Abstract
Bladder cancer (BCa) is the second most common urological malignancy, and the incidence of BCa has dramatically increased recently. Various toll-like receptors (TLRs) signaling pathway proteins were proven to be associated with BCa susceptibility. However, the effect of genetic variants in TLRs signaling pathway genes on risk of BCa has not been elucidated clearly. Previous studies mainly focused on the coding region of target genes, while in this study, polymorphisms in the non-coding region, microRNA (miRNA) binding sites were investigated as potential targets. We used bioinformatics approach to screen 100 BCa related TLRs signaling pathway genes. Candidate polymorphisms were select in this region and 8 polymorphisms were confirmed. Rs72552316, located at the 3’UTR of the TLR7 gene, exhibited significant association with risk of BCa, indicating a strong relationship with decreased risk of BCa (P ≤ 0.0001). Furthermore, no association was detected between all the polymorphisms and recurrence-free survival time of overall study population or non-muscle invasive BCa subgroups. In conclusion, rs72552316 in the miRNA binding sites of TLR7 might contribute to BCa susceptibility, and this finding provided new targets for high BCa risk population screening.
Keywords: Polymorphism, bladder cancer, TLRs signaling pathway genes, microRNA binding sites
Introduction
Bladder cancer (BCa) accounts for approximately 2% of all human malignancies and it has the highest incidence and mortality rate among urinary system tumors [1,2]. In China, the occurrence of BCa has dramatically increased in recent years [3]. Various risk factors have been demonstrated to be closely associated with the development of BCa, including smoking, occupational and environmental exposures, and chronic irritation [4]. Recently, several studies have identified those polymorphisms in individuals’ genetic susceptibility contributed to the susceptibility of BCa [5]. However, the precise gene interactions and signaling pathways in the development of BCa are still unclear.
Toll-like receptors (TLRs) belong to the family of pattern-recognition receptors (PRRs). Furthermore, TLRs play an important role in innate immune system as the most known pathogen sensors, and detect invariant pathogen molecules through pathogen-associated molecular patterns (PAMPs). Interestingly, various studies have shown that TLRs might influence tumor initiation and progression through regulating the activation of transcription factors such as NF-κB, interferon regulatory factors (IRFs) or AP-1 via mitogen-activated protein kinase (MAPKs) signaling integrators. As a consequence, inflammatory responses are affected by these transcription factors, leading to the change of inflammatory-related cytokines and type I interferon [6-8]. Recently, the development of several major cancers has been proven to be associated with TLRs, including BCa [9-15]. It has been shown that TLRs might facilitate bladder tumor development and progression by the induction of tumor-associated inflammatory responses and immune escape, and the initiation of bladder tumor regrowth after local radiotherapy [15-18].
MicroRNAs (miRNAs) contain 21-25 nucleotides and belong to non-coding small RNA family. Gene expression was controlled by post-transcriptional regulation through the inhibition of specific mRNAs by strict matching between the seeding region, 2-7 nucleotides in the 5’ miRNA sequence, and the non-coding region, 3’ untranslated region (3’UTR) of the target genes [19,20]. Recently, several studies identified that miRNAs could act as oncogenes or tumor suppressors by targeting 3’UTR of cancer-related genes [21,22]. Thus, any disruption in the microRNA binding sites such as SNPs could disturb the binding process to cause cancer development and progression [23].
Previous studies have demonstrated that SNPs on coding regions of TLRs signaling pathway genes were associated with BCa susceptibility. Shen et al. [24] showed that TLR4 + 3725GC polymorphism CC genotype was associated with increased risk of Bca initiation and progression. In addition, polymorphism rs4129009 in TLR10 was found to play a role in modulating urothelial cancer risk and progression [25]. However, the association between SNPs in the miRNA binding sites of TLRs signaling pathway genes and BCa susceptibility has not been discussed before. So we conducted this hospital-based case-control study with an integrative bioinformatics SNPs selecting approach in order to provide data for screening high-risk individuals in Chinese Han population.
Materials and methods
Study population
We recruited 317 patients from the Department of Urology, West China Hospital Sichuan University during the period from 2000 to 2012. All enrolled patients in this study were sporadic cases with pathological diagnosis (mean age 63.35 years; 242 males and 75 females). Those with history of other cancers were excluded. 268 healthy individuals with no evidence of cancer or immune disease were recruited in a routine check-up or health awareness campaigns as controls (mean age 64.13 years; 199 males and 69 females) from West China Hospital Sichuan University. All controls were matched to cases by gender and age (± 5 years) with the same ethnicity and had no evidence of immune disease. Clinical and epidemiology information were collected from all subjects, including tumor grade, tumor stage, smoking status and history, and event (recurrence/non recurrence, and death/live). At the end of the interview, 1 mL peripheral blood of all the subjects were collected and then frozen at -80°C. Informed consents were signed by all subjects after completely understanding the purpose of this case-control study.
The follow-up process was carried out by interviewing the patients via telephone or in the outpatient. The recurrence-free survival time was the specific period from the date of surgery to the date of recurrence or death. Malignant urothelial tumors that have not invaded the detrusor were defined as non-muscle invasive BCa, while the others were muscle invasive BCa.
SNP selection
In order to select potential SNPs within miRNAs binding sites, we initially used bioinformatics approach to select TLRs signaling pathway genes in the databases (http://www.biocarta.com, http://cgap.nci.nih.gov/Pathways) and a total of 100 genes were included in this study. Their names and designations were shown in Table S1. In addition, 90 cancer-related miRNAs which potentially target TLRs signaling pathway genes were found in Patrocles (http://www.patrocles.org) (Table S2). Then three different web databases, including TargetScanS (http://genes.mit.edu/tscan/targetscanS2005.html), PicTar (http://pictar.mdc-berlin.de), and PolymiRTS (http://compbio.uthsc.edu/miRSNP), were searched to identify the SNPs within miRNAs binding sites of the selected 100 genes based on these 90 miRNAs. After bioinformatics comparison, the searching results were intersected and 42 SNPs were chosen (Table S3). Furthermore, minor allele frequency (MAF) based on the frequencies in Asia population of these 42 SNPs (http://www.ncbi.nlm.nih.gov/snp/) were checked to exclude SNPs with the frequency of no more than 5%, and LDSelect program (http://droog.gs.washington.edu/ldSelect.html) was used to remove SNPs in high linkage disequilibrium. Finally, 24 SNPs in the potential miRNA binding sites were selected to accomplish the test.
DNA isolation and SNP genotyping
Genomic DNA was extracted from peripheral blood samples with QIAmp DNA extraction kit (Qiagen) according to the manufacturer’s protocol. After extraction, DNA purity and concentration were determined by spectrophotometer. Genotyping of the selected microRNA binding site SNPs were done with Sequenom MassARRAY & iPLEX assay of Capitalbio Company. In brief, primers for PCR and iPLEX reaction were designed by Genotyping Tools & MassARRAY Assay Design software, and then synthesized. PCR amplification was performed in 384-well plate and the product was dealt with shrimp alkaline phosphatase in order to dephosphorylate unincorporated dNTPs in PCR system. Then the Mass ARRAY iPLEX reaction was performed. After the iPLEX reaction, each base of SNP sites will create iPLEX products with different molecular weight. At last, matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) can identify the different iPLEX product, and then we can get the genotyping information of each SNP site for every subject in the result of MALDI-TOF with TYPER 4.0 software.
Statistical analysis
In this study, Hardy-Weinberg equilibrium was detected in each SNP in controls. Pearson x2 or Fisher’s exact tests was carried out to analysis the potential difference in the SNP genotypes between patients and controls. Student’s t test was used to explore the difference in the distribution of age. The odds ratio (OR) and 95% confidence interval (95% CI) was calculated with the risk option of crosstabs. We used Kaplan-Meier and Cox proportional hazard models to examine the correlation between the genotypes and recurrence-free survival time. All the analyses in this study were univariable analyses. Statistical analysis was done using the Statistical Package for Social Sciences software, v.13.0 (SPSS, Chicago, IL) and P < 0.05 was considered statistically significant with two-side tests.
Results
SNPs identification
According to previously described process, we obtained 24 SNPs in the 3’UTR of TLRs signaling pathway genes. However, 16 were removed for no proper PCR condition. Finally, 8 SNPs were selected for genotyping, including TLR7 rs10127190, TLR4 rs11536887, MAP3K7 rs3734657, TOLLIP rs41314515, TLR7 rs5743786, TLR7 rs72552316, TLR6 rs5743823, and TLR4 rs7869402 (Table 1).
Table 1.
Information about predicted miRNAs binding sites SNPs
| gene | SNP | Variation | MAFa | PCR primers | Putative microRNAs |
|---|---|---|---|---|---|
| TLR7 | Rs10127190 | [T/A] | NA | F 5’-ACGTTGGATGACTTGCCACTCTTTTACAGG-3’ | hsa-miR-19a, hsa-miR-19b |
| R 5’-ACGTTGGATGCTTTCTTATCTCTCTGTGTC-3’ | |||||
| TLR4 | Rs11536887 | [A/G] | 0.016 | F 5’-ACGTTGGATGCGAGTGACAAAGTGACAGAG-3’ | - |
| R 5’-ACGTTGGATGAAGACGTGCTTCAAATATCC-3’ | |||||
| MAP3K7 | Rs3734657 | [C/T] | 0.016 | F 5’-ACGTTGGATGAGAAGTCAGCAGCAGAAACG-3’ | hsa-miR-194, hsa-miR-212, hsa-miR-132 |
| R 5’-ACGTTGGATGGTCTTTCTTTGCATATTTC-3’ | |||||
| TOLLIP | Rs41314515 | [C/T] | 0.0028 | F 5’-ACGTTGGATGTGCACCCAAGAACAGGTGTG-3’ | hsa-miR-608 |
| R 5’-ACGTTGGATGGATTCCCGTGAAAGAGCACC-3’ | |||||
| TLR7 | Rs5743786 | [T/C] | 0.000 | F 5’-ACGTTGGATGAGGAGGACTCCAAGAGTGTG-3’ | hsa-miR-548a-3p, hsa-miR-548e, hsa-miR-548f |
| R 5’ACGTTGGATGAGGAATCCATATAATTGGC-3’ | |||||
| TLR7 | Rs72552316 | [C/T] | NA | F 5’-ACGTTGGATGGTAGGTGGACCATATGCATT-3’ | hsa-miR-1265, hsa-miR-4764-5p, hsa-miR-541-5p |
| R 5’-ACGTTGGATGTTGGGCCTGCTTCTGGGTT-3’ | |||||
| TLR6 | Rs5743823 | [T/C] | 0.005 | F 5’-ACGTTGGATGGGAAATTCAACTTAAGAAACC-3’ | hsa-miR-452 |
| R 5’-ACGTTGGATGCCTCCAGACAGTTACTTACG-3’ | |||||
| TLR4 | Rs7869402 | [C/T] | 0.092 | F 5’-ACGTTGGATGTTTAGGGAGACACAGATGGC-3’ | hsa-miR-539 |
| R 5’-ACGTTGGATGACCTTCACACGTAGTTCTCC-3’ |
MAF (Minor Allele Frequency) was cited from http://www.ncbi.nlm.nih.gov/SNP.
Characteristics of the study objects
The demographic information of the study subjects and clinical characteristics of each patient are presented in Table 2. We found no significant difference in the distribution of age between the cases (63.35 ± 12.95) and the controls (64.13 ± 12.38) (P = 0.443). The percentage of patients in clinical grade I, II, and III was 13.6% (n = 43), 34.1% (n = 108), and 48.9% (n = 155), respectively. The rest 3.4% (n = 11) were mixed grade tumor. In addition, patients with superficial BCa accounted for 43.5% (n = 138), while the remaining 56.5% (n = 179) were invasive BCa patients.
Table 2.
Demographic characteristics in the cases and controls
| Variables | Cases (n = 317) (%) | Controls (n = 268) (%) | P | |
|---|---|---|---|---|
| Sex | Male | 242 (76.3) | 199 (74.3) | |
| Female | 75 (23.7) | 69 (25.7) | ||
| Age (Mean age ± SD) | 63.35 ± 12.95 | 64.13 ± 12.38 | 0.443 | |
| Smoking | Non smokers | 168 (53.0) | ||
| Smokers | 149 (47.0) | |||
| Grade* | I | 43 (13.6) | ||
| II | 108 (34.1) | |||
| III | 155 (48.9) | |||
| Mixed* | 11 (3.4) | |||
| Stage | Superficial | 138 (43.5) | ||
| Invasive | 179 (56.5) | |||
| Event | Recurrence/non recurrence | 71/54 | ||
| Death/live | 38/87 |
Association of SNPs with BCa susceptibility
The genotype and allele frequencies of 8 selected SNPs in all subjects were listed in Table 3. All the SNPs were in Hardy-Weinberg equilibrium (P > 0.05). Genotype and allele distributions of TLR7 gene polymorphism rs72552316 exhibited significant difference between cases and controls. The frequency of the TC genotype and C allele was significant higher in controls than in patients (P ≤ 0.0001). However, for three polymorphisms (MAP3K7 rs3734657, TLR6 rs5743823, TLR4 rs7869402), the genotype and allele frequencies did not differ significantly between the cases and the controls (P > 0.05). For the remaining 4 polymorphisms, no mutant was found.
Table 3.
The polymorphisms of the TLR7 gene and the risk of BCa
| Gene name | Polymorphism | Patients (n = 317) | Controls (n = 317) | OR (95%) | P valuea | |||
|---|---|---|---|---|---|---|---|---|
|
| ||||||||
| n | % | n | % | |||||
| TLR7 | rs10127190 | TT (584) | 316 | 100.00 | 268 | 100.00 | 1 ref | - |
| TA (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| AA (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| T (1168) | 632 | 100.00 | 536 | 100.00 | 1 ref | - | ||
| A (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| TLR4 | rs11536887 | AA (583) | 317 | 100.00 | 268 | 100.00 | 1 ref | - |
| AG (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| GG (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| A (1170) | 634 | 100.00 | 536 | 100.00 | 1 ref | - | ||
| G (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| MAP3K7 | rs3734657 | CC (537) | 289 | 91.17 | 248 | 92.54 | 1 ref | - |
| CT (47) | 28 | 8.83 | 19 | 7.09 | 0.791(0.431-1.451) | 0.447 | ||
| TT (1) | 0 | 0.00 | 1 | 0.37 | - | - | ||
| C (1121) | 606 | 95.58 | 515 | 96.08 | 1 ref | |||
| T (49) | 28 | 4.42 | 21 | 3.92 | 0.883(0.495-1.573) | 0.671 | ||
| TOLLIP | rs41314515 | CC (584) | 317 | 100.00 | 267 | 99.63 | 1 ref | - |
| TC (1) | 0 | 0.00 | 1 | 0.37 | - | - | ||
| TT (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| C (1169) | 634 | 100.00 | 535 | 99.81 | 1 ref | - | ||
| T (1) | 0 | 0.00 | 1 | 0.19 | - | - | ||
| TLR7 | rs5743786 | TT (585) | 317 | 100.00 | 268 | 100.00 | 1 ref | - |
| TC (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| CC (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| T (1170) | 634 | 100.00 | 536 | 100.00 | 1 ref | - | ||
| C (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| TLR7 | rs72552316 | TT (367) | 317 | 100.00 | 50 | 18.66 | 1 ref | - |
| TC (218) | 0 | 0.00 | 218 | 81.34 | - | 0.000 | ||
| CC (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| T (952) | 634 | 100.00 | 318 | 59.33 | 1 ref | - | ||
| C (218) | 0 | 0.00 | 218 | 40.67 | - | 0.000 | ||
| TLR6 | rs5743823 | TT (580) | 313 | 99.05 | 267 | 99.63 | 1 ref | |
| TC (4) | 3 | 0.95 | 1 | 0.37 | 0.391(0.040-3.779) | 0.735 | ||
| CC (0) | 0 | 0.00 | 0 | 0.00 | - | - | ||
| C (1164) | 629 | 99.53 | 535 | 99.81 | 1 ref | - | ||
| T (4) | 3 | 0.47 | 1 | 0.19 | 0.392(0.041-3.779) | 0.736 | ||
| TLR4 | rs7869402 | CC (492) | 267 | 84.49 | 225 | 83.96 | 1 ref | - |
| CT (86) | 46 | 14.56 | 40 | 14.93 | 1.032(0.652-1.634) | 0.893 | ||
| TT (6) | 3 | 0.95 | 3 | 1.11 | 1.187(0.237-5.973) | 1.000 | ||
| C (1070) | 580 | 91.77 | 490 | 91.42 | 1 ref | - | ||
| T (98) | 52 | 8.23 | 46 | 8.58 | 1.047(0.692-1.585) | 0.828 | ||
The bold numbers mean the P value is < 0.05.
No association between SNPs and BCa recurrence-free survival
The follow-up was conducted in 317 BCa patients, of which 125 patients completed with 71 patients suffering from BCa recurrence. The mean and median follow-up time in this study was 38.25 ± 3.62 months and 24 months respectively. And the mean and median recurrence-free survival time was 38.87 ± 2.79 months and 25 months respectively. No relationship was detected between the eight polymorphisms and BCa recurrence-free survival time among the 125 patients (Table 4). In the subgroup of non-muscle invasive BCa cases, no correlation was found either (Table 5).
Table 4.
Recurrence-free survival analysis of 125 bladder cancer patients
| polymorphisms | genotype | N (all, n = 125) | N (reoccurence, n = 71) | HR (95% CI) |
|---|---|---|---|---|
| rs10127190 | TT | 125 (100.0%) | 71 (56.8%) | 1 |
| TA | 0 | 0 | - | |
| AA | 0 | 0 | - | |
| rs11536887 | AA | 125 (100.0%) | 71 (56.8%) | 1 |
| AG | 0 | 0 | - | |
| GG | 0 | 0 | - | |
| rs3734657 | CC | 117 (93.6%) | 66 (56.4%) | 1 |
| CT | 8 (6.4%) | 5 (62.5%) | 1.244 (0.501, 3.090) | |
| TT | 0 | 0 | - | |
| rs41314515 | CC | 125 (100.0%) | 71 (56.8%) | 1 |
| TC | 0 | 0 | - | |
| TT | 0 | 0 | - | |
| rs5743786 | TT | 125 (100.0%) | 71 (56.8%) | 1 |
| TC | 0 | 0 | - | |
| CC | 0 | 0 | - | |
| rs72552316 | TT | 125 (100.0%) | 71 (56.8%) | 1 |
| TC | 0 | 0 | - | |
| CC | 0 | 0 | - | |
| rs5743823 | TT | 123 (98.4%) | 70 (56.9) | 1 |
| TC | 2 (1.6%) | 1 (50.0%) | 0.946 (0.131, 6.816) | |
| CC | 0 | 0 | - | |
| rs7869402 | CC | 103 (82.4%) | 56 (54.4%) | 1 |
| CT | 19 (15.2%) | 13 (68.4%) | 1.278 (0.698, 2.339) | |
| TT | 3 (2.4%) | 2 (66.7%) | 1.041 (0.514, 2.107) |
Table 5.
Recurrence-free survival analysis of 50 non-muscle invasive bladder cancer patients
| polymorphisms | genotype | N (all, n = 50) | N (reoccurence, n = 29) | HR (95% CI) |
|---|---|---|---|---|
| rs10127190 | TT | 50 (100.0%) | 29 (58.0%) | 1 |
| TA | 0 | 0 | - | |
| AA | 0 | 0 | - | |
| rs11536887 | AA | 50 (100.0%) | 29 (58.0%) | 1 |
| AG | 0 | 0 | - | |
| GG | 0 | 0 | - | |
| rs3734657 | CC | 46 (92.0%) | 27 (58.7%) | 1 |
| CT | 4 (8.0%) | 2 (50.0%) | 0.671 (0.159, 2.824) | |
| TT | 0 | 0 | - | |
| rs41314515 | CC | 50 (100.0%) | 29 (58.0%) | 1 |
| TC | 0 | 0 | - | |
| TT | 0 | 0 | - | |
| rs5743786 | TT | 50 (100.0%) | 29 (58.0%) | 1 |
| TC | 0 | 0 | - | |
| CC | 0 | 0 | - | |
| rs72552316 | TT | 50 (100.0%) | 29 (58.0%) | 1 |
| TC | 0 | 0 | - | |
| CC | 0 | 0 | - | |
| rs5743823 | TT | 50 (100.0%) | 29 (58.0%) | 1 |
| TC | 0 | 0 | - | |
| CC | 0 | 0 | - | |
| rs7869402 | CC | 40 (80.0%) | 22 (55.0%) | 1 |
| CT | 9 (18.0%) | 6 (66.7%) | 1.106 (0.448, 2.734) | |
| TT | 1 (2.0%) | 1 (100.0%) | 1.167 (0.427, 3.185) |
Discussion
Previous studies mostly focused on the association between polymorphisms in the coding regions of TLRs signaling pathway genes and BCa susceptibility. In this study, polymorphism rs72552316 in miRNA binding sites of TLR7 gene was found to be related with the occurrence and progression of BCa. In addition, TC carriers of this polymorphism were associated with a decreased risk of BCa, indicating that rs72552316 might be used as a potential predicted target for the evaluation of BCa susceptibility.
TLR7 have been reported to affect various bladder tumor biological behaviors. TLR7 is a receptor that can recognize nucleic acid ligand, expressed on endosomal membranes of antigen presenting cells and leukocytes as dimers structurally. TLR7 activation could induce viability decline, proliferation suppression, and apoptosis of tumor cells, with immunogenic effects on tumors. A number of studies have reported the critical role of TLR7 in tumor development and progression, including melanoma, basal cell carcinoma and BCa [26,27]. Activation of TLR7 in bladder tumor cells was associated with decreased proliferation and apoptosis induction, which was mediated by down-regulation of a suppressive apoptotic molecule Bcl-2. In addition, increased proinflammatory cytokine, such as IL-6 and TNF-α, contributed to the immune mediated BCa cell cytotoxicity through MyD88/NF-κB pathway [28-30]. Moreover, Smith and his colleagues observed growth inhibition of bladder tumor in vivo after TLR7 stimulation, which suggested a promising target for BCa therapy. Given the multifaceted role of TLR7 in the occurrence and progression of BCa, it is not surprising that rs72552316 in miRNA-binding sites of TLR7 could affect BCa susceptibility and progression in Chinese Han population. However, due to the limited validated studies investigating the association between different genotypes of this polymorphism and diverse TLR7 mRNA/protein expression, it is too early to define the feasibility of rs72552316 to predict BCa risk.
In rs72552316 (TLR7), the base transition from T to C may affect the binding process between miRNAs and the target region in 3’UTR of TLR7. If miRNA loss combination to the corresponding miRNA binding site, the downstream process such as mRNA synthesis, might return to normal level or even increase. Thus, expression level of TLR7 in TC carriers could be higher than in cases. Due to the potential activation of TLR7, tumor growth would be suppressed to a certain extent. However, it is still under exploring about the exact regulation mechanisms between these miRNAs and BCa. Furthermore, whether this predicted target is the exact miRNA binding site needs further study.
No relationship was detected between the other seven polymorphisms and the risk of BCa in this study. The number of previous studies regarding to the relationship between these polymorphisms and BCa was extremely limited, which suggested that BCa susceptibility might be independent of the seven polymorphisms.
Up to date, several studies have investigated the prognostic role of TLRs signaling pathway genes in predicting survival outcome of BCa patients. In this study, no significant association was found between the eight polymorphisms and recurrence-free survival time of 125 patients who completed the follow-up. However, treatment and prognosis for muscle invasive bladder cancer (MIBC) and non-muscle invasive bladder cancer (NMIBC) are quite different. Radical cystectomy or radiotherapy was the most commonly used procedure in MIBC, and approximately 50% of patients ultimately die of distant metastases [31]. NMIBC is currently treated using a combination of transurethral resection (TURBT) and intravesical therapy, and 50-70% of patients will develop disease recurrence within two years of their initial diagnosis [32,33]. The recurrence rate is high in both low-and high-grade disease, and the degree of malignancy of NMIBC increases with the increase of recurrence. Until now, no certain factors have been proven to be sufficient to predict the recurrence of NMIBC. In order to verify the prognostic role of these polymorphisms, subgroup survival analysis for patients with NMIBC was conducted in this study. However, no significant relationship was found either. Since the index for the evaluation of survival outcome varied, significant association might be found between these polymorphisms and other indicators, such as overall-survival time. In addition, the number of patients who completed the follow-up was insufficient, which might affect the outcome of survival analysis. Sample size expansion should be adopted in the future for more accurate detection.
In conclusion, our data demonstrated the potential relationship between rs72552316 in TLR7 and BCa susceptibility. This finding might be helpful for the development of better tools to screen high BCa risk population and predict prognosis of BCa patients. However, recruitment of regional population, limitation of SNPs selection, and deficiency of functional analysis should not be ignored when we explain the significance of this study. In order to confirm our results, prospective, large scale and long-time follow-up studies needed to be conducted in the future.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 81101939).
Disclosure of conflict of interest
None.
Supporting Information
References
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