检测不同癌症亚型之间的共同和独特特征是旨在改善个体化治疗的研究的重要焦点。与主要基于预测技术的现有方法不同，我们的研究旨在提高对描述性导致癌症的分子机制的认识，因此不需要先前的知识来验证。在这里，我们提出了一种基于对比集挖掘的方法来捕捉癌症转录组数据中的高阶关系。通过这种方式，我们能够从几种癌症亚型中提取有价值的见解，这些见解以与疾病影响的功能通路相关的高度特异的遗传关系的形式呈现。为了达到这个目的，我们将多个癌症基因表达数据库按样本所关联的亚型划分，以检测哪些基因组与每个癌症亚型相关。为了证明所提方法的潜力和实用性，我们对乳腺癌、肾脏癌和结肠癌亚型的RNA-Seq基因表达数据进行了广泛的分析。通过广泛的文献研究进一步评估了所获得的遗传关系的可能作用，而通过生存分析评估了其预后情况，在各种癌症亚型中发现了与生存相关的基因表达模式。一些基因关联在文献中被描述为潜在的癌症生物标志物，而其他结果尚未被描述，可能成为未来研究的起点。 版权所有 © 2023 作者。由 Elsevier B.V. 出版。保留所有权利。

The task of detecting common and unique characteristics among different cancer subtypes is an important focus of research that aims to improve personalized therapies. Unlike current approaches mainly based on predictive techniques, our study aims to improve the knowledge about the molecular mechanisms that descriptively led to cancer, thus not requiring previous knowledge to be validated. Here, we propose an approach based on contrast set mining to capture high-order relationships in cancer transcriptomic data. In this way, we were able to extract valuable insights from several cancer subtypes in the form of highly specific genetic relationships related to functional pathways affected by the disease. To this end, we have divided several cancer gene expression databases by the subtype associated with each sample to detect which gene groups are related to each cancer subtype. To demonstrate the potential and usefulness of the proposed approach we have extensively analysed RNA-Seq gene expression data from breast, kidney, and colon cancer subtypes. The possible role of the obtained genetic relationships was further evaluated through extensive literature research, while its prognosis was assessed via survival analysis, finding gene expression patterns related to survival in various cancer subtypes. Some gene associations were described in the literature as potential cancer biomarkers while other results have been not described yet and could be a starting point for future research.Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.