- Open Access
Downregulation of dystroglycan glycosyltransferases LARGE2 and ISPD associate with increased mortality in clear cell renal cell carcinoma
© Miller et al. 2015
- Received: 21 March 2015
- Accepted: 17 July 2015
- Published: 30 July 2015
Dystroglycan (DG) is a cell-surface laminin receptor that links the cytoskeleton to the extracellular matrix in a variety of epithelial tissues. Its function as a matrix receptor requires extensive glycosylation of its extracellular subunit αDG, which involves at least 13 distinct genes. Prior work has shown loss of αDG glycosylation in an assortment of carcinomas, including clear cell renal cell carcinoma (ccRCC) though the cause (s) and functional consequences of this loss are still unclear.
Using The Cancer Genome Atlas (TCGA) database, we analyzed the DG glycosylation pathway to identify changes in mRNA expression and correlation with clinical outcomes. We validated our findings with a cohort of 65 patients treated with radical nephrectomy by analyzing DG glycosylation via immunohistochemistry and gene expression via qRT-PCR.
Analysis of TCGA database revealed frequent dysregulation of a subset of DG glycosyltransferases. Most notably, there was a frequent, significant downregulation of GYLTL1B (LARGE2) and ISPD. DG glycosylation is frequently impaired in ccRCC patient samples and most strongly associates with downregulation of GYLTL1B.
Reduced levels of GYLTL1B and ISPD mRNA associated with increased patient mortality and are the likely cause of αDG hypoglycosylation in ccRCC.
- (MESH Terms)
- Renal cell
Renal cell carcinoma is a highly prevalent disease that will newly affect approximately 64,000 people in 2014 . ccRCC is the most common histologic subtype of renal cell carcinoma and exhibits a 5-year disease-specific survival rates of 50–69 % [2, 3]. Currently, the primary prognostic information for ccRCC is the Fuhrman nuclear grade (a grading system based on nuclear size and morphology) and disease staging at the time of resection . Molecular understanding of this disease has begun to emerge in recent years with two critical papers defining the molecular subtypes of ccRCC [5, 6].
DG is an extracellular matrix receptor which links the extracellular matrix to the actin cytoskeleton . DG is composed of the glycosylated extracellular alpha subunit that is non-covalently bound to the transmembrane beta subunit . DG expression and glycosylation are frequently downregulated in many tumor types [9–17], and loss of αDG glycosylation associates with increased mortality in ccRCC patients [18, 19]. Loss of αDG glycosylation contributes to both invasive and proliferative phenotypes in cancer cells [20–22]. Proper glycosylation is absolutely required for αDG’s function as an extracellular matrix receptor . Therefore derangements of the αDG glycosylation pathway may underlie its dysfunction in cancer. Indeed several studies have identified reduced expression of individual enzymes including LARGE, LARGE2 and β3GNT1 in breast and prostate cancer associated with hypoglycosylation of αDG [12, 21, 22, 24]. However, to date, there has not been a comprehensive analysis of the DG glycosylation pathway in any tumor type and mechanisms underlying loss of αDG glycosylation in ccRCC remain undefined.
The glycosylation of αDG is complex and relies upon the concerted action of at least 13 distinct genes. The initial O-mannosylation of DG requires the combined activity of the protein O-mannosyltransferases 1 and 2 along with the isoprenoid-synthase domain containing protein (ISPD) whose enzymatic activity is unknown [25, 26]. Following O-mannosylation, several enzymes including POMGnT1, fukutin, and fukutin related protein (FKRP) accomplish further glycan modification [27–29] DG requires phosphorylation of its O-mannose for recognition by LARGE and subsequent glycan chain extension . This phosphorylation depends upon a number of more recently identified proteins including glycosyltransferase-like domain containing 2 (GTDC2), β-1,3-N-acetylgalactosaminyltransferase2 (B3GALNT2), and SGK196 . From this phosphorylated glycan, LARGE and/or LARGE2, working in concert with B3GnT1, then act through combined xylosyltransferase and glucoronyltransferase activities to generate a repeating disaccharide that is the functional, matrix-binding glycan for DG [21, 30, 32–34]. Loss of function of any of these enzymes, with the exception of LARGE2, has been shown to cause one of a spectrum of muscular dystrophies referred to as the alpha-dystroglycanopathies (for a recent review see ). LARGE2, unlike the other glycosyltransferases, is not highly expressed in skeletal muscle or neural tissue, but does show higher-level expression in the kidney . Due to the number of enzymes required for functional DG glycosylation, a large-scale database of tumor genetics is necessary for optimal investigation of the key components of the pathway.
Herein, we utilize the TCGA database to analyze the DG glycosyltransferase pathway and identify a number of genes in this pathway that strongly correlate with tumor grade and stage. We further demonstrate that downregulation of these genes associate with increased overall mortality. Furthermore, we demonstrate a reduction in αDG glycosylation and expression within a case control cohort of ccRCC patients. Finally, we showed that the levels of GYLTL1B (the gene encoding the LARGE2 enzyme) mRNA most strongly correlate with hypoglycosylation of αDG in a cohort of ccRCC patient samples.
All TCGA data was obtained using the University of Iowa Institute for Clinical & Translational Science’s (ICTS) data portal. ICTS created a custom database system for storing the large volumes of data required by the TCGA Dataset. This database utilizes a distributed open source platform, Cassandra from the Apache Foundation. Data was extracted from each of the data files downloaded from TCGA website, then loaded into a representative Cassandra table. Once the data was loaded for each type, we were then able to query and combine the data based on the barcode values for each sample. This combination work has been done in several ways. The first attempt was completed using Perl Scripts and direct access to the files. The current system uses a JAVA Web Application, connecting directly to the Cassandra database via a JDBC Driver (https://research.icts.uiowa.edu/tcga/login.html). Clinical data was obtained from the clinical_kirc.tar.gz (06/14/2012), transcript information from the IlluminaHiSeq_RNASeqV2.Level_126.96.36.199 (01/08/2012), and the methylation status from KIRC, HumanMethylation450.Level_188.8.131.52 (05/13/2013) databases. Only normalized, gene specific transcript data was obtained and integrated (rsem.genes.normalized_results). All gene RNA-Seq by Expectation Maximization (RSEM) values were collected and log-transformed to correct for non-normal distribution. The cBioPortal was utilized for copy number alterations and mutational analysis [37, 38].
RNA Extraction & qRT-PCR
13 cases of ccRCC resected in the past 12 months and with available tumor and matched normal tissue were selected. One hematoxylin and eosin (H&E) stained slide and 10 unstained slides (6 μm in thickness) were obtained from FFPE tissue blocks. Areas of interest were marked on the H&E stained slides by pathologist. The H&E stained slide was used as a guide for microdissection of tissues from unstained sections. The paraffin flakes were deparaffinized with 1200 μL of xylene, vortexed, and centrifuged (16,000 g x 5 min). The tissue pellet was washed with 95 % ethanol twice before proceeding with RNA extraction. Total RNA extraction was performed with the RNeasy FFPE Kit (Qiagen, Valencia, CA) according to the manufacturer instruction. Reverse transcription (RT) was performed on 1 ng of RNA with iScript cDNA Synthesis Kit (BioRad, Hercules, CA). 1 μl of each RT reaction mixture, TaqMan probes against GYLTL1B (Hs00403017_g1), DAG1 (Hs00189308_m1), LARGE (Hs00893935_m1), and ISPD (Hs00417152_m1) were used with the TaqMan Universal PCR Master Mix) for the subsequent quantitative real-time PCR (qPCR) according to manufacturer’s instruction (Applied Biosystems, Foster City, CA). The results were analyzed by the delta-delta Ct method and using the housekeeping gene PPIA (Hs04194521_s1) as a reference for calculation.
All human samples, retrospective and de-identified, were obtained and handled according to the IRB approved protocol #201306718. Formalin-fixed, paraffin-embedded (FFPE) patients’ samples were obtained from the archives of Department of Pathology, University of Iowa (UI) Hospitals and Clinics (Iowa City, IA). All patients had received partial or radical nephrectomy with negative surgical margins. The slides were reviewed and the diagnoses of ccRCC were confirmed by two pathologists. Blocks with the highest tumor percentage and lowest amount of contaminating materials (non-neoplastic cells, necrosis, etc.) were selected for immunohistochemistry and gene expression studies.
Immunohistochemistry (IHC) studies for DG were performed by the UI Department of Pathology Core Lab as described previously . Antibodies used for staining include IIH6 (1:100, Santa Cruz Biotechnology, Dallas, TX) and 8D5 (1:100, Leica Biosystems, Buffalo Grove, IL). The pathologists were blinded to staging status at the time of analysis. IHC stained slides were scored by two pathologists independently according to a quartile system whereby: 3: positive (≥90 % of cells showing intensely membrane staining); 2: heterogeneous (regional positivity with >10 % of cells negative); 1: reduced (>10 % of cells negative and decreased intensity of membrane staining); and 0: loss (≤1 % of cells positive). There was 100 % agreement between the 2 independent pathologists. Staining controls are provided as Additional file 1 Figure S1.
To compare expression in tumor-normal matched samples, we carried out paired t-tests of differences in expression on the log scale. Associations between expression and stage/grade were calculated using a proportional odds regression model, adjusting for age and sex. Here, stage and grade were treated as ordinal outcomes. The effects of differential expression on mortality were assessed using a proportional hazards model, again adjusting for age and sex. Separate models were fit for each gene to assess the marginal associations between each gene and disease progression as well as a joint model including expression levels for all genes in order to isolate the effects of individual genes within the context of the entire DG glycosylation pathway. Kaplan-Meier curves were also fit to illustrate the effects of differential expression on overall mortality. Fisher’s exact test was used to assess the association between loss of expression or glycosylation and disease recurrence.
The DG glycosylation pathway is perturbed in ccRCC
Correlation of DG-associated glycosyltransferases with nuclear grade and tumor stage in ccRCC
Pathway analysis identifies genes associated with patient mortality
DG Hypoglycosylation in ccRCC Correlates with Loss of GYLTL1B mRNA
DG Expression and glycosylation are reduced in ccRCC as assessed by immunohistochemical staining
Cohort characteristics of patients utilized within this study
Number of patients
Median Time to Relapse (years)
Median Followup (years)
GYLTL1B gene expression associates with increased methylation of its promoter region
DG has been identified as a potential prognostic biomarker in a number of different malignancies [14–17] and several studies have sought to identify the underlying mechanisms responsible for its hypoglycosylation [12, 21, 22, 24]. Herein we utilized the TCGA database to perform a large-scale, unbiased analysis of the αDG glycosylation pathway. Our results indicate a significant and substantial loss of GYLTL1B expression levels through both the TCGA and our tissue-based analysis. LARGE2 was only recently recognized as being a critical mediator of dystroglycan in prostate epithelial cells , and these findings support its function in renal epithelium as well. Additionally, this work indicates a strong inverse association between loss of ISPD and mortality suggesting a critical role for this enzyme during disease progression. Interestingly, this indicates disruption of dystroglycan glycosylation at both early (ISPD) and late (LARGE2) events involved in the production of the laminin-binding DG glycan. This study is the first to indicate a role for ISPD in contributing to DG hypoglycosylation in tumors. Unlike other genes that have been implicated in DG hypoglycosylation in cancer, such as B3GnT1, LARGE, and LARGE2; ISPD does not yet have a defined glycosyltransferase enzymatic activity and how it is involved in the process of DG glycosylation is unclear . However, we have not shown here that loss of ISPD is causally involved in the loss of DG glycosylation in ccRCC, and this is a clear focus for future studies.
We found that DAG1 and POMGNT2 both exhibit high rates of loss of heterozygosity in ccRCC, most likely due to their proximity to the VHL gene. Thus, ccRCC represents a tumor type that is uniquely sensitized to disruption of DG function. Loss of heterozygosity for any of the genes involved in DG glycosylation has not been reported in any other tumor type to date. Despite this, we did not find evidence for secondary mutations in DAG1 (or in any of the other genes examined). Nonetheless, the frequency of DG disruption at the protein level is very high, with nearly 90 % of α- and β-DG stained samples exhibiting reduction compared to the normal tubules. While this work showed that a number of the DG glycosyltransferases were downregulated in ccRCC tumors, it was still unclear whether downregulation of any of these enzymes lead to hypoglycosylation of αDG. However, our data indicates that GYLTL1B was significantly downregulated in ccRCC tumors that showed loss of αDG glycosylation by immunostaining when compared to adjacent normal tissue. Given this observation and our findings from the TCGA dataset, it is highly likely that a reduction of GYLTL1B expression is a frequent causative event in αDG hypoglycosylation in ccRCC, consistent with our previous findings in prostate cancer .
Building upon the findings above, we sought to determine whether DG could be used as a predictor of disease recurrence following surgical resection with curative intent. While loss of DG immunostaining has previously been linked to grade and increased mortality in patients with ccRCC [18, 19], we did not observe any association between DG expression and disease recurrence in our cohort of 63 patients. We note that these previous studies utilized a binary scoring system of high/low. We believe that our quartile system provides a more accurate representation of the various staining patterns we observed in our studies. Consistent with the previous studies, we did observe a loss of αDG staining in patients that recur, although this finding was not statistically significant in our study. Indeed, a limitation of this aspect of our study is the relatively small size of our retrospective cohort. A larger cohort would be necessary to evaluate significance of the association between DG staining and disease recurrence. It will also be of interest to determine if loss of DG glycosylation is associated with lympho-vascular invasion, which was also not significantly associated with DG glycosylation status (data not shown).
Little is known about the transcriptional control of the genes that are involved in DG glycosylation. We found evidence that the promoter region of GYLTL1B is hypermethylated relative to normal tissue indicating that this mechanism may be involved in silencing its expression in ccRCC, similar to what has been proposed for LARGE in breast cancer . While the magnitude of this methylation is not strikingly high, it demonstrates a clear inverse association with gene expression indicating at least a measure of gene regulation. Utilization of bioinformatics allowed us to analyze if any of these genes might be coordinately regulated. This analysis identified a number of genes that have moderate levels of correlation that suggest the possibility of shared upstream regulatory control. (Additional file 2: Figure S2). This finding is interesting in light of our recent publication demonstrating selective regulatory control of LARGE and LARGE2 by SNAIL and/or ZEB1 . Additionally, ZEB1 has been implicated in promoting chromatin modifications, possibly coupled to DNA methylation that could account for LARGE2 promoter hypermethylation [43–45].
We thank Dr. Michael Gailey for his assistance in reviewing and scoring histopathology samples. This study was supported by NIH grant CA130916.
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