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Abstract

BACKGROUND:

MicroRNAs (miRNAs), a group of non-coding post-transcriptional regulators of gene expression, are dysregulated in clear cell renal cell carcinoma (ccRCC) and play an important role in carcinogenesis. Our prior work identified a subset of miRNAs in pT1 ccRCC tumors associated with progression to metastatic disease.

OBJECTIVE:

To investigate the impact of two of these dysregulated miRNA, miR-15a-5p and -26a-5p, in an effort to elucidate the mechanisms underpinning aggressive forms of stage I ccRCC.

METHODS:

The ccRCC cell line 786-O was transfected with pre-miRs-15a-5p and -26a-5p to rescue expression. Cell proliferation was measured via MT Cell Viability Assay. O-GlcNAc-transferase (OGT), a known protein in ccRCC proliferation, was identified by bioinformatics analysis as a target of both miRNA and validated via luciferase reporter assay to confirm binding of each miR to the 3 ${}^{\prime}$ untranslated region (UTR). OGT protein expression was evaluated via western blotting.

RESULTS:

Luciferase assay confirmed specificity of miR-15a-5p and -26a-5p for the OGT UTR. Western blot analysis for OGT showed reduced expression following co-transfection of both miRNAs compared to negative control or individual transfection. Co-transfection of these miRNAs greatly reduced proliferation when compared to negative control or the individual transfections.

CONCLUSION:

Our results indicate that the dysregulation of miR-15a-5p and -26a-5p contribute cooperatively to the proliferation of ccRCC through their regulation of OGT. These results give insight into the pathogenesis of aggressive early stage ccRCC and suggest potential therapeutic targets for future research.

Keywords

Biomarkers clear cell renal cell cancer (ccRCC)microRNA O-GlcNAc-transferase (OGT)proliferation

1. Introduction

Renal cell carcinoma (RCC) is a heterogeneous group of cancer variants that arise from the renal tubular epithelium [1]. RCC can be subdivided based on pathology (histologic subtypes), genetic, or behavioral characteristics [2]. Of these subgroups, clear cell RCC (ccRCC) is both the most common, representing 75–80% of all RCC, and most overrepresented in metastatic RCC, at about 90% of cases [3]. Traditionally, pathologic stage has been the strongest prognostic indicator for ccRCC progression [4], however recent studies have begun to look at additional features, not traditionally included in staging rubrics, with the goal of improving both prognostic accuracy and tailoring treatment algorithms. As a result of the rise of active surveillance of small tumors during the past decade [5], interest in alternative methods of classifying these tumors to inform clinical management has also risen. One area of particular interest involves pathologic stage I (pT1, N0, Mx) ccRCC. These tumors are associated with favorable patient outcomes; however, the rise of wide-spread cross sectional imaging has created a disproportionate increase in incidence of pT1 RCC compared to other tumor stages [6]. Despite the excellent prognosis for these tumors, with disease specific 5-year survival cited at 81–92%, a small minority of patients will progress to metastatic disease [7]. A prior study from our laboratory addressed this issue by investigating the use of microRNAs (miRNAs) as biomarkers to identify tumors that progress to metastasis [7].

miRNAs are a class of small ( $\sim$ 20 nucleotide) non-coding RNA molecules that post-transcriptionally regulate gene expression via RNA interference [8]. The miRNAs often function by base-pairing to the 3 ${}^{\prime}$ UTR of the target transcript and negatively regulate its expression [8]. Dysregulation of miRNAs has been proposed to be integral in cancer pathogenesis in urologic cancers [9]. The mechanisms through which miRNA dysregulation exerts its pathophysiologic effect vary widely according to the binding profiles of the specific miRNAs involved. One common thread seen across many cancer types is interference with key biological processes such as proliferation, differentiation, and apoptosis [10]. Specifically for ccRCC, miRNA signatures have been shown to associate closely with early relapse after nephrectomy [11]. We previously identified miR-15a-5p and miR-26a-5p, among others, as downregulated in small ccRCC tumors (pT1 $\leqslant$ 5 cm) in patients that subsequently progressed to metastatic disease, and decreased expression of miR-15a-5p and miR-26a-5p were significantly associated with poorer cancer specific survival [7].

Prior studies have established the prognostic value of miR-15a-5p in a wide variety of cancers, however its role in RCC is in dispute [12]. Specifically, miR-15a-5p has been demonstrated to suppress tumor proliferation in a wide variety of cancer types [13, 14, 15], with downregulation leading to unchecked proliferation. In studies comparing its expression in ccRCC tumors compared to corresponding adjacent normal tissue, reports are conflicting [16, 17].

Numerous studies have linked a downregulation of miR-26a-5p with poor patient prognosis in RCC. Expression levels of miR-26a-5p were lower in RCC tumors of patients who developed tumor relapse [11]. Decreased miR-26a-5p expression was also correlated with aggressiveness in ccRCC tumors [10]. As is seen with miR-15a-5p, miR-26a-5p has also been reported to suppress tumor proliferation in various tumor types, including osteosarcoma and breast cancer [18, 19].

The influence of a particular miRNA at the cellular level is determined by the specific targets and pathways through which they interact. An individual miRNA can interact with the transcripts of multiple potential protein targets, and each protein transcript can be affected by many different miRNAs binding at unique sites [20]. Additionally, multiple miRNA may act on a single target resulting in a cooperative effect, greatly amplifying the impact of each individual miRNA on the target [20]. The role a specific miRNA plays in a disease state can be elucidated by first using algorithmic methods to identify potential protein targets [21] and then using experimental methods to verify those targets [20]. This was the approach we took to examine the impact of miR-15a-5p and miR-26a-5p in ccRCC.

One such target predicted to interact with both of these miRNAs is O-GlcNAc-transferase (OGT) [22, 23, 24], which is an enzyme that catalyzes the addition of an O-Linked $\beta$ - $N$ -acetylglucosamine (O-GlcNAc) sugar to a target protein [25]. This protein is considered to be a hallmark of cancer [26], and additionally has been shown to be dysregulated in ccRCC [27, 28]. Wang et al. identified OGT as an oncogene that is directly related to ccRCC cellular proliferation, and through immunohistochemistry analysis demonstrated that its expression is positively correlated with Fuhrman grade [28]. Further, they showed that OGT interacts with EGFR [28], a key protein in the epithelial-mesenchymal transition (EMT) cancer pathway [29]. EMT is involved in tumor progression with metastatic expansion, marked by improved migratory and invasive properties [30]. For these reasons, OGT and its downstream targets are an attractive protein for further investigation, and to our knowledge studies into the regulation of OGT in ccRCC are lacking.

The aim of this study is to identify the potential influence of miR-15a-5p and miR-26a-5p on the proliferation of ccRCC cells, in addition to their potential interaction with OGT. We investigated the link and potential cooperation between these miRNA and OGT in order to better understand the roles of miR-15a-5p and miR-26a-5p in the development of metastatic potential in small stage I ccRCC tumors.

2. Materials and methods

2.1 Cell culture

The human ccRCC line 786-O (ATCC ${}^{\text{\textregistered}}$ CRL-1932 ${\texttrademark}$ , American Type Culture Collection, Manassas, VA, USA) was cultured under standard conditions (37 ${}^{\circ}$ C, 5% CO ${}_{2}$ ). 786-O, derived from a primary epithelial clear cell adenocarcinoma, was maintained in RPMI 1640 (ATCC) supplemented with 10% fetal bovine serum and penicillin/streptomycin. 786-O cells are VHL-mutant, with altered HIF and VEGF (Vascular endothelial growth factor) pathways [31].

2.2 Prediction of miRNA targets

Target search for predicted protein targets was conducted using a combination of miRcode (http://mircode. org/) [22], miRmap (https://mirmap.ezlab.org/) [23], and PicTar (https://pictar.mdc-berlin.de/) [24]. We chose a gene based on predicted interaction on all search engines for miR-15a-5p and miR-26a-5p. We additionally focused on a target that has previously been demonstrated to play a role in tumorigenesis, and has been shown to be dysregulated in ccRCC.

2.3 Verification of protein targets in ccRCC

Protein and gene transcript expression levels in ccRCC tumors were investigated using the Human Protein Atlas Pathology database (https://www.proteinatlas. org/) [27]. Survival analyses were performed in an effort to identify targets where high expression correlated with poorer survival, using the TCGA transcript expression dataset for ccRCC, downloaded from the Human Protein Atlas. The median expression level was used as the cutoff, and groups were compared by log-rank tests where a $p$ -value less than 0.05 was deemed to be significant.

2.4 Cell transfection of pre-miR constructs

Cells were seeded into CELLSTAR six-well dishes (Greiner, Kremsmunster, Austria) at a density of 2 $\times$ 10 ${}^{4}$ cells/mL. Cell densities were determined using a hemocytometer (American Optical Corporation, Buffalo, NY, USA). Cells were transfected with pre-miR-15a-5p (Cat. #AM17100, Assay ID PM10235, Ambion, Austin, TX, USA), pre-miR-26a-5p (Cat. #AM17100, Assay ID PM10249, Ambion), pre-miR-Precursor Negative Control #1 (Cat. #AM17110, Ambion), or a combination of pre-miR-15a-5p and pre-miR-26a-5p at a final concentration of 30 nM using siPORT NeoFX transfection reagent (Invitrogen, Carlsbad, CA, USA) according to manufacturer’s protocol. The combination transfection was prepared at the same overall miRNA concentration and volume as the negative control and individual pre-miR transfections (15 nM of each pre-miR for a combined overall concentration of 30 nM). RNA from an additional well of cells was harvested using the Qiagen miRNeasy mini kit (Qiagen, Hilden, Germany). RNA was analyzed via qRT-PCR (Applied Biosystems, Foster City, CA, USA) to verify overexpression of each miRNA compared to control. RNU43 functioned as the normalization control.

2.5 Luciferase assay

A vector was prepared containing the human OGT 3 ${}^{\prime}$ UTR linked to the Firefly luciferase gene in addition to the Renilla luciferase gene for normalization (Genecopoeia, Rockville, MD, USA). The OGT 3 ${}^{\prime}$ UTR contained the theoretical interaction site between each miRNA and OGT. Cells were seeded into CELLSTAR 24-well dishes (Greiner) at a density of 5 $\times$ 10 ${}^{4}$ cells/ml. 24 hours after plating, the media was replaced with Opti-MEM™ Reduced Serum Medium (31985062, Invitrogen). Cells were transfected with either pre-miR-15a-5p, pre-miR-26a-5p, pre-miR-Precursor Negative Control #1, or a combination of pre-mir-15a-5p and pre-miR-26a-5p (30 nM total) in addition to the designed vector (0.2 $\mu$ g). The Endofectin transfection agent was used in the delivery of the vector and the pre-miRNA into the cells (Genecopoeia). Cells were lysed 24 hours after transfection following the manufacturer’s protocol and luminescence was measured using the Luc-Pair™ Duo-Luciferase Assay kit (Genecopoeia). Firefly luminescence was normalized relative to Renilla luminescence. A minimum of 5 independent trials was performed per condition.

2.6 Western blot analysis

Cell lysates were prepared from six-well dishes displaying 70–80% confluency, 48 hours after transfection. Cells were lysed in 100 $\mu$ L/well of boiled 1 $\times$ SDS-Laemmli (250 mM Tris-HCl, 4% SDS, 10% glycerol, 0.003% bromophenol blue) by scraping the dishes manually followed by shearing with a 24 gauge needle. Total protein concentration for each sample was determined using the BCA assay (Pierce, Waltham, MA, USA). Gels were loaded using an equal volume and concentration of each protein sample, along with 2 $\mu$ L/100 $\mu$ L $\beta$ ME. Each sample was boiled for 5 minutes and loaded in a novel lane in a 4–15% gradient polyacrylamide gel (Mini-PROTEAN ${}^{\text{\textregistered}}$ TGX™, Bio-Rad, Hercules, CA). Proteins were transferred onto nitrocellulose using the iBlot™ 2 gel transfer system (Invitrogen). Membranes were blocked in 10% milk in TBS with 0.05% Tween (TBST) for 3 hours and placed on OGT primary antibody (ab177941, Abcam, Cambridge, UK) at a concentration of 1:1000 overnight at 4 ${}^{\circ}$ C. Blots were then washed with TBST and incubated for 1 hour at room temperature with rabbit specific secondary antibody (NA934V, Amersham Biosciences, Amersham, UK) at a concentration of 1:1000. Blots were washed again in TBST and developed using an ECL kit (Pierce). Densitometry was conducted using the ImageJ software (National Institute of Health, Bethesda, MD, USA) as previously described [32], and OGT values were normalized using GAPDH primary antibody (2118, Cell Signaling Technology, Danvers, MA, USA) at a concentration of 1:2000. A minimum of 9 independent trials was performed per condition.

2.7 Cell proliferation assay

Cells were seeded into 35 mm dishes (Corning, Corning, NY, USA) at a density of 1.56 $\times$ 10 ${}^{4}$ cells/mL. Cells were transfected with either pre-miR-15a-5p, pre-miR-26a-5p, pre-miR-Precursor Negative Control #1, or a combination of pre-miR-15a-5p and pre-mir-26a-5p. Half of the media was replaced with RealTime-Glo™ MT Cell Viability Assay (Promega, Madison, WI, USA) reagents in serum-free RPMI, at a final concentration of 1:2000, 48 hours after cell transfection. Measurements were taken in the GloMax ${}^{\text{\textregistered}}$ 20/20 Luminometer (Promega) 1 hour following the addition of the Cell Viability Assay reagents. Each experiment was set up in duplicate and a minimum of 8 independent trials was performed per condition.

2.8 Statistical analysis

A one-way repeated measures ANOVA was conducted with post hoc Fisher’s least significant difference tests using GraphPad Prism version 8.0.0 for Mac (GraphPad Software, San Diego, CA, USA, www.graphpad.com) to determine whether a statistically significant difference in traits exists for cells transfected with the pre-miR negative control or the pre-miR-15a-5p and/or -26a-5p. Corresponding plots depict the mean relative response rate for cells transfected with the pre-miR-15a-5p and/or -26a-5p relative to the pre-miR negative control. A $p$ -value $<$ 0.05 was considered statistically significant.

3. Results

3.1 OGT is overexpressed in ccRCC

OGT expression levels in stage I ccRCC were analyzed using data from the Human Protein Atlas database [27] v19.3 (https://www.proteinatlas.org/EN SG00000147162-OGT/pathology/renal+cancer/KIRC), setting the median expression level as cutoff. High expression levels of OGT were significantly correlated with poorer patient outcomes (Fig. 1). The mean survival time estimate for stage I patients with high or low expression of OGT was 7.2 years (95% CI: 6.5–7.9 years) or 10.1 years (95% CI: 9.0–11.1 years), respectively, $p=$ 0.031.

Figure 1.

High OGT expression in stage I ccRCC tumors is associated with poor patient outcomes. OGT expression and survival data for stage I ccRCC patients available from the Human Protein Atlas v. 19.3 (https://www.proteinatlas.org/ENSG00000147162-OGT/pathology/ renal+cancer/KIRC). Kaplan-Meier analysis shows the relationship between OGT expression and patient outcomes ( $n=$ 263). Higher OGT expression was significantly associated with decreased survival ( $p=$ 0.031), determined by log-rank tests.

3.2 OGT is a direct target of miR-15a-5p and miR-26a-5p

Using the miRNA binding algorithms miRcode [22], miRmap [23], and PicTar [24] we found that miRs-15a-5p and -26a-5p both have high predicted interactions with OGT with good inter-algorithm agreement. The conserved binding sites identified were the 3 ${}^{\prime}$ UTR bases of OGT that were consistent across all search engines, corresponding to position 907-913 for miR-15a-5p and 1533–1538 for miR-26a-5p (Fig. 2A). A luciferase reporter assay was conducted to confirm activity of the 3 ${}^{\prime}$ UTR of OGT in ccRCC cells. The luminescence ratio (Renilla/Firefly) was calculated to compare 3 ${}^{\prime}$ UTR binding activity between groups. Decreased expression ratios were observed for all three test conditions. While no significant difference was seen between the ratio for miR-15a-5p with the negative control, transfection with miR-26a-5p or both miRNA in combination significantly decreased the luminescence ratio (Fig. 2B). Western blot analysis was used to validate the influence of these miRNA on OGT expression. Here, as seen in our luciferase reporter assay, while no significant difference was observed between miR-15a-5p and negative control, transfection with miR-26a-5p or a combination of the two miRNA resulted in significantly decreased OGT expression compared to negative control (Fig. 2C and D). Furthermore, cells transfected with both miRNA exhibited significantly decreased OGT expression compared to cells transfected with either miR alone (Fig. 2C and D). Average luciferase and western blot ratios are detailed in Table 1.

Figure 2.

Effect of miR-15a-5p and miR-26a-5p transfection on OGT expression and cancer phenotype. (A) Predicted interaction between the human 3 ${}^{\prime}$ UTR and miR-15a-5p and miR-26a-5p as predicted by bioinformatics. (B) Relative luciferase activity was assayed in 786-O cells following co-transfection with the pEZX-MT06 vector and miRNA(s) of interest. (C) Representative images of GAPDH and OGT protein expression following transfection with miRs-15a-5p (30 nM), -26a-5p (30 nM), or a combination of the two (15 nM each). (D) Normalized OGT expression levels in 786-O cells following miRNA transfection as determined by western blot. (E) Cellular proliferation in 786-O cells following transfection with miRs-15a-5p (30 nM), -26a-5p (30 nM), or a combination of the two (15 nM each). ${}^{*}P<$ 0.05 vs. given group.

Table 1

Luciferase, Western blot, and Proliferation statistics. Mean ratios and $p$ -values of each transfection condition vs. negative control

Luciferase
$n=$ 5					Ratio
$p$ -value	NC	15a	26a	15a $+$ 26a	mean
NC	x	0.1845	0.0428	0.0049	1
15a		x	0.2711	0.282	0.7365
26a			x	0.4823	0.624
15a $+$ 26a				x	0.5515
Western blot
$n=$ 9					Ratio
$p$ -value	NC	15a	26a	15a $+$ 26a	mean
NC	x	0.1087	0.0414	0.0013	1
15a		x	0.1723	0.0116	0.8703
26a			x	0.0269	0.8181
15a $+$ 26a				x	0.6515
Proliferation statistics
$n=$ 8					Ratio
$p$ -value	NC	15a	26a	15a $+$ 26a	mean
NC	x	0.001	0.0028	$<$ 0.0001	1
15a		x	0.2506	0.0037	0.8774
26a			x	0.0126	0.8333
15a $+$ 26a				x	0.7538

3.3 miR-15a-5p and miR-26a-5p cooperatively suppress ccRCC cell proliferation

miRs-15a-5p and -26a-5p have previously been shown to inhibit cellular proliferation in other cancer types [13, 15, 18]. We performed a cell proliferation assay to assess the impact of these miRNA in ccRCC. Both miRs-15a-5p and -26a-5p significantly inhibited cell proliferation when transfected into 786-O cells compared to negative control (Fig. 2E). Additionally, in cells transfected with both miRs-15a-5p and -26a-5p proliferation was significantly less than cells transfected with either miRNA alone (Fig. 2E). Average proliferation ratios are detailed in Table 1.

4. Discussion

In order to improve prognostic accuracy for ccRCC patients, many studies have focused on identifying biomarker signatures that correlate with patient outcomes. These investigations cover an array of cellular processes including, but not limited to, cellular proliferation, angiogenesis, chemoresistance, metabolism, and mitochondrial dysfunction [33, 34]. In addition, these biomolecules can be measured in a variety of matrices. For example: Papale et al. found the urinary concentration of RKIP and p-RKIP to be prognostic of patient outcomes in ccRCC patients [35]. $\alpha$ Klotho in both serum and tissue samples was also found to be predictive of ccRCC, with low expression observed in patients with more advanced disease [36].

Evidence is mounting suggesting that dysregulation of miRNAs can be predictive of patient outcomes as well, playing an important role in cancer pathophysiology. These translational regulators have been linked to altered protein expression of both oncogenic and tumor suppressor proteins. Unsurprisingly, alterations in miRNA expression have also been linked to patient outcomes, presumably through alterations of key cancer related protein expression. Our previous study identified miRs-15a-5p and -26a-5p, among others, as dysregulated miRNA biomarkers in small stage I ccRCC tumors with an aggressive phenotype. These were also shown to have prognostic significance [7]. While each of these miRNA can potentially bind to and silence hundreds of different mRNA transcripts, here we have gained further insight into the tumor suppressor functionality of miR-15a-5p and miR-26a-5p in ccRCC. This study investigated the impact of these miRNAs, individually and in combination, on cell proliferation and on OGT in ccRCC.

We demonstrate a cooperative relationship between miR-15a-5p and miR-26a-5p with respect to proliferation in 786-O cells. Despite the same amount of miRNA added, cells transfected with both pre-miR-15a-5p and pre-miR-26a-5p exhibited a significantly reduced proliferation rate compared to cells transfected with a negative control pre-miR or either miRNA individually. Through luciferase and western blot analysis, we show that both of these miRNA, when transfected together bind to and reduce the expression of OGT in 786-O cells. The same is true of miR-26a-5p when transfected alone. While transfection with miR-15a-5p was unable to significantly decrease OGT expression in these two assays, co-transfection of both miRNA produced an amplified and significant reduction in expression. We hypothesize that the decreased expression of these miRNA in ccRCC leads to an increase in OGT expression, which then contributes to tumor progression. This is supported by OGT upregulation correlating with poorer patient outcomes, even as early as stage I, as seen in the TCGA dataset presented by the Human Protein Atlas. Additionally, OGT protein and mRNA levels were higher in ccRCC tissues compared to that of adjacent normal tissue [28].

Studies on miR-15a-5p and miR-26a-5p have found both miRNA to be downregulated in ccRCC in addition to other cancer types: although previous reports for miR-15a-5p in ccRCC are conflicting [7, 12, 16, 17]. Within the broader miR-15a/16 cluster, miR-15a is characterized by reduced expression in various cancers and plays an important role in tumor progression [37]. In addition to reduced expression, miR-15a-5p has been linked to suppressed proliferation in numerous cancers, such as nasopharyngeal, hepatocellular, and osteosarcoma [13, 14, 15]. Specifically in ccRCC, miR-15a-5p expression has been shown to be reduced in tumor tissue compared to its adjacent normal tissue [16], and our previous study found it to be decreased in aggressive stage I ccRCC [7] as compared to stage I tumors that did not progress to metastatic disease. While the great body of evidence suggests that miR-15a-5p acts as a tumor suppressor, a recent study by Mytsyk et al. found a correlation between the upregulation of this miRNA and poor patient outcomes [12] in ccRCC. While our findings support the former studies, the role of miR-15a-5p in ccRCC is unclear, and we present our findings in anticipation that these results will help to guide future studies in an effort to fully elucidate its oncogenic effects.

As with miR-15a-5p, miR-26a-5p is also downregulated in various cancer types, such as osteosarcoma and breast [18, 19], and its expression level is inversely related to cellular proliferation [18, 19, 38]. Kurozumi et al. identified a tumor suppressive function of miR-26a-5p through its interaction with the proteins LOXL2 and PLOD2 [39]. The mechanism behind the downregulation of miR-26a-5p has been investigated in other cancer types. In prostate cancer, the Myc oncogene binds directly to the miR-26a-5p promoter and negatively regulates expression [40]. As Myc is both activated and upregulated in ccRCC [41], the same mechanism may explain the reduced expression of miR-26a-5p in ccRCC as well. While not as strongly repressed by Myc as miR-26a-5p, there is evidence to suggest that miR-15a-5p expression is also directly reduced by Myc in B cell lymphoma [42]. Future studies are needed to verify these findings in ccRCC, however Myc may be responsible for the expression levels seen in both of our miRNA’s of interest.

Of the abundant potential protein targets from which to choose for each of these miRNA, we chose to focus on OGT, which in addition to its own importance in cancer biology is targeted by both miR-15a-5p and miR-26a-5p [22, 23, 24]. Patient outcomes are significantly worse in ccRCC patients with high OGT expression [27]. OGT, an enzyme that catalyzes the addition of an O-GlcNAc sugar to a target protein [25], acts on a wide array of different protein targets, allowing the protein to have a far-reaching impact on numerous cellular processes [43]. In both prostate and breast cancers, a reduction in cellular O-GlcNAc levels led to G1 cell cycle arrest, inhibiting cellular growth and division [43]. In RCC cells, Wang et al. demonstrated the suppression of cellular proliferation upon OGT knockdown [28]. In addition to its impact on proliferation, OGT has been shown to affect EMT, angiogenesis, and epigenetics [43]. However, while much is understood of the impact of OGT on the cell, the regulation of OGT is not as clear, especially in ccRCC. To our knowledge, no previous study has investigated the regulation of OGT in ccRCC. In breast cancer cells, Sodi et al. demonstrated that the mTOR/Myc axis regulates OGT expression [44]. Because the stability and activity of Myc is dependent on O-GlcNAclyation, there may be a feed-forward regulation mechanism between Myc and OGT [44]. As Myc itself is upregulated in ccRCC [41] and is known to additionally repress the expression of miR-26a-5p and miR-15a-5p in some cancer types, it is possible that these four players may be converging to promote metastatic potential in ccRCC. Because both miR-15a-5p and miR-26a-5p are downregulated and function to attenuate OGT expression, it is plausible that the dysregulation in OGT is a direct result of the dysregulation in these miRNA.

It is important to note that while we present the conservative binding sites predicted by three search engines, there are other potential target sites that were predicted for each miRNA. One limitation of our study was that we did not investigate the validity of the different predicted sites. We present the sites that had the highest prediction score and were the only sites predicted by all three informatics tools. Investigating these potential alternative sites in future work to learn more about why these two miRNA have a cooperative effect with respect to OGT may be worthwhile. Another potential limitation is the use of only one cell line. Evaluation of the influence of miRs-15-5p and -26a-5p on OGT in other ccRCC cell lines in future study may add to the evidence presented here.

In conclusion, the downregulation of miR-15a-5p and miR-26a-5p in aggressive ccRCC tumors is likely at least partially responsible for the upregulation of OGT in these same tumors. We show that these miRNA modulate both cellular proliferation and OGT expression directly in a cooperative manner.

Footnotes

Acknowledgments

R.K. Mellon Family Foundation support to the Lahey Hospital Medical Center Division of Urology.

Supplementary data

The supplementary files are available to download from http://dx.doi.org/10.3233/CBM-200553.

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MicroRNAs MiR-15a and MiR-26a cooperatively regulate O-GlcNAc-transferase to control proliferation in clear cell renal cell carcinoma

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Materials and methods

2.1 Cell culture

2.2 Prediction of miRNA targets

2.3 Verification of protein targets in ccRCC

2.4 Cell transfection of pre-miR constructs

2.5 Luciferase assay

2.6 Western blot analysis

2.7 Cell proliferation assay

2.8 Statistical analysis

3. Results

3.1 OGT is overexpressed in ccRCC

4. Discussion

Footnotes

Acknowledgments

Supplementary data

References