Shotgun Lipidomics for Differential Diagnosis of HPV-Associated Cervix Transformation.

A dramatic increase in cervical diseases associated with human papillomaviruses (HPV) in women of reproductive age has been observed over the past decades. An accurate differential diagnosis of the severity of cervical intraepithelial neoplasia and the choice of the optimal treatment requires the search for effective biomarkers with high diagnostic and prognostic value. The objective of this study was to introduce a method for rapid shotgun lipidomics to differentiate stages of HPV-associated cervix epithelium transformation. Tissue samples from 110 HPV-positive women with cervicitis (n = 30), low-grade squamous intraepithelial lesions (LSIL) (n = 30), high-grade squamous intraepithelial lesions (HSIL) (n = 30), and cervical cancers (n = 20) were obtained. The cervical epithelial tissue lipidome at different stages of cervix neoplastic transformation was studied by a shotgun label-free approach. It is based on electrospray ionization mass spectrometry (ESI-MS) data of a tissue extract. Lipidomic data were processed by the orthogonal projections to latent structures discriminant analysis (OPLS-DA) to build statistical models, differentiating stages of cervix transformation. Significant differences in the lipid profile between the lesion and surrounding tissues were revealed in chronic cervicitis, LSIL, HSIL, and cervical cancer. The lipids specific for HPV-induced cervical transformation mainly belong to glycerophospholipids: phosphatidylcholines, and phosphatidylethanolamines. The developed diagnostic OPLS-DA models were based on 23 marker lipids. More than 90% of these marker lipids positively correlated with the degree of cervix transformation. The algorithm was developed for the management of patients with HPV-associated diseases of the cervix, based on the panel of 23 lipids as a result. ESI-MS analysis of a lipid extract by direct injection through a loop, takes about 25 min (including preparation of the lipid extract), which is significantly less than the time required for the HPV test (several hours for hybrid capture and about an hour for PCR). This makes lipid mass spectrometric analysis a promising method for express diagnostics of HPV-associated neoplastic diseases of the cervix.

Feature selection for OPLS discriminant analysis of cancer tissue lipidomics data.

The mass spectrometry-based molecular profiling can be used for better differentiation between normal and cancer tissues and for the detection of neoplastic transformation, which is of great importance for diagnostics of a pathology, prognosis of its evolution trend, and development of a treatment strategy. The aim of the present study is the evaluation of tissue classification approaches based on various data sets derived from the molecular profile of the organic solvent extracts of a tissue. A set of possibilities are considered for the orthogonal projections to latent structures discriminant analysis: all mass spectrometric peaks over 300 counts threshold, subset of peaks selected by ranking with support vector machine algorithm, peaks selected by random forest algorithm, peaks with the statistically significant difference of the intensity determined by the Mann-Whitney U test, peaks identified as lipids, and both identified and significantly different peaks. The best predictive potential is obtained for OPLS-DA model built on nonpolar glycerolipids (Q2 = 0.64, area under curve [AUC] = 0.95); the second one is OPLS-DA model with lipid peaks selected by random forest algorithm (Q2 = 0.58, AUC = 0.87). Moreover, models based on particular molecular classes are more preferable from biological point of view, resulting in new explanatory mechanisms of pathophysiology and providing a pathway analysis. Another promising features for OPLS-DA modeling are phosphatidylethanolamines (Q2 = 0.48, AUC = 0.86).

Label-free cervicovaginal fluid proteome profiling reflects the cervix neoplastic transformation.

Cervicovaginal fluid (CVF) is a valuable source of clinical information about the female reproductive tract in both nonpregnant and pregnant women. The aim of this study is to specify the CVF proteome at different stages of cervix neoplastic transformation by label-free quantitation approach based on liquid chromatography tandem mass spectrometry (LC-MS/MS) method. The proteome composition of CVF from 40 women of reproductive age with human papillomavirus (HPV)-associated cervix neoplastic transformation (low-grade squamous intraepithelial lesion [LSIL], high-grade squamous intraepithelial lesion [HSIL], and CANCER) was investigated. Hierarchical clustering and principal component analysis (PCA) of the proteomic data obtained by a label-free quantitation approach show the distribution of the sample set between four major clusters (no intraepithelial lesion or malignancy [NILM], LSIL, HSIL and CANCER) depending on the form of cervical lesion. Multisample ANOVA with subsequent Welch's t test resulted in 117 that changed significantly across the four clinical stages, including 27 proteins significantly changed in cervical cancer. Some of them were indicated as promising biomarkers previously (ACTN4, VTN, ANXA1, CAP1, ANXA2, and MUC5B). CVF proteomic data from the discovery stage were analyzed by the partial least squares-discriminant analysis (PLS-DA) method to build a statistical model, allowing to differentiate severe dysplasia (HSIL and CANCER) from the mild/normal stage (NILM and LSIL), and receiver operating characteristic (ROC) area under the curve (AUC) were obtained on an independent set of 33 samples. The sensitivity of the model was 77%, and the specificity was 94%; AUC was equal to 0.87. CVF proteome proved to be reflect the stage of cervical epithelium neoplastic process.