Bioinformatics Based Effect of ZNF492 Mutation on Transcriptome of Ovarian Cancer and Its Clinical Value

doi:10.12280/gjfckx.20220662

Abstract

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

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.

Figures/Tables 9

References 28

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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