甲状腺癌铁死亡预后风险模型的构建及其潜在机制的生物信息学分析

doi:10.13481/j.1671-587X.20230217

摘要/Abstract

摘要：

目的筛选甲状腺癌（TC）差异预后铁死亡基因（PFRGs），构建TC铁死亡相关基因（FRGs）预后风险模型，并阐述其潜在作用机制。方法从癌症基因组图谱（TCGA）数据库获取基因表达及临床数据，从铁死亡疾病数据库（FerrDb）和人类基因数据库（GeneCards）中获取FRGs，采用R软件筛选TCPFRGs；从TCGA和GTEx数据库获取TC组织和甲状腺组织中PFRGs mRNA表达数据，从人类蛋白图谱（HPA）数据库获取免疫组织化学结果，验证PFRGs mRNA和蛋白表达的差异；采用时间依赖性受试者工作特征（time-ROC）曲线和Kaplan-Meier曲线评估PFRGs与TC患者生存和预后的关系；采用单因素和多因素Cox回归分析计算PFRGs表达的风险评分，纳入TC患者临床数据，进行独立预后分析，并构建Nomogram图；TCGA数据库中PFRGs与各基因表达的相关性采用Spearman相关分析，计算相关系数并筛选共表达基因；采用生物信息学方法对PFRGs共表达基因进行蛋白-蛋白互作（PPI）网络图、基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析。结果在TC中差异分析筛选出3 317个上调基因和3 456个下调基因，单因素Cox回归分析筛选出343个差异表达基因（DEGs）与TC患者的生存和预后相关，其中包括CD44、膜联蛋白A1（ANXA1）和核受体亚家族4A类成员1（NR4A1）。Kaplan-Meier和time-ROC曲线显示CD44、ANXA1和NR4A1的表达与TC患者生存和预后有关联（P=0.048，P=0.005，P=0.036），且均具有良好的1、3和5年生存预测作用。构建3个基因风险评分系统，风险评分作为TC患者临床预后因子［风险比（HR）=8.882，95%CI：1.561~50.547，P=0.014）］，风险评分越高，生存预后越差［P=0.011，ROC曲线下面积（AUC）=0.761、0.767和0.722］；风险评分联合TC患者临床特征构建的Nomogram图（C-index=0.938）对TC患者的生存具有较好的预测作用。共表达与富集分析，TC铁死亡主要与其共表达基因（DUSP1、DUSP5、DUSP6、FOS、IL1RAP、JUN、MET、RASGRF1、TGFA、TGFBR1、TNFRSF1A）介导MAPK信号通路，影响MAPK活性和p-MAPK活性，调控MAPK失活。结论基于生物信息学筛选出的TC差异PFRGs CD44、ANXA1和NR4A1与TC患者生存和预后相关，由3个基因构建的预测模型具有较好的预测能力，其作用机制可能与多基因网状调控MAPK信号通路有关。

关键词: 甲状腺肿瘤, 铁死亡, 风险评分, Nomogram图, 生存预后

Abstract:

Objective To screen the differential prognostis ferroptosis genes of thyroid cancer （TC） and construct the prognostic risk model of TC ferroptosis related genes（FRGs）， and to clarify its potential mechanism at the molecular level. Methods The gene expression and clinical data were obtained from The Cancer Genome Atlas （TCGA） Database. The FRGs were obtained from FerrDb and GeneCards Databases， and R software was used to screen the PFRGs of TC；the PFRGs mRNA expressions in TC and thyroid tissues were obtained from TCGA and GTEx Databases， and the immunohistochemical results were obtainted from the Human Protein Atlas （HPA） Database to verify the differences in the expressions of PFRGs mRNA and protein； time-receiver operating characteristic （time-ROC） curve and Kaplan-Meier curve were used to evaluate the relationships between PFRGs and survival and prognosis of the TC patients； univariate and multivariate Cox regression analysis were used to calculate the risk scores of PFRGs expression， the clinical data of the TC patients were included， the independent prognostic analysis was performed， and the Nomogram chart was constructed. Spearman correlation analysis was used to obtain the correlation between the expressions of PFRGs and the expressions of other genes in TCGA Database and expressions of various genes，the correlation coefficient was calculated and the co-expressing genes were screened；the co-expression genes of PFRGs were analyzed by protein-protein interaction（PPI） network diagram，Geno Ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） enrichment analysis. Results A total 3 317 up-regulated genes and 3 456 down-regulated genes in TC were screened out by differential analysis；343 differentially expressed genes（DEGs） screened out by univariate Cox regression were associated with the survival and prognosis of the TC patients， including CD44， Annexin A1（ANXA1）， and nuclear receptor subfamily 4 group A member 1（NR4A1）. The Kaplan-Meier and time-ROC curves results showed that the expressions of CD44， ANXA1， and NR4A1 were associated with the survival and prognosis of the TC patients （P=0.048， P=0.005， P=0.036）， and all had good 1-year， 3-year， and 5-year survival prediction effects；the risk scoring system of three genes was constructed to calculate the risk score，and the risk score was a prognostic factor of the TC patients ［hazard ratio（HR）=8.882， 95%CI=1.561－50.547，P=0.014］， and the higher the risk score was，the worse the survival prognosis was［P=0.011，area under curve（AUC）=0.761，0.767，and 0.722）； the Nomogram chart （C-index=0.938） constructed by the risk score combined with the clinical characteristics of the TC patients had a good predictive effect on the survival of the TC patients. The co-expression and enrichment analysis results showed that TC ferroptosis and its co-expressing genes （DUSP1， DUSP5， DUSP6，FOS，IL1RAP， JUN， MET， RASGRF1，TGFA，TGFBR1，andTNFRSF1A）mediated the MAPK signaling pathway and affected the MAP kinase/serine/threonine tyrosine phosphatase activity and MAP kinase phosphatase activity，and regulated the inactivation of the MAPK activity. Conclusion The TC differential PFRGs CD44， ANXA1， and NR4A1 based on bioinformatics analysis are related to the survival and prognosis of the TC patients. The prediction model constructed by three genes has a good predictive ability，and its mechanism may be related to the polygenic network’s regulation of the MAPK signaling pathway.

Key words: Thyroid neoplasm, Ferroptosis, Risk score, Nomogram, Survival prognosis

中图分类号:

R736.3

杨仁义,彭书旺,王永恒,董宇轩,段姗姗. 甲状腺癌铁死亡预后风险模型的构建及其潜在机制的生物信息学分析[J]. 吉林大学学报(医学版), 2023, 49(2): 402-413.

Renyi YANG,Shuwang PENG,Yongheng WANG,Yuxuan DONG,Shanshan DUAN. Construction of ferroptosis prognostic risk model of thyroid cancer and bioinformatics analysis on its potential mechanism[J]. Journal of Jilin University(Medicine Edition), 2023, 49(2): 402-413.

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Genes	Log₂FC	Adjusted P	Cox P	HR	95%CI
CD44	1.311	<0.001	0.048	0.319	0.103－0.990
ANXA1	1.376	<0.001	0.005	0.121	0.027－0.533
NR4A1	－1.519	<0.001	0.036	3.838	1.093－13.474

Degree		Closeness		Betweenness
Gene	Value	Gene	Value	Gene	Value
JUN	23	JUN	38.033	FOS	1 383.878
FOS	22	FOS	36.900	JUN	1 375.111
ATF3	15	ATF3	31.733	TGFA	748.000
FOSB	13	FOSB	30.233	EGR3	647.507
DUSP1	10	DUSP1	29.067	TNFRSF1A	446.000
PPP1R15A	9	BTG2	28.867	PTPRE	446.000
NR4A2	9	PPP1R15A	28.400	ATF3	393.206
BTG2	9	NR4A2	28.067	NR4A3	274.714
ZFP36	7	DUSP6	27.283	DUSP6	265.825
TGFBR1	7	ZFP36	27.067	INHBB	230.000

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