Profiling of High-Throughput Mass Spectrometry Data for Ovarian Cancer Detection

Backgroud: The impact of cancer in the society has created the necessity of new and faster theoretical models for the early diagnosis of cancer. Methods: In the work, A mass spectrometry (MS) data analysis method based on star-like graph of protein and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the MS data set. Firstly, the MS data is reduced and transformed into the corresponding protein sequence. And then, the topological indexes of the star-like graph are calculated to describe each MS data of cancer sample. Finally, the SVM model is suggested to classify the MS data. Results: Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models. The average prediction accuracy, sensitivity, and specificity of the model were 96.45%, 96.88%, and 95.67%, respectively, for [0,1] normalization data. and the model were 94.43%, 96.25%, and 91.11%, respectively, for [-1,1] normalization data. Conclusion: The model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

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Evaluation and validation of an analytical approach for high-throughput metabolomic fingerprinting using direct introduction–high-resolution mass spectrometry: Applicability to classification of urine of scrapie-infected ewes

European Journal of Mass Spectrometry ◽

10.1177/1469066718806450 ◽

2018 ◽

Vol 25 (2) ◽

pp. 251-258 ◽

Cited By ~ 4

Author(s):

Estelle Rathahao-Paris ◽

Sandra Alves ◽

Nawel Boussaid ◽

Nicole Picard-Hagen ◽

Véronique Gayrard ◽

...

Keyword(s):

Mass Spectrometry ◽

High Resolution ◽

High Throughput ◽

Biological Samples ◽

Matrix Effects ◽

Mass Measurement ◽

Direct Injection ◽

Mass Spectrometry Data ◽

Direct Injection Mass Spectrometry

Direct injection–mass spectrometry can be used to perform high-throughput metabolomic fingerprinting. This work aims to evaluate a global analytical workflow in terms of sample preparation (urine sample dilution), high-resolution detection (quality of generated data based on criteria such as mass measurement accuracy and detection sensitivity) and data analysis using dedicated bioinformatics tools. Investigation was performed on a large number of biological samples collected from sheep infected or not with scrapie. Direct injection–mass spectrometry approach is usually affected by matrix effects, eventually hampering detection of some relevant biomarkers. Reference compounds were spiked in biological samples to help evaluate the quality of direct injection–mass spectrometry data produced by Fourier Transform mass spectrometry. Despite the potential of high-resolution detection, some drawbacks still remain. The most critical is the presence of matrix effects, which could be minimized by optimizing the sample dilution factor. The data quality in terms of mass measurement accuracy and reproducible intensity was evaluated. Good repeatability was obtained for the chosen dilution factor (i.e., 2000). More than 150 analyses were performed in less than 16 hours using the optimized direct injection–mass spectrometry approach. Discrimination of different status of sheeps in relation to scrapie infection (i.e., scrapie-affected, preclinical scrapie or healthy) was obtained from the application of Shrinkage Discriminant Analysis to the direct injection–mass spectrometry data. The most relevant variables related to this discrimination were selected and annotated. This study demonstrated that the choice of appropriated dilution faction is indispensable for producing quality and informative direct injection–mass spectrometry data. Successful application of direct injection–mass spectrometry approach for high throughput analysis of a large number of biological samples constitutes the proof of the concept.

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