Dataset Information

Amyloids as protein traps in bladder cancer

ABSTRACT: Despite the current diagnostic and therapeutic approaches to bladder cancer being widely accepted, there have been few significant advancements in this field over the past decades. This underscores the necessity for a paradigm shift in the approach to bladder cancer. The role of amyloids in cancer remains unclear despite their identification in several other pathologies. In this study, we present evidence of amyloids in bladder cancer, both in vitro and in vivo. In a murine model of bladder cancer, a positive correlation was observed between amyloids and tumor stage, indicating an association between amyloids and bladder cancer progression. Subsequently, the amyloid proteome of the RT4 non-invasive and HT1197 invasive bladder cancer cell lines was identified and included oncogenes, tumor suppressors, and highly expressed cancer-related proteins. It is proposed that amyloids function as structures that sequester key proteins. Therefore, amyloids should be considered in the study and diagnosis of bladder cancer.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Cesar Garcia

LAB HEAD: Lucia Canclini

PROVIDER: PXD053864 | Pride | 2024-12-19

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 20

Similar Datasets

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.

			Action	DRS
	Fasta_HT1197_mutant.fasta	Fasta
	Fasta_RT4_mutant.fasta	Fasta
	HT1197-PKAF1.raw	Raw
	HT1197-PKAF2.raw	Raw
	HT1197-PKAF3.raw	Raw

Dataset Information

Amyloids as protein traps in bladder cancer

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets