Background:To date, no good marker for screening or disease monitoring of endometrial cancer (EC) is available. The aims of this study were to investigate HE4 gene, protein expression and serum HE4 (sHE4) levels in a panel of ECs and normal endometria (NEs) and to correlate sHE4 with patient clinicopathological characteristics and prognosis.Methods:Using quantitative real-time PCR we tested 46 ECs and 20 NEs for HE4 gene expression. Protein expression was analysed by immunohistochemistry on tissue microarrays in 153 ECs and 33 NEs. Pre-operative serum samples from 138 EC and 76 NE patients were analysed with HE4–EIA assay. Association between sHE4 and patient clinicopathological characteristics or outcome was evaluated.Results:Protein and HE4 gene were significantly upregulated in EC tissues and sera, compared with controls. High sHE4 levels were significantly associated with worse EC clinical characteristics. By univariate survival analysis, high sHE4 levels significantly correlated with decreased overall survival, progression-free survival and disease-free survival, retaining their independent prognostic value on the poorly differentiated EC cohort.Conclusion:We demonstrate, for the first time, that high sHE4 levels correlates with an aggressive EC phenotype and may constitute an independent prognostic factor for poorly differentiated-ECs. Determination of sHE4 could be clinically useful in identifying high-risk EC patients for a more aggressive adjuvant therapy.

HE4 is more sensitive and specifi c than CA125in distinguishing endometrial cancer patients from women with normal endometrium, regardless of tumour stage and grade. sHE4 appears to be associated with a more aggressive tumour variant and it could be clinically useful, in identifying high-risk endometrial cancer patients, for a tailored surgical and postoperative therapy. HE4 significant correlation with decreased Overall Survival, Disease Free Survival and Progression Free Survival suggests its potential role as a novel prognostic marker for endometrial cancer.

Abstract. Current efforts to identify protein biomarkers of disease use mainly mass spectrometry (MS) to analyze tissue and blood specimens. The low-molecular-weight "peptidome" is an attractive information archive because of the facile nature by which the low-molecular-weight information freely crosses the endothelial cell barrier of the vasculature, which provides opportunity to measure disease microenvironmentassociated protein analytes secreted or shed into the extracellular interstitium and from there into the circulation. However, identifying useful protein biomarkers (peptidomic or not) which could be useful to detect early detection/monitoring of disease, toxicity, doping, or drug abuse has been severely hampered because even the most sophisticated, high-resolution MS technologies have lower sensitivities than those of the immunoassays technologies now routinely used in clinical practice. Identification of novel low abundance biomarkers that are indicative of early-stage events that likely exist in the sub-nanogram per milliliter concentration range of known markers, such as prostate-specific antigen, cannot be readily detected by current MS technologies. We have developed a new nanoparticle technology that can, in one step, capture, concentrate, and separate the peptidome from high-abundance blood proteins. Herein, we describe an initial pilot study whereby the peptidome content of ovarian and prostate cancer patients is investigated with this method. Differentially abundant candidate peptidome biomarkers that appear to be specific for early-stage ovarian and prostate cancer have been identified and reveal the potential utility for this new methodology

Human papillomaviruses (HPVs), particularly HPV-16/18, are linked to cervical cancer development. Full-length, recombinant HPV-16/18 E7 oncoproteins were used in a new streptavidin-biotin capture ELISA method to investigate anti-HPV E7 antibody prevalence in serum. Sera from 99 healthy women, 70 cervical cancer patients, and 30 patients with cervical pre-invasive neoplasia were analyzed. Anti-HPV-16/18 E7 positivity was found in 53% of cervical cancer patients, in 40% with cervical pre-invasive neoplasia, and in 8% of healthy women. Serum samples from 12 cervical cancer patients were obtained at different time intervals during the treatment. Eleven out of 12 showed a correspondence between HPV-E7 antibody levels (decreasing versus increasing) and the type of response (clinically complete or partial response versus progression or stable disease) at each serological evaluation. Five patients with recurrent HPV-16/18-positive cervical carcinoma were analyzed before and after vaccination with HPV-16/18 E7-pulsed autologous dendritic cells; anti-HPV-16/18 E7 positivity was found in 3 out of 5 women. In conclusion, this assay could potentially be used as an adjunctive tool to monitor the type of response to treatment and possibly to detect antibody induction in cervical cancer patients after vaccination, as a potential marker to evaluate its efficacy.

Some aspects of endometrial cancer (EC) preoperative work-up are still controversial, and debatable are the roles played by lymphadenectomy and radical surgery. Proper preoperative EC staging can help design a tailored surgical treatment, and this study aims to propose a new algorithm able to predict extrauterine disease diffusion. 293 EC patients were consecutively enrolled, and age, BMI, children’s number, menopausal status, contraception, hormone replacement therapy, hypertension, histological grading, clinical stage, and serum HE4 and CA125 values were preoperatively evaluated. In order to identify before surgery the most important variables able to classify EC patients based on FIGO stage, we adopted a new statistical approach consisting of two-steps: 1) Random Forest with its relative variable importance; 2) a novel algorithm able to select the most representative Regression Tree (RERT) from an ensemble method. RERT, built on the above mentioned variables, provided a sensitivity, specificity, NPV and PPV of 90%, 76%, 94% and 65% respectively, in predicting FIGO stage > I. Notably, RERT outperformed the prediction ability of HE4, CA125, Logistic Regression and single cross-validated Regression Tree. Such algorithm has great potential, since it better identifies the true early-stage patients, thus providing concrete support in the decisional process about therapeutic options to be performed.