Ovarian cancer (OV) is the main cause of deaths worldwide in female reproductive system malignancies. Enhancer RNAs (eRNAs) are derived from the transcription of enhancers and has attracted increasing attention in cancers recently. However, the biological functions and clinical significance of eRNAs in OV have not been well described presently. We used an integrated data analysis to identify prognostic-related eRNAs in OV. Tissue-specific enhancer-derived RNAs and their regulating genes were considered as putative eRNA-target pairs using the computational pipeline PreSTIGE. Gene expression profiles and clinical data of OV and 32 other cancer types were obtained from the UCSC Xena platform. Altogether, 71 eRNAs candidates showed significant correlation with overall survival (OS) of OV samples (Kaplan-Meier log-rank test, P<0.05). Among which, 23 were determined to be correlated with their potential target genes (Spearman's r > 0.3, P<0.001). It was found that among the 23 prognostic-related eRNAs, the expression of forkhead box P4 antisense RNA 1 (FOXP4-AS1) had the highest positive correlation with its predicted target gene FOXP4 (Spearman's r = 0.61). Moreover, the results were further validated by RT-qPCR analysis in an independent OV cohort. Our results suggested the eRNA FOXP4-AS1 expression index may be a favorable independent prognostic biomarker candidate in OV.
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http://dx.doi.org/10.1042/BSR20204008 | DOI Listing |
Biometrics
January 2025
Department of Biostatistics, University of Michigan at Ann Arbor, Ann Arbor, MI 48109, United States.
Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer.
View Article and Find Full Text PDFJ Thorac Oncol
January 2025
Department of General Internal Medicine and Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Hypothesis: To evaluate how comorbidities affect mortality benefits of lung cancer screening (LCS) with low-dose computed-tomography (LDCT).
Methods: We developed a comorbidity index (PLCO-ci) using LCS-eligible participants' data from the Prostate Lung Colorectal and Ovarian (PLCO) trial (training set) and the National Lung Screening Trial (NLST) (validation set). PLCO-ci predicts 5-year non-lung cancer (LC) mortality using a regularized Cox model; with performance evaluated by the area under the ROC curve (ROC).
Med Image Anal
January 2025
Nuffield Department of Medicine, University of Oxford, Oxford, UK; Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, UK. Electronic address:
Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide images (WSI) is non-trivial. The task is further complicated by the lack of annotations for subtyping and contextual histomorphological features that might span multiple scales.
View Article and Find Full Text PDFPhytomedicine
January 2025
School of Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China. Electronic address:
Background: Melittin, a major peptide component of bee venom, has demonstrated promising anti-cancer activity across various preclinical cell models, making it a potential candidate for cancer therapy. However, its molecular mechanisms, particularly in ovarian cancer, remain largely unexplored. Ovarian cancer is a life-threatening gynecological malignancy with poor clinical outcomes and limited treatment options.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Radiology, Montpellier Research Center Institute, PINKCC Laboratory, Montpellier, France.
Objective: To provide up-to-date European Society of Urogenital Radiology (ESUR) guidelines for staging and follow-up of patients with ovarian cancer (OC).
Methods: Twenty-one experts, members of the female pelvis imaging ESUR subcommittee from 19 institutions, replied to 2 rounds of questionnaires regarding imaging techniques and structured reporting used for pre-treatment evaluation of OC patients. The results of the survey were presented to the other authors during the group's annual meeting.
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