可变多聚腺苷酸化在肿瘤中的研究进展
Advances in Alternative Polyadenylation in Tumors
DOI: 10.12677/acm.2024.143902, PDF, HTML, XML, 下载: 34  浏览: 75 
作者: 周梦浩, 梁华庚*：华中科技大学同济医学院附属协和医院泌尿外科，湖北 武汉
关键词: 可变多聚腺苷酸化；肿瘤；基因表达调控；RNA测序；Alternative Polyadenylation (APA)； Tumor； Gene Expression Regulation； RNA Sequencing
摘要: 可变多聚腺苷酸化(Alternative Polyadenylation, APA)作为一种转录后调控机制，其在基因表达调控中的作用越来越受到重视。近期的许多研究发现APA在肿瘤的发生发展和耐药机制中发挥着重要作用。本文主要总结了APA在基因表达调控和癌症等疾病中的功能作用和新的APA相关的研究方法和数据库。
Abstract: The role of alternative polyadenylation (APA) as a post-transcriptional regulatory mechanism in the regulation of gene expression has received increasing attention. Many recent studies have found that APA plays an important role in tumor development and drug resistance mechanisms. This paper mainly summarizes the functional role of APA in gene expression regulation and diseases such as cancer and new APA-related research methods and databases.
文章引用：周梦浩, 梁华庚. 可变多聚腺苷酸化在肿瘤中的研究进展[J]. 临床医学进展, 2024, 14(3): 1741-1749. https://doi.org/10.12677/acm.2024.143902

1. 简介

在真核生物的细胞核中，前体mRNA (Precursor-mRNA, pre-mRNA)合成后，需要进行5'端和3'端的修饰以及对pre-mRNA进行剪接，才能成为成熟的mRNA，被转运到核糖体，指导蛋白质翻译。需要注意的是，编码区两侧的5'和3'序列不会被翻译，因此被称为非翻译区(Untranslated Region, UTR)。在这其中，5'UTR是核糖体组装进行mRNA翻译的主要位点。与之相对应的，3'UTR在转录后控制基因表达中发挥着各种作用，包括但不限于mRNA的稳定性、翻译和亚细胞定位 [1] 。

当mRNA识别位于3'UTR中的poly(A)位点(Polyadenylation Sites, PASs)后，会发生可变多聚腺苷酸化(Alternative Polyadenylation, APA)。APA是研究最广泛的3'UTR加工事件之一，其结果通常导致3'UTR的缩短。早期的研究表明，3'UTR的缩短在癌症中普遍存在 [2] [3] 。

APA的应用非常普遍，大约有70%的人类基因的3'UTR中存在APA，而约50%的基因则含有3个或更多的PASs。PAS的选择是一个动态过程，主要由顺式元件决定 [4] [5] 。这种机制在真核生物中似乎是高度保守的，仅出现在哺乳动物和植物中，在约70%的酵母基因中几乎没有发生任何可变剪接(Alternative Splicing, AS)。这证明了其在进化中的重要性 [6] 。值得注意的是，APA在非编码RNA (Non-coding RNA, ncRNA)中的应用也相当普遍 [7] 。一项对小鼠全基因组的研究发现，约79%的mRNA基因和66%的长链非编码RNA (Long Non-coding RNA, lncRNA)基因中至少存在一种重要的APA亚型 [8] 。

2. APA分类

现有文献已经涉及了使用各种命名法和模式对不同类型的PAS进行分类，但在这里，我们将它们大致分为3'UTR-APA和上游区域-APA (Upstream Regions APA, UR-APA)。当PAS大部分位于3'UTR时，被称为3'UTR-APA，它改变了3'UTR的长度，但保持了基因产物的一致性。3'UTR-APA的频繁发生显著影响mRNA的多个方面，包括稳定性、翻译效率等。这凸显了它在基因表达中的重要调控作用。另一类APA发生在最后一个外显子的上游，因此被称为UR-APA，其进一步改变了编码蛋白质的可能性 [7] [9] [10] 。UR-APA在不同程度上截断蛋白质产物。它包括三个亚类：末端外显子APA、内含子APA、内部外显子APA。这些APA亚型在细胞过程中扮演着重要的角色，影响了蛋白质的多样性和基因表达的调控 [11] [12] 。

3. APA因子

pre-mRNA 3'端加工复合物由四个亚单位组成。裂解和聚腺苷酸化特异因子(Cleavage and Polyadenylation Specificity Factor, CPSF)组成员包括CPSF1 (又称CPSF160)、CPSF2 (又称CPSF100)、CPSF3 (又称CPSF73)、CPSF4 (又称CPSF30)、FIP1 (又称FIP1L1)和WDR33。研究表明，CPSF1在pre-mRNA 3'端的形成中发挥着至关重要的作用。在拟南芥中，CPSF2被发现具有锚定PASs并介导转录终止的能力 [13] 。作为一种pre-mRNA 3'端加工内切酶，CPSF3参与了转录本周期的终止，包括RNA的裂解 [14] [15] 。FIP1通过与多聚腺苷酸聚合酶的相互作用来调节PAS。WDR33是3'端加工过程中的关键组分之一，它在与AAUAAA的结合中扮演主要角色 [16] [17] 。

切割激活因子(Cleavage Stimulation Factor, CSTF)由CSTF1、CSTF2和CSTF3组成。CSTF可以加强CPSF对PAS的识别能力。Yang等人的研究发现CSTF1在DNA损伤反应过程中的染色质重塑环节发挥了重要作用 [18] [19] 。CSTF2可以直接与RNA相互作用。其有一个CSTF2t的对等物，两者的功能在一定程度上相同，敲除两者都会引起APA的明显变化 [20] [21] 。CSTF3也是APA和核定位的一个关键成分 [22] 。mRNA 3'端的加工处理一般主要由CSTF1、CSTF2和CSTF3负责 [22] [23] 。

切割因子I (Cleavage Factor I, CFI)和切割因子II (Cleavage Factor II, CFII)是APA的调控因子，也是哺乳动物裂解机制的两个核心成分 [10] [24] 。在3'UTR长度的调节中，CFI具有十分重要的作用。具体来说，CFI可以选择性地与末端外显子中的远端PAS结合，从而提升远端PAS的使用率。Rüegsegger等人的研究表明CFI可以加强CPSF与PAS作用的稳定性 [25] 。此外，在HEK293细胞中，CFI (尤其是CFIm25和CFIm68)功能缺失会导致整个转录组的近端PAS使用率增加 [26] [27] 。CFII是3'端处理机制中特征最少的成分。CFII由CLP1和PCF11组成。CFII与RNA的亲和力主要被PCF11影响，而CFII的裂解能力则取决于CLP1 [28] [29] 。

4. APA相关技术

4.1. PolyaID

基因组中PASs的分布应与局部基因结构进行共同演化。否则，虚假的多腺苷酸化可能导致过早的转录终止并生成异常蛋白质。为了深入了解跨越人类基因组的PASs优化的机制，研究者开发了深度机器学习模型，以前所未有的核苷酸水平分辨率识别全基因组的潜在PAS，并计算它们在基因组背景中的强度和使用情况。该模型定量地测量了位置特异性基序的重要性以及它们在PAS形成和剪切异质性中的相互作用 [30] 。

4.2. stAPAminer

空间转录组学(Spatial Transcriptomics, ST)技术为破译转录组景观的空间背景提供了机会。stAPAminer的工具包可以从ST数据中挖掘APA的空间模式。从ST数据中识别并量化了APA位点。其中，设计了一个基于k-近邻算法的估算模型来恢复APA信号，然后确定了具有APA使用变异空间模式的APA基因。stAPAminer利用ST的强大功能，以空间分辨率探索空间APA模式的转录图谱。该工具包可在 https://github.com/BMILAB/stAPAminer和https://ngdc.cncb.ac.cn/biocode/tools/BT007320上获取 [31] 。

4.3. REPAC

REPAC是一个从RNA测序(RNA Sequencing, RNA-seq)数据分析APA的框架。REPAC利用注释PASs上游50 bp窗口的表达量估计值，拟合一个广义线性回归模型，以评估不同条件下发生的PASs使用率差异 [32] 。

4.4. ImmAPA

免疫相关APA事件(Immune-related APA Event, ImmAPA)评分管道，这是一种综合算法，用于描述APA事件在肿瘤免疫相关通路中的调控作用。在ImmAPAs中，排名第一的COL1A1 3'UTR使用与预后恶化和肿瘤免疫逃脱密切相关。此外，通过机器学习方法构建的免疫检查点抑制剂(Immune Checkpoint Blockade, ICB)相关ImmAPA评分模型可有效预测免疫疗法的疗效 [33] 。

4.5. APARENT2

残差神经网络模型APARENT2能更准确地从DNA序列推断3'端剪切和多聚腺苷酸化。该模型推广到APA的情况，适用于PASs数量可变的APA [34] 。

4.6. DeeReCT-APA

APA深度调控代码和工具(Deep Regulatory Code and Tools for Alternative Polyadenylation, DeeReCT-APA)用于定量预测给定基因中所有替代PAS的使用情况。为了适应不同基因可能具有不同数量的PAS，DeeReCT-APA将问题视为目标长度可变的回归任务。基于卷积神经网络–长短期记忆(Convolutional Neural Network-long Short-term Memory, CNN-LSTM)架构，DeeReCT-APA利用CNN层提取序列特征，使用双向LSTM明确模拟竞争PAS之间的相互作用，并输出代表基因所有PAS使用水平的百分比分数。代码和数据可在https://github.com/lzx325/DeeReCT-APA-repo上获取 [35] 。

4.7. QuantifyPoly (A)

加权密度峰聚类方法(QuantifyPoly (A))可以准确量化全基因组的多腺苷酸化选择。在已发表的动物和植物3'端测序数据集上应用QuantifyPoly (A)，它们的多腺苷酸化图谱被重塑为无数新的PAS群。这些新型PAS群大多在不同的生物样本中显示出显著的动态使用情况，或与反式作用因子的结合位点相关联。这些PAS群的上游序列富含聚腺苷酸化信号UGUA、UAAA和/或AAUAAA，其方式与物种有关。PAS群也具有物种特异性，植物的微异质性通常高于动物。QuantifyPoly (A)广泛适用于任何类型的3'端测序数据和物种，可准确量化和构建复杂而动态的多腺苷酸化图谱 [36] 。

4.8. Aptardi

Aptardi (从RNA-Seq数据和DNA序列信息进行APA转录组分析)在机器学习范式中利用DNA序列和RNA测序预测表达的PAS。Aptardi将DNA核苷酸序列、基因组对齐的RNA-Seq数据和初始转录组作为输入数据。程序会对这些初始转录本进行评估，以确定生物样本中表达的PAS，并相应地优化转录本的3'末端部分。Aptardi模型的平均精确度是标准转录组装配程序的两倍。Aptardi模型的召回率(算法检测到的真实PAS的比例)提高了三倍以上 [37] 。

4.9. MAAPER

越来越多的RNA-seq方法，尤其是用于单细胞转录组分析的方法，会产生接近PAS的读数，称为近位点读数。而MAAPER利用近位点读数进行APA分析。MAAPER预测PAS的准确度和灵敏度都很高，并能以强大的统计学方法检查不同类型的APA事件 [38] 。

4.10. SCAPTURE

SCAPTURE可以从基于3'标签的单细胞RNA测序(Single-cell RNA Sequencing, scRNA-seq)中识别、评估和量化裂解位点和PAS。SCAPTURE以高灵敏度和准确性在单细胞中从头检测PASs，从而能检测到以前未注释的PASs。量化的替代PAS转录本完善了基因表达之外的细胞身份分析，丰富了从scRNA-seq数据中提取的信息 [39] 。

4.11. scDaPars

scRNA-seq的APA动态分析(Dynamic Analysis of APA from Single-cell RNA-seq, scDaPars)可使用3'末端或全长scRNA-seq数据在单细胞和单基因分辨率下精确量化APA事件。在真实数据和模拟数据的验证结果表明，scDaPars能恢复因单细胞中测序的mRNA数量较少而导致的缺失APA事件。在应用于癌症和人类内胚层分化数据时，scDaPars不仅揭示了细胞类型特异性APA调控，还识别出了在传统基因表达分析中一般无法发现的细胞亚群 [40] 。

5. APA相关数据库

5.1. ipaQTL-atlas

ipaQTL-atlas (http://bioinfo.szbl.ac.cn/ipaQTL)是一个内含子多腺苷酸化的综合门户网站。ipaQTL-atlas基于对GTEx数据库中838位个体的15,170个RNA-seq数据的分析，包含约98万个与内含子APA事件相关的SNPs。ipaQTL-atlas提供了访问内含子多腺苷酸化信息的一站式门户，可极大地推动APA相关疾病易感基因的发现 [41] 。

5.2. scAPAdb

scAPAdb (http://www.bmibig.cn/scAPAdb)提供了一个全面的、人工编辑的单细胞水平PAS、APA事件和poly (A)信号图谱。目前，其收集了来自超过360个scRNA-seq实验的APA信息，涵盖6个物种，包括人类、小鼠和其他几个植物物种。此外，该数据库还提供数据批量下载，用户可以通过基因名称、基因功能等各种关键字查询数据库 [42] 。

5.3. scAPAatlas

scAPAatlas (http://www.bioailab.com:3838/scAPAatlas)用于探索不同细胞类型的APA，并解释潜在的生物学功能。基于从24个人类组织和25个小鼠正常组织中获取的scRNA-seq数据，研究人员系统地识别了不同细胞类型的细胞特异性APA事件，并研究了APA与基因表达水平之间的相关性。同时估算了细胞类型特异性APA事件与microRNA或RNA结合蛋白(RNA Binding Protein, RBP)之间的相互作用 [43] 。

5.4. 3'aQTL-atlas

3'aQTL-atlas (3′UTR APA Quantitative Trait Loci, 3'aQTLs)提供了一份全面的列表 (https://wlcb.oit.uci.edu/3aQTLatlas)，其中包含约149万个与目标基因APA相关的单核苷酸多态性(Single-Nucleotide Polymorphisms, SNPs)，这些SNPs是基于GTEx数据库中的15,201个RNA-seq样本。它还包括按基因/SNP跨组织的3'aQTL搜索、3'aQTL基因组浏览器、3'aQTL方框图和全基因组关联研究(Genome-wide association studies, GWAS)-3'aQTL共定位事件可视化。3'aQTL-atlas旨在将APA确立为一种新兴的分子表型，以解释大部分GWAS风险SNPs，从而对APA的遗传基础以及人类性状和疾病中与APA相关的易感基因有重要的新见解 [44] 。

6. APA与肿瘤

人类蛋白质编码基因中约70%的miRNA靶标和约11%的腺苷酸/尿苷酸富集区域位于3'UTR中 [45] 。在对整体APA景观的系统调查中，发现肿瘤样本中的总体APA比匹配的正常样本短，并且癌细胞系中的APA比肿瘤样本中的缩短更广泛 [46] 。3'UTR的缩短导致miRNA结合位点的减少和一些mRNA不稳定元件的消失。在正常细胞中，原癌基因在蛋白质编码区使用远端PAS，转录本通常由miRNA和/或RBPs调控 [47] 。随着APA事件的发生，一旦细胞选择近端PAS产生短的3'UTR，就有可能消除miRNA和/或RBP的结合位点，导致mRNA失去正常控制并诱导癌变 [48] 。

目前已经有许多关于APA调节因子在不同肿瘤中发挥作用的研究。比如说，Chen等人的研究发现PABPN1调控mRNA的APA抑制膀胱癌进展 [49] ；Xiong等人的研究发现PABPN1通过抑制SGPL1和CREG1的APA促进透明细胞肾细胞癌的进展 [50] ；Tan等人的研究发现NUDT21缺失诱导的MORC2APA促进了KIRC癌症的发生 [51] 。除此之外，还有研究发现APA调节因子在肿瘤的耐药机制方面也起着关键的作用。在B细胞祖细胞急性淋巴细胞白血病(B cell progenitor acute lymphoblastic leukemia, B-ALL)中，NUDT21通过APA限制CD19水平，降低B-ALL对嵌合抗原受体T细胞疗法(Chimeric Antigen Receptor T-Cell Immunotherapy, CAR-T)和双特异性T细胞啮合疗法blinatumomab (靶向表面糖蛋白CD19)的敏感性。在肺癌中，PD-L1的剪接变体PD-L1-vInt4可能是癌症对抗PD-L1疗法产生耐药性的原因之一 [52] 。乳腺癌对新辅助化疗(Neoadjuvant Chemotherapy, NAC)的耐药性是由一种新型p62 mRNA异构体驱动的，它能摆脱miRNA介导的抑制，并导致p62蛋白表达增加 [53] 。

7. 总结

随着高通量测序技术的发展，科研人员开始对APA的调控方式、APA的功能以及APA在肿瘤中的作用有了更清晰的认识。越来越多的证据表明，APA是基因表达的一个新的调控机制，并且现在研究中许多计算工具和数据库被开发，目的是用于更准确高效地检测APA事件。其中大多数是从标准RNA-seq数据中推断PAS的使用信息。另外一些则利用深度学习模型预测出不同生物条件下的新型APA事件。总之，这些工具能够有效帮助科研人员研究全基因组APA图谱，并且在对APA调控基因表达和功能多样性的理解方面也起到了重要作用。许多疾病(包括癌症)的病理生理学过程，都会发生广泛的APA，APA事件正在成为极具潜力的临床生物标志物。尽管目前的研究丰富了我们对APA的认识，但其某些功能我们依旧不够了解，如PAS与不同RBPs的亲和力和APA调控的更多其他细节等。因此，对于APA的调控、APA对生物过程的影响以及APA在肿瘤耐药性中的作用机制的进一步研究仍是十分必要的。

NOTES

*通讯作者。

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