Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, 030001, PR, China.
MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Taiyuan, Shanxi, 030001, PR, China.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae606.
Renal cell carcinoma (RCC) ranks among the most prevalent cancers worldwide, with both incidence and mortality rates increasing annually. The heterogeneity among RCC patients presents considerable challenges for developing universally effective treatment strategies, emphasizing the necessity of in-depth research into RCC's molecular mechanisms, understanding the variations among RCC patients and further identifying distinct molecular subtypes for precise treatment. We proposed a metagene-based similarity network fusion (Meta-SNF) method for RCC subtype identification with multi-omics data, using a non-negative matrix factorization technique to capture alternative structures inherent in the dataset as metagenes. These latent metagenes were then integrated to construct a fused network under the Similarity Network Fusion (SNF) framework for more precise subtyping. We conducted simulation studies and analyzed real-world data from two RCC datasets, namely kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) to demonstrate the utility of Meta-SNF. The simulation studies indicated that Meta-SNF achieved higher accuracy in subtype identification compared with the original SNF and other state-of-the-art methods. In analyses of real data, Meta-SNF produced more distinct and well-separated clusters, classifying both KIRC and KIRP into four subtypes with significant differences in survival outcomes. Subsequently, we performed comprehensive bioinformatics analyses focused on subtypes with poor prognoses in KIRC and KIRP and identified several potential biomarkers. Meta-SNF offers a novel strategy for subtype identification using multi-omics data, and its application to RCC datasets has yielded diverse biological insights which are highly valuable for informing clinical decision-making processes in the treatment of RCC.
肾细胞癌（RCC）是全球最常见的癌症之一，其发病率和死亡率每年都在上升。RCC 患者之间的异质性给开发普遍有效的治疗策略带来了巨大挑战，这强调了深入研究 RCC 的分子机制、了解 RCC 患者之间的差异以及进一步确定不同的分子亚型以进行精确治疗的必要性。我们提出了一种基于元基因的相似网络融合（Meta-SNF）方法，用于使用多组学数据对 RCC 亚型进行鉴定，该方法使用非负矩阵分解技术来捕获数据集中固有替代结构作为元基因。然后，这些潜在的元基因被整合到相似网络融合（SNF）框架下构建融合网络，以进行更精确的亚型划分。我们进行了模拟研究，并分析了来自两个 RCC 数据集（即肾透明细胞癌（KIRC）和肾乳头状细胞癌（KIRP））的真实数据，以证明 Meta-SNF 的实用性。模拟研究表明，与原始 SNF 和其他最先进的方法相比，Meta-SNF 在亚型鉴定方面具有更高的准确性。在真实数据的分析中，Meta-SNF 产生了更明显和更好分离的聚类，将 KIRC 和 KIRP 分为四个具有显著生存结果差异的亚型。随后，我们针对 KIRC 和 KIRP 中预后较差的亚型进行了全面的生物信息学分析，并鉴定了几个潜在的生物标志物。Meta-SNF 为使用多组学数据进行亚型鉴定提供了一种新策略，其在 RCC 数据集上的应用产生了丰富的生物学见解，对指导 RCC 治疗的临床决策过程具有重要价值。
