Relationship between long non-coding RNA overexpression and poor prognostic of small cell lung cancer: a meta-analysis

doi:10.3969/j.issn.1004-583X.2021.08.002

Abstract

Abstract:

Objective This meta-analysis aims to come to a judgement the clinical value of long non-coding RNA(LncRNA) as a prognostic marker and potential clinicopathological value in small cell lung cancer(SCLC).Methods A comprehensive search of available literature resources included PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, CQVIP Journal Database, and Chinese Biology Medicine database to study the association between LncRNA overexpression and prognosis for SCLC up to May 2020. Related information was screened and extracted from the qualified studies by two independent reviewers. After the quality evaluation, the statistical software STATA12.0 was used to perform Meta quantitative comprehensive analysis. Results A total of thirteen case-control studies including 1302 SCLC patients were involved. The results revealed that: LncRNA overexpression patients had poor disease stage (Odds ratio, [OR]=6.10, 95% confidence interval[CI] (4.69, 7.93), P<0.01), poorer chemosensitivity (OR=5.17, 95%CI[3.44, 7.77], P<0.01), poorer survival status (OR=5.96, 95%CI[4.22, 8.40], P<0.01), overall survival (OS) (hazard ratio, [HR]=3.33, 95%CI[2.65, 4.19], P<0.01) and progression-free survival (PFS) (HR=4.88, 95%CI[3.00, 7.94), P<0.01).Conclusion LncRNA overexpression can predict poor survival and poor clinicopathological features in SCLC patients, may be a potential prognostic marker. However, the above conclusion still needs to be supported by more high-quality studies.

Key words: long non-coding RNA, overexpression, small cell lung cancer, prognosis, meta-analysis

CLC Number:

R734.2

Xu Ying, Han Caijuan, Fan Hong, Meng Zerong, Wang Xiaojun, Liu Hua. Relationship between long non-coding RNA overexpression and poor prognostic of small cell lung cancer: a meta-analysis[J]. Clinical Focus, 2021, 36(8): 685-690.

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URL: https://huicui.hebmu.edu.cn/EN/10.3969/j.issn.1004-583X.2021.08.002

https://huicui.hebmu.edu.cn/EN/Y2021/V36/I8/685

Figures/Tables 9

References 24

[1]	Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020[J]. CA Cancer J Clin, 2020, 70(1):7-30. doi: 10.3322/caac.v70.1 URL
[2]	薛世民, 贾娟, 沈华. 非小细胞肺癌组织中lncRNA HOTAIR的表达及临床意义[J]. 临床肿瘤学杂志, 2016, 21(9):780-784.
[3]	施宇佳, 张倩. 长链非编码RNA与肺癌的相关性研究进展[J]. 国际呼吸杂志, 2018, 38(1):56-59.
[4]	尹艳桃, 倪亚光, 覃丽. lncRNA在肿瘤中的表达及作用机制[J]. 中国生物化学与分子生物学报, 2015, 31(4):352-359.
[5]	郑志刚, 韩军, 廖继红. 小细胞肺癌组织中lncRNAMEG3的表达及其与预后关系的研究[J]. 临床肿瘤学杂志, 2017, 22(1).
[6]	刘丽丽, 吕立丽, 谷雪. lncRNA CASC11在小细胞肺癌中的表达及意义[J]. 癌症进展, 2019, 17(23):2847-2850.
[7]	周支瑞, 张天嵩, 李博, 等. 生存曲线中Meta分析适宜数据的提取与转换[J]. 中国循证心血管医学杂志, 2014, 6(03):243-247.
[8]	刘清, 丁利. 如何获取与验证生存曲线中生存数据进行Meta分析[J]. 中国循证医学杂志, 2019, 19(9):1124-1130.
[9]	Ioannidis JP, Trikalinos TA. The appropriateness of asymmetry tests for publication bias in meta-analyses: a large survey[J]. CMAJ, 2007, 176(8):1091. pmid: 17420491
[10]	蒋骞, 雷静, 周业琴, 等. 小细胞肺癌中LncRNA AC009336.24的表达及其与化疗耐药的关系[J]. 肿瘤防治研究, 2018, 45(4):225-229.
[11]	赵冲, 汪强, 武奋萍, 等. LncRNAAC010145.4与小细胞肺癌预后及化疗耐药的关系[J]. 肿瘤防治研究, 2017, 44(10):677-681.
[12]	曾丽, 杨濡溪, 王佳, 等. 小细胞肺癌组织中LncRNA EXOC7的表达及其与预后的关系[J]. 肿瘤, 2017, 37(3):269-274.
[13]	柳斌, 刘晓琴, 杨业, 等. 小细胞肺癌组织中LncRNA AK09398的表达及与预后的关系研究[J]. 中国肺癌杂志, 2016(11):754-759.
[14]	李文, 张玉高, 石敏, 等. LncRNA RP11-513G11.1在小细胞肺癌患者化疗耐药和预后测评中的作用[J]. 中国肿瘤生物治疗杂志, 2018(11):1166-1170.
[15]	李晓华, 代斌, 周婷, 等. LncRNA RP11-259P1.1在小细胞肺癌组织中的表达及其临床意义[J]. 中国肿瘤生物治疗杂志, 2018, 25(10):1042-1047.
[16]	Niu Y, Ma F, Huang W, et al. Long non-coding RNA TUG1 is involved in cell growth and chemoresistance of small cell lung cancer by regulating LIMK2b via EZH2[J]. Mol Cancer, 2017, 16(1):5. doi: 10.1186/s12943-016-0575-6 URL
[17]	Zhang Y, Li Y, Han L, et al. SBF2-AS1: An oncogenic lncRNA in small-cell lung cancer[J]. J Cell Biochem, 2019, 120(9):15422-15428. doi: 10.1002/jcb.v120.9 URL
[18]	Fu Y, Zhang P, Nan H, et al. LncRNA CASC11 promotes TGF-β1, increases cancer cell stemness and predicts postoperative survival in small cell lung cancer[J]. Gene, 2019, 704:91-96. doi: 10.1016/j.gene.2019.04.019 URL
[19]	Chen W, Hang Y, Xu W, et al. BLACAT1 predicts poor prognosis and serves as oncogenic lncRNA in small-cell lung cancer [J]. J Cell Biochem, 2018 Sep 11. [Epub ahead of print]
[20]	Chen S, Wu H, Lv N, et al. LncRNA CCAT2 predicts poor prognosis and regulates growth and metastasis in small cell lung cancer[J]. Biomed Pharmacother, 2016, 82:583-588. doi: 10.1016/j.biopha.2016.05.017 URL
[21]	Huang C, Liu S, Wang H, et al. LncRNA PVT1 overexpression is a poor prognostic biomarker and regulates migration and invasion in small cell lung cancer[J]. Am J Transl Res, 2016, 8(11):5025-5034.
[22]	Sun Y, Hu B, Wang Q, et al. Long non-coding RNA HOTTIP promotes BCL-2 expression and induces chemoresistance in small cell lung cancer by sponging miR-216a[J]. Cell Death Dis, 2018, 9(2):85. doi: 10.1038/s41419-017-0113-5 URL
[23]	马宣, 虎迎春, 孟凡珍, 等. 小细胞肺癌组织中LncRNA表达及其与预后关系的研究[J]. 宁夏医学杂志, 2019, 41(2):103-105.
[24]	王刚, 吕金燕, 王若雨, 等. 依托泊苷与拓扑替康对小细胞肺癌患者的远期预后及LincRNA水平的影响[J]. 检验医学与临床, 2018, 15(5):586-588.

纳入研究	国家	LncRNAs类型	检测方法	样本数 (高/低)	截点值	随访时间 (月)	结局指标	HR来源	NOS 评分
Jiang 2018^[10]	China	AC009336.24	qRT-PCR	67/53	mean ratio	66	①②③	HR/K-M	7
Zhao 2017^[11]	China	AC010145.4	qRT-PCR	36/31	7.225	60	①②③	HR/K-M	7
Zeng 2017^[12]	China	EXOC7	qRT-PCR	46/40	7.91	60	①②③	HR/K-M	7
Liu 2016^[13]	China	AK09398	qRT-PCR	66/52	mean ratio	66	①②③	K-M	6
Li X 2018^[14]	China	RP11-513G11.1	qRT-PCR	51/47	mean ratio	60	①②③	HR/K-M	7
Li XH 2018^[15]	China	RP11-259P1.1	qRT-PCR	81/77	8.52	72	①②③	HR/K-M	6
Niu 2017^[16]	China	TUG1	qRT-PCR	16/17	NA	40	①③	K-M	6
Zhang 2019^[17]	China	SBF2-AS1	qRT-PCR	48/48	median value	40	①③	HR	6
Fu 2019^[18]	China	CASC11	qRT-PCR	37/34	mean ratio	60	①	K-M	7
Chen 2018^[19]	China	BLACAT1	qRT-PCR	53/52	fold change	60	①③	HR	7
Chen 2016^[20]	China	CCAT2	qRT-PCR	56/56	median value	60	①③	HR	7
Huang 2016^[21]	China	PVT1	qRT-PCR	60/60	NA	96	①③	HR	7
Sun 2018^[22]	China	HOTTIP	qRT-PCR	68/50	fold-change	100	①③	K-M	6

纳入研究	国家	LncRNAs类型	检测方法	样本数 (高/低)	截点值	随访时间 (月)	结局指标	HR来源	NOS 评分
Jiang 2018^[10]	China	AC009336.24	qRT-PCR	67/53	mean ratio	66	①②③	HR/K-M	7
Zhao 2017^[11]	China	AC010145.4	qRT-PCR	36/31	7.225	60	①②③	HR/K-M	7
Zeng 2017^[12]	China	EXOC7	qRT-PCR	46/40	7.91	60	①②③	HR/K-M	7
Liu 2016^[13]	China	AK09398	qRT-PCR	66/52	mean ratio	66	①②③	K-M	6
Li X 2018^[14]	China	RP11-513G11.1	qRT-PCR	51/47	mean ratio	60	①②③	HR/K-M	7
Li XH 2018^[15]	China	RP11-259P1.1	qRT-PCR	81/77	8.52	72	①②③	HR/K-M	6
Niu 2017^[16]	China	TUG1	qRT-PCR	16/17	NA	40	①③	K-M	6
Zhang 2019^[17]	China	SBF2-AS1	qRT-PCR	48/48	median value	40	①③	HR	6
Fu 2019^[18]	China	CASC11	qRT-PCR	37/34	mean ratio	60	①	K-M	7
Chen 2018^[19]	China	BLACAT1	qRT-PCR	53/52	fold change	60	①③	HR	7
Chen 2016^[20]	China	CCAT2	qRT-PCR	56/56	median value	60	①③	HR	7
Huang 2016^[21]	China	PVT1	qRT-PCR	60/60	NA	96	①③	HR	7
Sun 2018^[22]	China	HOTTIP	qRT-PCR	68/50	fold-change	100	①③	K-M	6

临床特征	纳入研究数(篇)	OR(95%CI)	P值	异质性检验结果		效应模型
临床特征	纳入研究数(篇)	OR(95%CI)	P值	I²(%)	P值	效应模型
年龄	12^{[10-17, 19-22]}	1.06 (0.84, 1.33)	0.623	0.0	0.973	固定
性别	12^{[10-17, 19-22]}	1.05 (0.83, 1.32)	0.701	0.0	0.939	固定
吸烟史	9^{[10-17, 19-21]}	1.11 (0.80, 1.551)	0.527	27	0.204	固定
疾病分期	12^{[10-17, 19-22]}	6.10 (4.69, 7.93 )	0.000	0.0	0.990	固定
淋巴结转移	10^{[10-17, 19-21]}	4.91 (3.17, 7.61 )	0.000	61.9	0.005	随机
远处转移	10^{[10-17, 19-21]}	5.22 (3.31, 8.24 )	0.000	59.8	0.008	随机
化疗敏感度	7^{[10-15, 22]}	5.17 (3.44, 7.77)	0.000	31.7	0.186	固定
生存状态	7^{[10-11, 13-16, 22]}	5.96 (4.22, 8.40)	0.000	0.589	0.589	随机

临床特征	纳入研究数(篇)	OR(95%CI)	P值	异质性检验结果		效应模型
临床特征	纳入研究数(篇)	OR(95%CI)	P值	I²(%)	P值	效应模型
年龄	12^{[10-17, 19-22]}	1.06 (0.84, 1.33)	0.623	0.0	0.973	固定
性别	12^{[10-17, 19-22]}	1.05 (0.83, 1.32)	0.701	0.0	0.939	固定
吸烟史	9^{[10-17, 19-21]}	1.11 (0.80, 1.551)	0.527	27	0.204	固定
疾病分期	12^{[10-17, 19-22]}	6.10 (4.69, 7.93 )	0.000	0.0	0.990	固定
淋巴结转移	10^{[10-17, 19-21]}	4.91 (3.17, 7.61 )	0.000	61.9	0.005	随机
远处转移	10^{[10-17, 19-21]}	5.22 (3.31, 8.24 )	0.000	59.8	0.008	随机
化疗敏感度	7^{[10-15, 22]}	5.17 (3.44, 7.77)	0.000	31.7	0.186	固定
生存状态	7^{[10-11, 13-16, 22]}	5.96 (4.22, 8.40)	0.000	0.589	0.589	随机

结局指标	影响因素	亚组	纳入研究数 (篇)	异质性检验结果		效应模型	Meta分析结果	P值
结局指标	影响因素	亚组	纳入研究数 (篇)	I²(%)	P值	效应模型	HR(95%CI)	P值
OS	样本量	≥100例	7^{[10, 13, 15, 19, 21-22]}	10.9	0.346	固定	3.20(2.59, 3.95)	<0.01
		<100例	6^{[11-12, 14, 16-18]}	64.9	0.014	随机	3.20(1.99, 5.13)	<0.01
	生存质量分析方法	单因素分析	9^{[10-12, 14-15, 17, 19-21]}	35.3	0.136	固定	3.43(2.85, 4.14)	<0.01
		多因素分析	9^{[10-12, 14-15, 17, 19-21]}	14.1	0.317	固定	2.51(2.05, 3.06)	<0.01
	提取资料	从文中提取资料	9^{[10-12, 14-15, 17, 19-21]}	35.3	0.136	固定	3.43(2.85, 4.14)	<0.01
		从生存曲线提取资料	4^{[13, 16, 18, 22]}	60.8	0.006	随机	2.59(1.44, 4.69)	0.002
PFS	样本量	≥100例	3^{[10, 13, 15]}	80.6	0.006	随机	3.88(1.87, 8.05)	<0.01
		<100例	3^{[11-12, 14]}	0.0	0.692	固定	6.53(4.15, 10.29)	<0.01