Bioinformatics analysis on expression of immune-related lncRNAs in hepatocellular carcinoma and its prognostic value

doi:10.3877/cma.j.issn.2095-3232.2021.05.019

Abstract

Abstract:

Objective

To investigate the expression level of immune-related lncRNAs in hepatocellular carcinoma (HCC) and its prognostic value based on TCGA database analysis.

Methods

The transcriptomic expression data of HCC were collected from TCGA database and immune-related lncRNAs were screened from the clinical data. Prognostic factors for the overall survival of patients with differentially-expressed immune-related lncRNAs were evaluated by Cox regression analysis. Prognosis-associated lncRNAs were selected and gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to identify the potential regulatory pathways of immune-related lncRNAs.

Results

20 immune-related lncRNAs associated with the clinical prognosis of HCC patients were found out by Cox regression analysis. Risk stratification was an independent prognostic factor for HCC patients (HR=1.340, P<0.05). Survival prognosis became worse as the risk score increased. In the high-risk group, the mortality was 51.5%, and 20.5% in the low-risk group, where significant difference was observed (χ²=36.089, P<0.05). Kaplan-Meier survival analysis showed that the 1-,3- and 5-year overall survival rates in the high-risk group were 73.5%, 47.0% and 33.8%, significantly lower than 96.4%, 79.8% and 64.4% in the low-risk group (χ²=42.378, P<0.05). Correlation analysis revealed that most lncRNAs were highly expressed in the high-risk group, and the expression levels were up-regulated with the progression of HCC. GO function and KEGG pathway enrichment analysis indicated that immune-related lncRNAs might play a biological role through cell cycle, splice, endocytosis, purine metabolism, RNA degradation and cell cycle signaling pathway.

Conclusions

Based on bioinformatics analysis, 20 immune-related lncRNAs are selected as the independent influencing factors. Immune-related lncRNAs play a pivotal role in the incidence and development of HCC, which may be taken as a potential marker for HCC screening and identifying patients who are sensitive to immunotherapy.

Key words: Carcinoma, hepatocellular, Long noncoding RNA, Immunity, Prognosis

Ensi Ma, Quanbao Zhang, Jianhua Li, Ruidong Li, Conghuan Shen, Yifeng Tao, Zhenyu Ma, Jing Zhao, Zhengxin Wang. Bioinformatics analysis on expression of immune-related lncRNAs in hepatocellular carcinoma and its prognostic value[J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2021, 10(05): 519-523.

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Figures/Tables 5

References 19

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序号	lncRNA	coef	HR值	P值	序号	lncRNA	coef	HR值	P值
1	GASAL1	0.470	2.542	0.047	11	AC040970.1	0.230	1.70	0.133
2	AL365203.2	0.388	1.956	0.007	12	AC009005.1	0.263	1.758	0.087
3	CYTOR	0.559	2.785	0.018	13	TMEM220-AS1	0.943	7.939	0.102
4	AL355574.1	-0.361	1.144	0.153	14	MIR4435-2HG	-0.771	0.919	0.027
5	AC023157.3	0.533	2.609	0.014	15	AL603839.3	0.404	2.316	0.069
6	ELFN1-AS1	0.364	1.774	0.001	16	DANCR	0.176	1.485	0.116
7	F11-AS1	-0.348	0.995	0.047	17	AL117336.3	0.820	3.633	0.001
8	AC015908.3	-1.233	0.900	0.032	18	MAPKAPK5-AS1	-0.552	1.034	0.065
9	AC145207.5	0.516	2.925	0.069	19	SNHG3	-0.301	1.033	0.077
10	MSC-AS1	0.525	2.242	0.001	20	AC073611.1	-0.582	1.103	0.094

序号	lncRNA	coef	HR值	P值	序号	lncRNA	coef	HR值	P值
1	GASAL1	0.470	2.542	0.047	11	AC040970.1	0.230	1.70	0.133
2	AL365203.2	0.388	1.956	0.007	12	AC009005.1	0.263	1.758	0.087
3	CYTOR	0.559	2.785	0.018	13	TMEM220-AS1	0.943	7.939	0.102
4	AL355574.1	-0.361	1.144	0.153	14	MIR4435-2HG	-0.771	0.919	0.027
5	AC023157.3	0.533	2.609	0.014	15	AL603839.3	0.404	2.316	0.069
6	ELFN1-AS1	0.364	1.774	0.001	16	DANCR	0.176	1.485	0.116
7	F11-AS1	-0.348	0.995	0.047	17	AL117336.3	0.820	3.633	0.001
8	AC015908.3	-1.233	0.900	0.032	18	MAPKAPK5-AS1	-0.552	1.034	0.065
9	AC145207.5	0.516	2.925	0.069	19	SNHG3	-0.301	1.033	0.077
10	MSC-AS1	0.525	2.242	0.001	20	AC073611.1	-0.582	1.103	0.094

变量	单因素分析			多因素分析
变量	HR值	95%CI	P值	HR值	95%CI	P值
年龄	0.998	0.980~1.016	0.827	0.997	0.978~1.015	0.717
性别	0.768	0.471~1.251	0.289	0.880	0.507~1.529	0.651
T分期	2.006	1.575~2.554	<0.001	1.899	0.751~4.799	0.175
N分期	2.305	0.562~9.458	0.246	2.508	0.424~14.829	0.310
M分期	4.386	1.371~14.038	0.012	1.440	0.378~5.488	0.594
病理分级	1.018	0.737~1.407	0.913	1.093	0.763~1.566	0.626
临床分期	2.095	1.612~2.723	<0.001	0.975	0.347~2.735	0.961
风险分层	1.381	1.266~1.506	<0.001	1.340	1.218~1.474	<0.001

变量	单因素分析			多因素分析
变量	HR值	95%CI	P值	HR值	95%CI	P值
年龄	0.998	0.980~1.016	0.827	0.997	0.978~1.015	0.717
性别	0.768	0.471~1.251	0.289	0.880	0.507~1.529	0.651
T分期	2.006	1.575~2.554	<0.001	1.899	0.751~4.799	0.175
N分期	2.305	0.562~9.458	0.246	2.508	0.424~14.829	0.310
M分期	4.386	1.371~14.038	0.012	1.440	0.378~5.488	0.594
病理分级	1.018	0.737~1.407	0.913	1.093	0.763~1.566	0.626
临床分期	2.095	1.612~2.723	<0.001	0.975	0.347~2.735	0.961
风险分层	1.381	1.266~1.506	<0.001	1.340	1.218~1.474	<0.001

定位	GO功能富集	标准化富集分数	P值
GO:0044839	G2期细胞进入M期的细胞周期过程	2.165 917 4	0.007
GO:0006338	在整个细胞分裂周期中发生的真核染色质动态结构变化	2.164 294 2	0.007
GO:1901987	调控细胞周期相变的频率、速率或程度	2.153 715 4	0.007
GO:1902749	调节细胞周期从G2期到M期的任何信号传导途径	2.151 192 0	0.009
GO:0045292	剪接体催化将同一RNA分子的两个片段连接	2.143 811 5	0.010
GO:0043044	真核染色质的动态结构变化	2.132 272 5	0.013
GO:0043130	与泛素选择性地和非共价地相互作用	2.115 901 7	0.018

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