Long noncoding RNA DANCR in various cancers: a meta-analysis and bioinformatics

Figures & data

Figure 1 Flow diagram of the study search and selection process in the meta-analysis.

Table 1 Summary of the 11 included articles

Study	Region	Tumor type	Sample size	DANCR expression		Survival information	HR	Laboratory method
				High	Low
Li et al, 2017^Citation17	China	glioma	86	43	43	os	0.55(0.27–1.13)(C)	qRT-PCR
Liu et al, 2015^Citation18	China	CRC	104	52	52	os	0.467(0.141–0.867)(R)	qRT-PCR
Mao et al, 2017^Citation16	China	GC	60	30	30	NA	NA	qRT-PCR
Hao et al, 2017^Citation19	China	GC	118	46	72	os	0.52 (0.23–1.17)(C)	qRT-PCR
Pan et al, 2018^Citation20	China	GC	65	40	25	NA	NA	qRT-PCR
Sha et al, 2017^Citation21	China	TNBC	63	32	31	os	0.58 (0.32–1.54)(C)	qRT-PCR
Wang et al, 2018^Citation22	China	NSCLC	128	64	64	os	0.47(024–0.94)(C)	qRT-PCR
Wen et al, 2018^Citation23	China	NPC	86	43	43	os	0.76(0.42–1.39)(C)	qRT-PCR
Yang et al, 2018^Citation24	China	glioma	82	41	41	os	0.561(0.287–0.892)(R)	qRT-PCR
Yong et al, 2018^Citation25	China	CRC	47	26	21	NA	NA	qRT-PCR
Zhan et al, 2018^Citation26	China	BC	106	70	36	NA	NA	qRT-PCR

Abbreviations: BC, Urothelial carcinoma of the bladder; C, HR was estimated by curve; CRC, colorectal cancer; GC, Gastric cancer; NPC, nasopharyngeal carcinoma; NA, Not available; NSCLC, non-small cell lung cancer; R, HR was reported; TNBC, Triple negative breast cancer.

Figure 2 Forest plot of studies evaluating (A) the relationship between DANCR expression and overall survival (OS) rate, (B) sensitivity analysis for OS, and (C) Begg’s publication bias plots of OS.

Table 2 The association between DANCR expression and clinical features

Clinicopathological parameters	Studies (n)	Patients (n)	OR (95% CI)	P-value	Heterogeneity
					I2 (%)	Ph	Model
Age	6	484	1.27 (0.87, 1.86)	0.219	0	0.649	Fixed
Gender	5	413	1.29(0.84, 1.96)	0.754	0	0.754	Fixed
Tumor size	5	398	0.72(0.36–1.42)	0.354	61.4	0.035	Random
Differentiation	4	325	0.36, (0.15, 0.68)	0.021	69.2	0.0021	Random
TNM stage	5	378	0.22, (0.14, 0.35)	0.0001	0	0.682	Fixed
LNM	5	398	0.21, (0.13, 0.35)	0.0001	3.3	0.388	Fixed

Note: Bold figures indicate statistically significant P<0.05.

Abbreviations: DM, distant metastasis; LNM, lymph node metastasis.

Figure 3 Forest plot of studies evaluating the relationship between DANCR expression and (A) differentiation, (B) lymph node metastasis, (C) stage, (D) tumor size, (E) gender, and (F) age.

Figure 4 Begg’s publication bias plots evaluating the relationship between DANCR expression and (A) differentiation (B) lymph node metastasis (C) Stage (D) Tumor size (E) Gender (F) Age.

Figure 5 The expression levels of DANCR in four kinds of cancer tissues and normal tissues. “*”|Log₂FC|>1 and P<0.01.

Abbreviations: LGG, brain lower grade glioma; THYM, Thymoma; DLBC, Diffuse Large B-cell Lymphoma; CHOL, Cholangiocarcinoma.

Figure 6 (A) Survival curves of DANCR are plotted for all kinds of cancers from TCGA dataset (n=10041). (B) The survival curve of patients with ACC. (C) The survival curve of patients with LIHC (D) the survival curve of patients with SKCM.

Abbreviations: ACC, Adrenocortical carcinoma; LIHC, Liver hepatocellular carcinoma; SKCM, Skin Cutaneous Melanoma; TCGA, The Cancer Genome Atlas.

Table S1 Reporting recommendations for tumor marker prognostic studies (REMARK) checklist

INTRODUCTION
1	State the marker examined, the study objectives, and any pre-specified hypotheses.
MATERIALS AND METHODS
Patients
2	Describe the characteristics (for example, disease stage or co-morbidities) of the study patients, including their source and inclusion and exclusion criteria.
3	Describe treatments received and how chosen (for example, randomized or rule-based).
Specimen characteristics
4	Describe type of biological material used (including control samples) and methods of preservation and storage.
Assay methods
5	Specify the assay method used and provide (or reference) a detailed protocol, including specific reagents or kits used, quality control procedures, reproducibility assessments, quantitation methods, and scoring and reporting protocols. Specify whether and how assays were performed blinded to the study endpoint.
Study design
6	State the method of case selection, including whether prospective or retrospective and whether stratification or matching (for example, by stage of disease or age) was used. Specify the time period from which cases were taken, the end of the follow-up period, and the median follow-up time.
7	Precisely define all clinical endpoints examined.
8	List all candidate variables initially examined or considered for inclusion in models.
9	Give rationale for sample size; if the study was designed to detect a specified effect size, give the target power and effect size.
Statistical analysis methods
10	Specify all statistical methods, including details of any variable selection procedures and other model-building issues, how model assumptions were verified, and how missing data were handled.
11	Clarify how marker values were handled in the analyses; if relevant, describe methods used for cutpoint determination.
RESULTS
Data
12	Describe the flow of patients through the study, including the number of patients included in each stage of the analysis (a diagram may be helpful) and reasons for dropout. Specifically, both overall and for each subgroup extensively examined report the number of patients and the number of events.
13	Report distributions of basic demographic characteristics (at least age and sex), standard (disease-specific) prognostic variables, and tumor marker, including numbers of missing values.
Analysis and presentation
14	Show the relation of the marker to standard prognostic variables.
15	Present univariable analyses showing the relation between the marker and outcome, with the estimated effect (for example, hazard ratio and survival probability). Preferably provide similar analyses for all other variables being analyzed. For the effect of a tumor marker on a time-to-event outcome, a Kaplan-Meier plot is recommended.
16	For key multivariable analyses, report estimated effects (for example, hazard ratio) with confidence intervals for the marker and, at least for the final model, all other variables in the model.
17	Among reported results, provide estimated effects with confidence intervals from an analysis in which the marker and standard prognostic variables are included, regardless of their statistical significance.
18	If done, report results of further investigations, such as checking assumptions, sensitivity analyses, and internal validation.
DISCUSSION
19	Interpret the results in the context of the pre-specified hypotheses and other relevant studies; include a discussion of limitations of the study.
20	Discuss implications for future research and clinical value.

Table S2 Assessing the quality of included studies based on reporting recommendations for tumor marker prognostic studies (REMARK) guideline

Study	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15	Q16	Q17	Q18	Q19	Q20	Tatal (%)
Li et al, 2017^Citation1	✓	✓	✓	✓	✓	✕	✕	✕	✕	✕	✓	✓	✓	✓	✓	✓	✓	✕	✓	✓	70
Liu et al, 2015^Citation2	✓	✓	✓	✓	✓	✕	✕	✕	✕	✕	✓	✓	✓	✓	✓	✓	✓	✕	✓	✓	70
Mao et al, 2017^Citation3	✓	✓	✓	✓	✓	✕	✕	✕	✕	✕	✓	✓	✓	✕	✕	✓	✓	✓	✓	✓	65
Hao et al, 2017^Citation4	✓	✓	✓	✓	✓	✕	✕	✕	✕	✕	✓	✓	✓	✓	✓	✓	✓	✕	✓	✓	70
Pan et al, 2018^Citation5	✓	✓	✓	✓	✓	✕	✕	✕	✕	✕	✓	✓	✓	✕	✕	✓	✓	✓	✓	✓	65
Sha et al, 2017^Citation6	✓	✓	✓	✓	✓	✕	✕	✕	✕	✕	✓	✓	✓	✓	✓	✓	✓	✕	✓	✓	70
Wang et al, 2018^Citation7	✓	✓	✓	✓	✓	✕	✕	✕	✕	✕	✓	✓	✓	✓	✓	✓	✓	✕	✓	✓	70
Wen et al, 2018^Citation8	✓	✓	✓	✓	✓	✕	✕	✕	✕	✕	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	75
Yang et al, 2018^Citation9	✓	✓	✓	✓	✓	✕	✕	✕	✕	✕	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	75
Yong et al, 2018^Citation10	✓	✓	✓	✓	✓	✕	✕	✕	✕	✕	✓	✓	✓	✓	✕	✕	✓	✓	✓	✓	65
Zhan et al, 2018^Citation11	✓	✓	✓	✓	✓	✕	✕	✕	✕	✕	✓	✓	✓	✓	✕	✕	✓	✕	✓	✓	60

Table S3 Risk of bias in individual studies

Study	Selection bias	Performance bias	Detection bias	Attrition bias	Reporting bias
Li et al, 2017^Citation1	H	H	H	L	H
Liu et al, 2015^Citation2	H	H	H	L	H
Mao et al, 2017^Citation3	H	L	L	U	H
Hao et al, 2017^Citation4	H	H	H	L	H
Pan et al, 2018^Citation5	H	L	L	U	H
Sha et al, 2017^Citation6	H	H	H	L	H
Wang et al, 2018^Citation7	H	H	H	L	H
Wen et al, 2018^Citation8	H	H	H	L	L
Yang et al, 2018^Citation9	H	L	L	U	L
Yong et al, 2018^Citation10	H	H	H	L	H
Zhan et al, 2018^Citation11	H	L	L	U	H

Abbreviations: H, high risk of bias; L, low risk of bias; U, unclear bias.

Li J, Zhou L. Overexpression of lncRNA DANCR positively affects progression of glioma via activating Wnt/β-catenin signaling. Biomed Pharmacother. 2018;102:602–607. doi:10.1016/j.biopha.2018.03.11629602127

 PubMed Web of Science ®Google Scholar

Liu Y, Zhang M, Liang L, et al. Over-expression of lncRNA DANCR is associated with advanced tumor progression and poor prognosis in patients with colorectal cancer. Int J Clin Exp Pathol. 2015;8(9):11480.26617879

 PubMed Web of Science ®Google Scholar

Mao Z, Li H, Du B, et al. LncRNA DANCR promotes migration and invasion through suppression of lncRNA-LET in gastric cancer cells. Biosci Rep. 2017;37:BSR20171070. doi:10.1042/BSR2017107028951520

 PubMed Web of Science ®Google Scholar

Hao YP, Qiu JH, Zhang DB, et al. Long non-coding RNA DANCR, a prognostic indicator, promotes cell growth and tumorigenicity in gastric cancer. Tumor Biol. 2017;39(6):101042831769979. doi:10.1177/1010428317699798

 Google Scholar

Pan L, Liang W, Gu J, et al. Long noncoding RNA DANCR is activated by SALL4 and promotes the proliferation and invasion of gastric cancer cells. Oncotarget. 2018;9(2):1915–1930. doi:10.18632/oncotarget.2301929416741

 PubMedGoogle Scholar

Sha S, Yuan D, Liu Y, et al. Targeting long non-coding RNA DANCR inhibits triple negative breast cancer progression. Biol Open. 2017;6(9):bio.023135. doi:10.1242/bio.023135

 PubMed Web of Science ®Google Scholar

Wang S, Jiang M. The long non-coding RNA-DANCR exerts oncogenic functions in non-small cell lung cancer via miR-758-3p. Biomed Pharmacother. 2018;103:94–100. doi:10.1016/j.biopha.2018.03.05329635134

 PubMed Web of Science ®Google Scholar

Wen X, Liu X, Mao YP, et al. Long non-coding RNA DANCR stabilizes HIF-1α and promotes metastasis by interacting with NF90/NF45 complex in nasopharyngeal carcinoma. Theranostics. 2018;8(20):5676–5689. doi:10.7150/thno.2853830555573

 PubMed Web of Science ®Google Scholar

Yang JX, Sun Y, Gao L, et al. Long non-coding RNA DANCR facilitates glioma malignancy by sponging miR-33a-5p. Neoplasma. 2018;65:790–798. doi:10.4149/neo_2018_170724N49829940760

 PubMed Web of Science ®Google Scholar

Wang Y, Lu Z, Wang N, et al. Long noncoding RNA DANCR promotes colorectal cancer proliferation and metastasis via miR-577 sponging. Exp Mol Med. 2018;50(5):57. doi:10.1038/s12276-018-0082-529717105

 PubMedGoogle Scholar

Zhan Y, Chen Z, Li Y, et al. Long non-coding RNA DANCR promotes malignant phenotypes of bladder cancer cells by modulating the miR-149/MSI2 axis as a ceRNA. J Exp Clin Cancer Res. 2018;37(1). doi:10.1186/s13046-018-0921-1.

 Google Scholar

Li J, Zhou L. Overexpression of lncRNA DANCR positively affects progression of glioma via activating Wnt/β-catenin signaling. Biomed Pharmacother. 2018;102:602–607. doi:10.1016/j.biopha.2018.03.11629602127

 PubMed Web of Science ®Google Scholar

Liu Y, Zhang M, Liang L, et al. Over-expression of lncRNA DANCR is associated with advanced tumor progression and poor prognosis in patients with colorectal cancer. Int J Clin Exp Pathol. 2015;8(9):11480.26617879

 PubMed Web of Science ®Google Scholar

Mao Z, Li H, Du B, et al. LncRNA DANCR promotes migration and invasion through suppression of lncRNA-LET in gastric cancer cells. Biosci Rep. 2017;37:BSR20171070. doi:10.1042/BSR2017107028951520

 PubMed Web of Science ®Google Scholar

Hao YP, Qiu JH, Zhang DB, et al. Long non-coding RNA DANCR, a prognostic indicator, promotes cell growth and tumorigenicity in gastric cancer. Tumor Biol. 2017;39(6):101042831769979. doi:10.1177/1010428317699798

 Google Scholar

Pan L, Liang W, Gu J, et al. Long noncoding RNA DANCR is activated by SALL4 and promotes the proliferation and invasion of gastric cancer cells. Oncotarget. 2018;9(2):1915–1930. doi:10.18632/oncotarget.2301929416741

 PubMedGoogle Scholar

Sha S, Yuan D, Liu Y, et al. Targeting long non-coding RNA DANCR inhibits triple negative breast cancer progression. Biol Open. 2017;6(9):bio.023135. doi:10.1242/bio.023135

 PubMed Web of Science ®Google Scholar

Wang S, Jiang M. The long non-coding RNA-DANCR exerts oncogenic functions in non-small cell lung cancer via miR-758-3p. Biomed Pharmacother. 2018;103:94–100. doi:10.1016/j.biopha.2018.03.05329635134

 PubMed Web of Science ®Google Scholar

Wen X, Liu X, Mao YP, et al. Long non-coding RNA DANCR stabilizes HIF-1α and promotes metastasis by interacting with NF90/NF45 complex in nasopharyngeal carcinoma. Theranostics. 2018;8(20):5676–5689. doi:10.7150/thno.2853830555573

 PubMed Web of Science ®Google Scholar

Yang JX, Sun Y, Gao L, et al. Long non-coding RNA DANCR facilitates glioma malignancy by sponging miR-33a-5p. Neoplasma. 2018;65:790–798. doi:10.4149/neo_2018_170724N49829940760

 PubMed Web of Science ®Google Scholar

Wang Y, Lu Z, Wang N, et al. Long noncoding RNA DANCR promotes colorectal cancer proliferation and metastasis via miR-577 sponging. Exp Mol Med. 2018;50(5):57. doi:10.1038/s12276-018-0082-529717105

 PubMedGoogle Scholar

Zhan Y, Chen Z, Li Y, et al. Long non-coding RNA DANCR promotes malignant phenotypes of bladder cancer cells by modulating the miR-149/MSI2 axis as a ceRNA. J Exp Clin Cancer Res. 2018;37(1). doi:10.1186/s13046-018-0921-1.

 Google Scholar