Song Junsoo, Ui Ayako, Mizuguchi Kenji, Watanabe Reiko
Laboratory for Computational Biology, Institute for Protein Research, The University of Osaka, 3-2 Yamadaoka, Suita-shi, Osaka 565-0871, Japan.
Department of Molecular Oncology, IDAC Fellow Laboratory, Institute of Development, Aging and Cancer (IDAC), Tohoku University, 4-1 Seiryo-machi, Aoba-ku, Sendai-shi, Miyagi-ken 980-8575, Japan.
Comput Struct Biotechnol J. 2025 Jun 5;27:2614-2625. doi: 10.1016/j.csbj.2025.06.015. eCollection 2025.
AT-rich interactive domain-containing protein 1A (ARID1A) is frequently mutated in endometrial cancers. Although patient stratification based on mutations or mRNA expression is commonly performed, this approach may not accurately reflect the functional state of ARID1A. This functional state is not only directly reflected in upstream events such as gene expression but also influenced by various regulatory including protein expression and the presence and type of mutations. Although protein expression is more directly correlated with phenotypic outcomes, integrating different omics data remains challenging due to disparities in data availability. To address this challenge, we developed a novel patient stratification method that integrates proteomics and transcriptomics to assess the functional state of ARID1A in patients with uterine corpus endometrial carcinoma. Initially, missing protein expression data were imputed using machine learning, and the patients were labelled based on their ARID1A protein expression. We then labelled the patients according to ARID1A activity, inferred by analysing the transcriptional regulation of genes directly controlled by ARID1A. Finally, patients were stratified by ARID1A functional state, considering both protein expression and the inferred activity label. This approach identified different gene expression patterns that are undetectable using conventional methods based on mRNA expression and mutation. Gene set enrichment and over-representation analyses confirmed that the proposed method revealed immune-related differences in patients with ARID1A-deficient uterine corpus endometrial carcinoma. These results highlight its potential to identify novel therapeutic targets and immune alterations that are undetected by conventional techniques.
富含AT的相互作用结构域蛋白1A（ARID1A）在子宫内膜癌中经常发生突变。尽管通常会根据突变或mRNA表达对患者进行分层，但这种方法可能无法准确反映ARID1A的功能状态。这种功能状态不仅直接反映在基因表达等上游事件中，还受到包括蛋白质表达以及突变的存在和类型等各种调控的影响。虽然蛋白质表达与表型结果的相关性更直接，但由于数据可用性的差异，整合不同的组学数据仍然具有挑战性。为了应对这一挑战，我们开发了一种新的患者分层方法，该方法整合蛋白质组学和转录组学来评估子宫体子宫内膜癌患者中ARID1A的功能状态。最初，使用机器学习估算缺失的蛋白质表达数据，并根据患者的ARID1A蛋白质表达进行标记。然后，通过分析由ARID1A直接控制的基因的转录调控来推断ARID1A活性，并据此对患者进行标记。最后，综合考虑蛋白质表达和推断的活性标记，根据ARID1A功能状态对患者进行分层。这种方法识别出了使用基于mRNA表达和突变的传统方法无法检测到的不同基因表达模式。基因集富集分析和过表达分析证实，所提出的方法揭示了ARID1A缺陷型子宫体子宫内膜癌患者中与免疫相关的差异。这些结果突出了其识别传统技术未检测到的新型治疗靶点和免疫改变的潜力。
