Dataset Information

Ficolin-2 and ficolin-3 in women with malignant and benign ovarian tumours.

ABSTRACT: Ficolins are serum pattern recognition molecules. They have opsonic properties and are able to activate complement via the lectin pathway. This paper reports investigations concerning ficolin-2 and ficolin-3 in ovarian cancer (OC). Their serum levels, single nucleotide polymorphisms of the corresponding FCN2 and FCN3 genes and specific mRNA expression in ovarian sections were investigated in 128 patients suffering from primary OC and 197 controls operated on for reasons other than malignancies. The latter consisted of two reference groups: those with benign tumours (n = 123) and those with normal ovaries (NO) (n = 74). Serum ficolin-2 and ficolin-3 concentrations were higher among patients with malignant disease when compared with either of the reference groups. A significant correlation between ficolin-2 and ficolin-3 concentrations was found, while no correlations with CA125 antigen or CRP were observed. No differences in the frequency of single nucleotide polymorphisms at sites -64, -4 (promoter), +6359, or +6424 (exon 8) (FCN2 gene) nor in the frame-shift mutation 1637delC (FCN3 gene) were found between investigated groups. In contrast to serum concentrations, the expression of FCN2 gene (reported for the first time in ovarian sections) was significantly lower in women with OC in comparison with patients with NO but not with benign ovarian tumours. In case of FCN3 gene, its expression levels in OC group inversely correlated with serum ficolin-3 and were lower in comparison with controls.

SUBMITTER: Szala A

PROVIDER: S-EPMC3717161 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Ficolin-2 and ficolin-3 in women with malignant and benign ovarian tumours.

Szala Agnieszka A Sawicki Sambor S Swierzko Anna St AS Szemraj Janusz J Sniadecki Marcin M Michalski Mateusz M Kaluzynski Andrzej A Lukasiewicz Jolanta J Maciejewska Anna A Wydra Dariusz D Kilpatrick David C DC Matsushita Misao M Cedzynski Maciej M

Cancer immunology, immunotherapy : CII 20130607 8

Ficolins are serum pattern recognition molecules. They have opsonic properties and are able to activate complement via the lectin pathway. This paper reports investigations concerning ficolin-2 and ficolin-3 in ovarian cancer (OC). Their serum levels, single nucleotide polymorphisms of the corresponding FCN2 and FCN3 genes and specific mRNA expression in ovarian sections were investigated in 128 patients suffering from primary OC and 197 controls operated on for reasons other than malignancies. ...[more]

PMID: 23744477

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Project description:BackgroundAccurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours.MethodsWe randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model.ResultsThe training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively.ConclusionsThe three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

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Dataset Information

Ficolin-2 and ficolin-3 in women with malignant and benign ovarian tumours.

Publications

Ficolin-2 and ficolin-3 in women with malignant and benign ovarian tumours.

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