Identifying cooperating cancer driver genes in individual patients through hypergraph random walk

IF 4 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Biomedical Informatics Pub Date : 2024-08-17 DOI:10.1016/j.jbi.2024.104710

Tong Zhang , Shao-Wu Zhang , Ming-Yu Xie , Yan Li

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Abstract

Objective

Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies.

Methods

Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients.

Results

The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments.

Conclusion

We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.

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通过超图随机漫步识别单个患者中相互合作的癌症驱动基因

目的：确定癌症驱动基因，尤其是罕见或患者特异性癌症驱动基因，是癌症治疗的首要目标。虽然研究人员提出了一些方法来解决这一问题，但这些方法大多是从单个基因水平识别癌症驱动基因，忽略了癌症驱动基因之间的合作关系。方法：在此，我们提出了一种新型的个性化合作癌症驱动基因（PCoDG）方法，利用超图随机游走来识别合作驱动个体患者癌症进展的癌症驱动基因。PCoDG 利用超图在表示多向关系方面的强大能力，首先使用个性化超图来描述个体患者的突变基因和差异表达基因之间的复杂相互作用。然后，利用基于超边缘相似性的超图随机漫步算法计算突变基因的重要性得分，并将这些得分与信号通路数据整合，以确定个体患者中的合作癌症驱动基因：在三个 TCGA 癌症数据集（即 BRCA、LUAD 和 COADREAD）上的实验结果表明，PCoDG 在识别个性化合作癌症驱动基因方面非常有效。PCoDG 发现的这些基因不仅为患者分层提供了与临床结果相关的宝贵见解，还为定制个性化治疗提供了有用的参考资源：我们提出了一种新方法，它能有效识别个体患者的合作癌症驱动基因，从而加深我们对个性化癌症驱动基因之间合作关系的理解，推动精准肿瘤学的发展。

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来源期刊

Journal of Biomedical Informatics 医学-计算机：跨学科应用

CiteScore

8.90

自引率

6.70%

发文量

243

审稿时长

32 days

期刊介绍： The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.