基于生物信息学的锌指蛋白492基因突变对卵巢癌转录组的影响及价值

doi:10.12280/gjfckx.20220662

摘要/Abstract

摘要：

目的：探讨锌指蛋白492（zinc finger protein 492，ZNF492）基因突变在卵巢癌的作用机制及其临床价值，为卵巢癌的诊治提供理论依据。方法：通过癌症基因图谱（The Cancer Genome Atlas，TCGA）下载ZNF492突变样本的转录组数据，将样本分成ZNF492突变组和非突变组，使用R包DESeq2分析2组间的差异表达基因，对差异表达基因进行京都基因及基因组百科全书（kyoto encyclopedia of genes and genomes，KEGG）通路富集分析和基因集富集分析（gene set enrichment analysis，GSEA）。获取卵巢癌患者年龄、肿瘤分期和分化的情况，通过ZNF492表达与卵巢癌患者临床特征的多因素COX回归预测模型进行列线图（Nomogram）模型构建及评价。结果：ZNF492突变组（n=377）与非突变组（n=2）差异基因中有100个上调基因和971个下调基因。2组间的差异基因及ZNF492突变组100个上调基因的通路富集分析均显示富集于转化生长因子-β（transforming growth factor-β，TGF-β）信号通路。多因素COX回归预测模型结果显示卵巢癌患者的年龄大（≥65岁）、肿瘤分期（Ⅲ~Ⅳ期）和分化（G3）高可能与患者较差的总生存期相关（HR>1），而ZNF492高表达（≥0.672）与较好的预后相关（HR<1），其中年龄较大（≥65岁）的患者预后较差（P<0.01）。基于以上4个因素，进一步构建Nomogram预测模型，模型验证结果显示C指数为0.593，3年和5年受试者操作特征曲线下面积（area under the curve，AUC）分别为0.610和0.566，临床决策曲线（decision curve analysis，DCA）提示患者3年或者5年生存率大于18%且小于82%时可采取干预措施。结论：突变的ZNF492可能通过调控TGF-β通路以及抗凋亡的潜在机制影响卵巢癌的发生、发展，并且对预测预后有较强临床实用价值，ZNF492突变有望成为卵巢癌循环肿瘤DNA的标志物和治疗靶点。

关键词: 卵巢肿瘤, 转录组, 基因, 肿瘤, 突变, ZNF492, 癌症基因图谱

Abstract:

Objective: To investigate the mechanism and clinical value of zinc finger protein 492 (ZNF492) gene mutation in ovarian cancer, and to provide a theoretical basis for the diagnosis and treatment of ovarian cancer. Methods: The transcriptome data of ZNF492 mutant samples were downloaded by TCGA, and the samples were divided into two groups: ZNF492 mutant and non-mutant groups. The R package DESeq2 was used to analyze the differential genes between the two groups. The differentially expressed genes were analyzed by kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis and gene set enrichment analysis (GSEA). The age, tumor stage and differentiation of ovarian cancer patients were obtained, and the Nomogram model was constructed and evaluated by multivariate COX regression prediction model of ZNF492 expression and clinical characteristics of ovarian cancer patients. Results: There were 100 up-regulated genes and 971 down-regulated genes in the differential genes between the ZNF492 mutant group (n=377) and the non-mutant group (n=2). The pathway enrichment analysis of differential genes between the two groups and 100 up-regulated genes in the ZNF492 mutant group showed that they were enriched transforming growth factor-β (TGF-β) signal path. The results of multivariate COX regression prediction model showed that higher age (≥65 years old), the higher tumor stage (Ⅲ-Ⅳ stage) and differentiation (G3) might be related to the poor overall survival (HR>1), while high expression of ZNF492 (≥0.672) was related to the better prognosis (HR<1), among which the higher age patients (≥65 years old) had a poor prognosis (P<0.01). Based on the above four factors, further construct the Nomogram prediction model. The model validation results showed that the C index was 0.593, the area under the curve (AUC) (0.610 and 0.566) of three-year and five-year ROC curves, and the clinical decision curve analysis (DCA) suggested that patients can take bold measures when the 3-year or 5-year survival rate was greater than 18% and less than 82%. Conclusions: Mutated ZNF492 may affect the occurrence and development of ovarian cancer by regulating TGF-β pathway and anti-apoptosis potential mechanism, and has strong clinical practical value in predicting prognosis. ZNF492 mutation is expected to be a marker of circulating tumour DNA and therapeutic target for ovarian cancer.

Key words: Ovarian neoplasms, Transcriptome, Genes, neoplasm, Mutation, ZNF492, TCGA

陆媛媛, 李力. 基于生物信息学的锌指蛋白492基因突变对卵巢癌转录组的影响及价值[J]. 国际妇产科学杂志, 2023, 50(2): 216-222.

LU Yuan-yuan, LI Li. Bioinformatics Based Effect of ZNF492 Mutation on Transcriptome of Ovarian Cancer and Its Clinical Value[J]. Journal of International Obstetrics and Gynecology, 2023, 50(2): 216-222.

图/表 9

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ID	描述	P	基因
hsa05031	安非他明成瘾	0.002	ARC/GRIA1/DRD1
hsa04971	胃酸分泌	0.002	KCNJ1/KCNJ15/SLC26A7
hsa04713	昼夜夹带	0.005	KCNJ3/CACNA1I/GRIA1
hsa04024	cAMP信号通路	0.007	TSHR/SSTR1/GRIA1/DRDI
hsa04960	醛固酮调节钠重吸收	0.008	KCNJ1/SCNN1G
hsa04080	神经活性配体-受体相互作用	0.008	TSHR/GPR83/SSTR1/GRIA1/DRDI
hsa04728	多巴胺能突触	0.011	KCNJ3/GRIA1/DRD1
hsa04929	促性腺激素释放激素分泌	0.022	KCNJ3/CACNA1I
hsa05032	吗啡成瘾	0.042	KCNJ3/DRD1
hsa04350	TGF-β信号通路	0.044	BMP2/NOG

ID	描述	P	基因
hsa05031	安非他明成瘾	0.002	ARC/GRIA1/DRD1
hsa04971	胃酸分泌	0.002	KCNJ1/KCNJ15/SLC26A7
hsa04713	昼夜夹带	0.005	KCNJ3/CACNA1I/GRIA1
hsa04024	cAMP信号通路	0.007	TSHR/SSTR1/GRIA1/DRDI
hsa04960	醛固酮调节钠重吸收	0.008	KCNJ1/SCNN1G
hsa04080	神经活性配体-受体相互作用	0.008	TSHR/GPR83/SSTR1/GRIA1/DRDI
hsa04728	多巴胺能突触	0.011	KCNJ3/GRIA1/DRD1
hsa04929	促性腺激素释放激素分泌	0.022	KCNJ3/CACNA1I
hsa05032	吗啡成瘾	0.042	KCNJ3/DRD1
hsa04350	TGF-β信号通路	0.044	BMP2/NOG

ID	中文注释	富集得分	标准化富集分数	P	基因
hsa05010	阿尔茨海默病	-0.896	-1.797	0.001	TLIBALB/TUBASE/AGER
hsa05022	多种神经退行性疾病通路	-0.753	-1.823	0.003	TRPC3/TUBAL3/TUBA3E/GRIA2/AGER
hsa05150	金黄色葡萄球菌感染	0.862	1.910	0.004	C4A/HLA-DQA1/HLA-DQA2/KRT32
hsa04350	TGF-β信号通路	0.889	1.754	0.006	BMP2/NOG
hsa05322	系统性红斑狼疮	0.887	1.749	0.006	C4A/HLA-DQA1/HLA-DQA2
hsa04024	cAMP信号通路	-0.658	-1.771	0.008	SSTR1/ADCY8/GRIA2/TSHR
hsa04210	凋亡	-0.925	-1.482	0.009	7DBA3E

ID	中文注释	富集得分	标准化富集分数	P	基因
hsa05010	阿尔茨海默病	-0.896	-1.797	0.001	TLIBALB/TUBASE/AGER
hsa05022	多种神经退行性疾病通路	-0.753	-1.823	0.003	TRPC3/TUBAL3/TUBA3E/GRIA2/AGER
hsa05150	金黄色葡萄球菌感染	0.862	1.910	0.004	C4A/HLA-DQA1/HLA-DQA2/KRT32
hsa04350	TGF-β信号通路	0.889	1.754	0.006	BMP2/NOG
hsa05322	系统性红斑狼疮	0.887	1.749	0.006	C4A/HLA-DQA1/HLA-DQA2
hsa04024	cAMP信号通路	-0.658	-1.771	0.008	SSTR1/ADCY8/GRIA2/TSHR
hsa04210	凋亡	-0.925	-1.482	0.009	7DBA3E

变量	赋值	HR	95%CI	P
ZNF492	基因表达量<0.672（中位数）=0， ≥0.672=1	0.791	0.607~1.032	0.084
年龄	年龄<65岁=0，≥65岁=1	1.429	1.092~1.868	0.009
肿瘤分期	Ⅰ、Ⅱ期=0，Ⅲ、Ⅳ期=1	1.999	0.874~4.573	0.101
分化	G1/G2=0，G3=1	1.090	0.723~1.646	0.680