Dataset Information

Detection of cervical cancer biomarker patterns in blood plasma and urine by differential scanning calorimetry and mass spectrometry.

ABSTRACT: Improved methods for the accurate identification of both the presence and severity of cervical intraepithelial neoplasia (CIN) and extent of spread of invasive carcinomas of the cervix (IC) are needed. Differential scanning calorimetry (DSC) has recently been shown to detect specific changes in the thermal behavior of blood plasma proteins in several diseases. This methodology is being explored to provide a complementary approach for screening of cervical disease. The present study evaluated the utility of DSC in differentiating between healthy controls, increasing severity of CIN and early and advanced IC. Significant discrimination was apparent relative to the extent of disease with no clear effect of demographic factors such as age, ethnicity, smoking status and parity. Of most clinical relevance, there was strong differentiation of CIN from healthy controls and IC, and amongst patients with IC between FIGO Stage I and advanced cancer. The observed disease-specific changes in DSC profiles (thermograms) were hypothesized to reflect differential expression of disease biomarkers that subsequently bound to and affected the thermal behavior of the most abundant plasma proteins. The effect of interacting biomarkers can be inferred from the modulation of thermograms but cannot be directly identified by DSC. To investigate the nature of the proposed interactions, mass spectrometry (MS) analyses were employed. Quantitative assessment of the low molecular weight protein fragments of plasma and urine samples revealed a small list of peptides whose abundance was correlated with the extent of cervical disease, with the most striking plasma peptidome data supporting the interactome theory of peptide portioning to abundant plasma proteins. The combined DSC and MS approach in this study was successful in identifying unique biomarker signatures for cervical cancer and demonstrated the utility of DSC plasma profiles as a complementary diagnostic tool to evaluate cervical cancer health.

SUBMITTER: Garbett NC

PROVIDER: S-EPMC3885574 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Detection of cervical cancer biomarker patterns in blood plasma and urine by differential scanning calorimetry and mass spectrometry.

Garbett Nichola C NC Merchant Michael L ML Helm C William CW Jenson Alfred B AB Klein Jon B JB Chaires Jonathan B JB

PloS one 20140108 1

Improved methods for the accurate identification of both the presence and severity of cervical intraepithelial neoplasia (CIN) and extent of spread of invasive carcinomas of the cervix (IC) are needed. Differential scanning calorimetry (DSC) has recently been shown to detect specific changes in the thermal behavior of blood plasma proteins in several diseases. This methodology is being explored to provide a complementary approach for screening of cervical disease. The present study evaluated the ...[more]

PMID: 24416269

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Project description:The thermoanalytical technique differential scanning calorimetry (DSC) has been applied to characterize protein denaturation patterns (thermograms) in blood plasma samples and relate these to a subject's health status. The analysis and classification of thermograms is challenging because of the high-dimensionality of the dataset. There are various methods for group classification using high-dimensional data sets; however, the impact of using high-dimensional data sets for cancer classification has been poorly understood. In the present article, we proposed a statistical approach for data reduction and a parametric method (PM) for modeling of high-dimensional data sets for two- and three- group classification using DSC and demographic data. We compared the PM to the non-parametric classification method K-nearest neighbors (KNN) and the semi-parametric classification method KNN with dynamic time warping (DTW). We evaluated the performance of these methods for multiple two-group classifications: (i) normal versus cervical cancer, (ii) normal versus lung cancer, (iii) normal versus cancer (cervical + lung), (iv) lung cancer versus cervical cancer as well as for three-group classification: normal versus cervical cancer versus lung cancer. In general, performance for two-group classification was high whereas three-group classification was more challenging, with all three methods predicting normal samples more accurately than cancer samples. Moreover, specificity of the PM method was mostly higher or the same as KNN and DTW-KNN with lower sensitivity. The performance of KNN and DTW-KNN decreased with the inclusion of demographic data, whereas similar performance was observed for the PM which could be explained by the fact that the PM uses fewer parameters as compared to KNN and DTW-KNN methods and is thus less susceptible to the risk of overfitting. More importantly the accuracy of the PM can be increased by using a greater number of quantile data points and by the inclusion of additional demographic and clinical data, providing a substantial advantage over KNN and DTW-KNN methods.

Project description:Thermogravimetry-coupled with differential scanning calorimetry, quadrupole mass spectrometry, and Fourier-transform infrared spectroscopy (TG-DSC-QMS-FTIR)-was applied to monitor the thermal stability (in an N2 pyrolytic atmosphere) and chemical properties of natural polymers, fulvic (FA) and humic acids (HA), isolated from chemically different soils. Three temperature ranges, R1, 40-220°C; R2, 220-430°C; and R3, 430-650°C, were distinguished from the DSC data, related to the main thermal processes of different structures (including transformations without weight loss). Weight loss (?M) estimated from TG curves at the above temperature intervals revealed distinct differences within the samples in the content of physically adsorbed water (at R1), volatile and labile functional groups (at R2) as well as recalcitrant and refractory structures (at R3). QMS and FTIR modules enabled the chemical identification (by masses and by functional groups, respectively) of gaseous species evolved during thermal decomposition at R1, R2 and R3. Variability in shape, area and temperature of TG, DSC, QMS and FTIR peaks revealed differences in thermal stability and chemical structure of the samples between the FAs and HAs fractions of different origin. The statistical analysis showed that the parameters calculated from QMS (areas of m/z = 16, 17, 18, 44), DSC (MaxDSC) and TG (?M) at R1, R2 and R3 correlated with selected chemical properties of the samples, such as N, O and COOH content as well as E2/E6 and E2/E4 indexes. This indicated a high potential for the coupled method to monitor the chemical changes of humic substances. A new humification parameter, HTD, based on simple calculations of weight loss at specific temperature intervals proved to be a good alternative to indexes obtained from other methods. The above findings showed that the TG-DSC-QMS-FTIR coupled technique can represent a useful tool for the comprehensive assessment of FAs and HAs properties related to their various origin.

Dataset Information

Detection of cervical cancer biomarker patterns in blood plasma and urine by differential scanning calorimetry and mass spectrometry.

Publications

Detection of cervical cancer biomarker patterns in blood plasma and urine by differential scanning calorimetry and mass spectrometry.

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