Abstract

BackgroundOncostatin M receptor (OSMR) represents a part of the interleukin-six (IL6) cytokine group that was discovered recently to be closely associated with cell’s growth and differentiation, inflammation, and enhancement of metastatic capacity. A comprehensive study suggests a close relationship between OSMR and papillary thyroid cancer, colorectal cancer, breast cancer, and other tumors. However, the relationship between OSMR and bladder cancer has yet to be determined.MethodsThree hundred six patients (including 142 patients with muscle-invasive bladder cancer and 164 patients with non-muscle-invasive bladder cancer) as well as 459 normal controls were included in this study. Two tag SNPs of OSMR, rs2278329, and rs2292016 were genotyped by TaqMan® SNP Genotyping Assay method and then the associations with bladder cancer were analyzed, as well as risk factors and prognosis.ResultsPatients with bladder cancer and controls did not differ significantly in terms of genotype frequencies and allele frequency distribution of rs2278329 (P = 0.77, OR = 0.97) and rs2292016 (P = 0.39, OR = 1.20) respectively. For rs2278329, no differences were found in terms of risk factors in stratified analyses. However, rs2292016 was associated with recurrence and tumor grade. GT/TT was found to increase the risk of relapse compared to the patients without allele T (GG genotype) (P = 0.016, OR = 1.878, 95% CI = 1.12–3.14) with the T allele of rs2292016 being a risk factor for recurrence (P = 0.032, OR = 0.67, 95% CI = 0.47–0.97). Besides, patients with GT genotype often present with high-grade bladder cancer (P = 0.003, OR = 2.33, 95% CI = 1.32–4.17). Multiple Cox regression analysis showed that rs2278329 and rs2292016 were related to the recurrence-free survival and overall survival in non muscle invasive bladder cancer (NMIBC) patients. For rs2278329, GA genotype could affect recurrence-free survival (P = 0.01, OR = 2.16, 95% CI = 1.17–3.98). For rs2292016, TT/GT genotype had a lower risk of death compared with GG homozygote genotype, and T was a protective factor for overall survival in bladder cancer (P = 0.029, OR = 0.22, 95% CI = 0.06–0.86).ConclusionsOSMR genotype frequencies were found to be associated with higher recurrence in bladder cancer, and it may serve as a biomarker candidate gene to predict prognosis of this disease. Further validation of OSMR as biomarker is required.

Highlights

  • Oncostatin M receptor (OSMR) represents a part of the interleukin-six (IL6) cytokine group that was discovered recently to be closely associated with cell’s growth and differentiation, inflammation, and enhancement of metastatic capacity

  • DNA sequencing analyzing was used to confirm the identities of three genotypes of each several gene polymorphisms (SNPs)

  • A serial of studies demonstrate that the onset and progression of bladder cancer are closely associated with multiple gene polymorphisms including CD44, XRCC1, and PDCD6 [8,9,10]

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Summary

Introduction

Oncostatin M receptor (OSMR) represents a part of the interleukin-six (IL6) cytokine group that was discovered recently to be closely associated with cell’s growth and differentiation, inflammation, and enhancement of metastatic capacity. A comprehensive study suggests a close relationship between OSMR and papillary thyroid cancer, colorectal cancer, breast cancer, and other tumors. Cancer of the bladder is a frequently encountered malignancy. It is associated with a poor quality of life. According to the 2012 World Health Organization statistics, the number of new bladder cancer cases was approximately 429,000 worldwide, which ranks ninth in the incidence of malignant tumors. MIBC patients' experience low 5-year survival rates at 69%, with metastasis lowering this survival rate rapidly to 6% [4]. Diagnosis and treatment of malignant tumors has a significant impact on the prognosis of patients. Cystoscopy and pathological biopsy represent the gold standard for the diagnosis of bladder cancer. The disadvantages include high cost, trauma, pain, and the inability of these approaches to predict the treatment outcomes

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