Tissue and serum proteomic profiling for diagnostic and prognostic bladder cancer biomarkers

Schwamborn, Kristina; Gaisa, Nadine T.; Henkel, Corinna

doi:10.1586/epr.10.82

Cited by 18 publications

(15 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The technologies used in biomarker design range from SELDI/MALDI-TOF MS through liquid chromatography or capillary electrophoresis mass spectroscopy and gel-based approaches to protein arrays. [32], [33] More recent metabolic approaches combined several techniques such as high performance liquid chromatography (HPLC) with gas chromatography or mass spectroscopy (MS) for the analysis of human urine metabolites in search for diagnostic, prognostic, and therapy monitoring biomarkers. [34]–[36] These studies support the potential for proteomic profiling as a non-invasive tool for detecting and monitoring bladder cancer.…”

Section: Discussionmentioning

confidence: 99%

“…[50] Their overexpression has been documented in several human malignancies including bladder cancer and was shown to be associated with tumor invasiveness. [23], [32], [49] Therefore, progressive deregulation of protein expression patterns in voided urine samples of patients with bladder cancer may be observed in aggressive variants of the disease. This may explain the relationship between global expression patterns of proteins in voided urine and clinical aggressiveness as identified by unsupervised clustering using the matrix of 473 protein peaks.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Bladder Cancer Using Proteomic Profiling of Urine Sediments

et al. 2012

View full text Add to dashboard Cite

We used protein expression profiles to develop a classification rule for the detection and prognostic assessment of bladder cancer in voided urine samples. Using the Ciphergen PBS II ProteinChip Reader, we analyzed the protein profiles of 18 pairs of samples of bladder tumor and adjacent urothelium tissue, a training set of 85 voided urine samples (32 controls and 53 bladder cancer), and a blinded testing set of 68 voided urine samples (33 controls and 35 bladder cancer). Using t-tests, we identified 473 peaks showing significant differential expression across different categories of paired bladder tumor and adjacent urothelial samples compared to normal urothelium. Then the intensities of those 473 peaks were examined in a training set of voided urine samples. Using this approach, we identified 41 protein peaks that were differentially expressed in both sets of samples. The expression pattern of the 41 protein peaks was used to classify the voided urine samples as malignant or benign. This approach yielded a sensitivity and specificity of 59% and 90%, respectively, on the training set and 80% and 100%, respectively, on the testing set. The proteomic classification rule performed with similar accuracy in low- and high-grade bladder carcinomas. In addition, we used hierarchical clustering with all 473 protein peaks on 65 benign voided urine samples, 88 samples from patients with clinically evident bladder cancer, and 127 samples from patients with a history of bladder cancer to classify the samples into Cluster A or B. The tumors in Cluster B were characterized by clinically aggressive behavior with significantly shorter metastasis-free and disease-specific survival.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Detection of Bladder Cancer Using Proteomic Profiling of Urine Sediments

et al. 2012

View full text Add to dashboard Cite

show abstract

“…However, this technique often will yield inconsistent or variable results and is time-consuming, leading to difficulties when trying to compare multiple datasets. 38 Protein array techniques are newly emerging approaches for proteomic analysis in recent years. These can be targeted approaches in which specific antibodies can be immobilized to form a protein microarray for the capture and quantification of specific protein concentrations.…”

Section: Methods For Proteomic Profilingmentioning

confidence: 99%

“…However, the disadvantages of this method are the high costs of antibodies and the fact that PTMs of proteins are more difficult to analyze. 38,40 …”

Section: Methods For Proteomic Profilingmentioning

confidence: 99%

The impact of advanced proteomics in the search for markers and therapeutic targets of bladder cancer

et al. 2017

View full text Add to dashboard Cite

Bladder cancer is the most common cancer of the urinary tract and can be avoided through proper surveillance and monitoring. Several genetic factors are known to contribute to the progression of bladder cancer, many of which produce molecules that serve as cancer biomarkers. Blood, urine, and tissue are commonly analyzed for the presence of biomarkers, which can be derived from either the nucleus or the mitochondria. Recent advances in proteomics have facilitated the high-throughput profiling of data generated from bladder cancer-related proteins or peptides in parallel with high sensitivity and specificity, providing a wealth of information for biomarker discovery and validation. However, the transmission of screening results from one laboratory to another remains the main disadvantage of these methods, a fact that emphasizes the need for consistent and standardized procedures as suggested by the Human Proteome Organization. This review summarizes the latest discoveries and progress of biomarker identification for the early diagnosis, projected prognosis, and therapeutic response of bladder cancer, informs the readers of the current status of proteomic-based biomarker findings, and suggests avenues for future work.

show abstract

“…Proteomic technologies can provide high-throughput and in-depth analysis. Many of these studies have been performed on different cancer types including lung (11), breast (12), colorectal (13), bladder (14), prostate (15), head and neck (16) and ovarian cancers (17). The different cancer proteomics have generated a set of putative cancer biomarkers.…”

Section: Introductionmentioning

confidence: 99%

A new bioinformatics insight into human cancer-associated proteins

2012

View full text Add to dashboard Cite

Abstract. Cancer is a complex disease caused by multiple factors including genetic mutations, and environmental factors. Cancer-associated proteins are potential biomarkers or targets for diagnostic and therapeutic interventions in cancer. The Universal Protein Resourse (UniPort) is a well-annotated comprehensive resourse for protein sequence records. In the present study, we performed data mining of UniProt proteins as a proteomic resource. We generated a catalog of 1653 cancer-associated proteins including 344 secretory proteins and 300 cell surface proteins. Integrated bioinformatic analysis including ontological classification, functional enrichment and pathway construction were performed. These proteins could serve as a reference for further studies to discover cancer targets, and the enriched bioinformatic analysis provides new insights into cancer proteomics research.

show abstract

Tissue and serum proteomic profiling for diagnostic and prognostic bladder cancer biomarkers

Cited by 18 publications

References 83 publications

Detection of Bladder Cancer Using Proteomic Profiling of Urine Sediments

Detection of Bladder Cancer Using Proteomic Profiling of Urine Sediments

The impact of advanced proteomics in the search for markers and therapeutic targets of bladder cancer

A new bioinformatics insight into human cancer-associated proteins

Contact Info

Product

Resources

About