INTEGRATED GENOMIC AND TRANSCRIPTOMIC ANALYSIS FOR BIOMARKER IDENTIFICATION IN CANCER

Abstract

Cancer is a very heterogeneous disease, with complex molecular changes, where reliable biomarkers are needed to detect the disease at an earlier stage and treat it based on the indication of the specific treatment. The more conventional methods of single-omics, that is, using genomic or transcriptomic data alone cannot adequately demonstrate the full range of molecular interactions that result in tumour evolution. This paper will overcome this shortcoming by suggesting a combined genomic and transcriptomic study process to identify strong biomarkers in cancer. Generally, publicly accessible datasets like those obtained by The Cancer Genome Atlas (TCGA) were used, and then it was preprocessed, analysed by differential expression, and the analysis of mutations. The combination of eight and transcriptomic expression patterns through a multi-omics-integration strategy combined with genomic mutation scores allowed to identify high-confidence candidate biomarkers. The analysis demonstrated that there were some highly disregulated genes with high mutation frequency and clinical scores. Moreover, the prognostic capability of the biomarkers identified was confirmed by the survival analysis, meaning that they are related to patient outcomes. The findings indicate that multi-omics methods are more effective in enhancing the discovery of biomarkers as opposed to traditional methods. This paper will serve as an effective template to identify cancer biomarkers, as well as a valuable subject of study with regard to precision medicine and tailored treatment regimens.