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Abstract

Long noncoding RNA urothelial carcinoma-associated 1 has previously played important roles in cancer. However, its role is still unknown in clear cell renal cell carcinoma. We utilized the most recent molecular and clinical data of clear cell renal cell carcinoma from The Cancer Genome Atlas project, and the relationship between urothelial carcinoma-associated 1 expression and the clinicopathological features was analyzed. Our results indicated that urothelial carcinoma-associated 1 overexpression was associated with male (p = 0.003), wild-type PBRM1 (p = 0.021), and BAP1 mutation (p = 0.022) in clear cell renal cell carcinoma, although lower expression was found in tumors compared with normal controls, validated in tumor tissues from The Cancer Genome Atlas and 21 clear cell renal cell carcinoma patients at our hospital. Moreover, urothelial carcinoma-associated 1 overexpression indicated poor prognosis independently (Hazard Ratio [HR]: 1.92, p = 0.000) in clear cell renal cell carcinoma; it might be a potential detrimental gene considered as a predictive biomarker involved in clear cell renal cell carcinoma.

Keywords

Urothelial carcinoma-associated 1 renal cell carcinoma gene prognosis

Introduction

Renal cell carcinoma (RCC) is one of the most common and deadly malignancies in the world. There are approximately 63,000 new cases and 14,000 deaths from RCC each year,¹ and also, the incidence has steadily increased in recent decades.^2,3 Clear cell renal cell carcinomas (ccRCCs) represent the majority, accounting for 90% of all RCCs.⁴ Patients with ccRCC show divergent clinical behavior and treatment response, which was due to their heterogeneity in histology, molecular alterations in tumor suppressor gene, and oncogenicity. VHL (Von Hippel–Lindau), PBRM1 (also referred to as BAF180), BAP1 (BRCA1 associated protein-1), and SETD2 (SET domain containing 2) are the most common hallmarks in ccRCC which located at the most frequently arm-level events of chromosome 3p.⁵ KDM5C, PTEN, MTOR, and TP53 were also frequently analyzed in ccRCC.^5–8 Among them, VHL gene is the most frequent and characteristic genetic change in ccRCC; its inactivation plays a major role in the pathogenesis of ccRCC. However, it is still unclear whether it is associated with clinicopathological parameters and prognosis.^9,10 Meanwhile, PBRM1, BAP1, and SETD2 were newly identified genes located in the 3p21 region, adjacent to the 3p25 region where VHL resides. Therefore, the region of 3p deleted in a majority of sporadic ccRCCs would simultaneously impair the four tumor-related genes, which might be functionally linked.¹¹ PBRM1, which was the second most mutated gene, was found in 33%–45% of ccRCC.^8,12 It acted as a suppressor role in diverse malignancies, including pancreatic cancer and breast cancer.^13,14 However, the role of PBRM1 mutation in clinical relevance in ccRCC remains to be determined due to contradictory findings.^15–17 In addition, the mutations of SETD2 and BAP1, which were regarded as tumor suppressor genes in ccRCC, were associated with metastasis, advanced stage, and worse overall and disease-specific survival although some result was inconsistent.^8,16,18–20 Recently, the results of whole-genome and whole-exome sequencing have revealed frequently mutated signaling pathways in ccRCC, including PI3K-AKT-mTOR and p53 pathway. MTOR, PTEN, PIK3CA, AKT2, CDK2NA, and TP53 in these pathways played important roles in proliferation, apoptosis, angiogenesis, and genomic stability. Furthermore, KDM5C/JARID1c (lysine (K)-specific demethylase 5C), which was commonly mutated in other adult epithelial cancers, also contributed to ccRCC.^5,15 Despite the fact that a lot of genomic studies have been reported in ccRCC, the molecular biomarkers were not routinely used in clinical practice. Therefore, our purpose was to find the relationship between urothelial carcinoma-associated 1 (UCA1) expression and the clinicopathological features of ccRCC.

Long noncoding RNAs (lncRNAs) are defined as a group of RNAs with length more than 200 nucleotides and do not encode for protein.²¹ Deregulation of a large number of lncRNAs is involved in human cancers, acting as oncogenes and tumor suppressors in cancer progression.^22–24 UCA1 is a new lncRNA belonging to the human endogenous retrovirus H (HERV-H) family; it was noted that UCA1 overexpression began from fertilization and was kept in numerous organs during embryonic development. However, it was only expressed in the heart, spleen, and placental tissue in adults. In cancers, UCA1 was first reported to enhance tumorigenic and invasive potential in urinary bladder cancer.²⁵ Overexpression of UCA1 can promote cell proliferation by activating through phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)-dependent signaling pathway.²⁶ In another way, UCA1 suppresses cell apoptosis by downregulating regulatory factors of cell apoptosis pathway such as tumor necrosis factor receptor superfamily member (Fas) and ataxia telangiectasia mutated (ATM).²⁷ In other malignancies, the mechanism of UCA1 in tumor progression has also been explored.^28,29 These results indicate that UCA1 plays an important role in the development and prognosis of malignant tumors. Although UCA1 has been discovered only a decade ago, several recent studies have focused on the oncogenic role of UCA1 in a variety of cancers; the aberrant expression of UCA1 has been detected in breast cancer, melanoma, tongue squamous cell carcinoma, colorectal cancer, and gastric cancer.^25,30–33 However, the information about UCA1 in ccRCC was unavailable. In this study, with high-dimensional genomic data in The Cancer Genome Atlas (TCGA), we analyzed the relationship between UCA1 expression and the clinicopathological features. Also, we made analysis of UCA1 expression in 21 patients with ccRCC at the First Affiliated Hospital of Nanjing Medical University.

Material and method

Data acquisition

Overall survival data were downloaded from Firehose (http://gdac.broadinstitute.org; August 2015). The clinical variables were obtained from the TCGA Data Portal (https://portal.gdc.cancer.gov/), and the molecular data were from the Pan-Cancer project (syn300013) on Synapse (http://www.synapse.org). A provisional dataset of 514 total patients with available RNA-Seq data for UCA1 expression was used. There were 384 patients with gene mutation and expression data for VHL, PBRM1, BAP1, SETD2, KDM5C, PTEN, MTOR, and TP53 (Figure 1). Data regarding clinical features (age, gender, race, tumor stage, and tumor grade) were obtainable in 457 patients.

Figure 1.

Heatmap of mutations event of ccRCC-related genes (mutation, red; n = 384).

Analysis of messenger RNA expression

The normalized messenger RNA (mRNA) expression data based on RNA-Seq (RNA-Seq by expectation-maximization (RSEM)) were obtained from the TCGA data portal. Molecular data from the platform of Illumina HiSeq 2000 RNA Sequencing V2 were utilized in our study. To determine the cutoff of UCA1 expression, the receiver operating characteristic (ROC) was used to identify the appropriate cutoff of “high” and “low” expressions of UCA1 (“high” vs “low,” with log₂ normalized), and survival event was used. The area under the ROC curve (AUC) was 0.58 (Table S1). Based on the highest value of the AUC, the optimal cutoff is −6.808 in which the sensitivity and specificity are 53.09% and 63.05%, respectively (Figure S1). The optimal point was selected by using the minimum distance between the point 100% sensitive and 100% specific and any point on the ROC curve.

Analysis of somatic mutation data

The mutation annotation format (MAF) files were obtained from Firehose (http://gdac.broadinstitute.org), and only non-silent mutations were retained for analysis. To prevent the potential bias introduced by ultramutated samples, the samples with >1000 mutations in their exomes were filtered out. The non-silent mutations with ⩾5% mutation frequency in a patient cohort were focused on because of their potential biological significance and detecting power in the analysis. For selected genes of interest, we extracted mutation information from the MAF file and generated the heatmap plot using R project.

Real-time quantitative PCR validation

After obtaining institutional review board (IRB) approval, total RNA was prepared with Trizol and reverse transcribed with the PrimeScript RT Reagent Kit (TaKaRa) from matched tumor and adjacent normal tissues of 21 chemotherapy-naive patients with ccRCC at the First Affiliated Hospital of Nanjing Medical University. The UCA1 was analyzed by using SYBR Green Real-Time PCR Master Mix with gene-specific primers (5′-TCGGGTAACTCTTACGGT-3′, 3′-GGTCCATTGAGGCTGTAG-5′) on the ABI 7500 Fast Real-Time PCR System (Applied Biosystems, CA, USA).

Statistical analysis

Fisher’s exact test was used to identify the correlation between the UCA1 expression and clinicopathological characteristics. Overall survival time was established from the date of pathological diagnosis to death or last contact if exact date of death is unavailable. The Kaplan–Meier method and log-rank test were carried out to evaluate various prognostic factors including age, gender, tumor stage, grade, UCA1 expression, and mutation of VHL, PBRM1, BAP1, SETD2, KDM5C, PTEN, MTOR, and TP53. A multivariate Cox model was used to evaluate the independent prognostic factors. All statistical tests were two-sided, and statistical significance was set at p < 0.05 using SPSS statistics software (Version 19.0; IBM).

Results

Patient characteristics

A cohort of 457 patients with a pathological diagnosis of ccRCC was enrolled from TCGA. Details of the clinical characteristics were summarized in Table 1. The median age was 61 years (range: 26–90). Moreover, the majority of patients were male (65.0%, 297/457), white (94.1%, 430/457), stage I or II (58.4%, 267/457), and histological grade 3 or 4 (54.9%, 251/457). Furthermore, in the subgroup of UCA1 overexpression (50.1%, 229/457), the median age was 60 years (range: 32–90). Among them, 70.3% (161/229) of the patients were male; 90.8% (208/229) of the patients were white, 43.7% (100/229) in a late stage, and 54.6% (125/229) in high grade.

Table1.

Clinical characteristics of 457 patients, stratified according to UCA1 expression.

Variable	Mean expression of UCA1	p
Age (median: 61)
⩾61 (231/457, 50.5%)	−9.5	0.102
<61 (226/457 49.5%)	−8.8
Gender
Male (297/457, 65.0%)	−8.6	0.003
Female (160/457, 35.0%)	−10.2
Stage
I-II (267/457, 58.4%)	−9.4	0.350
III-IV (190/457, 41.6%)	−8.9
Grade
1-2 (206/457, 45.1%)	−9.4	0.487
3-4 (251/457, 54.9%)	−9.0
VHL
Mutation (201/384, 52.3%)	−9.5	0.542
Wild (183/384, 47.7%)	−9.2
PBRM1
Mutation (127/384, 33.1%)	−10.3	0.021
Wild (257/384, 66.9%)	−8.9
BAP1
Mutation (41/384, 9.9%)	−7.6	0.022
Wild (343/384, 90.1%)	−9.6
SETD2
Mutation (43/384, 11.2%)	−9.59	0.774
Wild (341/384, 88.8%)	−9.34
KDM5C
Mutation (25/384, 6.5%)	−9.46	0.931
Wild (359/384, 93.5%)	−9.36
PTEN
Mutation (16/384, 4.2%)	−10.57	0.358
Wild (368/384, 95.8%)	−9.31
MTOR
Mutation (25/384, 6.5%)	−8.70	0.519
Wild (359/384, 93.5%)	−9.41
TP53
Mutation (8/384, 2.1%)	−6.60	0.137
Wild (376/384, 97.9%)	−9.43

UCA1: urothelial carcinoma-associated 1.

UCA1 overexpression in male/PBRM1-wild/BAP1-mutation patients

To understand the biological characteristics of UCA1, we conducted the correlation analysis between UCA1 expression and clinicopathological characteristics. Then, it was found that UCA1 expression was higher in male patients compared with female patients (−8.6 vs −10.2, p = 0.003). In addition, in the 385 patients with genetic mutation information, UCA1 overexpression was found frequently in PBRM1 wild type compared with the mutations (−8.9 vs −10.3, p = 0.021) and in BAP1 mutations compared with the wild type (−7.6 vs −9.6, p = 0.022) (Figure 2). No correlation was observed between UCA1 expression and age/tumor stage/grade, nor the VHL, SETD2, KDM5C, PTEN, MTOR, and TP53 mutations (Table 1). Also, we made correlation analysis about all the eight most commonly mutated genes (Figure 3). However, lower expression was found in tumors compared with normal controls in the TCGA cohort, and also validated in tumor tissues with real-time quantitative polymerase chain reaction (PCR) from 21 ccRCC patients at our hospital (p < 0.05, Figure S2).

Figure 2.

UCA1 expression in (a) PBRM1 mutational group versus wild-type group (p = 0.021) and (b) BAP1 mutational group versus wild-type group (p = 0.022).

Figure 3.

Correlation of the most common mutated renal genes by Spearman’s nonparametric analysis in tumor tissue of ccRCC (data retrieved from TCGA). The color legend at the top represents the odds ratio (OR) of the ccRCC-mutated genes.

UCA1 overexpression as a poor prognostic factor

Overall survival can be obtained in 457 patients. It was noted that patients with UCA1 overexpression had shorter survival than patients with low UCA1 expression (p = 0.001; Figure 4). Meanwhile, it was revealed that patients with age ⩾61 years, late stage, high grade, and gene mutation (BAP1 and TP53) had a significantly shorter survival with univariate analysis (Table 2, Figure 4). Furthermore, multivariate Cox regression analysis also confirmed that UCA1 overexpression, age ⩾61 years, late stage, and TP53 mutation were independent poor prognostic factors (Table 2).

Figure 4.

Kaplan–Meier curve demonstrating significant difference in overall survival for patients with (a) age ⩾61 years versus <61 years, (b) stage III-IV versus stage I-II, (c) grade 3-4 versus grade 1-2, (d) high expression versus low expression of UCA1, (e) BAP1 wild-type group versus mutational group, and (f) TP53 wild-type group versus mutational group.

Table 2.

Univariate and multivariate analysis of overall survival in ccRCC.

Variable	Category	Univariate (n = 457)			Multivariate (n = 384)
		HR	95% CI	p	HR	95% CI	p
Age	⩾61 years/<61 years	1.75	1.27–2.40	0.001	1.95	1.37–2.78	0.000
Gender	Male/female	0.95	0.69–1.30	0.729	NT	NT	NT
Stage	Stage III-IV/stage I-II	3.80	2.74–5.28	0.000	3.38	2.35–4.86	0.000
Grade	Grade3-4/grade 1-2	2.62	1.85–3.73	0.000	1.45	0.98–2.13	0.065
UCA1 expression^a	High/low	1.69	1.26–2.35	0.001	1.92	1.36–2.70	0.000
VHL^a	Mutation/wild	0.97	0.70–1.36	0.868	NT	NT	NT
PBRM1^a	Mutation/wild	0.87	0.61–1.25	0.461	NT	NT	NT
BAP1^a	Mutation/wild	2.08	1.32–3.25	0.001	1.37	0.94–2.37	0.242
SETD2^a	Mutation/wild	1.34	0.83–2.15	0.229	NT	NT	NT
KDM5C^a	Mutation/wild	1.58	0.88–2.86	0.129	NT	NT	NT
PTEN^a	Mutation/wild	1.25	0.55–2.83	0.598	NT	NT	NT
MTOR^a	Mutation/wild	1.34	0.77–2.33	0.302	NT	NT	NT
TP53^a	Mutation/wild	3.69	1.62–3.90	0.002	3.66	1.58–8.45	0.002

ccRCC: clear cell renal cell carcinoma; CI: confidence interval; NT: not tested; HR: hazard ratio.

Total n = 457, events = 162, censored = 295.

n = 384.

Discussion

CcRCC, which originates from renal proximal tubules, is the most frequent subtype of kidney cancer.³⁴ ccRCC has a poor prognosis with the metastatic rate of 15.3%–21.5% at presentation and a 5-year cancer-specific survival rate of 55%–82.7%, due to late stage and resistance to chemoradiotherapy.^35–40 Previously, some genes have been identified to correlate with tumorigenicity and prognosis of ccRCC; however, there were conflicting reports. Recently, numerous evidence has shown the central role of lncRNA in the regulation of cell proliferation, differentiation, and apoptosis.^41,42 Then a novel therapeutic strategy may be revealed. UCA1 was one of lncRNAs, which was first identified as a potential biomarker for bladder cancer.²⁵ The gene was mapped to human chromosome 19p13.12 positive strand, and consisted of three exons and two introns, but lacks any conserved long open reading frames (ORFs).⁴³ UCA1 promoted the tumorigenicity and increased invasion and drug resistance of BLS-211 cells by regulating several pathways.⁴³ Furthermore, UCA1 also played oncogenic roles in several cancers, including breast cancer, colorectal cancer, esophageal squamous cell carcinoma, gastric cancer, melanoma, and ovarian cancer.^{28,31,33,42–45} UCA1 overexpression also promoted proliferation, migration, invasion, and chemoresistance in these cancers. As for ccRCC, a low expression rate was found in renal cancer compared with bladder cancer.²⁵ We were trying to figure out more biological values of the UCA1 expression in ccRCC.

In the clinicopathological analysis, patients with PBRM1 mutation and wild-type BAP1 were considerably more likely to have low expression of UCA1 than patients with wild-type PBRM1 and BAP1 mutation. PBRM1 encodes polybromo-1 protein, which is a subunit of the SWItch/Sucrose NonFermentable (SWI/SNF) transcription-modulating chromatin remodeling complex and controlling DNA accessibility for transcription.¹² Moreover, PBRM1 acts as a crucial role in regulating pathways associated with chromosomal stability and cellular proliferation in ccRCC. PBRM1 and BAP1 mutations tend to be mutually exclusive in ccRCC, which exhibit different biological actions. Importantly, patients with PBRM1 mutations have longer recurrence-free survival (RFS) than with wild-type PBRM1 in ccRCC.⁸ However, patients with BAP1 mutations have poor overall survival.^46–49 Moreover, patients with BAP1 mutations have a higher risk of death than with PBRM1 mutations (Hazard Ratio [HR]: 2.5–3).⁵⁰ It was consistent with our results that patients with wild-type PBRM1 and BAP1 mutations also had UCA1 overexpression, which contribute to poorer outcome compared with their counterparts. The relationship between UCA1 overexpression, PBRM1 mutations, and BAP1 mutations deserved attention and further investigation on tumorigenicity. Our results also made clear that no correlation existed between UCA1 expression and age, tumor stage, and important genes such as VHL, SETD2, KDM5C, PTEN, MTOR, and TP53 in ccRCC.

As for prognostic factors, we found out that patients with UCA1 overexpression had poor outcomes compared with UCA1 low expression. Furthermore, a Cox proportional hazards model was performed, including all important factors. We found that patients with BAP1 mutation or TP53 mutation exclusively were associated with significantly worse survival than wild-type BAP1 (HR: 2.08, 95% CI: 1.32–3.25, p < 0.05) and wild-type TP53 (HR: 3.69, 95% CI: 1.62–3.90, p < 0.05), which was confirmed by previous reports.^46–49,51 Besides, multivariate analysis proved the independent biomarker of UCA1 expression in ccRCC (HR: 1.92, 95% CI: 1.36–2.70, p < 0.05). Meanwhile, old age, late stage, and TP53 mutation were also validated to be independent poor prognostic factors (p < 0.05).^51–53 Meanwhile, we also made analysis of UCA1 expression in the TCGA cohort and validated in tumor tissues with real-time quantitative PCR from 21 patients at our hospital. It demonstrated that lower expression was found in tumors compared with normal tissues. However, UCA1 overexpression was found in other cancers^{25,31–33,42,43}; it might be interesting and indicated that UCA1 played important and diverse roles in different cancers.

This was the first time to reveal the comprehensive relationship of UCA1 expression in the largest samples of ccRCC with TCGA data. Several potential limitations of this study should be claimed. First, some important clinical characteristics such as race and smoking history were not enrolled in our analysis, because the majority of patients (94.1%, 430/457) were white, 4.4% (20/457) were black, and 1.5% (7/457) were Asian only. Likewise, only 3.3% (15/457) of patients could achieve smoking information. Second, although the cutoff value of UCA1 expression was taken by ROC, AUC along with the sensitivity and specificity was not being satisfied. Third, we calculated the survival time from pathological diagnosis to death or last contact but without considering treatment history; the effect of treatment on survival may be ignored. Finally, up to now, UCA1 cannot discriminate between cancer and normal tissue because of conflicting data.

In conclusion, the novelty finding was that UCA1 overexpression was found frequently in male/PBRM1-wild/BAP1-mutation ccRCC patients, although the UCA1 expression was lower in tumor than normal tissues in ccRCC. Moreover, UCA1 overexpression could be a poor prognostic factor in ccRCC from our analysis of TCGA. Thus, UCA1 is a potential detrimental gene in ccRCC. Some studies have found that lncRNA deregulation in primary tumor tissues is mirrored in various body fluids, including plasma and urine, which make the diagnosis much more convenient and minimally invasive than conventional tissue biopsies.^31,54 Thus, UCA1 may have a great potential to become a novel predictive biomarker and therapeutic targets in ccRCC.

Footnotes

Acknowledgements

The authors gratefully acknowledge contributions from The Cancer Genome Atlas (TCGA) Research Network.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Natural Science Foundation of China (81472782), Natural Science Foundation of Jiangsu Province (BK20141491), Six Talent Peaks Foundation of Jiangsu Province (2012-WS-026), “333” Talents Project of Jiangsu Province, and PAPD (the Priority Academic Program Development of Jiangsu Higher Education Institutions).

References

Siegel

Zou

. Cancer statistics, 2014. CA Cancer J Clin 2014; 64: 9–29.

Yap

Ong

. Clinical prognostic factors and survival outcome in renal cell carcinoma patients—a Malaysian single centre perspective. Asian Pac J Cancer Prev 2013; 14: 7497–7500.

Sun

Thuret

Abdollah

. Age-adjusted incidence, mortality, and survival rates of stage-specific renal cell carcinoma in North America: a trend analysis. Eur Urol 2011; 59: 135–141.

Bukowski

Olencki

Wang

. Phase II trial of interleukin-2 and interferon-alpha in patients with renal cell carcinoma: clinical results and immunologic correlates of response. J Immunother 1997; 20: 301–311.

Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 2013; 499: 43–49.

Pawlowski

Muhl

Sulser

. Loss of PBRM1 expression is associated with renal cell carcinoma progression. Int J Cancer 2013; 132: E11–E17.

Stein

Majores

Rohde

. KDM5C is overexpressed in prostate cancer and is a prognostic marker for prostate-specific antigen-relapse following radical prostatectomy. Am J Pathol 2014; 184: 2430–2437.

Gossage

Murtaza

Slatter

. Clinical and pathological impact of VHL, PBRM1, BAP1, SETD2, KDM6A, and JARID1c in clear cell renal cell carcinoma. Genes Chromosomes Cancer 2014; 53: 38–51.

Patard

Rioux-Leclercq

Masson

. Absence of VHL gene alteration and high VEGF expression are associated with tumour aggressiveness and poor survival of renal-cell carcinoma. Br J cancer 2009; 101: 1417–1424.

10.

Schraml

Struckmann

Hatz

. VHL mutations and their correlation with tumour cell proliferation, microvessel density, and patient prognosis in clear cell renal cell carcinoma. J Pathol 2002; 196: 186–193.

11.

Beroukhim

Brunet

Di Napoli

. Patterns of gene expression and copy-number alterations in von-Hippel Lindau disease-associated and sporadic clear cell carcinoma of the kidney. Cancer Res 2009; 69: 4674–4681.

12.

Varela

Tarpey

Raine

. Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 2011; 469: 539–542.

13.

Shain

Giacomini

Matsukuma

. Convergent structural alterations define SWItch/Sucrose NonFermentable (SWI/SNF) chromatin remodeler as a central tumor suppressive complex in pancreatic cancer. Proc Natl Acad Sci USA 2012; 109: E252–E259.

14.

Burrows

Smogorzewska

Elledge

. Polybromo-associated BRG1-associated factor components BRD7 and BAF180 are critical regulators of p53 required for induction of replicative senescence. Proc Natl Acad Sci USA 2010; 107: 14280–14285.

15.

Sato

Yoshizato

Shiraishi

. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet 2013; 45: 860–867.

16.

Hakimi

Ostrovnaya

Reva

. Adverse outcomes in clear cell renal cell carcinoma with mutations of 3p21 epigenetic regulators BAP1 and SETD2: a report by MSKCC and the KIRC TCGA research network. Clin Cancer Res 2013; 19: 3259–3267.

17.

Kim

Lee

Moon

. The impact of PBRM1 expression as a prognostic and predictive marker in metastatic renal cell carcinoma. J Urol 2015; 194: 1112–1119.

18.

Pena-Llopis

Vega-Rubin-de-Celis

Liao

. BAP1 loss defines a new class of renal cell carcinoma. Nat Genet 2012; 44: 751–759.

19.

Kapur

Christie

Raman

. BAP1 immunohistochemistry predicts outcomes in a multi-institutional cohort with clear cell renal cell carcinoma. J Urol 2014; 191: 603–610.

20.

Hakimi

Chen

Wren

. Clinical and pathologic impact of select chromatin-modulating tumor suppressors in clear cell renal cell carcinoma. Eur Urol 2013; 63: 848–854.

21.

Mercer

Dinger

Mattick

. Long non-coding RNAs: insights into functions. Nat Rev Genet 2009; 10: 155–159.

22.

Wang

Zhao

. Nuclear factor of activated T cells 5 maintained by HOTAIR suppression of miR-568 upregulates S100 calcium binding protein A4 to promote breast cancer metastasis. Breast Cancer Res 2014; 16: 454.

23.

Yao

Wang

. Large intervening non-coding RNA HOTAIR is an indicator of poor prognosis and a therapeutic target in human cancers. Int J Mol Sci 2014; 15: 18985–18999.

24.

Park

Lee

Park

. Roles of long non-coding RNAs on tumorigenesis and glioma development. Brain Tumor Res Treat 2014; 2: 1–6.

25.

Wang

Zhang

Wang

. Rapid identification of UCA1 as a very sensitive and specific unique marker for human bladder carcinoma. Clin Cancer Res 2006; 12: 4851–4858.

26.

Yang

Wang

. Long non-coding RNA UCA1 regulated cell cycle distribution via CREB through Pi3-K dependent pathway in bladder carcinoma cells. Gene 2012; 496: 8–16.

27.

Wang

Chen

Yang

. Long non-coding RNA UCA1a(CUDR) promotes proliferation and tumorigenesis of bladder cancer. Int J Oncol 2012; 41: 276–284.

28.

Huang

Zhou

Watabe

. Long non-coding RNA UCA1 promotes breast tumor growth by suppression of p27 (Kip1). Cell Death Dis 2014; 5: e1008.

29.

Wang

Liu

. CREB up-regulates long non-coding RNA, HULC expression through interaction with microRNA-372 in liver cancer. Nucleic Acids Res 2010; 38: 5366–5383.

30.

Shi

Sun

Liu

. Long non-coding RNAs: a new frontier in the study of human diseases. Cancer Lett 2013; 339: 159–166.

31.

Zheng

Dai

. Aberrant expression of UCA1 in gastric cancer and its clinical significance. Clin Transl Oncol 2015; 17: 640–646.

32.

Wang

Chen

. Upregulated lncRNA-UCA1 contributes to progression of lung cancer and is closely related to clinical diagnosis as a predictive biomarker in plasma. Int J Clin Exp Med 2015; 8: 11824–11830.

33.

Zhang

. Overexpression of long non-coding RNA UCA1 predicts a poor prognosis in patients with esophageal squamous cell carcinoma. Int J Clin Exp Pathol 2014; 7: 7938–7944.

34.

Wood

Silverman

. Renal cell carcinoma: diagnosis, staging, and surveillance. AJR Am J Roentgenol 2008; 191: 1220–1232.

35.

Patard

Leray

Rioux-Leclercq

. Prognostic value of histologic subtypes in renal cell carcinoma: a multicenter experience. J Clin Oncol 2005; 23: 2763–2771.

36.

Dogan

Canda

Akbulut

Re: histological subtype is an independent predictor of outcome for patients with renal cell carcinoma: Leibovich

B.C.

Lohse

C. M.

Crispen

P. L.

Boorjian

S. A.

Thompson

R. H.

Blute

M. L.

Cheville

J. C

. J Urol 2010; 183: 1309–1316. J Urol 2010; 184: 2211; author reply 2211–2212.

37.

Ficarra

Martignoni

Galfano

. Prognostic role of the histologic subtypes of renal cell carcinoma after slide revision. Eur Urol 2006; 50: 786–793; discussion 793–784.

38.

Tanaka

Kuramitsu

Wang

. Glyoxalase 1 as a candidate for indicating the metastatic potential of SN12C human renal cell carcinoma cell clones. Oncol Rep 2013; 30: 2365–2370.

39.

Singer

Gupta

Marchalik

. Evolving therapeutic targets in renal cell carcinoma. Curr Opin Oncol 2013; 25: 273–280.

40.

Wei

Yang

. High expression of pituitary tumor-transforming gene-1 predicts poor prognosis in clear cell renal cell carcinoma. Mol Clin Oncol 2015; 3: 387–391.

41.

Liu

Shi

. The long non-coding RNA HNF1A-AS1 regulates proliferation and metastasis in lung adenocarcinoma. Oncotarget 2015; 6: 9160–9172.

42.

Wang

Ying

. Upregulated lncRNA-UCA1 contributes to progression of hepatocellular carcinoma through inhibition of miR-216b and activation of FGFR1/ERK signaling pathway. Oncotarget 2015; 6: 7899–7917.

43.

Wang

Xie

. UCA1, a non-protein-coding RNA up-regulated in bladder carcinoma and embryo, influencing cell growth and promoting invasion. FEBS Lett 2008; 582: 1919–1927.

44.

Wang

Zhou

Xie

. Involvement of SRPK1 in cisplatin resistance related to long non-coding RNA UCA1 in human ovarian cancer cells. Neoplasma 2015; 62(3): 432–438.

45.

Tian

Zhang

Hao

. Potential roles of abnormally expressed long noncoding RNA UCA1 and Malat-1 in metastasis of melanoma. Melanoma Res 2014; 24: 335–341.

46.

Piva

Santoni

Matrana

. BAP1, PBRM1 and SETD2 in clear-cell renal cell carcinoma: molecular diagnostics and possible targets for personalized therapies. Expert Rev Mol Diagn 2015; 15: 1201–1210.

47.

Piva

Giulietti

Occhipinti

. Computational analysis of the mutations in BAP1, PBRM1 and SETD2 genes reveals the impaired molecular processes in renal cell carcinoma. Oncotarget 2015; 6: 32161–32168.

48.

Kapur

Pena-Llopis

Christie

. Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation. Lancet Oncol 2013; 14: 159–167.

49.

Brugarolas

. PBRM1 and BAP1 as novel targets for renal cell carcinoma. Cancer J 2013; 19: 324–332.

50.

Boorjian

. Commentary on “Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation.” Kapur

Pena-Llopis

Christie

Zhrebker

Pavia-Jimenez

Rathmell

Xie

Brugarolas

. Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX.: Lancet Oncol 2013; 14(2):159–67. [Epub 2013 Jan 16]. doi:10.1016/s1470-2045(12)70584-3. Urol Oncol 2014; 32: 934–935.

51.

Bousquet

El Bouchtaoui

Leboeuf

. Tracking sub-clonal TP53 mutated tumor cells in human metastatic renal cell carcinoma. Oncotarget 2015; 6: 19279–19289.

52.

Jung

Lee

Kwak

. Young age is independent prognostic factor for cancer-specific survival of low-stage clear cell renal cell carcinoma. Urology 2009; 73: 137–141.

53.

Parker

Cheville

Frank

. Application of the stage, size, grade, and necrosis (SSIGN) score for clear cell renal cell carcinoma in contemporary patients. Eur Urol 2017; 71: 665–673.

54.

. The long non-coding RNAs, a new cancer diagnostic and therapeutic gold mine. Mod Pathol 2013; 26: 155–165.

Supplementary Material

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The long noncoding RNA urothelial carcinoma-associated 1 overexpression as a poor prognostic biomarker in clear cell renal cell carcinoma

Abstract

Keywords

Introduction

Material and method

Data acquisition

Analysis of messenger RNA expression

Analysis of somatic mutation data

Real-time quantitative PCR validation

Statistical analysis

Results

Patient characteristics

UCA1 overexpression in male/PBRM1-wild/BAP1-mutation patients

UCA1 overexpression as a poor prognostic factor

Discussion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

References

Supplementary Material