WHO grade of gliomas is currently the major indicator used for prognosis and for treatment protocols. The 2007 WHO grading system 
uses histopathological characteristics such as brisk mitotic activity (Ki-67: MIB Li) 
, increased cellularity, necrosis, and frequent invasion of brain parenchyma to classify gliomas into four broad grades. Sophisticated methods of assessing survival based on other clinical parameters have also been developed 
In combination with the current clinical methods, gene expression profiles are potentially powerful predictors of survival 
with additional potential as markers for diagnosis, as a guide to therapy, and even as potential therapeutic targets. Genetic analyses over the past several years have defined the major targets that are associated with the formation of glioma including EGRF, PTEN, TP53, IDH1/2, CDKN2A, MGMT, and others 
, which likely have downstream effects on other genes to improve tumor survival. For example, p53 inhibits tumor cell growth through the indirect regulation of CDC20, one of the genes of interest in this report 
. While the lesions involved in causing progression in glioma may vary from patient to patient, these changes likely converge on a few critical regulatory pathways. Any expression changes in these common pathways could prove useful in prognosis because the changes may occur in most tumors, regardless of the prime movers in disease progression in any particular tumor.
We further investigated hundreds of microarray profiles of glioma and a few normal brain tissues that had previously been used successfully to predict survival time in gliomas 
. Our goal was to identify a small subset of markers that could reliably substitute for or improve uponKi-67 measurements using MIB Li 
or multigene expression profiles, and then to confirm these markers on independent samples.
To identify pathways that might undergo changes in RNA expression with increasing grade using the WebarrayDB cross-platform analysis suite 
. Among a number of different groups of functionally related genes that correlated with grade, one group contained genes associated with the spindle assembly checkpoint (SAC). Importantly, if these genes were to be of potential clinical utility, expression of at least two of these SAC genes appeared to correlate better with grade than did Ki-67 RNA (). The SAC is involved in accurate chromosome segregation between two daughter cells during mitosis. There are already data in the literature that suggest that SAC gene expression may be associated with aggressiveness of cancers. Studies of malignant bladder cancer and breast cancer suggested that increased mRNA levels of mitotic checkpoint genes are correlated with tumor progression 
. A strong correlation between expression of BUB and gastric cancer cell proliferation has been observed 
. Combined expression of BUB1B and PINK1 was the best predictor of overall survival in adrenocortical tumors by microarray 
. Many immunohistochemistry investigations have shown that overexpression of BUBR1, the protein of BUB1B, significantly correlates with higher histological grade, advanced pathological stage, and high cell proliferation in different types of tumors, e.g. with tumor recurrence and disease progression in bladder cancer 
; with deep invasion, lymph node metastasis, liver metastasis, and poor prognosis in gastric cancer 
; and with advanced stage, serous histology and high grade in ovarian cancer 
. On the other hand, a lower level of BUBR1 correlated with low recurrence-free survival rates in ovarian cancer 
and aneuploidy in colorectal cancer 
. BUBR1 was used as an independent predictor for poor prognosis in pancreatobiliary-type tumors by tissue microarray 
. However, correlations of SAC gene expression with aggressiveness have not been reported for glioma except for CDC20 that has higher expression in glioblastoma than in low grade glioma 
To validate the RNA expression changes in SAC genes in the microarray data (), we performed qPCR analysis on RNA from six additional normal brain samples and 38 additional gliomas that had survival time data. The eight SAC genes were significantly overexpressed at the RNA level in glioblastomas (grade IV) in comparison to controls (), and all were almost monotonically increased in expression along with grade, indicating that they might serve as prognostic glioma markers. Two genes, BUB1B and CDC20, outperformed Ki-67 RNA. This is consistent with past reports for colorectal carcimona in whichMIB-1 Li had no significant correlation with Ki-67 mRNA expression 
BUB1 and TTK were significantly differentially expressed between low grade gliomas and normal brain tissues (p
<0.01, ). Further research will indicate if these two genes have utility in classifying early stage tumors. These genes have previously been associated with cancer though not glioma; BUB1 mutation was associated with lymph node metastasis and shorter relapse-free survival after surgery in colorectal cancers 
, TTK had an increased expression level in anaplastic thyroid carcinoma 
, and TTK expression correlated with tumor node metastasis (TNM) stage in gastric cancer 
We used gene expression of SAC genes to build models that correlate with grade. A4-gene regression model (Equation 1) was sufficient to generate 92.1% identity with grade. Considering the potential bias in identification of grades by histological diagnoses and the arbitrary boundaries between adjacent grades, this performance is extremely good.
In a multivariate proportional hazards model without genes (Model 1 in ) WHO grade is identified as a significant factor for survival (p
<0.05). However, WHO grade adds no power to classification when SAC gene expression profiles are added to the model (Model 2 in ). MIB labeling index and mitotic index (MI) also add no additional information in model 2. WHO grade is not present when this model is optimized to identify factors that best predict outcome (Model 3 in ). Although MIB Li is retained in this model, it is a minor factor compared to BUB1B expression. Thus, qPCR assays of SAC genes, particularly BUB1B, might be used as an objective complement to histological diagnosis for identification of glioma grades, MIB Li, MI, and the multigene RNA profiles that have been proposed 
illustrates that BUB1B was able to distinguish among grade IV samples with significance (p
0.003). Two previous studies are used microarray data to subclassify glioblastomas (grade IV) 
. The SAC genes were not prominent among these genes. However, given that our study encompassed all grades, this lack of concordance is not surprising. We will need larger datasets in order to validate whether we are able to define subclasses of glioblastoma using just SAC genes. In the future, it will be worth studying whether all three sets of predictors can be combined into an even more powerful predictor, at least for grade IV glioblastomas.
Our model of BUB1B RNA expression provided median survival estimates very close to the observed median survival rate for the WHO grades in leave-one-out cross validation. There are inevitably some outliers in any model of clinical data () because the models cannot take into account other important factors such as differences in sample sources, treatments, genes in other pathways, and other potential biological factors. This issue can be addressed by using samples with more detailed follow-up examinations and qPCR assays for more related genes. It is notable how few outliers are seen even without this additional information.
Why do SAC gene expression levels increase with increasing grade in gliomas? The simplest explanation is that expression is simply correlated with the rate of cell division, which is, in turn correlated with survival time.
Another possibility is that increased SAC gene expression is a homeostatic response to defects in other molecular components. The mitotic spindle assembly checkpoint ensures that cells with defective mitotic spindles or defective interaction between the spindles and kinetochores do not initiate chromosomal segregation during mitosis. The SAC can protect the cell from chromosome mis-segregation and aneuploidy during cell division 
. Increased chromosomal instability is a major driving force for tumor development and progression 
. In general, tumor cells become increasingly aneuploid with tumor progression 
. Increased SAC gene expression is correlated with aneuploidy in breast cancer 
. Previous studies have proven that defects in the mitotic checkpoint might contribute to tumorigenesis 
. However, total loss of checkpoint gene function can be catastrophic even for cancer cells 
, making the SAC a potentially interesting target for therapy in brains, where the side effect of inhibiting cell division may have little consequence.
Another possible mechanism for the changes in expression we observed would be mutations in one or more SAC gene. To date, only one study has found such mutations; BUB1 mutation is associated with lymph node metastasis and shorter relapse-free survival after surgery in colorectal cancers 
. In contrast, studies have failed to find mutations in BUB1, BUB1B and BUB3 as a significant causation of chromosomal instability in glioblastomas 
. Mutations were not found in mitotic checkpoint genes in breast cancer 
, bladder cancer 
or gastric cancer 
. Thus, change in SAC gene expression could be due to lesion in other genes that act to increase cell division. A search for epigenetic changes in SAC genes may be fruitful.
Preliminary immunohistochemistry evidence indicates that the proteins encoded by the SAC genes investigated here are also induced in gliomas, with BUB1B again being the most significant (). Thus immunohistochemistry might be useful as an alternative to qPCR as a prognostic assay. Furthermore, the spatial resolution of immunohistochemistry within cells or across a tumor might identify tumors where only a portion is highly aggressive, leading to a more accurate survival time prediction compared to qPCR-based estimates from bulk samples.
A single marker such as BUB1B will not capture all the variability in subtypes of glioma. Instead, such a gene may be useful in combination with other markers. Over the past decade, there has been an increasing use of molecular markers in the assessment and management of glioma patients 
. For example, the methylation of MGMT has been shown to be useful as a prognostic biomarker in some circumstances 
; EGFR vIII expression enables identification of a subgroup of tumors with more aggressive behavior 
; and IDH1/IDH2 mutations have strong prognostic value in grade III astrocytomas and in glioblastomas 
. A recent study identified a classifier based on the RNA levels of nine genes that has potential value for therapy optimization in glioblastoma (grade IV), the most advanced form of glioma 
. Another report divided glioblastoma into different subtypes using cluster analysis of microarray expression data from the literature 
. Such differentiation into subclasses could lead to different therapy strategies 
as well as better clinical trial designs.
In conclusion, two SAC genes, BUB1 and TTK, showed increased RNA expression compared to normal brain even in the lowest grades of glioma, perhaps indicating their future utility for differentiating among low grade gliomas. Another SAC gene, BUB1B is highly correlated with survival time, outperforming other markers, including grade and Ki-67 mRNA level. Measuring the expression of BUB1B gene might be a useful addition to the repertoire of clinicians for staging gliomas. This ability to use just one or a small handful of genes to predict outcome could have an impact on clinical trials where matching patients across treatment arms more accurately would lead to a considerable increase in power.