定量蛋白质组学增强了我们使用各种技术（包括统计测试）研究蛋白质动力学及其与疾病的关系的能力，以辨别条件之间的显着差异。虽然大多数关注点是不同条件之间的差异，但探索相似之处可以提供有价值的见解。然而，直接从分析物水平（例如蛋白质、基因或代谢物）探索相似性并不是标准做法，也没有被广泛采用。在这项研究中，我们提出了一个名为 QuEStVar（通过统计假设检验对稳定性和变异性进行定量探索）的统计框架，能够通过组合统计框架探索特征的定量稳定性和变异性。 QuEStVar 在比较条件时利用差异和等效测试来扩展分析物的统计分类。我们将我们的方法应用于癌细胞系的广泛数据集，并揭示了跨不同组织和癌症亚型的定量稳定的核心蛋白质组。这组蛋白质的功能分析强调了癌细胞通过转录、翻译和核细胞质运输等生物过程维持致瘤环境恒定条件的分子机制。

Quantitative proteomics has enhanced our capability to study protein dynamics and their involvement in disease using various techniques, including statistical testing, to discern the significant differences between conditions. While most focus is on what is different between conditions, exploring similarities can provide valuable insights. However, exploring similarities directly from the analyte level, such as proteins, genes, or metabolites, is not a standard practice and is not widely adopted. In this study, we propose a statistical framework called QuEStVar (Quantitative Exploration of Stability and Variability through statistical hypothesis testing), enabling the exploration of quantitative stability and variability of features with a combined statistical framework. QuEStVar utilizes differential and equivalence testing to expand statistical classifications of analytes when comparing conditions. We applied our method to an extensive data set of cancer cell lines and revealed a quantitatively stable core proteome across diverse tissues and cancer subtypes. The functional analysis of this set of proteins highlighted the molecular mechanism of cancer cells to maintain constant conditions of the tumorigenic environment via biological processes, including transcription, translation, and nucleocytoplasmic transport.