Emphasizing the Role of Multi-omics Approach to Increase Survival Rate of Breast and Prostate Cancer Patients
Authors
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Division of Biomedical and Life-Sciences, School of Science, Navrachana University, Vadodara - 391410, Gujarat ,IN
Khushali Upadhyay -
Division of Biomedical and Life-Sciences, School of Science, Navrachana University, Vadodara - 391410, Gujarat ,INForam Patel
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Division of Biomedical and Life-Sciences, School of Science, Navrachana University, Vadodara - 391410, Gujarat ,INYashshvini Patel
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Division of Biomedical and Life-Sciences, School of Science, Navrachana University, Vadodara - 391410, Gujarat ,INA. V. Ramachandran
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Division of Biomedical and Life-Sciences, School of Science, Navrachana University, Vadodara - 391410, Gujarat ,INDarshee Baxi
DOI:
https://doi.org/10.18311/jer/2023/34985Keywords:
Biomarker Discovery, Cancer, Omics, Personalized Medicine, Risk AssessmentAbstract
The understanding of cancer biology has greatly advanced since the advent of genomics. A remarkable heterogeneity at the whole-genome (or omics) level exists amongst even histologically comparable cancers, demonstrating the enormous complexity of the cancer genome. A powerful resource that has the potential to translate high-throughput omics to better and quick overall survival is the massive accrual and public accessibility of multi-omics databases with accompanying clinical annotation, including tumor histology, patient response, and outcome. In this new era of high-throughput omics, this paper emphasizes the distinct benefits of a multidimensional approach to genomic analysis. It discusses the implications of translational omics research for the cancer population. Single-level data analysis of high-throughput technologies has constraints because it only displays a small window of cellular processes. Understanding the links across several cellular organization levels made possible by data integration across various platforms, including genomes, epigenomics, transcriptomics, proteomics, and metabolomics, is important. This review examines a few popular frameworks for integrating multi-omics data. It provides a general overview of multi-omics applications in tumor classification, prognosis, diagnostics, and the function of data integration in searching for novel biomarkers and treatment options.
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