Clinical significance of altered <it>nm23-H1, EGFR, RB and p53 </it>expression in bilharzial bladder cancer

Abstract

Abstract Background Clinical characterization of bladder carcinomas is still inadequate using the standard clinico-pathological prognostic markers. We assessed the correlation between nm23-H1, Rb, EGFR and p53 in relation to the clinical outcome of patients with muscle invasive bilharzial bladder cancer (MI-BBC). Methods nm23-H1, Rb, EGFR and p53 expression was assessed in 59 MI-BBC patients using immunohistochemistry and reverse transcription (RT-PCR) and was correlated to the standard clinico-pathological prognostic factors, patient's outcome and the overall survival (OS) rate. Results Overexpression of EGFR and p53 proteins was detected in 66.1% and 35.6%; respectively. Loss of nm23-H1and Rb proteins was detected in 42.4% and 57.6%; respectively. Increased EGFR and loss of nm23-H1 RNA were detected in 61.5% and 36.5%; respectively. There was a statistically significant correlation between p53 and EGFR overexpression (p nm23 loss (protein and RNA), lymph node status (p EGFR RNA overexpression (p= 0.003) as well as between the incidence of metastasis and altered Rb expression (p = 0.026), p53 overexpression (p p = 0.04). Advanced disease stage correlated significantly with increased EGFR (protein and RNA) (p = 0.003 & 0.01), reduced nm23-H1 RNA (p = 0.02), altered Rb (p = 0.023), and p53 overexpression (p = 0.004). OS rates correlated significantly, in univariate analysis, with p53 overexpression (p = 0.011), increased EGFR (protein and RNA, p = 0.034&0.031), nm23-H1 RNA loss (p = 0.021) and aberrations of ≥ 2 genes. However, multivariate analysis showed that only high EGFR overexpression, metastatic recurrence, high tumor grade and the combination of ≥ 2 affected markers were independent prognostic factors. Conclusion nm23-H1, EGFR and p53 could be used as prognostic biomarkers in MI-BBC patients. In addition to the standard pathological prognostic factors, a combination of these markers (≥ 2) has synergistic effects in stratifying patients into variable risk groups. The higher is the number of altered biomarkers, the higher will be the risk of disease progression and death.