Ovarian cancer is a highly heterogeneous malignancy with complex molecular underpinnings that extend beyond genomic mutations to encompass transcriptomic and epigenomic alterations. Advances in next-generation sequencing and bioinformatics have enabled comprehensive profiling of gene expression patterns, non-coding RNAs, DNA methylation, histone modifications, and chromatin accessibility, offering novel insights into ovarian tumor biology. Transcriptomic analyses reveal dysregulated coding and non-coding RNA networks, while epigenomic studies uncover epigenetic modifications that regulate gene expression and chromatin structure, together shaping the cancer phenotype. The integration of transcriptomic and epigenomic data through sophisticated bioinformatics pipelines allows the identification of key regulatory networks and molecular subtypes, enhancing our understanding of ovarian cancer heterogeneity and progression. Bioinformatics tools facilitate differential expression analysis, epigenetic mapping, and multi-omics data integration, revealing potential biomarkers and therapeutic targets. These approaches have also illuminated mechanisms of chemoresistance and immune evasion, providing avenues for personalized therapy and improved patient stratification. Datasets will be critical to harness the full potential of transcriptomic and epigenomic research. Ultimately, bioinformatics-driven insights into the ovarian cancer transcriptome and epigenome promise to inform early diagnosis, prognostication, and the development of targeted therapies, advancing precision oncology in this lethal
E. I. Obeagu· Annals of Medicine and Surge...· 0 citations
Breast cancer remains the most frequently diagnosed cancer and a leading cause of cancer-related mortality among women worldwide, underscoring the need for accurate, minimally invasive biomarkers to support precision oncology. Conventional tissue biopsy remains the standard for molecular characterization but is limited by its invasiveness, inability to capture spatial and temporal tumor heterogeneity, and challenges in serial monitoring. Circulating tumor DNA (ctDNA), a tumor-derived fraction of cell-free DNA, has emerged as a promising liquid biopsy biomarker capable of providing real-time genomic information throughout disease progression. This narrative review examines recent advances in ctDNA biology, analytical technologies, clinical applications, current limitations, and future directions in breast cancer management. A structured literature search of PubMed/MEDLINE, Scopus, Embase, Web of Science, and Google Scholar identified relevant English-language publications from 2015 to 2026. Current evidence indicates that highly sensitive platforms, including digital PCR, BEAMing, and next-generation sequencing, can detect clinically actionable alterations in genes such as PIK3CA, ESR1, TP53, ERBB2, AKT1, and BRCA1/2. ctDNA has demonstrated particular utility in identifying minimal residual disease, monitoring therapeutic response, detecting emerging resistance mechanisms, and guiding targeted treatment selection in advanced breast cancer. However, applications in early cancer detection, population screening, and artificial intelligence-assisted clinical decision-making remain investigational. Widespread clinical implementation is constrained by low ctDNA abundance in early-stage disease, analytical variability, limited assay standardization, and cost considerations. Continued technological innovation, prospective multicenter validation, standardized testing protocols, and evidence-based clinical guidelines are essential to fully integrate ctDNA into routine precision breast cancer care.
E. I. Obeagu, C. Okafor· Breast Cancer: Basic and Cli...· 0 citations
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