多组学在癌症生物标志物发现和癌症分型中的应用。

Advances in clinical chemistry Pub Date : 2025-01-01 Epub Date: 2024-10-29 DOI:10.1016/bs.acc.2024.10.004
Seunghwan Choi, Joon-Yong An
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引用次数: 0

摘要

多组学的出现开创了癌症研究的新时代,其特点是整合基因组学、转录组学和蛋白质组学分析,以揭示癌症生物学的复杂性,并促进新的生物标志物的发现。本章提供了多组学概念的全面概述,详细介绍了基础技术的重大进展及其对我们理解癌症的贡献。它深入研究了基因组学和转录组学的发展,蛋白质组学的突破,以及多组学方法的总体进展,突出了它们对癌症生物标志物发现的集体影响。此外,本章探讨了多组学研究必不可少的计算方法,包括描述癌症亚型的聚类技术,估计分子特征和活动的策略,以及解释多组学数据集的途径富集分析的效用。特别关注的是这些方法的应用,以识别不同的癌症亚型,从而使癌症治疗更加个性化。通过对多组学的科学原理、技术进步和实际应用的详细讨论,本章旨在强调多组学在推进癌症研究和为个性化医疗铺平道路方面的关键作用。本文提供的见解不仅阐明了癌症生物标志物发现的现状,而且预测了肿瘤学多组学研究的未来方向,倡导一种更综合、更细致的方法来理解和对抗癌症。
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Multiomics in cancer biomarker discovery and cancer subtyping.

The advent of multiomics has ushered in a new era of cancer research characterized by integrated genomic, transcriptomic and proteomic analysis to unravel the complexities of cancer biology and facilitate the discovery of novel biomarkers. This chapter provides a comprehensive overview of the concept of multiomics, detailing the significant advances in the underlying technologies and their contributions to our understanding of cancer. It delves into the evolution of genomics and transcriptomics, breakthroughs in proteomics, and overarching progress in multiomic methodologies, highlighting their collective impact on cancer biomarker discovery. Furthermore, this chapter explores the computational methods essential for multiomic studies, including clustering techniques for delineating cancer subtypes, strategies for estimating molecular features and activities, and utility of pathway enrichment analyses for interpreting multiomic datasets. Particular focus has been placed on the application of these methods for identifying distinct cancer subtypes, thereby enabling a more personalized approach to cancer treatment. Through a detailed discussion of the scientific principles, technological advancements, and practical applications of multiomics, this chapter aims to underscore the pivotal role of multiomics in advancing cancer research and paving the way for personalized medicine. The insights provided herein not only illuminate the current landscape of cancer biomarker discovery, but also forecast future directions of multiomics research in oncology, advocating for a more integrated and nuanced approach to understanding and combating cancer.

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