Al-Harazi Olfat, Kaya Ibrahim H, El Allali Achraf, Colak Dilek
Biostatistics, Epidemiology and Scientific Computing Department, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Front Genet. 2021 Nov 1;12:721949. doi: 10.3389/fgene.2021.721949. eCollection 2021.
The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including , , , , , and . The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.
开发可靠的方法来识别复杂疾病的强大生物标志物对于疾病诊断和预后研究至关重要。将多组学数据与蛋白质-蛋白质相互作用（PPI）网络整合以研究疾病可能有助于在分子水平上更好地理解疾病特征。在本研究中，我们开发并测试了一种基于网络的新方法来检测结直肠癌（CRC）患者的子网标志物。我们使用全基因组基因表达谱和拷贝数改变（CNA）数据集进行了综合组学分析，随后为显著改变的基因构建了基因相互作用网络。然后，我们将构建的基因网络聚类为子网，并为每个显著子网分配一个分数。我们使用这些分数作为特征值开发了支持向量机（SVM）分类器，并在独立的CRC转录组数据集上测试了该方法。网络分析产生了15个子网标志物，揭示了几个可能在结直肠癌中起重要作用的枢纽基因，包括 ， ， ， ， ，和 。15个子网分类器在检测CRC患者时显示出超过98%的准确率。与单个基因生物标志物相比，基于综合多组学和网络分析的子网标志物可能会带来更好的疾病分类、诊断和预后。
