Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates.
Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates.
Int J Mol Sci. 2024 May 7;25(10):5071. doi: 10.3390/ijms25105071.
Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.
前列腺癌(PCa)是全球主要的致死因素之一,主要影响 65 岁及以上的男性。组学领域最近受到关注,因为它能够深入了解前列腺癌等疾病的生化机制。这涉及到识别和定量作为早期检测、治疗评估和靶点识别的关键生化信号的低分子量代谢物和蛋白质。一系列分析方法用于识别和测量这些分子,揭示它们在疾病环境中的改变的生物学途径。代谢组学和蛋白质组学通过复杂的软件生成经过详细统计分析的精细化数据,提供实质性的见解。本综述旨在强调多组学对 PCa 研究的主要贡献,涵盖其核心原则、在肿瘤生物学特征描述、生物标志物发现、预后研究中的作用,以及各种分析技术,如质谱和核磁共振,数据处理,以及通过综合“组学”方法实现的最近的临床应用。这种方法旨在解决当前 PCa 治疗相关的挑战。因此,我们的研究旨在展示这些强大工具在研究中的有价值应用,为深入了解前列腺癌的复杂生化环境和推进针对特定治疗方法的发展提供巨大潜力。