A novel tumor suppressing gene, ARHGAP9, is an independent prognostic biomarker for bladder cancer
- Authors:
- Published online on: November 19, 2019 https://doi.org/10.3892/ol.2019.11123
- Pages: 476-486
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Copyright: © Piao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Bladder cancer (BC), one of the most common malignancies worldwide, is classified into two subtypes based on cancer cell infiltration into the muscle layer of the bladder. Non-muscle invasive BC (NMIBC) is less aggressive but has a high recurrence rate, whereas muscle invasive BC (MIBC) tends to metastasize and has a relatively poor prognosis (1–3). High throughput techniques such as microarray analysis and next generation sequencing, which are used commonly in the fields of genetics and epigenetics, have identified several genes involved in cancer pathogenesis, and have led to identification of cancer biomarkers and to development of novel effective gene targeted therapies (4). In a previous study, we used next generation sequencing and miRNA microarray assays to identify several miRNAs and their target genes that are differentially expressed in BC (5). We found that a novel gene, Rho GTPase-activating protein 9 (ARHGAP9), is down-regulated in BC. In addition, hsa-miR-3620, which interacts with ARHGAP9, is up-regulated.
Rho GTPases are key regulators of the actin cytoskeleton, which plays an important role in cell adhesion and migration. The switch mechanism of Rho GTPases is controlled by binding to GTP or GDP (6–8). ARHGAP9 contains a diverse combination of functional protein domains, including the RhoGAP, SH3, WW, and PH domains (9). Binding of the RhoGAP domain to GTP-bound Rho proteins accelerates GTPase activity, and defective Rho GTPase signaling is implicated in tumorigenesis and metastasis (10,11). Silencing ARHGAP9 inhibits proliferation, migration, and invasion of breast cancer cells (12). Activated ARHGAP9 inhibits adhesion of a human leukemia cell line, KG-1, to fibronectin and collagen through activation of cdc42 and Rac1 but not RhoA (6).
Here, we asked whether ARHGAP9 is a novel prognostic biomarker for BC. We used real-time polymerase chain reaction (PCR) to compare expression of ARHGAP9 mRNA in human BC and control tissues (the latter comprised normal tissue surrounding BC and normal bladder mucosa); and analyzed its ability to predict prognosis of NMIBC and MIBC. ARHGAP9, known as a MAP kinase docking protein, was encoded by ARHGAP9 gene, which shares 16 bases with Gli1 in their 3′ ends (9,13). Accordingly, we asked whether ARHGAP9 plays a role in the MAPK and Hedgehog signaling pathways.
Materials and methods
Patients and tissue samples
The biospecimens used in the present study were provided by the Chungbuk National University Hospital, a member of the National Biobank of Korea, which is supported by the Ministry of Health, Welfare, and Family Affairs. The study was approved by the Institutional Review Board at Chungbuk National University (GR2010-12-010), and the experiments were undertaken with the informed written consents of all participants. The study methodologies conformed with the standards set by the Declaration of Helsinki. The baseline characteristics of the case subjects (n=237 bladder tissue samples) are shown in Table I. Among these, 140 samples were from primary BC patients and were histologically verified as transitional cell carcinomas; the remaining 97 samples used as the control set comprised normal bladder mucosa or normal tissues from the area surrounding BC. To reduce the chances of confounding factors affecting the analyses, patients diagnosed with concomitant carcinoma in situ or carcinoma in situ lesions alone were excluded. Voided urine cytology was tested before surgical treatment to assist BC diagnosis and/or prognosis. Fresh-frozen specimens were obtained during surgical resection of transitional cell carcinoma at Chungbuk National University Hospital. All tumors were macro-dissected, typically within 15 min of surgical resection. Each specimen was confirmed by pathological analysis of a part of fresh-frozen specimens obtained from radical cystectomy and transurethral resection of bladder tumor (TURBT). Tumors were staged (2002 TNM Classification) and graded (2004 WHO Classification), according to standard criteria (14). Clinically metastatic disease and non-cystectomy cases were not excluded from the study. Each patient was followed and managed suggested management according to standard recommendations (15–17). Surveillance was performed by cystoscopic examination and upper urinary tract imaging in accordance with European Association of Urology guidelines (16). Recurrence was defined as relapse of primary NMIBC of the same pathologic stage, and progression of NMIBC and MIBC was defined as TNM stage progression after disease recurrence. The mean follow-up period for NMIBC patients was 72.95 months (range, 3.2–172.2). The mean follow-up period for MIBC patients was 36.18 months (range, 3.0–141.4).
 | Table I.Clinicopathological features of primary BC patient and control tissues (surrounding normal tissues and normal bladder mucosae). |
RNA extraction
Total RNA was extracted from tissues using TRIzol reagent (Invitrogen), as described previously (18), and stored at −80°C. Next, cDNA was synthesized from 1 µg of total RNA using a First Strand cDNA Synthesis kit (Clontech, TAKARA), according to the manufacturer's protocol.
Microarray analysis
Five hundred nanograms of total RNA was used for labeling and hybridization prior to analysis, according to the manufacturer's protocols (Illumina). After the bead chips were scanned with an Illumina Bead Array Reader, the Robust Multiarray Average in R package was used to perform global correction, quantile normalization, and median polish summarization of the microarray data. P-values (t test) were calculated from bead mRNA signal intensities (19–21). The full set of microarray data set are available online at http://www.ncbi.nlm.nih.gov/geo/under data series accession number GSE13507 (21).
mRNA sequencing
Total sequencing reads were subjected to preprocessing as follows: Adapter trimming was performed using cutadapt with default parameters, and quality trimming (Q30) was performed using FastQC with default parameters. Processed reads were mapped to the human reference genome [Ensembl 72 (GRCh37: hg19)] using tophat and cufflink with default parameters (22). Fragments Per Kilobase of exon per million fragments Mapped (FPKM) values were normalized and quantitated using R package Tag Count Comparison (TCC) (23) to determine statistical significance (e.g., P and Q values) and differential expression (e.g., -fold changes).
Quantitative PCR analysis
Tissue mRNAs were amplified by quantitative PCR performed using a Rotor Gene 6000 instrument (Qiagen) and quantified using the 2−∆∆cq method (24). QuantitativePCR reactions were carried out using the SYBR Premix Ex Taq II (Clontech, TAKARA). The following primers were used to amplify candidate genes: ARHGAP9 (Gene ID: ENSG00000123329), sense, 5′-CAGAGCAGTGCCTCTCTC-3′ (18 bp, Tm 58°C); antisense, 5′-CTGCTGGGTCAGATGTCTC-3′ (19 bp, Tm 58°C) and the amplicon size was 179 bp. The control GAPDH (Gene ID: ENSG00000111640) primers were as follows: sense, 5′-CATGTTCGTCATGGGTGTGA-3′ (20 bp, Tm 60°C); antisense, 5′-ATGGCATGGACTGTGGTCAT-3′ (20 bp, Tm 60°C) and the amplicon size was 156 bp. The PCR reaction was performed in a final volume of 10 µl, comprising 5 µl of 2× SYBR Premix EX Taq buffer, 0.5 µl of each 5′and 3′ primer (10 pM/µl), and 2 µl, of sample cDNA. A known concentration of the PCR product was then 10-fold serially diluted from 100 pg/µl to 0.1 pg/µl and used to establish a standard curve. The real-time PCR conditions were as follows: 1 cycle at 96°C for 20 sec, followed by 40 cycles of 3 sec at 96°C for denaturation, 15 sec at 60°C for annealing, and 15 sec at 72°C for extension. The melting program was performed at 72–95°C at a heating rate of 1°C per 45 sec. Rotor-Gene Q software 2.3.1.49 was used for capturing and analyzing spectral data. All samples were run in triplicate. Gene expression was normalized to the expression of GAPDH.
Statistical analysis
To reduce variation among microarrays, the intensity values for each microarray were rescaled using a quantile normalization method (19). Gene expression values were loge-transformed and median-centered across samples. The significance of various clinicopathological variables was evaluated using univariate and multivariate Cox proportional hazard regression models. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to investigate relative risk. Survival curves to determine the prognostic value of the genetic biomarker were plotted using the Kaplan-Meier method and compared using the log-rank test. The Kruskal-Wallis H test and Mann-Whitney U test were used to examine expression of ARHGAP9 in BC tissues versus control tissues. Correlations between ARHGAP9 and genes involved in the MAPK and Hedgehog signaling pathways were examined by calculating non-parametric Spearman's correlation coefficients. Statistical analyses were performed using IBM SPSS Statistics ver. 20.0 (IBM) and GraphPad Prism 7 (GraphPad Software). P-values <0.05 were considered significant.
Results
Expression of ARHGAP9 mRNA in BC tissue
Microarray analysis revealed that expression of mRNA encoding ARHGAP9 in BC tissues was lower than that in control samples. The validation test showed that the real-time PCR results were identical to those of the microarray, i.e., expression of mRNA encoding ARHGAP9 was significantly lower in NMIBC and MIBC tissues than in normal control tissues (P<0.001; Fig. 1).
Expression of ARHGAP9 correlates with NMIBC prognosis
Univariate and multivariate Cox regression analyses revealed that expression of ARHGAP9 in NMIBC patients was an independent predictor of recurrence-free survival (RFS) (HR, 2.436; 95% CI, 1.132–5.243; P=0.023; Table II). Kaplan-Meier analysis demonstrated that NMIBC patients with ARHGAP9 expression levels in the upper 50th percentile experienced less recurrence than those with expression levels in the lower 50th percentile (log-rank test, P=0.043; Fig. 2A). Particularly, for T1 high grade(HG) BC patients, univariate and multivariate Cox regression analysis identified ARHGAP9 expression as an independent risk factor for T1HG BC recurrence (HR, 7.264; 95% CI, 1.291–45.091; P=0.025) and progression (HR, 14.987; 95% CI, 1.093–205.567; P=0.043; Table III). The RFS and progression-free survival (PFS) of T1HG BC patients with ARHGAP9 expression levels in the upper 50th percentile experienced less recurrence and progression than those with expression levels in the lower 50th percentile (log-rank test, P=0.013 and 0.026 respectively; Fig. 2B and C).
| Table III.Univariate and multivariate Cox regression analysis to predict T1 high grade NMIBC recurrence and progression. |
Expression of ARHGAP9 correlates with MIBC prognosis
For MIBC patients, univariate and multivariate Cox regression analysis identified ARHGAP9 expression as an independent risk factor for disease progression (HR, 5.241; 95% CI, 1.456–18.870; P=0.011) and cancer-specific death (HR, 2.923; 95% CI, 1.192–7.163; P=0.019) (Tables IV and V). PFS and cancer specific survival (CSS) of patients with ARHGAP9 expression in the upper 50th percentile were significantly higher than those of patients in the lower 50th percentile (log-rank test, P=0.020 and 0.031, respectively; Fig. 3A and B).
| Table V.Univariate and multivariate Cox regression analysis for predicting the cancer-specific survival of patients with MIBC. |
Relationship between ARHGAP9 and genes regulating the MAPK and Hedgehog signaling pathways in BC
To identify whether expression of ARHGAP9 correlates with that of genes regulating the MAPK and Hedgehog signaling pathways, we undertook gene network depiction and analysis using the GeneMANIA (http://www.genemania.org) web tool. We selected seven genes (ARHGAP9, epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1, also known as ERK2), mitogen-activated protein kinase 14 (MAPK14, also known as p38α), mitogen-activated protein kinase kinase 3 (MKK3), mitogen-activated protein kinase kinase 6 (MKK6), and glioma-associated oncogene homolog 1 (Gli1)) showing potential inter-correlations (Supplementary Fig. S1). Non-parametric Spearman's correlation coefficients (based on microarray data) identified interactions among ARHGAP9, EGFR, MAPK1 (ERK2), MAPK14 (p38α), MKK3, MKK6, and Gli1. Table VI shows that expression of ARHGAP9 correlated positively with that of Gli1, which regulates the Hedgehog signaling pathway. In addition, ARHGAP9 interacted with MKK6 and MAPK1 (ERK2), both of which are essential components of the MAPK signal transduction pathway (P<0.05 for both).
| Table VI.Spearman correlation coefficients of Gli1, ARHGAP9, EGFR, MKK3, MKK6, MAPK1 (ERK2) and MAPK14 (p38α) in BC. |
Discussion
ARHGAP9 sits adjacent to Gli1 on human chromosome 12q13.3; two genes have overlapping 16 bases in their 3′-ends (13), suggesting that Gli1 and ARHGAP9 may regulate each other. Studies suggest that Gli1 is down-regulated in BC (25); indeed, Gli1 is considered to be the most reliable biomarker of Hedgehog pathway activity (25–27). The microarray data presented herein shows that mRNA expression of Gli1 and ARHGAP9 were down-regulated in BC tissues, and that there was a positive correlation between the two (Table VI); this indicates that ARHGAP9, which lies adjacent to Gli1, might be a novel regulator of Gli1.
As a novel MAP kinase docking protein, ARHGAP9 associates specifically with ERK2 and p38α via complementarily charged residues within the WW domain of ARHGAP9 and the CD domains of ERK2 and p38α. This interaction suppresses MAP kinase activation; but does not affect that of RhoGAP (9). MAPK activation is a common event in tumor progression and metastasis. Inhibition of ERK1/2 and p38 MAP kinase pathways in BC could inhibit proliferation and growth (28). The key target in this signal transduction pathway is EGFR, a receptor tyrosine kinase (29). Binding of EGF to EGFR in BC activates EGFR, which is already overexpressed; furthermore, the Ras-MAPK pathway is activated through the MAPK/ERK pathway. This continuous ‘ON’ status of MAPK signaling results in overexpression of MEK2 and MKK3, 4, and 6, which lie upstream of MAP kinase (i.e., ERK2 and p38α) and activate ERK2 and p38α, leading to reduced interaction between ARHGAP9 and ERK2 or p38α in BC (this is probably attributable to competitive displacement by overexpressed docking proteins) (Fig. 4). The microarray data revealed a competitive correlation between expression of ARHGAP9 mRNA and that of MKK6, and a positive correlation between ARHGAP9 and ERK2 (Table VI). These findings suggest that ARHGAP9 acts as a tumor suppressor gene in BC. EGFR acts as a receptor molecule in the MAPK signaling pathway, and is a prognostic marker for many cancer types, including BC (30). Our previous study showed that EGFR is a progression-related gene in MIBC; increased expression of EGFR is associated with a poor prognosis (31). Here, we found that lower expression of ARHGAP9 was related to poor PFS and CSS (Fig. 3A and B), which is consistent with previous results. However, no definitive evidence has been demonstrated on the recurrence rate of MIBC after radical cystectomy, and the definition of local and distant recurrence is not standardized (32). In our preliminary study, twenty-six MIBC patients received radical cystectomy and only three of them were manifested recurrence, such result should be examined in further study with more samples for the statistically significant validation of the survival analysis.
Furthermore, the ARHGAP9 mRNA expression could predict the recurrence of NMIBC, that is, lower expression of ARHGAP9 was related to poor RFS (Fig. 2A). In particular, T1HG BC patients with higher expression of ARHGAP9 experienced less recurrence and progression (Fig. 2B and C). A more careful monitoring and optimal treatment recommendation should be implemented for T1HG BCs because of their highly recurrent nature and risk of progression to MIBC (33), which highlights the strategy for predicting prognosis. This study indicates that ARHGAP9 gene has a good performance in predicting prognosis of T1HG BC patients.
In addition, TCGA data from the Human Pathology Atlas (https://www.proteinatlas.org/ENSG00000123329-ARHGAP9/pathology/tissue/urothelial+cancer) show that BC patients with higher expression of ARHGAP9 mRNA tend to survive longer, though it is not statistically significant (P=0.069). On the basis of the results of this study, we can conclude that ARHGAP9 regulates growth and proliferation of BC by regulating the MAPK signaling pathway. Future studies should use real-time PCR assays to validate the results of microarray tests to confirm reliability of the data. For a better understanding of ARHGAP9, its protein levels in BC should be evaluated and the experimental samples should be increased to reduce the statistical limitations in the future. Moreover, the function of miR-3620, which interacted with ARHGAP9 mRNA, could be clarified by validating the function of ARHGAP9 in the future.
In conclusion, our findings provide a novel tumor suppressor gene in BC, which could be served as an independent prognostic marker for stratification of NMIBC and MIBC patients into favorable and poor prognosis. Moreover, a new paradigm in BC tumorigenesis and pathogenesis is estimated, since this novel gene seems to involve in the crucial tumorigenesis signaling pathways.
Supplementary Material
Supporting Data
Acknowledgements
The biospecimens used in the present study were provided by the Chungbuk National University Hospital, a member of the National Biobank of Korea, which is supported by the Ministry of Health, Welfare, and Family Affairs. All samples derived from the National Biobank of Korea were obtained with informed consent under institutional review board-approved protocols. The authors would like to thank Ms. Eun-Ju Shim from the National Biobank of Korea at Chungbuk National University Hospital for preparing samples and her excellent technical assistance.
Funding
The present study was supported by the International Science and Business Belt Program of the Ministry of Science, ICT and Future Planning (grant no. 2015-DD-RD-0070); the National Research Foundation of Korea funded by the Korean government (grant no. 2018R1A2B2005473); and the Basic Science Research Program of the National Research Foundation of Korea, funded by the Ministry of Education (grant no. 2017R1D1A1B03033629).
Availability of data and materials
The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.
Authors' contributions
XMP, PJ, SJY and WJK designed the study and all experiments. XMP performed the experiments. YHK, YJB, YX, SPS and SKM collected patient samples. XMP, CY, HWK and WTK assisted with data collection. XMP, JYL, IYK, YHC, EJC and SJY analyzed the data. WJK provided funding. XMP, SJY and WJK wrote the manuscript.
Ethics approval and consent to participate
The collection and analysis of all samples were approved by the Institutional Review Board at Chungbuk National University (approval no. GR2010-12-010). The study methodologies conformed with the standards set by the Declaration of Helsinki. All samples derived from the National Biobank of Korea were obtained with informed consent under institutional review board-approved protocols.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Glossary
Abbreviations
Abbreviations:
|
ARHGAP9 |
Rho GTPase-activating protein 9 |
|
BC |
Bladder cancer |
|
CI |
confidence interval |
|
CIS |
carcinoma in situ |
|
CSS |
cancer-specific survival |
|
EGFR |
epidermal growth factor receptor |
|
Gli1 |
glioma-associated oncogene homolog 1 |
|
HR |
hazard ratio |
|
MAPK1 |
mitogen-activated protein kinase 1 (also known as ERK2) |
|
MAPK14 |
mitogen-activated protein kinase 14 (also known as p38α) |
|
MIBC |
muscle invasive BC |
|
MKK3 |
mitogen-activated protein kinase kinase 3 |
|
MKK6 |
mitogen-activated protein kinase kinase 6 |
|
NGS |
next generation sequencing |
|
NMIBC |
non-muscle invasive BC |
|
PFS |
progression-free survival |
|
Real-time PCR |
real-time polymerase chain reaction |
|
RFS |
recurrence-free survival |
|
T1HG |
T1 high grade |
References
|
Lotan Y, Black PC, Caba L, Chang SS, Cookson MS, Daneshmand S, Kamat AM, McKiernan JM, Pruthi RS, Ritch CR, et al: Optimal trial design for studying urinary markers in bladder cancer: A collaborative review. Eur Urol Oncol. 1:223–230. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Soukup V, Čapoun O, Cohen D, Hernandez V, Babjuk M, Burger M, Compérat E, Gontero P, Lam T, MacLennan S, et al: Prognostic performance and reproducibility of the 1973 and 2004/2016 World Health Organization grading classification systems in non-muscle-invasive bladder cancer: A European Association of Urology non-muscle invasive bladder cancer guidelines panel systematic review. Eur Urol Suppl. 72:801–813. 2017. View Article : Google Scholar | |
|
Westhoff E, Witjes JA, Fleshner NE, Lerner SP, Shariat SF, Steineck G, Kampman E, Kiemeney LA and Vrieling A: Body mass index, diet-related factors, and bladder cancer prognosis: A systematic review and meta-analysis. Bladder Cancer. 4:91–112. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Sethi S, Kong D, Land S, Dyson G, Sakr WA and Sarkar FH: Comprehensive molecular oncogenomic profiling and miRNA analysis of prostate cancer. Am J Transl Res. 5:200–211. 2013.PubMed/NCBI | |
|
Lee JY, Yun SJ, Jeong P, Piao XM, Kim YH, Kim J, Subramaniyam S, Byun YJ, Kang HW, Seo SP, et al: Identification of differentially expressed miRNAs and miRNA-targeted genes in bladder cancer. Oncotarget. 9:27656–27666. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Furukawa Y, Kawasoe T, Daigo Y, Nishiwaki T, Ishiguro H, Takahashi M, Kitayama J and Nakamura Y: Isolation of a novel human gene, ARHGAP9, encoding a rho-GTPase activating protein. Biochem Bioph Res Commun. 284:643–649. 2001. View Article : Google Scholar | |
|
Hall A: Rho GTPases and the control of cell behaviour. Biochem Soc Trans. 33:891–895. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Etienne-Manneville S and Hall A: Rho GTPases in cell biology. Nature. 420:629–635. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Ang BK, Lim CY, Koh SS, Sivakumar N, Taib S, Lim KB, Ahmed S, Rajagopal G and Ong SH: ArhGAP9, a novel MAP kinase docking protein, inhibits Erk and p38 activation through WW domain binding. J Mol Signal. 2:12007. View Article : Google Scholar : PubMed/NCBI | |
|
Sahai E and Marshall CJ: RHO-GTPases and cancer. Nat Rev Cancer. 2:133–142. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Jaffe AB and Hall A: Rho GTPases in transformation and metastasis. Adv Cancer Res. 84:57–80. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Wang T and Ha M: Silencing ARHGAP9 correlates with the risk of breast cancer and inhibits the proliferation, migration, and invasion of breast cancer. J Cell Biochem. 119:7747–7756. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Katoh Y and Katoh M: Integrative genomic analyses on GLI1: Positive regulation of GLI1 by Hedgehog-GLI, TGFβ-Smads, and RTK-PI3K-AKT signals, and negative regulation of GLI1 by Notch-CSL-HES/HEY, and GPCR-Gs-PKA signals. Int J Oncol. 35:187–192. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Sharma S, Ksheersagar P and Sharma P: Diagnosis and treatment of bladder cancer. Am Fam Physician. 80:717–723. 2009.PubMed/NCBI | |
|
Hall MC, Chang SS, Dalbagni G, Pruthi RS, Seigne JD, Skinner EC, Wolf JS Jr and Schellhammer PF: Guideline for the management of nonmuscle invasive bladder cancer (stages Ta, T1, and Tis): 2007 update. J Urology. 178:2314–2330. 2007. View Article : Google Scholar | |
|
Babjuk M, Böhle A, Burger M, Capoun O, Cohen D, Compérat EM, Hernández V, Kaasinen E, Palou J, Rouprêt M, et al: EAU guidelines on non-muscle-invasive urothelial carcinoma of the bladder: Update 2016. Eur Urol. 71:447–461. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Witjes JA, Compérat E, Cowan NC, De Santis M, Gakis G, Lebret T, Ribal MJ, Van der Heijden AG and Sherif A; European Association of Urology, : EAU guidelines on muscle-invasive and metastatic bladder cancer: Summary of the 2013 guidelines. Eur Urol. 65:778–792. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Kim WT, Kim J, Yan C, Jeong P, Choi SY, Lee OJ, Chae YB, Yun SJ, Lee SC and Kim WJ: S100A9 and EGFR gene signatures predict disease progression in muscle invasive bladder cancer patients after chemotherapy. Ann Oncol. 25:974–979. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Bolstad BM, Irizarry RA, Åstrand M and Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 19:185–193. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U and Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Kim WJ, Kim EJ, Kim SK, Kim YJ, Ha YS, Jeong P, Kim MJ, Yun SJ, Lee KM, Moon SK, et al: Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer. Mol Cancer. 9:32010. View Article : Google Scholar : PubMed/NCBI | |
|
Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL and Pachter L: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 7:562–578. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Sun J, Nishiyama T, Shimizu K and Kadota K: TCC: An R package for comparing tag count data with robust normalization strategies. BMC Bioinformatics. 14:2192013. View Article : Google Scholar : PubMed/NCBI | |
|
Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 25:402–408. 2001. View Article : Google Scholar : PubMed/NCBI | |
|
Pignot G, Vieillefond A, Vacher S, Zerbib M, Debre B, Lidereau R, Amsellem-Ouazana D and Bieche I: Hedgehog pathway activation in human transitional cell carcinoma of the bladder. Br J Cancer. 106:1177–1186. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Lee J, Platt KA, Censullo P and Ruiz i Altaba A: Gli1 is a target of Sonic hedgehog that induces ventral neural tube development. Development. 124:2537–2552. 1997.PubMed/NCBI | |
|
Kimura H, Stephen D, Joyner A and Curran T: Gli1 is important for medulloblastoma formation in Ptc1+/- mice. Oncogene. 24:4026–4036. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Kumar B, Sinclair J, Khandrika L, Koul S, Wilson S and Koul HK: Differential effects of MAPKs signaling on the growth of invasive bladder cancer cells. Int J Oncol. 34:1557–1564. 2009.PubMed/NCBI | |
|
Spiess PE and Czerniak B: Dual-track pathway of bladder carcinogenesis: Practical implications. Arch Pathol Lab Med. 130:844–852. 2006.PubMed/NCBI | |
|
Nicholson RI, Gee JM and Harper ME: EGFR and cancer prognosis. Eur J Cancer. 37 (Suppl 4):S9–S15. 2001. View Article : Google Scholar : PubMed/NCBI | |
|
Kim WJ, Kim SK, Jeong P, Yun SJ, Cho IC, Kim IY, Moon SK, Um HD and Choi YH: A four-gene signature predicts disease progression in muscle invasive bladder cancer. Mol Med. 17:478–485. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Mari A, Campi R, Tellini R, Gandaglia G, Albisinni S, Abufaraj M, Hatzichristodoulou G, Montorsi F, van Velthoven R, Carini M, et al: Patterns and predictors of recurrence after open radical cystectomy for bladder cancer: A comprehensive review of the literature. World J Urol. 36:157–170. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Yun SJ, Kim SK and Kim WJ: How do we manage high-grade T1 bladder cancer? Conservative or aggressive therapy? Investig Clin Urol. 57 (Suppl 1):S44–S51. 2016. View Article : Google Scholar : PubMed/NCBI |
