BreCAN-DB: a repository cum browser of personalized DNA breakpoint profiles of cancer genomes

Cancer Associated PRDM9: Implications for Linking Genomic Instability and Meiotic Recombination

The PR domain-containing 9 or PRDM9 is a gene recognized for its fundamental role in meiosis, a process essential for forming reproductive cells. Recent findings have implicated alterations in the PRDM9, particularly its zinc finger motifs, in the onset and progression of cancer. This association is manifested through genomic instability and the misregulation of genes critical to cell growth, proliferation, and differentiation. In our comprehensive study, we harnessed advanced bioinformatic mining tools to delve deep into the intricate relationship between PRDM9F and cancer. We analyzed 136,752 breakpoints and found an undeniable association between specific PRDM9 motifs and the occurrence of double-strand breaks, a phenomenon evidenced in every cancer profile examined. Utilizing R statistical querying and the Regioner package, 55 unique sequence variations of PRDM9 were statistically correlated with cancer, from a pool of 1024 variations. A robust analysis using the Enrichr tool revealed prominent associations with various cancer types. Moreover, connections were noted with specific phenotypic conditions and molecular functions, underlining the pervasive influence of PRDM9 variations in the biological spectrum. The Reactome tool identified 25 significant pathways associated with cancer, offering insights into the mechanistic underpinnings linking PRDM9 to cancer progression. This detailed analysis not only confirms the pivotal role of PRDM9 in cancer development, but also unveils a complex network of biological processes influenced by its variations. The insights gained lay a solid foundation for future research aimed at deciphering the mechanistic pathways of PRDM9, offering prospects for targeted interventions and innovative therapeutic approaches in cancer management.

Computer-aided biomarker discovery for precision medicine: data resources, models and applications

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.