对组学发现研究的系统回顾,以确定局部晚期直肠癌对新辅助放化疗的相关代谢途径。

IF 3.3 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM
Aaron Kler, Matthew Fok, Gabrielle J Grundy, Marco Sciacovelli, Warwick B Dunn, Dale Vimalachandran
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引用次数: 0

摘要

背景:局部晚期直肠癌(LARC)对新辅助治疗(NAT)有不同的反应。因此,确定使用NAT治疗LARC时涉及的生物学途径的变化对于开发改善临床结果的治疗方法至关重要,因为NAT既可变又不可预测。尽管个别研究试图辨别在转录组学、蛋白质组学和代谢组学水平上的反应差异,但还没有一个统一的系统综述来识别该患者群体中代谢途径的关键变化。综述目的:本系统综述旨在了解代谢组学、蛋白质组学和转录组学如何证明LARC中NAT反应的紊乱代谢途径如何为进一步的临床研究提供靶点。综述的关键科学概念:13项研究符合纳入标准,包括7项代谢组学研究,5项蛋白质组学研究和1项转录组学研究。代谢组学分析显示,氨基酸代谢、三羧酸(TCA)循环和甘油磷脂代谢发生了一致的变化。蛋白质组学研究结果支持这些结果,强调糖酵解和糖异生的破坏。联合通路分析显示相关性强(r = 0.99, p
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A systematic review of omics discovery studies to identify pertinent metabolic pathways for locally advanced rectal cancer in response to neoadjuvant chemoradiotherapy.

A systematic review of omics discovery studies to identify pertinent metabolic pathways for locally advanced rectal cancer in response to neoadjuvant chemoradiotherapy.

A systematic review of omics discovery studies to identify pertinent metabolic pathways for locally advanced rectal cancer in response to neoadjuvant chemoradiotherapy.

A systematic review of omics discovery studies to identify pertinent metabolic pathways for locally advanced rectal cancer in response to neoadjuvant chemoradiotherapy.

Background: Locally advanced rectal cancer (LARC) has variable responses to neoadjuvant therapy (NAT). Therefore, identifying changes in biological pathways involved when LARC is treated with NAT is crucial for developing treatments to improve clinical outcomes, as NAT is both variable and unpredictable. Although individual studies have attempted to discern how the response differs at a transcriptomic, proteomic and metabolomic level, there has not been a unifying systematic review discerning the key changes in metabolic pathways in this patient population.

Aim of review: This systematic review aims to understand how metabolomics, proteomics and transcriptomics can demonstrate how the perturbed metabolic pathways of the NAT response in LARC can provide targets for further clinical research.

Key scientific concepts of review: Thirteen studies met the inclusion criteria, including seven metabolomic, five proteomic, and one transcriptomic study. Metabolomic analyses revealed consistent alterations in amino acid metabolism, the tricarboxylic acid (TCA) cycle, and glycerophospholipid metabolism. Proteomic findings supported these results, highlighting disruptions in glycolysis and gluconeogenesis. Joint pathway analysis demonstrated a strong correlation (r = 0.99, p < 0.0001) between metabolic changes observed across omics platforms. Key pathways such as alanine, branched-chain amino acid, and aspartate metabolism were commonly altered and may contribute to radio-resistance through enhanced energy production, reactive oxygen species (ROS) neutralization, and DNA repair mechanisms. The convergence of multi-omic data underscores the biological relevance of these metabolic reprogramming events. However, due to the limited availability of transcriptomic data meeting inclusion criteria, these findings are primarily driven by metabolomic and proteomic analyses, which constrains the extent of full multi-omic integration. Future studies should aim to validate these findings in clinical cohorts and explore how targeting these "survival" pathways could optimize treatment response in LARC.

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来源期刊
Metabolomics
Metabolomics 医学-内分泌学与代谢
CiteScore
6.60
自引率
2.80%
发文量
84
审稿时长
2 months
期刊介绍: Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to: metabolomic applications within man, including pre-clinical and clinical pharmacometabolomics for precision medicine metabolic profiling and fingerprinting metabolite target analysis metabolomic applications within animals, plants and microbes transcriptomics and proteomics in systems biology Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.
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