Nema Rajeev
Department of Biosciences Manipal University Jaipur, Dehmi Kalan, Jaipur-Ajmer Expressway, Jaipur, Rajasthan, India.
Rep Pract Oncol Radiother. 2024 Dec 4;29(5):649-650. doi: 10.5603/rpor.102823. eCollection 2024.
Multi-omics approaches are revolutionizing cancer research and treatment by integrating single-modality omics methods, such as the transcriptome, genome, epigenome, epi-transcriptome, proteome, metabolome, and developing omics (single-cell omics). These technologies enable a deeper understanding of cancer and provide personalized treatment strategies. However, challenges such as standardization and appropriate methods for funneling complex information into clinical consequences remain. The tumor microenvironment (TME) is a complex system containing cancer cells, immune cells, stromal cells, and secreted molecules. To overcome these challenges, researchers can establish standardized protocols for data collection, analysis, and interpretation. Collaborations and data sharing among research groups and institutions can create a comprehensive and standardized multi-omics database, facilitating cross-validation and comparison of results. Multi-omics profiling enables in-depth characterization of diversified tumor types and better reveal their function in cancer immune escape. Datasets play a fundamental role in multi-omics approaches, with artificial intelligence and machine learning (AI) rapidly advancing in multi-omics for cancer.
多组学方法通过整合转录组、基因组、表观基因组、表观转录组、蛋白质组、代谢组等单模态组学方法以及发展中的组学（单细胞组学），正在革新癌症研究和治疗。这些技术能够更深入地了解癌症，并提供个性化的治疗策略。然而，诸如标准化以及将复杂信息转化为临床结果的适当方法等挑战仍然存在。肿瘤微环境（TME）是一个包含癌细胞、免疫细胞、基质细胞和分泌分子的复杂系统。为了克服这些挑战，研究人员可以建立数据收集、分析和解释的标准化方案。研究小组和机构之间的合作与数据共享可以创建一个全面且标准化的多组学数据库，便于结果的交叉验证和比较。多组学分析能够深入表征多样化的肿瘤类型，并更好地揭示它们在癌症免疫逃逸中的作用。数据集在多组学方法中起着基础性作用，人工智能和机器学习（AI）在癌症多组学领域正迅速发展。
