Construction of model for prognosis prediction based on TCGA database for differentially expressed genes of endometrial cancer

doi:10.3877/cma.j.issn.1673-5250.2021.02.009

Objective

To screen prognostic related differentially expressed genes of patients with endometrial cancer (EC) and construct a prognostic prediction model for patients with EC.

Methods

This study was searched by " Uteri" " TCGA-UCEC" " transcriptome profiling" " gene expression quantification and HTSeq-FPKM" as search key words in The Cancer Genome Atlas (TCGA) database from establishment of TCGA database to January 15, 2021. A total of 542 EC patients and 35 normal women who met inclusion criteria of this study were selected as research subjects. The steps for constructing a prognostic prediction model for patients with EC differential genes based on TCGA database in this study were as follows. ①R language linear models for microarray data (LIMMA) package was used to perform differential gene analysis on RNA-seq microarray gene expression data of TCGA database, and to screen candidate differential genes that affect the occurrence and development of EC gene. ②Kaplan-Meier method, LASSO algorithm regression, and univariate Cox proportional hazard regression analysis methods were used to screen survival-related differential genes of EC patients. Multivariate Cox proportional hazard regression analysis method was used to determine the prognostic differential genes of EC patients. ③Prognosis prediction model of EC patients was constructed with EC differential genes. ④Survival receiver operating characteristic (ROC) curve software package was used to detect the accuracy of the prediction model and draw a nomogram.

Results

①Among EC patients in this study, a total of 466 EC differential genes were found, of which 179 were up-regulated genes and 287 were down-regulated genes. ②Among 96 EC survival-related differential genes in EC patients of this study, 7 were found to be prognostic-related differential genes, including progesterone receptor (PGR), sushi repeat containing protein X-linked (SRPX), gamma-glutamyl hydrolase (GGH), secretoglobin family 2A member 1 (SCGB2A1), insulin like growth factor binding protein 5 (IGFBP5), cyclin dependent kinase inhibitor 2A (CDKN2A), and neuromedin U (NMU) genes. Univariate Cox proportional hazards regression analysis of these seven differential genes showed that they were all prognostic factors affecting prognosis of EC patients (P<0.05). Multivariate Cox proportional hazard regression analysis showed that differential genes of GGH, IGFBP5, and CDKN2A were independent risk factors that affect prognostic of EC patients (P<0.05). If expression levels of differential genes GGH, IGFBP5, and CDKN2A in EC patient are higher, the patient′s prognosis will be worse. ③Overall survival (OS) prediction model of EC patients was as follows: ln[h(t, X)/ h₀(t)]=1.300x_GGH+ 1.200x_IGFBP5+ 1.200x_CDKN2A. Among them, h(t, X) represented the hazard rate function of subject at time t, h₀(t) represented the hazard rate function when x_GGH, x_IGFBP5, x_CDKN2A were all 0, and x_GGH, x_IGFBP5, x_CDKN2A represented expression level of GGH, IGFBP5, CDKN2A differential genes. ④Survival risk of 542 EC patients was scored by established model and the patients were divided into high-risk subgroup (n=271, risk score higher than median score) and low-risk subgroup (n=271, risk score lower than median score) according to the median value of risk score. And OS period of low-risk subgroup was significantly longer than that of high-risk subgroup, and the difference was statistically significant (χ²=33.000, P<0.001). ROC curve analysis of this model for predicting OS period of EC patients showed that area under curve (AUC) was 0.700 (95%CI: 0.673-0.751, P<0.001). At the same time, Nomogram was constructed to quantitatively predict EC patients 1, 3, 5 year OS rate.

Conclusion

The constructed prognosis prediction model of EC patients with GGH, IGFBP5 and CDKN2A differential genes can provide data support for clinical prediction of prognosis of EC patients and search for corresponding targeted therapy drugs.

Key words: Endometrial carcinoma, TCGA database, Biological markers, Prognosis, Female

图1 EC差异基因分层聚类热图(根据差异基因表达情况进行聚类)

表1 EC患者预后相关差异基因的单因素与多因素Cox比例风险回归分析结果

差异基因	单因素Cox比例风险回归分析
差异基因	B	SE	Wald值	P值	HR值	HR值95%CI
PGR	-0.321	0.066	23.806	<0.001	0.726	0.638～0.825
GGH	0.340	0.093	13.438	0.002	1.406	1.172～1.686
SCGB2A1	-0.165	0.033	25.322	<0.001	0.848	0.795～0.904
IGFBP5	0.232	0.067	11.872	<0.001	1.261	1.105～1.439
CDKN2A	0.262	0.056	21.869	<0.001	1.300	1.164～1.450
SRPX	0.368	0.087	17.885	<0.001	1.445	1.219～1.715
NMU	0.312	0.080	15.152	<0.001	1.367	1.168～1.599
差异基因	多因素Cox比例风险回归分析
差异基因	B	SE	Wald值	P值	HR值	HR值95%CI
PGR	-0.139	0.086	2.616	0.106	0.870	0.735～1.030
GGH	0.260	0.100	6.702	0.010	1.300	1.100～1.600
SCGB2A1	-0.085	0.044	3.660	0.056	0.919	0.842～1.002
IGFBP5	0.152	0.069	4.872	0.027	1.200	1.000～1.300
CDKN2A	0.217	0.057	14.588	0.001	1.200	1.100～1.400
SRPX	0.133	0.104	1.649	0.199	1.140	0.932～1.400
NMU	0.094	0.089	1.118	0.290	1.099	0.923～1.308

表1 EC患者预后相关差异基因的单因素与多因素Cox比例风险回归分析结果

差异基因	单因素Cox比例风险回归分析
差异基因	B	SE	Wald值	P值	HR值	HR值95%CI
PGR	-0.321	0.066	23.806	<0.001	0.726	0.638～0.825
GGH	0.340	0.093	13.438	0.002	1.406	1.172～1.686
SCGB2A1	-0.165	0.033	25.322	<0.001	0.848	0.795～0.904
IGFBP5	0.232	0.067	11.872	<0.001	1.261	1.105～1.439
CDKN2A	0.262	0.056	21.869	<0.001	1.300	1.164～1.450
SRPX	0.368	0.087	17.885	<0.001	1.445	1.219～1.715
NMU	0.312	0.080	15.152	<0.001	1.367	1.168～1.599
差异基因	多因素Cox比例风险回归分析
差异基因	B	SE	Wald值	P值	HR值	HR值95%CI
PGR	-0.139	0.086	2.616	0.106	0.870	0.735～1.030
GGH	0.260	0.100	6.702	0.010	1.300	1.100～1.600
SCGB2A1	-0.085	0.044	3.660	0.056	0.919	0.842～1.002
IGFBP5	0.152	0.069	4.872	0.027	1.200	1.000～1.300
CDKN2A	0.217	0.057	14.588	0.001	1.200	1.100～1.400
SRPX	0.133	0.104	1.649	0.199	1.140	0.932～1.400
NMU	0.094	0.089	1.118	0.290	1.099	0.923～1.308

图2 对EC差异基因采取LASSO回归与多因素Cox比例风险回归分析结果图(图2A、2B：96个EC差异基因的LASSO回归模型；图2C：GGH、IGFBP5、CDKN2A差异基因的多因素Cox比例风险回归模型森林图)

图3 EC患者OS期预测模型预测准确性分析(图3A：高危与低危亚组EC患者OS曲线分析；图3B：该模型预测EC患者OS期的ROC曲线分析；图3C～3E：该模型中的高危与低危EC患者分布)

图4 EC患者OS期预测模型列线图

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