MAGI'S LAB, Rovereto (TN), Italy.
MAGI EUREGIO, Bolzano, Italy.
Clin Ter. 2023 Nov-Dec;174(Suppl 2(6)):95-103. doi: 10.7417/CT.2023.2476.
In the last decade, Prostate Cancer (PCa) has emerged as the second most prevalent and serious medical condition, and is considered one of the leading factors contributing to global mortality rates. Several factors (genetic as well as environmental) contribute to its development and seriousness. Since the disease is usually asymptomatic at early stages, it is typically misdiagnosed or over-diagnosed by the diagnostic procedures currently in use, leading to improper treatment. Effective biomarkers and diagnostic techniques are desperately needed in clinical settings for better management of PCa patients. Studies integrating omics sciences have shown that the accuracy and dependability of diagnostic and prognostic evaluations have increased because of the use of omics data; also, the treatment plans using omics can be facilitated by personalized medicine. The present review emphasizes innovative multi-omics methodologies, encompassing proteomics, genomics, microbiomics, metabolomics, and transcriptomics, with the aim of comprehending the molecular alterations that trigger and contribute to PCa. The review shows how early genomic and transcriptomic research has made it possible to identify PCa-related genes that are controlled by tumor-relevant signaling pathways. Proteomic and metabolomic analyses have recently been integrated, advancing our understanding of the complex mechanisms at play, the multiple levels of regulation, and how they interact. By applying the omics approach, new vulnerabilities may be discovered, and customized treatments with improved efficacy will soon be accessible.
在过去的十年中,前列腺癌(PCa)已成为第二大常见且严重的疾病,被认为是导致全球死亡率的主要因素之一。一些因素(遗传和环境)促成了其发展和严重性。由于疾病在早期通常没有症状,因此目前使用的诊断程序通常会误诊或过度诊断,导致治疗不当。在临床环境中迫切需要有效的生物标志物和诊断技术,以更好地管理 PCa 患者。整合组学科学的研究表明,由于使用了组学数据,诊断和预后评估的准确性和可靠性有所提高;此外,通过个性化医学可以促进使用组学的治疗计划。本综述强调了创新的多组学方法,包括蛋白质组学、基因组学、微生物组学、代谢组学和转录组学,旨在了解引发和促成 PCa 的分子变化。该综述展示了早期的基因组学和转录组学研究如何能够识别受肿瘤相关信号通路控制的与 PCa 相关的基因。蛋白质组学和代谢组学分析最近已经结合在一起,提高了我们对复杂机制、多个调节水平以及它们如何相互作用的理解。通过应用组学方法,可以发现新的弱点,并很快获得疗效更好的定制治疗。