Project description:This study aimed to identify the genetic signatures associated with disease prognosis in bladder cancer. We used 165 primary bladder cancer samples, 23 recurrent non-muscle invasive tumor tissues, 58 normal looking bladder mucosae surrounding cancer and 10 normal bladder mucosae for microarray analysis. Hierarchical clustering was used to stratify the prognosis-related gene classifiers. For validation, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) of top-ranked 14 genes was performed. On unsupervised hierarchical clustering using prognosis related gene-classifier, tumors were divided into 2 groups. The high risk gene signatures had significantly poor prognosis compared to low risk gene signatures (P<0.001 by the log-rank test, respectively). The prognosis-related gene classifiers correlated significantly with recurrence of non-muscle invasive bladder cancer (hazard ratio, 4.09; 95% confidence interval [CI], 1.94 to 8.64; P<0.001), and progression (hazard ratio, 23.68; 95% confidence interval [CI], 4.91 to 114.30; P<0.001), cancer-specific survival (hazard ratio, 29.25; 95% confidence interval [CI], 3.47 to 246.98; P=0.002) and overall survival (hazard ratio, 23.33; 95% confidence interval [CI], 4.97 to 109.50; P<0.001) of muscle invasive bladder cancer (p < 0.001, respectively). No patient with non-muscle invasive bladder cancer experienced cancer progression in low risk gene signature group. Prognosis-related gene classifiers validated by RT- PCR showed identical results. Prognosis related gene-classifiers provided strong predictive value for disease outcome. These gene classifiers could assist in selecting patients who might benefit from more aggressive therapeutic intervention or surveillance. Keywords: Gene expression, Bladder cancer, Prognosis 165 primary bladder cancer samples and 23 recurrent non-muscle invasive tumor tissues from 14 patients were taken in the Chungbuk National University Hospital. Only histologically verified transitional cell carcinoma samples were selected. Simultaneously 58 normal looking bladder mucosae surrounding cancer were obtained during the operation, which were histologically confirmed normal. Also, 10 normal bladder mucosae were obtained from patients with benign disease. The normal controls were determined to be free of cancer after revealing no malignant cells on urine cytology and no observable bladder cancer on cystoscopic examination during operation for their diseases, and were histologically reconfirmed normal.
Project description:RNAseq was done on Breast cancer PDX samples uisng Library protocol =llumina TruSeq Stranded Total RNA Kit with Ribo-Zero Gold , HiSeq 125 Cycle Paired-End Sequencing v4
Project description:Patients with bladder cancer need frequent controls over long follow-up time due to high recurrence rate and risk of conversion to muscle invasive cancer with poor prognosis. We identified cancer-related molecular signatures in apparently healthy bladder in patients with subsequent muscular invasiveness during follow-up. Global proteomics of the normal tissue biopsies revealed specific proteome fingerprints in these patients prior to subsequent muscular invasiveness. In these presumed normal samples, we detected modulations of proteins previously associated with different cancer types. This study indicates that analyzing apparently healthy tissue of a cancer-invaded organ may predict disease progression.
Project description:Previous studies successfully revealed molecular characteristics of bladder cancers, dealing with non-muscle invasive bladder cancer and muscle invasive bladder cancer, separately. At the molecular level, however, there is a great need to aggregate these subtypes, which may share biological characteristics. This study aimed to identify distinct molecular subtypes of BC and the clinical and/or biological characteristics of each subtype. We used seven gene expression data sets for bladder cancer, which included data from 118 primary bladder cancer samples and 27 recurrent bladder tumor tissues from the Yonsei University Severance Hospital. Hierarchical clustering revealed four molecular subtypes of BC with different clinical outcomes: class 1 with low-grade NMIBC and the best prognosis; class 2 characterized by active FGFR3 and inhibited immune response pathways; class 3 with high-grade NMIBC and the worst progression-free survival; and class 4 mainly comprised of MIBC along with EMT activation. By applying the classifier based on these characteristics, we stratified all BC samples into newly identified molecular subtypes. When comparing previously reported subtypes, our subtypes well agreed with their molecular characteristics regardless of breast cancer-based biology, and showed a strong prognostic relevance in class 3. Integrative analysis of mutation and gene expression suggested that class 3 may have the potential benefit from anti-PD-L1 immunotherapy. Our classifier, constructed by NMIBC and MIBC integration, successfully stratified BC patients into distinct subtypes with different clinical outcomes and a possible treatment option.