Institute of Computing, Universidade Federal do Amazonas, Manaus, BR, Brazil.
New York Univesity, New York, USA.
BMC Bioinformatics. 2024 Mar 1;25(1):92. doi: 10.1186/s12859-024-05715-8.
In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately.
This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature.
The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.
近年来，研究人员在理解乳腺癌及其各种亚型的异质性方面取得了重大进展。然而，今天可用的基因组和蛋白质组数据的丰富性需要有效的框架、工具和计算工具来进行有意义的分析。尽管 PAM50 基因特征作为一种预后工具取得了成功，但它依赖于许多基因，这在成本和复杂性方面带来了挑战。因此，需要更有效的方法来使用减少的基因集准确地对乳腺癌亚型进行分类。
本研究探讨了使用源自 PAM50 基因特征的减少基因集实现精确乳腺癌亚型分类的潜力。通过使用“Few-Shot Genes Selection”方法，我们从 PAM50 中随机选择较小的子集，并使用度量标准和线性模型（特别是支持向量机 (SVM) 分类器）来评估它们的性能。此外，我们旨在评估更紧凑的基因集是否可以在简化分类过程的同时保持性能。我们的研究结果表明，某些减少的基因子集可以与完整的 PAM50 基因特征表现相当或更好。
确定的基因子集（36 个基因）有可能有助于开发更具成本效益和简化的乳腺癌研究和临床环境中的诊断工具。
