基于肺腺癌组织中可变剪接数据构建剪接因子-可变剪接调控网络的生物信息学分析

doi:10.13481/j.1671-587X.20230118

摘要/Abstract

摘要：

目的利用生物信息学方法筛选与肺腺癌（LUAD）患者生存相关的可变剪接（AS）事件，构建剪接因子（SF）-AS调控网络，为LUAD患者的预后评价提供新思路。方法由癌症基因组图谱（TCGA）数据库中下载LUAD患者转录组数据和临床数据。从SF表达和RNA靶点（SpliceAid2）数据库中下载LUAD患者AS数据。对LUAD患者生存相关AS事件进行单因素Cox回归分析。采用LASSO回归分析和多因素Cox回归分析构建LUAD患者预后风险模型，基于该模型计算每种AS事件的风险评分，采用 Kaplan-Meier（K-M）生存分析和受试者工作特征曲线（ROC）评价模型的可靠性。通过Cox 回归分析风险模型、临床参数与LUAD患者独立预后的关系。采用Pearson检验对SF和与预后相关的AS事件进行相关性分析，并通过Cytoscape软件构建SF-AS互作网络。结果筛选获取与生存相关的AS事件43 948个，共7种类型，主要以外显子活跃（ES）事件和可变终止子（AT）事件为主。构建AS事件预后风险模型，基于该模型风险评分将LUAD患者分为高风险组和低风险组，K-M生存分析，高风险组LUAD患者总生存（OS）率较低风险组低（P<0.05）。ROC曲线分析，LUAD患者预后风险模型预测性良好，曲线下面积（AUC）为0.824。SF-AS调控网络，12个与预后相关SFs正向或负向调节AS事件，可预测LUAD患者的不良预后。结论筛选了与LUAD患者预后相关的AS事件和上游的调控因子SF，构建了SF-AS网络，为进一步研究AS事件与LUAD患者预后的相关性提供理论依据。

关键词: 生物信息学技术, 肺腺癌, 可变剪接, 剪接因子, 癌症基因组图谱数据库

Abstract:

Objective To screen the survival-associated alternative splicing （AS） events in the lung adenocarcinoma （LUAD） patients using bioinformatics method and construct the splicing factor （SF）-AS regulatory network， and to provide a new approach for the prognostic evaluation of the LUAD patients. Methods The transcriptome data and clinical data of the LUAD patients were downloaded from The Cancer Genome Atlas （TCGA） database， and the AS data of the LUAD patients were downloaded from the SF expression data and RNA target motifs（SpliceAid2）database. The survival-associated AS events of the LUAD patients were analyzed by univariate Cox regression analysis. The prognostic risk model of the LUAD patients was established by LASSO regression analysis and multivariate Cox regression analysis，and the risk score of each AS event was calculated based on the model. Kaplan-Meier （K-M） survival analysis and receiver operating characteristic （ROC） curve were used to evaluate the reliability of the model. The relationships between risk model or clinical parameters and independent prognosis of the LUAD patients were analyzed by using Cox regression analysis. The Pearson test was used to analyze the correlation of SF with AS events associated with prognosis. Cytoscape software was utilized to construct the SF-AS interaction network. Results A total of 43 948 cases of survival-associated AS events were screened， including 7 types of events. Exon skip （ES） events and alternate terminator （AT） events were the main events. The prognostic risk model of AS events was constructed， and the LUAD patients were divided into high and low risk groups based on the risk scores of the model.The K-M survival analysis results showed that the overall survival （OS） rate of the LUAD patients in high risk group was lower than that in low risk group （P<0.05）.The ROC curve analysis indicated that prognostic risk model of the LUAD patients had a good predictive effect，and the area under curve （AUC） was 0.824. The SF-AS regulatory network showed that 12 prognosis-related SFs modulated AS events positively or negatively，and they could predict the poor prognosis of the LUAD patients. Conclusion The prognostis-related AS events of LUAD patients and their upstream regulatory factor SF are screened，the SF-AS network is constructed，and the study provides the theoretical basis for further research on the correlation of AS events with prognosis of the LUAD patients.

Key words: Bioinformatics analysis, Lung adenocarcinoma, Alternative splicing, Splicing factor, The Cancer Genome Atlas database

中图分类号:

R738.6

唐吴月,李硕杰,庞丽娟. 基于肺腺癌组织中可变剪接数据构建剪接因子-可变剪接调控网络的生物信息学分析[J]. 吉林大学学报(医学版), 2023, 49(1): 139-149.

Wuyue TANG,Shuojie LI,Lijuan PANG. Bioinformatics analysis of splicing factor-alternative splicing regulatory network based on alternative splicing data in lung adenocarcinoma tissue[J]. Journal of Jilin University(Medicine Edition), 2023, 49(1): 139-149.

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参考文献 32

1	ZHANG G H， DONG R， KONG D M， et al. The effect of GLUT1 on the survival rate and immune cell infiltration of lung adenocarcinoma and squamous cell carcinoma： a meta and bioinformatics analysis［J］. Anticancer Agents Med Chem， 2022， 22（2）： 223-238.
2	JIA K R， WU Y C， HUANG J， et al. Survival-associated alternative splicing events in pan-renal cell carcinoma［J］. Front Oncol， 2019， 9： 1317.
3	WAN Q， SANG X， JIN L， et al. Alternative splicing events as indicators for the prognosis of uveal melanoma［J］. Genes （Basel）， 2020， 11（2）： E227.
4	CHEN H Q， CARROT-ZHANG J， ZHAO Y， et al. Genomic and immune profiling of pre-invasive lung adenocarcinoma［J］. Nat Commun， 2019， 10（1）： 5472.
5	RAVEH E， MATOUK I J， GILON M， et al. The H19 long non-coding RNA in cancer initiation， progression and metastasis—a proposed unifying theory［J］. Mol Cancer， 2015， 14： 184.
6	ZHANG Y J， YAO X Y， ZHOU H， et al. OncoSplicing： an updated database for clinically relevant alternative splicing in 33 human cancers［J］. Nucleic Acids Res， 2022， 50（D1）： D1340-D1347.
7	SONG Q L， YI F T， ZHANG Y H， et al. CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell［J］. BMC Cancer， 2019， 19（1）： 499.
8	LIN C W， YU B W， ZHANG M， et al. Systematic analyses of the differentially expressed alternative splicing events in gastric cancer and its clinical significance［J］. Front Genet， 2020， 11： 522831.
9	COOMER A O， BLACK F， GREYSTOKE A， et al. Alternative splicing in lung cancer［J］. Biochim Biophys Acta Gene Regul Mech， 2019， 1862（11/12）： 194388.
10	DE FRAIPONT F， GAZZERI S， CHO W C， et al. Circular RNAs and RNA splice variants as biomarkers for prognosis and therapeutic response in the liquid biopsies of lung cancer patients［J］. Front Genet， 2019， 10： 390.
11	WAN L D， YU W Y， SHEN E H， et al. SRSF6-regulated alternative splicing that promotes tumour progression offers a therapy target for colorectal cancer［J］. Gut， 2019， 68（1）： 118-129.
12	URBANSKI L M， LECLAIR N， ANCZUKÓW O. Alternative-splicing defects in cancer： Splicing regulators and their downstream targets， guiding the way to novel cancer therapeutics［J］. Wiley Interdiscip Rev RNA， 2018， 9（4）： e1476.
13	CERASUOLO A， BUONAGURO L， BUONAGURO F M， et al. The role of RNA splicing factors in cancer： regulation of viral and human gene expression in human papillomavirus-related cervical cancer［J］. Front Cell Dev Biol， 2020， 8： 474.
14	SOUBISE B， JIANG Y， DOUET-GUILBERT N，et al. RBM22， a key player of pre-mRNA splicing and gene expression regulation， is altered in cancer［J］. Cancers （Basel）， 2022， 14（3）： 643.
15	HUANG X Y， SHI H Y， SHI X H， et al. LncRNA FBXL19-AS1 promotes proliferation and metastasis of cervical cancer through upregulating COL1A1 as a sponge of miR-193a-5p［J］. J Biol Res （Thessalon）， 2021， 28（1）： 20.
16	MELNYK J E， STERI V， NGUYEN H G， et al. The splicing modulator sulfonamide indisulam reduces AR-V7 in prostate cancer cells［J］. Bioorg Med Chem， 2020， 28（20）： 115712.
17	WOJTUSZKIEWICZ A， ASSARAF Y G， MAAS M J，et al. Pre-mRNA splicing in cancer： the relevance in oncogenesis， treatment and drug resistance［J］. Expert Opin Drug Metab Toxicol， 2015， 11（5）： 673-689.
18	LIM B， KIM C， KIM J H， et al. Genetic alterations and their clinical implications in gastric cancer peritoneal carcinomatosis revealed by whole-exome sequencing of malignant ascites［J］.Oncotarget，2016，7（7）：8055-8066.
19	SONG H， WANG Y P， SHI C J， et al. SH3KBP1 promotes glioblastoma tumorigenesis by activating EGFR signaling［J］. Front Oncol， 2020， 10： 583984.
20	SHI Y X， ZHU T， ZOU T， et al. Prognostic and predictive values of CDK1 and MAD2L1 in lung adenocarcinoma［J］. Oncotarget， 2016， 7（51）： 85235-85243.
21	LI J F， HE X， WU X T， et al. miR-139-5p inhibits lung adenocarcinoma cell proliferation， migration， and invasion by targeting MAD2L1［J］. Comput Math Methods Med， 2020， 2020： 2953598.
22	ZHANG J F， ZHANG J， XU S P， et al. Hypoxia-induced TPM2 methylation is associated with chemoresistance and poor prognosis in breast cancer［J］. Cell Physiol Biochem， 2018， 45（2）： 692-705.
23	LIU S W， ZENG F P， FAN G W， et al. Identification of hub genes and construction of a transcriptional regulatory network associated with tumor recurrence in colorectal cancer by weighted gene co-expression network analysis［J］. Front Genet， 2021， 12： 649752.
24	VARISLI L. Identification of new genes downregulated in prostate cancer and investigation of their effects on prognosis［J］.Genet Test Mol Biomarkers，2013，17（7）： 562-566.
25	LIU Q， FANG L M， WU C J. Alternative splicing and isoforms： from mechanisms to diseases［J］. Genes， 2022， 13（3）： 401.
26	PÉREZ-CALERO C， BAYONA-FELIU A， XUE X Y， et al. UAP56/DDX39B is a major cotranscriptional RNA-DNA helicase that unwinds harmful R loops genome-wide［J］.Genes Dev，2020，34（13/14）： 898-912.
27	AWASTHI S， CHAKRAPANI B， MAHESH A，et al. DDX39B promotes translation through regulation of pre-ribosomal RNA levels［J］. RNA Biol， 2018， 15（9）： 1157-1166.
28	ZHANG H N， HE C C， GUO X X， et al. DDX39B contributes to the proliferation of colorectal cancer through direct binding to CDK6/CCND1［J］. Cell Death Discov， 2022， 8（1）： 30.
29	XU Z Z， LI X M， LI H X， et al. Suppression of DDX39B sensitizes ovarian cancer cells to DNA-damaging chemotherapeutic agents via destabilizing BRCA1 mRNA［J］.Oncogene，2020，39（47）：7051-7062.
30	WEI J H， LU J， CAO Y， et al. DDX39B predicts poor survival and associated with clinical benefit of anti-PD-L1 therapy in ccRCC［J］. Curr Cancer Drug Targets， 2021， 21（10）： 849-859.
31	ISHIMI Y. Regulation of MCM2-7 function［J］. Genes Genet Syst， 2018， 93（4）： 125-133.
32	WANG J Z， ZHU W， HAN J， et al. The role of the HIF-1α/ALYREF/PKM2 axis in glycolysis and tumorigenesis of bladder cancer［J］. Cancer Commun （Lond）， 2021， 41（7）： 560-575.

Item	P value	Hazard ratio
Age	0.883	0.999(0.979-1.019)
Gender	0.977	1.006(0.688-1.471)
TNM stage	<0.001	1.655(1.393-1.966)
T stage	<0.001	1.594(1.270-2.001)
M stage	0.077	1.762(0.941-3.298)
N stage	<0.001	1.803(1.457-2.231)
AA-risk score	0.021	1.402(1.044-1.982)
AD-risk score	0.003	1.671(1.245-2.390)
AP-risk score	0.008	0.608(0.459-0.895)
AT-risk score	0.002	1.667(1.241-2.288)
ES-risk score	0.006	1.486(1.092-2.176)
ME-risk score	<0.001	1.677(1.243-2.339)
RI-risk score	0.026	1.367(0.997-1.871)

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