目的:乳腺癌是一种多面性疾病,其特点是具有多种特征和相当高的死亡率,强调及时发现和干预的必要性。近年来,利用多组学数据来识别乳腺癌中的生物标志物和分类亚型已经获得了重要的吸引力。这种从局部到整体的研究思路也将是未来生命科学研究的必然趋势。深度学习可以整合和分析多组学数据来预测癌症亚型,这可以进一步推动靶向治疗。然而,很少有文章利用深度学习的性质进行特征选择。因此,本文提出了一种基于神经网络和二进制灰狼优化的BreastCAncer生物标记(NNBGWO-BRCAMarker)发现框架,利用多组学数据获得一系列生物标志物,用于对乳腺癌亚型进行精确分类。
方法:NNBGWO-BRCAMarker包括两个阶段:在第一阶段,使用从训练的前馈神经网络获得的权重选择相关基因;在第二阶段,利用二进制灰狼优化算法进一步筛选所选基因,导致一组潜在的乳腺癌生物标志物。
结果:使用NNGBO-BRCAMarker鉴定的80种生物标志物进行训练时,具有RBF内核的SVM分类器实现了0.9242±0.03的分类精度,实验结果证明了这一点。我们进行了全面的基因集分析,预后分析,和可药用性分析,揭示了25个药物基因,16个富集途径与乳腺癌的特定亚型密切相关,和8个与预后结果相关的基因。
结论:提出的框架成功地从多组数据中鉴定出80个生物标志物,能够准确分类乳腺癌亚型。这一发现可能为临床医生在进一步研究中提供新的见解。
OBJECTIVE: Breast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes.
METHODS: NNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers.
RESULTS: The SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes.
CONCLUSIONS: The proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.