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用于识别异构体变体作为癌症患者治疗靶点的多组学方法。

Multi-omics approach to identifying isoform variants as therapeutic targets in cancer patients.

作者信息

Shaw Timothy I, Zhao Bi, Li Yuxin, Wang Hong, Wang Liang, Manley Brandon, Stewart Paul A, Karolak Aleksandra

机构信息

Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.

Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.

出版信息

Front Oncol. 2022 Nov 24;12:1051487. doi: 10.3389/fonc.2022.1051487. eCollection 2022.

Abstract

Cancer-specific alternatively spliced events (ASE) play a role in cancer pathogenesis and can be targeted by immunotherapy, oligonucleotide therapy, and small molecule inhibition. However, identifying actionable ASE targets remains challenging due to the uncertainty of its protein product, structure impact, and proteoform (protein isoform) function. Here we argue that an integrated multi-omics profiling strategy can overcome these challenges, allowing us to mine this untapped source of targets for therapeutic development. In this review, we will provide an overview of current multi-omics strategies in characterizing ASEs by utilizing the transcriptome, proteome, and state-of-art algorithms for protein structure prediction. We will discuss limitations and knowledge gaps associated with each technology and informatics analytics. Finally, we will discuss future directions that will enable the full integration of multi-omics data for ASE target discovery.

摘要

癌症特异性可变剪接事件(ASE)在癌症发病机制中发挥作用,并且可以成为免疫疗法、寡核苷酸疗法和小分子抑制的靶点。然而,由于其蛋白质产物、结构影响和蛋白质异构体功能的不确定性,确定可操作的ASE靶点仍然具有挑战性。在此,我们认为综合多组学分析策略可以克服这些挑战,使我们能够挖掘这一尚未开发的靶点来源以用于治疗开发。在本综述中,我们将概述当前通过利用转录组、蛋白质组和用于蛋白质结构预测的最新算法来表征ASE的多组学策略。我们将讨论与每种技术和信息学分析相关的局限性和知识空白。最后,我们将讨论未来的方向,这些方向将实现多组学数据的全面整合以用于ASE靶点发现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/070e/9730332/bb652cda950f/fonc-12-1051487-g001.jpg

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