gong Tang; Chungyeul Kim; Joffre Baker; Maureen Cronin; Frederick L Baehner; Michael G Walker; Drew Watson; Taesung Park; William Hiller; Edwin R Fisher; D Lawrence Wickerham; John Bryant; Norman Wolmark
Journal:  N Engl J Med       Date:  2004-12-10       Impact factor: 91.245

4.  Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.

Authors:  Yixin Wang; Jan G M Klijn; Yi Zhang; Anieta M Sieuwerts; Maxime P Look; Fei Yang; Dmitri Talantov; Mieke Timmermans; Marion E Meijer-van Gelder; Jack Yu; Tim Jatkoe; Els M J J Berns; David Atkins; John A Foekens
Journal:  Lancet       Date:  2005 Feb 19-25       Impact factor: 79.321

5.  Genes that mediate breast cancer metastasis to lung.

Authors:  Andy J Minn; Gaorav P Gupta; Peter M Siegel; Paula D Bos; Weiping Shu; Dilip D Giri; Agnes Viale; Adam B Olshen; William L Gerald; Joan Massagué
Journal:  Nature       Date:  2005-07-28       Impact factor: 49.962

Review 6.  On the road to cancer: aneuploidy and the mitotic checkpoint.

Authors:  Geert J P L Kops; Beth A A Weaver; Don W Cleveland
Journal:  Nat Rev Cancer       Date:  2005-10       Impact factor: 60.716

7.  Increased expression of mitotic checkpoint genes in breast cancer cells with chromosomal instability.

Authors:  Bibo Yuan; Yi Xu; Ju-Hyung Woo; Yunyue Wang; Young Kyung Bae; Dae-Sung Yoon; Robert P Wersto; Ellen Tully; Kathleen Wilsbach; Edward Gabrielson
Journal:  Clin Cancer Res       Date:  2006-01-15       Impact factor: 12.531

8.  A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients.

Authors:  Hongyue Dai; Laura van't Veer; John Lamb; Yudong D He; Mao Mao; Bernard M Fine; Rene Bernards; Marc van de Vijver; Paul Deutsch; Alan Sachs; Roland Stoughton; Stephen Friend
Journal:  Cancer Res       Date:  2005-05-15       Impact factor: 12.701

9.  Bioconductor: open software development for computational biology and bioinformatics.

Authors:  Robert C Gentleman; Vincent J Carey; Douglas M Bates; Ben Bolstad; Marcel Dettling; Sandrine Dudoit; Byron Ellis; Laurent Gautier; Yongchao Ge; Jeff Gentry; Kurt Hornik; Torsten Hothorn; Wolfgang Huber; Stefano Iacus; Rafael Irizarry; Friedrich Leisch; Cheng Li; Martin Maechler; Anthony J Rossini; Gunther Sawitzki; Colin Smith; Gordon Smyth; Luke Tierney; Jean Y H Yang; Jianhua Zhang
Journal:  Genome Biol       Date:  2004-09-15       Impact factor: 13.583

10.  Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts.

Authors:  Yudi Pawitan; Judith Bjöhle; Lukas Amler; Anna-Lena Borg; Suzanne Egyhazi; Per Hall; Xia Han; Lars Holmberg; Fei Huang; Sigrid Klaar; Edison T Liu; Lance Miller; Hans Nordgren; Alexander Ploner; Kerstin Sandelin; Peter M Shaw; Johanna Smeds; Lambert Skoog; Sara Wedrén; Jonas Bergh
Journal:  Breast Cancer Res       Date:  2005-10-03       Impact factor: 6.466

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  45 in total

1.  Identification of transcription factors (TFs) and targets involved in the cholangiocarcinoma (CCA) by integrated analysis.

Authors:  L Yang; S Feng; Y Yang
Journal:  Cancer Gene Ther       Date:  2016-11-18       Impact factor: 5.987

2.  ERα upregulates the expression of long non-coding RNA LINC00472 which suppresses the phosphorylation of NF-κB in breast cancer.

Authors:  Zhanwei Wang; Dionyssios Katsaros; Nicoletta Biglia; Yi Shen; Lenora Loo; Xiao Yu; Hongyan Lin; Yuanyuan Fu; Wen-Ming Chu; Peiwen Fei; Yan Ni; Wei Jia; Xiaobei Deng; Biyun Qian; Herbert Yu
Journal:  Breast Cancer Res Treat       Date:  2019-03-04       Impact factor: 4.872

3.  Identification of differentially expressed protein-coding genes in lung adenocarcinomas.

Authors:  Luyao Wang; Shicheng Li; Yuanyong Wang; Zhenxue Tang; Chaolong Liu; Wenjie Jiao; Jia Liu
Journal:  Exp Ther Med       Date:  2019-12-06       Impact factor: 2.447

4.  Identification of key genes and biological pathways in lung adenocarcinoma via bioinformatics analysis.

Authors:  Yuanyuan Wang; Zihao Zhou; Liang Chen; Yuzheng Li; Zengyuan Zhou; Xia Chu
Journal:  Mol Cell Biochem       Date:  2020-11-01       Impact factor: 3.396

5.  BUB1 Is Identified as a Potential Therapeutic Target for Pancreatic Cancer Treatment.

Authors:  Ming Li; Xiaoyang Duan; Yajie Xiao; Meng Yuan; Zhikun Zhao; Xiaoli Cui; Dongfang Wu; Jian Shi
Journal:  Front Public Health       Date:  2022-06-13

6.  STING Promotes Homeostasis via Regulation of Cell Proliferation and Chromosomal Stability.

Authors:  Diana Rose E Ranoa; Ryan C Widau; Stephen Mallon; Akash D Parekh; Claudia M Nicolae; Xiaona Huang; Michael J Bolt; Ainhoa Arina; Renate Parry; Stephen J Kron; George-Lucian Moldovan; Nikolai N Khodarev; Ralph R Weichselbaum
Journal:  Cancer Res       Date:  2018-11-27       Impact factor: 12.701

7.  Identification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework.

Authors:  Ehsan Pournoor; Zaynab Mousavian; Abbas Nowzari Dalini; Ali Masoudi-Nejad
Journal:  Sci Rep       Date:  2020-03-19       Impact factor: 4.379

8.  LINC00472 expression is regulated by promoter methylation and associated with disease-free survival in patients with grade 2 breast cancer.

Authors:  Yi Shen; Zhanwei Wang; Lenora W M Loo; Yan Ni; Wei Jia; Peiwen Fei; Harvey A Risch; Dionyssios Katsaros; Herbert Yu
Journal:  Breast Cancer Res Treat       Date:  2015-11-13       Impact factor: 4.872

9.  Novel aromatic polyketides from soil Streptomyces spp.: purification, characterization and bioactivity studies.

Authors:  Sunita Bundale; Deovrat Begde; Dhanashree Pillai; Karishma Gangwani; Nandita Nashikkar; Tukaram Kadam; Avinash Upadhyay
Journal:  World J Microbiol Biotechnol       Date:  2018-04-24       Impact factor: 3.312

10.  [Screening of cell cycle-related genes regulated by KIAA0101 in gastric cancer].

Authors:  Zhi Wang; Chengxue Dang; Rong Yan; Hao Zhang; Dawei Yuan; Kang Li
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2018-09-30
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