基于基因表达谱预测肿瘤浸润免疫细胞类型及比例的解卷积算法

A deconvolution algorithm for predicting the type and proportion of tumor-infiltrating immune cells based on the gene expression profile

肿瘤浸润免疫细胞通过发挥促肿瘤和抗肿瘤的作用,可以深刻地影响肿瘤的进展以及抗癌治疗的成功.因此,对于肿瘤浸润免疫细胞的量化有望揭示免疫系统在人类癌症中的多方面作用及其参与肿瘤逃逸机制和对治疗的反应.解卷积的目的就是试图在复杂组织里存在的免疫细胞中寻找新的免疫疗法,其核心思想是利用算法和免疫细胞的表达特征,从细胞混合物的表达数据中量化免疫细胞比例信息,以准确刻画肿瘤样本测序数据的免疫浸润情况.为此,提出了一个新的基于逐步回归策略的解卷积算法模型,并使用真实的肿瘤样本微阵列数据和RNA-Seq测序数据来测试该算法的准确性.与CIBERSORT和dtangle相比较,具有良好的解卷积性能.

Tumor-infiltrating immune cells can profoundly affect tumor progression and the success of anticancer treatment by exerting anti-tumor effects.Therefore,quantification of tumor-infiltrating immune cells is expected to reveal the multifaceted effects of the immune system in human cancers and its involvement in tumor escape mechanisms and response to treatment.Deconvolution aims to find new immunotherapy in the immune cells existing in complex tissues.The core idea is to use the expression characteristics of the algorithm and immune cells to quantify the proportion of immune cells from the expression data of the cell mixture to accurately reveal the immune infiltration of tumor-sample sequencing data.To this end,this paper proposes a new deconvolution algorithm based on the stepwise regression strategy,and uses the real tumor sample of microarray data and RNA-Seq sequencing data to test the accuracy of the algorithm.The results show that this deconvolution algorithm has good deconvolution performance compared with CIBERSORT and dtangle.