Progression from superficial to invasive carcinoma of the bladder: genetic evidence of either clonal heterogeneous events

The potential diagnosis role of TP53 mutation in advanced bladder cancer: A meta-analysis

BACKGROUND

Bladder cancer is one of the most common urological cancers all over the world, and NMIBC occupies almost 80% of recently diagnosed bladder cancer cases. Progress and recurrence of bladder cancer are the main problems during the disease. The level of TP53 mutation is obviously higher in the high stage than the lower. This meta-analysis is to evaluate the potential diagnosis feature of TP53 mutation by the expression of TP53 mutation of Ta stage vs high stage in bladder cancer.

METHODS

A systematic search of databases was conducted, and some relevant articles were selected. Next, the meta-analysis was carried out according to the standard guidelines.

RESULTS

There were seven researches in which 677 participants were selected at the basis of inclusion standard. TP53 mutation was associated highly with increased diagnosis of bladder cancer. We found that the high stage of bladder cancer has obviously higher level of TP53 mutation than the lower stage, and these patients of MIBC have higher expression of TP53 mutation compared with NMIBC. No significant publication bias has been observed in this meta-analysis. The expression of TP53 mutation might be a diagnose-related biomarker for lots of patients with bladder cancer.

CONCLUSIONS

The results of this meta-analysis provided further evidences that the expression of TP53 mutation was associated with the diagnosis efficiency of advanced bladder cancer. Higher expression of TP53 mutation was observed in the high stage of bladder cancer or the MIBC, and lower expression of TP53 mutation in the Ta stage of bladder cancer or the NMIBC. The expression level of TP53 mutation was probably a critical diagnosed biomarker in advanced bladder cancer.

Identifying Superficial, Muscle-Invasive, and Metastasizing Transitional Cell Carcinoma of the Bladder

PURPOSE

Expression profiling by DNA microarray technology permits the identification of genes underlying clinical heterogeneity of bladder cancer and which might contribute to disease progression, thereby improving assessment of treatment and prediction of patient outcome.

EXPERIMENTAL DESIGN

Invasive (20) and superficial (22) human bladder tumors from 34 patients with known outcome regarding disease recurrence and progression were analyzed by filter-based cDNA arrays (Atlas Human Cancer 1.2; BD Biosciences Clontech) containing 1185 genes. For 9 genes, array data were confirmed using real-time reverse transcription-PCR. Additionally, Atlas array data were validated using Affymetrix GeneChip oligonucleotide arrays with 22,283 human gene fragments and expressed sequence tags sequences in a subset of three superficial and six invasive bladder tumors.

RESULTS

A two-way clustering algorithm using different subsets of gene expression data, including a subset of 41 genes validated by the oligonucleotide array (Affymetrix), classified tumor samples according to clinical outcome as superficial, invasive, or metastasizing. Furthermore, (a) a clonal origin of superficial tumors, (b) highly similar gene expression patterns in different areas of invasive tumors, and (c) an invasive-like pattern was observed in bladder mucosas derived from patients with locally advanced disease. Several gene clusters that characterized invasive or superficial tumors were identified. In superficial bladder tumors, increased mRNA levels of genes encoding transcription factors, molecules involved in protein synthesis and metabolism, and some proteins involved into cell cycle progression and differentiation were observed, whereas transcripts for immune, extracellular matrix, adhesion, peritumoral stroma and muscle tissue components, proliferation, and cell cycle controllers were up-regulated in invasive tumors.

CONCLUSIONS

Gene expression profiling of human bladder cancers provides insight into the biology of bladder cancer progression and identifies patients with distinct clinical phenotypes.