Surface-enhanced Laser Desorption/ionization Time-of-flight Mass Spectrometry: Serum Protein Profiling in Seminoma Patients

Overview

Journal World J Urol

Specialty Urology

Date 2009 Jun 17

PMID 19529944

Citations 3

Authors

Romy Strenziok

Stefan Hinz

Christian Wolf

Tim Conrad

Hans Krause

Kurt Miller

Mark Schrader

Affiliations

Soon will be listed here.

Abstract

Purpose: Surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS) allows rapid protein profiling of complex biological mixtures. We analyzed testicular germ cell cancer serum samples to differentiate between cancer and controls with a special focus on beta-hCG-negative seminomas.

Methods: Proteomic spectra were generated by the ProteinChip system and analyzed by the proteomic platform "proteomic.net". For statistical analysis, an artificial intelligence learning algorithm was used.

Results: The classification algorithm correctly identified the pattern in 90.4% of the patients. Decision trees predicted seminomas with 91.5% sensitivity and 89.4% specificity. Seminoma patients with normal beta-hCG serum level were correctly predicted with 80% sensitivity and 70% specificity.

Conclusions: Our study demonstrates protein profiles of testicular germ cell cancer patients that differ in a highly significant degree from normal controls. Validation of these findings may enable proteomic profiling to become a valuable tool, especially for aftercare.

Citing Articles

Liquid Biopsies in the Clinical Management of Germ Cell Tumor Patients: State-of-the-Art and Future Directions.

Lobo J, Leao R, Jeronimo C, Henrique R Int J Mol Sci. 2021; 22(5).

PMID: 33800799 PMC: 7961393. DOI: 10.3390/ijms22052654.

Proteomics for the Identification of Biomarkers in Testicular Cancer-Review.

Milardi D, Grande G, Vincenzoni F, Pierconti F, Pontecorvi A Front Endocrinol (Lausanne). 2019; 10:462.

PMID: 31354629 PMC: 6639829. DOI: 10.3389/fendo.2019.00462.

Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data.

Conrad T, Genzel M, Cvetkovic N, Wulkow N, Leichtle A, Vybiral J BMC Bioinformatics. 2017; 18(1):160.

PMID: 28274197 PMC: 5343371. DOI: 10.1186/s12859-017-1565-4.

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