Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

癌症蛋白质组学已成为一种强大的技术，可用于表征驱动恶性肿瘤转化的蛋白质标记物，追踪由治疗药物触发的蛋白质组变异，并发现用于治疗肿瘤疾病的新型靶标和药物。为了促进癌症的诊断/预后并加速药物靶标的发现，已开发出多种用于肿瘤标记物鉴定和样品分类的方法，并将这些方法成功地应用于癌症蛋白质组学研究。这篇综述文章描述了这些方法的最新进展，以及它们在癌症相关研究中的当前应用。首先，概述了许多流行的特征选择方法，并对其优点和缺点进行了客观评估。其次，这些方法根据其基础算法分为三大类。最后，讨论了各种样本分离算法。这篇综述全面概述了肿瘤制造者鉴定和患者/样品/组织分离方面的进展，这可以为癌症蛋白质组学的研究提供指导。