Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population

The low specificity and sensitivity of the carcinoembryonic antigen test makes it not an ideal biomarker for the detection of colorectal cancer. We developed and evaluated a proteomic approach for the simultaneous detection and analysis of multiple proteins for distinguishing individuals with colore...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Clinical cancer research Ročník 10; číslo 24; s. 8380
Hlavní autori: Chen, Yi-ding, Zheng, Shu, Yu, Jie-kai, Hu, Xun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 15.12.2004
Predmet:
Adult
Aged
Aged, 80 and over
Biomarkers, Tumor - metabolism
Blood Proteins - analysis
CA-19-9 Antigen - metabolism
Case-Control Studies
Colorectal Neoplasms - blood
Diagnosis, Differential
Female
Humans
Male
Middle Aged
Neural Networks (Computer)
Protein Array Analysis
Sensitivity and Specificity
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods
ISSN:1078-0432
On-line prístup:Zistit podrobnosti o prístupe
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The low specificity and sensitivity of the carcinoembryonic antigen test makes it not an ideal biomarker for the detection of colorectal cancer. We developed and evaluated a proteomic approach for the simultaneous detection and analysis of multiple proteins for distinguishing individuals with colorectal cancer from healthy individuals. We subjected serum samples (including 55 colorectal cancer patients and 92 age- and sex-matched healthy individuals) from 147 individuals, for analysis by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. Peaks were detected with Ciphergen SELDI software version 3.0. Using a multilayer artificial neural network with a back propagation algorithm, we developed a classifier for separating the colorectal cancer groups from the healthy groups. The artificial neural network classifier separated the colorectal cancer from the healthy samples, with a sensitivity of 91% and specificity of 93%. Four top-scored peaks, at m/z of 5,911, 8,930, 8,817, and 4,476, were finally selected as the potential "fingerprints" for detection of colorectal cancer. The combination of SELDI-TOF mass spectrometry with the artificial neural networks in the analysis of serum protein yields significantly higher sensitivity and specificity values for the detection and diagnosis of colorectal cancer.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1078-0432
DOI:10.1158/1078-0432.CCR-1162-03