Colorectal cancer (CRC) screening of the average risk population is only indicated according to age. We aim to elaborate a model to stratify the risk of CRC by incorporating environmental data and single nucleotide polymorphisms (SNP). The MCC-Spain case-control study included 1336 CRC cases and 2744 controls. Subjects were interviewed on lifestyle factors, family and medical history. Twenty-one CRC susceptibility SNPs were genotyped. The environmental risk model, which included alcohol consumption, obesity, physical activity, red meat and vegetable consumption, and nonsteroidal anti-inflammatory drug use, contributed to CRC with an average per factor OR of 1.36 (95% CI 1.27 to 1.45). Family history of CRC contributed an OR of 2.25 (95% CI 1.87 to 2.72), and each additional SNP contributed an OR of 1.07 (95% CI 1.04 to 1.10). The risk of subjects with more than 25 risk alleles (5 quintile) was 82% higher (OR 1.82, 95% CI 1.11 to 2.98) than subjects with less than 19 alleles (1 quintile). This risk model, with an AUROC curve of 0.63 (95% CI 0.60 to 0.66), could be useful to stratify individuals. Environmental factors had more weight than the genetic score, which should be considered to encourage patients to achieve a healthier lifestyle.
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http://dx.doi.org/10.1038/srep43263 | DOI Listing |
Brief Bioinform
November 2024
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China.
Identifying phage-host interactions (PHIs) is a crucial step in developing phage therapy, which is the promising solution to addressing the issue of antibiotic resistance in superbugs. However, the lifestyle of phages, which strongly depends on their host for life activities, limits their cultivability, making the study of predicting PHIs time-consuming and labor-intensive for traditional wet lab experiments. Although many deep learning (DL) approaches have been applied to PHIs prediction, most DL methods are predominantly based on sequence information, failing to comprehensively model the intricate relationships within PHIs.
View Article and Find Full Text PDFDiscov Oncol
January 2025
Department of General Surgery, The Second Affiliated Hospital of the Air Force Medical University, Xi'an, 710038, China.
A common digestive system cancer with a dismal prognosis and a high death rate globally is breast cancer (BRCA). BRCA recurrence, metastasis, and medication resistance are all significantly impacted by cancer stem cells (CSCs). However, the relationship between CSCs and the tumor microenvironment in BRCA individuals remains unknown, and this information is critically needed.
View Article and Find Full Text PDFBreast Cancer Res Treat
January 2025
Division of Medical Oncology, Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA.
Purpose: There is an increasing incidence of young breast cancer (YBC) patients with uncertainty surrounding the factors and patterns that are contributing.
Methods: We obtained characteristics and survival data from 206,156 YBC patients (≤ 40 years of age) diagnosed between 2005 and 2019 from the National Cancer Database (NCDB). Patients were subdivided into two comparison groups based on year of diagnosis (2005-2009, Old vs.
Am J Sports Med
January 2025
Department of Pharmacology and Biostatistics, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Background: Patellar instability is frequently encountered by orthopaedic surgeons. One of the major risk factors of this condition is underlying trochlear dysplasia (TD). Recent trends have indicated the use of multiple procedures to correct patellar instability under these conditions.
View Article and Find Full Text PDFHGG Adv
January 2025
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Inherited genetics represents an important contributor to risk of esophageal adenocarcinoma (EAC), and its precursor Barrett's esophagus (BE). Genome-wide association studies have identified ∼30 susceptibility variants for BE/EAC, yet genetic interactions remain unexamined. To address challenges in large-scale G×G scans, we combined knowledge-guided filtering and machine learning approaches, focusing on genes with (A) known/plausible links to BE/EAC pathogenesis (n=493) or (B) prior evidence of biological interactions (n=4,196).
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