BACKGROUND: Accurate 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.
METHODS: We 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.
RESULTS: The 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.
CONCLUSIONS: The 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.

Cancer-associated cachexia (CAC) is a complex syndrome of progressive muscle wasting and adipose loss with metabolic dysfunction, severely increasing the morbidity and mortality risk in cancer patients. However, there are limited studies focused on the underlying mechanisms of the progression of CAC due to the complexity of this syndrome and the lack of preclinical models that mimics its stagewise progression.
We characterized the initiation and progression of CAC in transgenic female mice with ovarian tumours. We measured proposed CAC biomarkers (activin A, GDF15, IL-6, IL-1β, and TNF-α) in sera (n = 6) of this mouse model. The changes of activin A and GDF15 (n = 6) were correlated with the decline of bodyweight over time. Morphometry and signalling markers of muscle atrophy (n ≥ 6) and adipose tissue wasting (n ≥ 7) were assessed during CAC progression.
Cancer-associated cachexia symptoms of the transgenic mice model used in this study mimic the progression of CAC seen in humans, including drastic body weight loss, skeletal muscle atrophy, and adipose tissue wasting. Serum levels of two cachexia biomarkers, activin A and GDF15, increased significantly during cachexia progression (76-folds and 10-folds, respectively). Overactivation of proteolytic activity was detected in skeletal muscle through up-regulating muscle-specific E3 ligases Atrogin-1 and Murf-1 (16-folds and 14-folds, respectively) with decreasing cross-sectional area of muscle fibres (P < 0.001). Muscle wasting mechanisms related with p-p38 MAPK, FOXO3, and p-AMPKα were highly activated in concurrence with an elevation in serum activin A. Dramatic fat loss was also observed in this mouse model with decreased fat mass (n ≥ 6) and white adipocytes sizes (n = 6) (P < 0.0001). The adipose tissue wasting was based on thermogenesis, supported by the up-regulation of uncoupling protein 1 (UCP1). Fibrosis in adipose tissue was also observed in concurrence with adipose tissue loss (n ≥ 13) (p < 0.0001).
Our novel preclinical CAC mouse model mimics human CAC phenotypes and serum biomarkers. The mouse model in this study showed proteolysis in muscle atrophy, browning in adipose tissue wasting, elevation of serum activin A and GDF15, and atrophy of pancreas and liver. This mouse line would be the best preclinical model to aid in clarifying molecular mediators of CAC and dissecting metabolic dysfunction and tissue atrophy during the progression of CAC.

Proper preoperative ovarian cancer (OC) diagnosis remains challenging. Serum free amino acid (SFAA) profiles were investigated to identify potential novel biomarkers of OC and assess their performance in ovarian tumor differential diagnosis. Serum samples were divided based on the histopathological result: epithelial OC (n = 38), borderline ovarian tumors (n = 6), and benign ovarian tumors (BOTs) (n = 62). SFAA profiles were evaluated using aTRAQ methodology based on high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). Levels of eleven amino acids significantly differed between OC+borderline and BOTs. The highest area under the receiver operating characteristic curve (AUC of ROC) (0.787) was obtained for histidine. Cystine and histidine were identified as best single markers for early stage OC/BOT and type I OC. For advanced stage OC, seven amino acids differed significantly between the groups and citrulline obtained the best AUC of 0.807. Between type II OC and BOTs, eight amino acids differed significantly and the highest AUC of 0.798 was achieved by histidine and citrulline (AUC of 0.778). Histidine was identified as a potential new biomarker in differential diagnosis of ovarian tumors. Adding histidine to a multimarker panel together with CA125 and HE4 improved the differential diagnosis between OC and BOTs.

UNASSIGNED: Human telomerase reverse transcriptase (hTERT), a crucial enzyme for telomere maintenance, has been associated with the development of ovarian cancer (OC). The purpose of this study was to investigate the difference of methylation rates of hTERT promoter in tumour tissues and plasma samples of patients with ovarian magnificent tumour and those with ovarian benign tumour, as well as in plasma samples of healthy women. This study further aimed to establish a possible association between increased methylation rate of hTERT promoter and circulating tumour DNAs (ctDNA) amongst patients with ovarian magnificent tumour.
UNASSIGNED: Tumour tissue samples and plasma samples were separately obtained from 17 patients with ovarian magnificent tumour (experiment group, group A) and from 15 patients with ovarian benign tumour (control group, group B). Another 15 plasma samples were acquired from healthy women (control group, group C). Promoter methylation was assessed by methylation-specific PCR (MSP). Statistical analysis was conducted using SPSS 22.0.
UNASSIGNED: Methylation of hTERT was observed in 76.5% of tumour tissue samples and in 70.6% of plasma samples from patients with ovarian magnificent tumour. It was also observed in 26.7% of tumour tissue samples and 20% of plasma samples from patients with ovarian benign tumour, and in 13.3% of plasma samples from healthy women. Comparing between plasmas and tissues, the respective rates of consistency, sensitivity and specificity were 70.59%, 76.9% and 50% in group A, and 80%, 50% and 90.9% in group B. Hence, the associations of hTERT methylation with ctDNAs (p=0.001) and tumour tissue samples (p=0.012) amongst patients with ovarian magnificent tumour were established.
UNASSIGNED: An increased methylation of hTERT promoter is related to ctDNAs and tumour tissues of patients with ovarian magnificent tumour.

The ALAN is drawing the attention of researchers and environmentalists for its ever-increasing evidence on its capacity of \"desynchronization\" of organismal physiology. Photoperiod and circadian cycles are critical parameters to influence the biology of reproduction in several animals, including fish. The present study is the first proof of the development of an ovarian tumour with the effect of light in zebrafish (Danio rerio), an excellent model for circadian-related studies. Results of three experimental conditions, continuous light for one week, LLW, one month, LLM, and for one year, LLY revealed a clear desynchronization of clock associated genes (Clock1a, Bmal1a, Per2, and Cry2a). Interestingly, loss of rhythmicity and low concentration of melatonin found in these conditions in whole brain, retina, ovary, and serum through ELISA. RNA-Seq data of ovarian samples revealed the upregulation of Mid2, Tfg, Irak1, Pim2, Tradd, Tmem101, Nfkbib genes and ultimately increase the expression of NF-κB, a cellular transformer for tumourigenesis, confirmed by the western blot. The appearance of TNFα, inflammatory cytokines and activator of NF-κB also increased. Histology approved the formation of thecoma and granulosa cell tumour in the one year exposed ovarian sample. The whole transcriptome data analysis revealed 1791 significantly upregulated genes in an ovarian tumour. Among these genes, DAVID functional annotation tool identified 438 genes, directly linked to other physiological disorders. This study evidenced of an ovarian tumour induced by ALAN in zebrafish.