👤 Dakheel A AlDakheel

Prostate cancer (PCa) remains a significant global health challenge, with approximately 1.6 million new cases and 366,000 deaths annually. Despite high survival rates for localized prostate cancer, recurrence poses a substantial risk due to inherent biological factors and residual disease. Early detection and intervention are essential for enhancing patient outcomes and reducing mortality. However, traditional diagnostics such as PSA tests, digital rectal examinations, and biopsies often lack specificity resulting in overdiagnosis. There is a pressing need for novel biomarkers to enhance precision medicine approaches for PCa. This study employs a machine learning approach to identify DNA methylation and RNA expression biomarkers predictive of PCa recurrence using datasets from The Cancer Genome Atlas (TCGA). We analyzed 49,133 genes, identifying 684 differentially methylated genes (DMGs) and 691 differentially expressed genes (DEGs) between recurrence and non-recurrence groups. Ten genes (TNNI2, SPIN2, COL5A3, RNF169, CCND1, FGFR1, SLC17A2, FAMM71F2, RREB1, AOX1) were found to have significant correlations between methylation and expression, forming the basis for our predictive model. A support vector machine (SVM) model was developed using these ten genes, achieving an area under the curve (AUC) of 0.773, demonstrating robust predictive capability. Multivariate regression analysis confirmed the SVM score as an independent predictor of recurrence (HR = 0.45; 95% CI 0.28-0.69, P < 0.001). The analysis of recurrence-free survival suggested that patients with low-risk scores experienced significantly better outcomes compared to those with high-risk scores. Functional enrichment analyses of DMGs revealed significant involvement in biological processes such as transcription regulation, signal transduction, and immune response, highlighting the potential mechanistic pathways of these biomarkers. Validation using real-time PCR confirmed differential expression and methylation patterns of the identified genes in prostate cancer (PC3) and non-cancerous cell lines (PNT2). In conclusion, our study hihglights the DNA methylation biomarkers linked to PCa recurrence and introduces a promising SVM model for early prediction, potentially improving treatment outcomes. Further research is needed to explore the biological roles of these genes in PCa aiming to refine therapeutic approaches. Show less

Nonunion of fractures occurs in about 15% of all fractures causing repeated surgical interference and prolonged morbidity. We performed this systematic review to assess genes and polymorphisms influencing fractures' nonunion (FNU). We searched between 2000 and July 2022 in PubMed, EMBASE, the Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews, Genome Wide Association Studies (GWAS) Catalog, and the Science Citation Index, with the keywords nonunion of fractures, genetic influence, and GWAS. The exclusion criteria were review articles and correspondence. The data were retrieved to determine the number of studies, genes, and polymorphisms and the total number of subjects screened. A total of 79 studies were reported on nonunion of fractures and genetic influence. After the inclusion and exclusion criteria, ten studies with 4402 patients' data were analyzed. Nine studies were case-controlled, and 1 GWAS. It was identified that patients with polymorphisms in the genes We believe that for patients who develop an early nonunion of fractures, a genetic study should be conducted for single nucleotide polymorphism (SNP) and genes so that alternative and more aggressive treatment can be performed to heal fractures without prolonged morbidity. Show less