基于m6A调节基因表达水平对子宫内膜癌免疫浸润及其预后影响的生物信息学分析

doi:10.13481/j.1671-587X.20210225

Abstract

Abstract: Objective

To analyze the expression levels of 20 m⁶A-related genes in uterine corpus endometrial carcinoma （UCEC） tissue with the bioinformatics methods data and to screen the hub genes related to prognosis， and to explore the correlations between the expression levels of hub genes and the immune infiltration degrees in UCEC.

Methods

The transcriptome sequencing， clinicopathological and survival data of 547 UCEC and 35 normal controls were obtained from The Cancer Genome Atlas （TCGA） database. The GSE17025 datasets that contained 81 samples of UCEC and 12 samples of adjacent cancer tissue were downloaded from Gene Expression Omnibus （GEO） database. Using the GEO2R online analysis tools and R software， the differentially expressed genes （DEGs） between UCEC tissue and adjacent normal tissue were identified. Survival analysis and Cox regression were carried out for DEGs. The genes that were significantly associated with overall survival （OS） were identified as the hub genes； protein-protein interaction （PPI） network of 20 m⁶A regulators and correlation analysis of the DEGs were performed using STRING database and R software， respectively. The correlations between the expression levels of hub genes and the clinicopathological features of UCEC were analyzed using the UALCAN database； the correlations between the target genes and immune infiltration degrees and the markers of immune cells were explored via Tumor Immune Estimation Resource （TIMER） and Gene Expression Profiling Interactive Analysis （GEPIA） database. Furthermore， survival analysis based on the expression of target genes was conducted in the related immune cell subgroup using Kaplan-Meier Plotter database.

Results

Most of the 20 m⁶A regulators were significantly differentially expressed in UCEC tissue compared with the adjacent normal tissue （P<0.05）； the IGF2BP3 mRNA was highly expressed in UCEC tissue （P<0.05）.High IGF2BP3 expression was correlated with shorter OS （P<0.05）. Furthermore， both univariate ［hazard ratio （HR）=1.106， 95% condifence interval （CI）： 1.019－1.202， P=0.016］ and multivariate （HR=1.097， 95% CI： 1.003－1.199，P=0.043） Cox regression analysis results showed that IGF2BP3 could be used as an independent risk factor for OS evaluation. IGF2BP3 mRNA exhibited higher expression in the UCEC tissue of advanced stage and pre-menopause patients and endometrial serous adenocarcinoma tissue （P<0.05）. The expression level of IGF2BP3 mRNA was positively associated with the degrees of immune infiltration by CD8+T cells （r=0.126， P<0.05），neutrophils （r=0.324，P<0.01） and dendritic cells （DC）（r=0.120， P<0.05） in UCEC. The expression level of IGF2BP3 mRNA was positively associated with the markers of tumor-associated macrophages（TAMs）， macrophages （M1 and M2）， helper T cells （Th1， Th2， and Th17） and regulatory T cells （Treg） in UCEC （0.1≤r ≤1，P<0.05）. The patients with high expression of IGF2BP3 mRNA had a shorter OS than those with low expression of IGF2BP3 mRNA in the low infiltration subgroup of B cells， Treg cells， and Th2 cells （P<0.05）.

Conclusion

The expression level of IGF2BP3 mRNA in UCEC tissue is significantly increased， which promotes the immune infiltration of UCEC and indicates the poor prognosis of UCEC patients. IGF2BP3 could be served as a prognostic molecular biomarker of UCEC and indicate the poor prognosis of UCEC patients， and may become a novel target for cancer therapy.

Key words: endometrial cancer, N6-methyladenosine, immune infiltration, bioinformatics

CLC Number:

R737.33

Lan ZHENG,Songzhe PIAO,Ran XU,Xinyue WANG,Yixuan WANG,Zhenhua LIN,Yang YANG. Bioinformatics analysis based on effect of expression levels of m⁶A regulators on immune infiltration and prognosis of uterine corpus endometrial carcinoma[J].Journal of Jilin University(Medicine Edition), 2021, 47(2): 438-452.

Figures/Tables 9

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Tab.1

Correlations between IGF2BP3 mRNA expression level and biological markers of immune cells analyzed with TIMER database"

		UCEC
Description	Gene marker	None		Purity
		r	P	r	P
CD8+T cells	CD8A	0.061	1.53E-01	0.133	2.25E-02
	CD8B	-0.095	2.66E-02	-0.053	3.68E-01
T cells (general)	CD3D	-0.079	6.62E-02	0.009	8.83E-01
	CD3E	-0.066	1.24E-01	0.016	7.80E-01
	CD2	-0.019	6.50E-01	0.068	2.45E-01
B cell	CD19	0.114	7.90E-03	0.188	1.25E-03
	CD79A	-0.003	9.53E-01	0.106	7.03E-02
	FCRL2	-0.166	1.00E-04	0.107	6.63E-02
	MS4A1	0.035	4.13E-01	0.113	5.40E-02
Monocytes	CD86	0.033	4.41E-01	0.074	2.09E-01
	CD115(CSF1R)	-0.094	2.74E-02	-0.027	6.43E-01
	C3AR1	0.027	5.30E-01	0.085	1.48E-01
	CD86	0.033	4.41E-01	0.074	2.09E-01
TAM	CCL2	-0.007	8.62E-01	0.018	7.65E-01
	CD68	0.069	1.07E-01	0.131	2.52E-02
	IL10	0.070	1.02E-01	0.117	4.53E-02
	CD84	0.089	3.88E-02	0.129	2.73E-02
M1 macrophages	INOS(NOS2)	0.090	3.55E-02	0.127	2.95E-02
	IRF5	0.217	3.13E-07	0.221	1.40E-04
M2 macrophages	CD163	0.153	3.49E-04	0.206	3.90E-04
	VSIG4	0.074	8.34E-02	0.118	4.37E-02
	MS4A4A	0.056	1.90E-01	0.095	1.05E-01
Neutrophils	CD66b(CEACAM8)	-0.008	8.53E-01	-0.005	9.26E-01
	CD11b(ITGAM)	-0.052	2.27E-01	-0.04	4.93E-01
	CCR7	-0.002	9.65E-01	0.102	8.19E-02
	KIR2DL1	0.002	9.57E-01	-0.040	5.00E-01
	KIR2DL3	0.051	2.38E-01	0.006	9.18E-01
	KIR2DL4	0.064	1.34E-01	0.129	2.70E-02
	CSF3R	0.078	7.04E-02	0.159	6.24E-03
	S100A12	-0.024	5.80E-01	0.065	2.69E-01
DC	HLA?DPB1	-0.106	1.30E-02	-0.016	7.90E-01
	HLA?DQB1	-0.105	1.39E-02	-0.038	5.13E-01
	HLA?DRA	-0.047	2.72E-01	0.027	6.40E-01
	HLA?DPA1	-0.040	3.56E-01	0.022	7.02E-01
	BDCA?1(CD1C)	-0.113	8.08E-03	-0.075	2.03E-01
	BDCA?4(NRP1)	0.055	2.03E-01	0.123	3.50E-02
	CD11c(ITGAX)	-0.004	9.21E-01	-0.003	9.65E-01
	CD209	0.104	1.48E-02	0.159	1.54E-02
Th1	T?bet(TBX21)	0.019	6.64E-01	0.093	1.12E-01
	STAT4	0.052	2.29E-01	0.114	5.22E-02
	STAT1	0.389	3.92E-21	0.455	2.10E-16

Description	Gene marker	None Purity
		r	P	r	P
	IFN?γ(IFNG)	0.074	8.23E-02	0.104	7.63E-02
	TNF?α(TNF)	0.043	3.11E-01	0.089	1.27E-01
Th2	GATA3	0.012	7.73E-01	-0.002	9.72E-01
	STAT6	0.085	4.64E-02	0.118	4.30E-02
	STAT5A	0.065	1.32E-01	0.160	6.00E-03
	IL13	-0.076	7.58E-02	-0.037	5.25E-01
Tfh	BCL6	-0.007	8.76E-01	-0.044	4.49E-01
	IL21	0.051	2.32E-01	0.113	5.36E-02
Th17	STAT3	0.119	5.31E-03	0.161	5.84E-03
	IL17A	0.063	1.41E-01	0.035	5.48E-01
Treg	FOXP3	0.007	8.69E-01	0.091	1.20E-01
	CCR8	0.083	5.28E-02	0.089	1.30E-01
	STAT5B	0.171	6.08E-05	0.232	6.07E-05
	TGFβ(TGFβ1)	0.077	7.06E-02	0.115	4.86E-02
T cell exhaustion	PD?1(PDCD1)	-0.016	7.04E-01	0.056	3.41E-01
	CTLA4	-0.040	3.47E-01	0.041	4.88E-01
	LAG3	0.079	6.40E-02	0.152	8.96E-03
	TIM?3(HAVCR2)	0.046	2.83E-01	0.112	5.46E-02
	GZMB	0.025	5.59E-01	0.074	2.04E-01
NK cells	KIR3DL3	0.067	1.20E-01	0.055	3.52E-01
	NCR1	0.010	8.13E-01	0.038	5.16E-01

Tab.1

Tab. 2

Fig. 7

References 30

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		UCEC
Description	Gene marker	Tumor		Normal
		r	P	r	P
B cells	CD19	0.140	0.072	-0.280	0.360
	CD79A	0.069	0.360	-0.066	0.830
TAM	CCL2	0.028	0.710	0.450	0.130
	CD68	0.190	0.014	-0.063	0.840
	IL?10	0.170	0.026	-0.120	0.700
	CD84	0.100	0.190	-0.170	0.580
M1 macrophages	INOS(NOS2)	0.320	1.60E-05	0.085	0.780
	IRF5	0.250	9.50E-04	0.660	0.014
M2 macrophages	CD163	-0.014	0.860	-0.200	0.510
	VSIG4	0.050	0.510	-0.094	0.760
	MS4A4A	0.068	0.370	-0.160	0.600
DC	HLA?DPB1	-0.002	0.980	0.130	0.670
	HLA?DQB1	-0.170	0.029	0.028	0.930
	HLA?DRA	0.110	0.140	0.280	0.350
	HLA?DPA1	0.096	0.210	0.250	0.410
	BDCA?1(CD1C)	-0.054	0.480	-0.260	0.390
	BDCA?4(NRP1)	0.130	0.097	-0.410	0.170
	CD11c(ITGAX)	0.051	0.500	0.540	0.054
	CD209	-0.015	0.850	-0.250	0.410
Th1	T?bet(TBX21)	0.009	0.910	0.290	0.330
	STAT4	0.130	0.098	0.140	0.430
	STAT1	0.450	5.50E-10	-0.094	0.760
	IFN?γ(IFNG)	0.100	0.170	0.140	0.640
	TNF?α(TNF)	0.019	0.800	0.330	0.270
Th2	GATA3	0.054	0.480	0.091	0.770
	STAT6	0.370	4.70E-07	-0.320	0.290
	STAT5A	0.130	0.083	-0.230	0.450
	IL?13	-0.024	0.750	0.600	0.029
Th17	STAT3	0.430	3.20E-09	0.091	0.770
	IL?17A	0.180	0.018	0.200	0.510
Treg	FOXP3	-0.005	0.950	0.180	0.550
	CCR8	0.320	2.10E-05	0.240	0.430
	STAT5β	0.490	7.30E-12	-0.300	0.310
	TGFβ(TGFβ1)	0.180	0.020	-0.110	0.710

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Bioinformatics analysis based on effect of expression levels of m⁶A regulators on immune infiltration and prognosis of uterine corpus endometrial carcinoma

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Bioinformatics analysis based on effect of expression levels of m⁶A regulators on immune infiltration and prognosis of uterine corpus endometrial carcinoma