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Polygenic risk score (PRS) and rare monogenic variant screening are valuable tools for predicting cancer risk and identifying individuals at high risk. Integrating both common and rare genetic variants is crucial for accurate risk assessment. However, estimating the impacts of rare variants on cancer and combining them with PRS remains challenging. Here, we analyze 454,711 exome sequencing and 487,409 array UK Biobank samples, focusing on breast and prostate cancers. We introduce an expanded PRS (EPRS) approach, yielding a systematic model for more effective risk stratification. By prioritizing and clustering genes with cancer-specific rare variants based on odds ratios and population-attributable fraction, we refine risk stratification by combining both monogenic and polygenic effects. Individuals in high-PRS groups with rare high-impact gene variants show up to 15- and 22-fold higher risk for breast and prostate cancers, respectively, compared to those in the intermediate-PRS groups without rare variants. Combined risk profiles vary across distinct rare variant clusters within the same PRS group for both cancers. Our EPRS approach enhances risk stratification for breast and prostate cancers, offering important insights for future research and potential applications to other cancer types.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464688 | PMC |
http://dx.doi.org/10.1038/s42003-024-06995-9 | DOI Listing |
Purpose Clear cell renal cell carcinoma (ccRCC), the dominant subtype of renal malignancy, has a rising global incidence and mortality. While surgery is the standard of care for localized cases, adjuvant therapy aims to improve outcomes in high-risk postoperative patients. To quantify the clinical value of adjuvant pharmacotherapy, this systematic review and meta-analysis assesses its effect on overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS) in patients with ccRCC.
View Article and Find Full Text PDFJ Electrocardiol
August 2025
Department of Cardiology, Kırşehir Ahi Evran Training and Research Hospital, Kırşehir, Turkey. Electronic address:
Background: Ischemia with non-obstructive coronary arteries (INOCA) represents a diagnostic and therapeutic challenge, often related to coronary microvascular dysfunction (CMD). Identifying non-invasive electrocardiographic markers that predict ischemia in this population remains a clinical priority. P-wave peak time (PWPT), reflecting atrial conduction delay, has been linked to ischemic pathophysiology.
View Article and Find Full Text PDFExp Cell Res
September 2025
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital and Institute, Beijing, China. Electronic address:
Background: Enteric glial cells (EGCs) have been implicated in colorectal cancer (CRC) progression. This study aimed to develop and validate a prognostic model integrating EGC- and CRC-associated gene expression to predict patient survival, recurrence, metastasis, and therapy response.
Methods: Bulk and single-cell RNA sequencing data were analyzed, and a machine learning-based model was constructed using the RSF random forest algorithm.
Mod Pathol
September 2025
Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. Electronic address:
Uterine leiomyosarcoma (uLMS) is a rare and deadly gynecologic malignancy. uLMS is histologically heterogeneous and presents with a wide spectrum of tumor differentiation, with a broad range of genomic DNA instability, which can make the diagnosis and prognosis of uLMS challenging. Methylation has emerged as a useful molecular tool in tumor classification and diagnosis in certain neoplasms.
View Article and Find Full Text PDFJ Cardiol
September 2025
Catholic Research Institute for Intractable Cardiovascular Disease, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; Department of Cardiology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. Electronic addr
Heart failure with preserved ejection fraction (HFpEF) accounts for more than half of all HF cases and its incidence and prevalence continue to increase, with a substantial burden of morbidity and mortality. Despite advances in our understanding of heterogeneous pathophysiology underlying HFpEF, the diagnosis, risk assessment, and management of this disease entity remain challenging in everyday practice. Artificial intelligence (AI) algorithm can handle large amounts of complex data and machine learning (ML), a subfield of AI, allows for the identification of relevant patterns by learning from big data.
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