基于scRNA-seq数据单细胞水平APA分析方法开发及其在肿瘤中的应用

选择性多聚腺苷酸化（APA）是一种作用于前体mRNA的3’末端加工的重要基因表达调控机制。APA可以通过调控3’UTR的长度的方式影响信使RNA的稳定性、转运以及翻译效率或者改变蛋白编码序列从而影响所编码蛋白质，进而在许多生物学过程中发挥重要的调控作用。研究表明，APA与肿瘤的发生发展密切相关，可以通过调控抑癌基因或癌基因的表达从而影响肿瘤的形成或转移等过程。近年来，单细胞转录组测序技术（scRNA-seq）被广泛应用于肿瘤研究中以揭示复杂的肿瘤细胞亚型和肿瘤免疫微环境，然而，单细胞水平挖掘APA与肿瘤的关系的研究仍少有报道。本项目旨在开发可以利用scRNA-seq数据进行单细胞水平APA分析的生物信息学方法，并将之应用于肿瘤数据分析，从而阐明APA与肿瘤内肿瘤细胞及肿瘤免疫微环境的关联性，并结合其他相关数据，揭示其潜在的调控机制，识别新的肿瘤标志物及药物靶点。

Alternative polyadenylation (APA) is recognized as a major mechanism of gene expression regulation by processing 3’ ends of precursor mRNA. APA could not only impact mRNA stability, translocation and translation through regulating the length of 3’UTR, but also impact protein product through changing the coding sequences. Through these mechanisms, APA play an important role in diverse biological processes. APA was reported to be involved in tumorigenesis and cancer progression through regulating expression of oncogenes and tumor suppressor genes. Single-cell RNA-seq (scRNA-seq) technologies were widely used in cancer studies to reveal complex subtypes of malignant cells and tumor immune microenvironment. However, systematic studies of APA at single-cell level in tumors are still lacking. Therefore, this study aims to develop a novel bioinformatic method to analyze APA at single-cell level using scRNA-seq data, and further apply this method to tumor scRNA-seq data in order to dissect the relationship across APA, malignant cells and tumor immune microenvironment. Furthermore, through integrating with other data, the underlying mechanism will be revealed in this study and novel tumor biomarkers and drug targets will be recognized, in order to pave the way towards precision medicine for tumor.