BIOINFORMATICS-BASED IDENTIFICATION OF PROGNOSTIC BIOMARKERS AND THERAPEUTIC TARGETS IN CANCER PRECISION MEDICINE
Abstract
With the complexity of tumor heterogeneity, molecular variability and therapeutic resistance of various cancer types, cancer is still one of the major factors contributing to mortality around the world. In the current study, we have explored the multi-omics data in TCGA-PANCAN to identify prognostic biomarkers and therapeutic targets in cancer precision medicine, adopting integrated bioinformatics and computational oncology strategies. To explore the molecular variability associated with cancer, a computational analytical framework was used that included dataset pre-processing, transcriptomic interpretation, evaluation of biomarkers, molecular interaction analysis, and evaluation of the potential
of the molecules for therapeutic intervention. The TCGA-PANCAN dataset comprised large-scale transcriptomic, genomic, methylation and microRNA data related to various types of cancer and molecular cancer subtypes. The results showed significant transcriptomic diversity and biomarker related differences between the various tumor types. There were several molecular biomarkers that showed significant differential expression patterns that have cancer progression and therapeutic relevance. Computational analysis also showed that integrated approaches of bioinformatics and
pharmacoinformatics may be useful in the identification of prognostic biomarkers, prediction of therapeutic targets and optimization of therapies for precision oncology. Artificial intelligence-driven computational systems also exhibited high levels of applicability in the areas of molecular interpretation, therapeutic specificity and predictive oncology frameworks. Multi-omics computational analysis, together with the application of machine learning and translational bioinformatics
could significantly advance future cancer diagnosis, therapeutic development and personalized healthcare strategies. In general, the study results suggest the increasing relevance of bioinformatics, computational oncology and precision medicine systems in contemporary cancer research and biomarker-based therapeutic innovation
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