Beijing StoneWise Technology Co Ltd, Danling SOHO, Beijing, China
Beijing StoneWise Technology Co Ltd, Danling SOHO, Beijing, China.
Life Sci Alliance. 2023 Nov 17;7(2). doi: 10.26508/lsa.202302253. Print 2024 Feb.
To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.
为了有效地理解疾病的潜在机制并为个性化治疗的发展提供信息，利用差异共表达（DCE）网络分析的力量至关重要。尽管 DCE 网络分析在精准医学中有很大的应用前景，但目前的方法存在一个主要的局限性：它们在多个样本上测量平均差异网络，这意味着个体患者的具体病因往往被忽视。为了解决这个问题，我们提出了 Cosinet，这是一种基于 DCE 的单样本网络重布线程度量化工具。通过分析两个乳腺癌数据集，我们证明 Cosinet 可以识别个体患者之间基因共表达模式的重要差异，并为每个个体生成得分，这些得分与总体生存率、无复发生存期和其他临床结果显著相关，即使在调整了年龄、肿瘤大小、HER2 状态和 PAM50 亚型等风险因素之后也是如此。Cosinet 代表了在精准医学背景下解锁 DCE 分析潜力的一项重大进展。
