上海交通大学学报(医学版) ›› 2021, Vol. 41 ›› Issue (4): 448-458.doi: 10.3969/j.issn.1674-8115.2021.04.006

• 论著·基础研究 • 上一篇    下一篇

基于TCGA数据库分析胃癌可变剪接与肿瘤免疫的关系

顾琦晟(), 张米粒, 曹灿, 李继坤()   

  1. 上海交通大学附属第一人民医院普外科,上海 201620
  • 收稿日期:2020-06-04 出版日期:2021-04-28 发布日期:2021-05-14
  • 通讯作者: 李继坤 E-mail:hmnb@sjtu.edu.cn;jkli65975@163.com
  • 作者简介:顾琦晟 (1994—),男,硕士生;电子信箱: hmnb@sjtu.edu.cn
  • 基金资助:
    国家自然科学基金(81472236)

Association of alternative splicing and tumor immune in gastric cancer based on TCGA data set

Qi-sheng GU(), Mi-li ZHANG, Can CAO, Ji-kun LI()   

  1. Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai 201620, China
  • Received:2020-06-04 Online:2021-04-28 Published:2021-05-14
  • Contact: Ji-kun LI E-mail:hmnb@sjtu.edu.cn;jkli65975@163.com
  • Supported by:
    National Natural Science Foundation of China(81472236)

摘要:

目的·基于癌症基因组图谱数据库TCGA(The Cancer Genome Atlas)探讨胃癌可变剪接与肿瘤免疫的关系。方法·下载TCGA数据库中375例胃癌患者及32例配对癌旁正常组织的转录组、基因组数据和所有纳入研究的胃癌患者的临床信息,以及Spliceseq数据库452例胃癌及配对癌旁样本的剪接百分比数据。在GEO(Gene Expression Omnibus)数据库中下载包含192例胃癌患者转录组芯片数据的数据集GSE15459。利用ConcensusClusterPlus包对样本的可变剪接数据进行分型,通过竞争性基因集测试算法(Competitive Gene Set Test Accounting for Inter-gene Correlation,CAMERA)和基因集变异分析(Gene Set Variation Analysis, GSVA)对样本进行通路和基因集分析。采用CIBERSORT方法,通过反卷积分析胃癌样本的免疫微环境情况。结果·经过滤,纳入445例胃癌及配对癌旁组织正常样本进行分析,共涉及4 051个基因和8 649个剪接事件。胃癌组织与癌旁正常组织相比,有大量异常剪接事件发生。基于胃癌的不同可变剪接事件频率,将胃癌分为4个亚型。亚型间的T分期、M分期、患病年龄、Lauren分型、病理分期和组织学分型等临床特征比较,差异均有统计学意义(P< 0.05)。4个亚型各自有不同的肿瘤标志特征和微环境状态。亚型与特定剪接因子的高表达有关(log2FC > 1且FDR < 0.05),且核心剪接因子的基因集表达与肿瘤的抗原提呈过程有潜在的重要关联(Pearson R= 0.44,P = 0.000)。结论·胃癌可变剪接事件的变化和胃癌的临床表型与肿瘤微环境密切相关。剪接因子的表达水平主导可变剪接水平的变化。剪接因子有望成为胃癌分型的标志物以及改善免疫治疗效果的重要靶点。

关键词: 可变剪接, 胃癌, 生物信息学, 肿瘤微环境, 癌症基因组图谱

Abstract:

Objective· To investigate the association of alternative splicing and tumor immune in gastric cancer based on The Cancer Genome Atlas (TCGA) data set.

Methods· Transcriptomic data, genomic data and corresponding clinical information of 375 tumor tissues and 32 paired adjacent normal tissues from gastric cancer patients were separately downloaded from TCGA portal. Percent-spliced-in matrix containing 452 gastric cancer tissues and paired normal tissues were downloaded from Spliceseq database. The microarray transcriptomic data GSE15459 including 192 gastric cancer patients were downloaded from Gene Expression Omnibus (GEO). ConcensusClusterPlus package was used for classifying the samples based on splicing events. Competitive Gene Set Test Accounting for Inter-gene Correlation (CAMERA) and Gene Set Variation Analysis (GSVA) were used to apply pathway and gene set analysis. CIBERSORT was used to interrogate the condition of tumor microenvironment of samples through deconvolution of profiling of mRNA expression.

Results· Four hundred and forty-five gastric cancer tissues and paired adjacent normal tissues were included into the study after filtering, involving 4 051 genes and 8 649 splicing events. There were aberrant splicing events existing in gastric cancer tissues compared to adjacent normal tissues. The gastric cancer samples could be divided into 4 subtypes based on different high-frequency splicing events. Many clinical characteristics like T stage, M stage, age, Lauren classification, pathological stage and histological stage among the 4 subtypes were significantly different (P<0.05). The tumor hallmark characteristics and condition of microenvironment were also marked different among the 4 subtypes. The high expression of certain splicing factors were responsible for clustering of the subtypes (log2FC > 1 and FDR < 0.05), and gene set of core splicing factors was strongly correlated to antigen presentation in tumor (Pearson R = 0.44, P = 0.000).

Conclusion· The variation of splicing events was closely related to clinical characteristics and tumor microenvironment in gastric cancer. Expression of splicing factors dominates the variation of alternative splicing events. Certain splicing factors were expected to be biomarkers for classification of gastric cancer, and targets for improving the efficacy of immunotherapy.

Key words: alternative splicing, gastric cancer, bioinformatics, tumor microenvironment, The Cancer Genome Atlas (TCGA)

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