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前列腺癌组织代谢组学评估:临床是否会将其用于诊断?

Evaluation of prostate cancer tissue metabolomics: would clinics utilise it for diagnosis?

机构信息

Department of Urology, King George's Medical University, Lucknow, India.

Centre of Biomedical Research, SGPGIMS Campus, Lucknow, India.

出版信息

Expert Rev Mol Med. 2023 Aug 7;25:e26. doi: 10.1017/erm.2023.22.

Abstract

The difficulty of diagnosing prostate cancer (PC) with the available biomarkers frequently leads to over-diagnosis and overtreatment of PC, underscoring the need for novel molecular signatures. The purpose of this review is to provide a summary of the currently available cellular metabolomics for PC molecular signatures. A comprehensive search on PubMed was conducted to find studies published between January 2004 and August 2022 that reported biomarkers for PC detection, development, aggressiveness, recurrence and treatment response. Although potential studies have reported the presence of distinguishing molecules that can distinguish between benign and cancerous prostate tissue. However, there are few studies looking into signature molecules linked to disease development, therapy response or tumour recurrence. The majority of these studies use high-dimensional datasets, and the number of potential metabolites investigated frequently exceeds the size of the available samples. In light of this, pre-analytical, statistical, methodological and confounding factors such as antiandrogen therapy (NAT) may also be linked to the identified chemometric multivariate differences between PC and relevant control samples in the datasets. Despite the methodological and procedural challenges, a range of methodological groups and processes have consistently identified a number of signature metabolites and pathways that appear to imply a substantial involvement in the cellular metabolomics of PC for tumour formation and recurrence.

摘要

目前可用的生物标志物在诊断前列腺癌 (PC) 时存在困难,这经常导致 PC 的过度诊断和过度治疗,凸显了对新型分子特征的需求。本综述的目的是提供目前用于 PC 分子特征的细胞代谢组学的概述。在 PubMed 上进行了全面检索,以查找 2004 年 1 月至 2022 年 8 月期间发表的报告用于 PC 检测、发展、侵袭性、复发和治疗反应的生物标志物的研究。虽然有一些潜在的研究报道了存在可区分良性和癌性前列腺组织的区分分子。然而,很少有研究关注与疾病发展、治疗反应或肿瘤复发相关的特征分子。这些研究中的大多数都使用了高维数据集,并且研究的潜在代谢物数量经常超过可用样本的大小。鉴于此,在分析前、统计、方法和混杂因素(如抗雄激素治疗 (NAT))也可能与数据集中 PC 和相关对照样本之间的化学计量多元差异有关。尽管存在方法和程序上的挑战,但一系列方法学小组和过程一致地确定了一些特征代谢物和途径,这些代谢物和途径似乎暗示它们在 PC 的细胞代谢组学中对肿瘤形成和复发具有实质性的参与。

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