Sequential reinforcement active feature learning for gene signature identification in renal cell carcinoma
Sequential reinforcement active feature learning for gene signature identification in renal cell carcinoma
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DOI：
10.1016/j.jbi.2022.104049
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发表时间：
2022-03
影响因子：
4.5
通讯作者：
Meng Huang;Xiucai Ye;A. Imakura;Tetsuya Sakurai
中科院分区：
文献类型：
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作者：
Meng Huang;Xiucai Ye;A. Imakura;Tetsuya Sakurai
肾细胞癌（renal cell carcinoma，RCC）是最致命的癌症之一，主要包括三种亚型：肾透明细胞癌（kidney clear cell carcinoma，KIRC）、肾乳头状细胞癌（kidney papillary cell carcinoma，KIRP）和肾嫌色细胞癌（kidney chromophobe，KICH）。基因标签识别在肾癌亚型的精确分类和个体化治疗中起着重要作用。然而，现有的基因选择方法大多集中于静态地为每个亚型选择相同的信息基因，而没有考虑到患者的异质性导致每个亚型的模式差异。在这项工作中，为了探索每个亚型的不同信息基因子集，我们提出了一种新的基因选择方法，称为顺序强化主动特征学习（SRAFL），它动态获取每个样本中的不同基因，以识别每个亚型的不同基因特征。所提出的SRAFL方法将癌症亚型分类器与强化学习（RL）代理相结合，该代理以成本敏感的方式从三种混合RCC亚型中依次选择每个样本中的活性基因。此外，基于模块的基因过滤运行之前的基因选择，以过滤冗余基因。我们主要评估所提出的SRAFL方法的基础上的mRNA和长的非编码RNA（lncRNA）表达谱RCC数据集从癌症基因组图谱（TCGA）。实验结果表明，该方法能够自动识别不同亚型的基因特征，从而准确地对RCC亚型进行分类。更重要的是，我们在这里首次表明，所提出的SRAFL方法可以考虑样本的异质性，为不同的RCC亚型选择不同的基因签名，这显示了未来基于精确度的RCC护理的更大潜力。
Renal cell carcinoma (RCC) is one of the deadliest cancers and mainly consists of three subtypes: kidney clear cell carcinoma (KIRC), kidney papillary cell carcinoma (KIRP), and kidney chromophobe (KICH). Gene signature identification plays an important role in the precise classification of RCC subtypes and personalized treatment. However, most of the existing gene selection methods focus on statically selecting the same informative genes for each subtype, and fail to consider the heterogeneity of patients which causes pattern differences in each subtype. In this work, to explore different informative gene subsets for each subtype, we propose a novel gene selection method, named sequential reinforcement active feature learning (SRAFL), which dynamically acquire the different genes in each sample to identify the different gene signatures for each subtype. The proposed SRAFL method combines the cancer subtype classifier with the reinforcement learning (RL) agent, which sequentially select the active genes in each sample from three mixed RCC subtypes in a cost-sensitive manner. Moreover, the module-based gene filtering is run before gene selection to filter the redundant genes. We mainly evaluate the proposed SRAFL method based on mRNA and long non-coding RNA (lncRNA) expression profiles of RCC datasets from The Cancer Genome Atlas (TCGA). The experimental results demonstrate that the proposed method can automatically identify different gene signatures for different subtypes to accurately classify RCC subtypes. More importantly, we here for the first time show the proposed SRAFL method can consider the heterogeneity of samples to select different gene signatures for different RCC subtypes, which shows more potential for the precision-based RCC care in the future.