The Clinical Prognostic Value of lncRNA SBF2-AS1 in Cancer Patients: A Meta-Analysis

Abstract

Background:

The mortality and recurrence of patients with cancer is of high prevalence. SET-binding factor 2 (SBF2) antisense RNA1 (lncRNA-SBF2-AS1) is a promising long non-coding RNA. There is increasing evidence that SBF2-AS1 is abnormally expressed in various tumors and is associated with cancer prognosis. However, the identification of the effect of lncRNA SBF2-AS1 in tumors remains necessary.

Materials and Methods:

Up to November 2, 2020, electronic databases, including PubMed, Cochrane Library, EMBASE, Medline, and Web of Science, were searched. The results were evaluated by pooled odds ratios (ORs) and hazard ratios (HRs) with 95% confidence intervals (CIs).

Results:

A total of 11 literatures on cancer patients were included for the present meta-analysis. The combined results revealed that high expression of SBF2-AS1 was significantly associated with unfavorable overall survival (OS) (HR = 1.48, 95% CI: 1.34-1.62, P < 0.00001) in a variety of cancers. In additional, the increase in SBF2-AS1 expression was also correlated with tumor size ((larger vs. smaller) OR = 2.34, 95% CI: 1.47-3.70, P = 0.0003), advanced TNM stage ((III/IV vs. I/II) OR = 2.78, 95% CI: 1.75-4.41, P < 0.0001), lymph node metastasis ((Positive vs. Negative) OR = 3.06, 95% CI: 1.93-4.86, P < 0.00001), and histological grade ((poorly vs. well/moderately) OR = 2.58, 95% CI: 1.47-4.52, P = 0.001) in patients with cancer. Furthermore, The Cancer Genome Atlas (TCGA) dataset valuated that SBF2-AS1 was upregulated in a variety of tumors, and predicted the worse prognosis.

Conclusions:

Our results of this meta-analysis demonstrate that high SBF2-AS1 expression may become a potential target for predicting the prognosis of human cancers.

Keywords

cancer overall survival prognosis long non-coding RNA SBF2-AS1

Introduction

Cancer is one of the major leading causes of death worldwide.¹ However, the exact mechanism of cancer remains under investigation. Furthermore, the surveillance of patients with early-stage cancer remains difficult. Hence, many cancer cases are identified at the advance-stage with dismal prognosis. According to the National Cancer Center in 2018, 1,735,350 new cancer cases and 609,640 deaths are expected, indicating that cancer remains as a primary public health problem and challenge.² Therefore, early diagnosis and interventions have become a vital link for improving the overall survival of cancer patients.

Noncoding RNAs, which usually include microRNAs (miRNAs), small molecular interfering RNAs (siRNAs) and long non-coding RNAs (lncRNAs), do not encode proteins.³ Long non-coding RNAs are a class of non-coding transcripts with more than 200 nucleotides in molecular length.⁴ In recent years, lncRNAs have been found to play tremendous regulatory roles in a variety of diseases, especially in the biological process of malignant tumors, including differentiation, migration, apoptosis, and dose compensation effect.^5,6 Studies have shown that lncNONHSAAT081507.1 (LINC81507) plays an inhibitory role in the progression of non-small cell lung cancer(NSCLC), and acts as a therapeutic target and potential biomarker for the diagnosis and prognosis of NSCLC.⁷ In cervical cancer, SIP1 expression is upregulated by lncRNA NORAD to promote the proliferation and invasion of cervical cancer cells.⁸ Accumulating studies in the field of oncology have indicated that the aberrant expression of lncRNA is associated with tumorigenesis, metastasis, and prognosis in cancers.⁹ Therefore, lncRNAs have attracted comprehensive attention as a potential indicator for cancer patients.^10
-12

SET-binding factor 2 (SBF2) antisense RNA1 (lncRNA-SBF2-AS1), which is 2708 bp in length, can direct the posttranslational modification of nucleolar small-molecule RNA, and has a link with the prognosis and development of cancer.¹³ Recently, accumulating evidences from fundamental and clinical researches have revealed that lncRNA SBF2-AS1 takes part in tumorigenesis, and is at the risk of poor prognostic effect in a variety of cancer types.^14,15 In non-small-cell lung cancer, the study conducted by Chen et al revealed that the high expression of SBF2-AS1 is a significant risk factor correlated to poor overall survival and advanced clinical stage.¹⁶ However, no systematic meta-analysis could explain the prognostic value of SBF2-AS1 in these cancers. Thus, a meta-analysis was performed to investigate the clinical prognostic value of lncRNA SBF2-AS1 in cancer patients.

Materials and Methods

Literature Search

The present meta-analysis, with respect to the prognosis, was performed on the basis of the guidelines on the Preferred Reporting Item for System Reviews and Meta-Analyses (PRISMA) and the meta-analysis of observational studies in epidemiology (MOOSE) statement. A full-scale and systematic literature retrieval was conducted. Two different reviewers independently completed the search on the electronic databases, which included PubMed, Cochrane Library, EMBASE, Medline, and Web of Science, and identified the relevant articles. The following search terms were used: “long noncoding RNA SBF2-AS1,” “lncRNA SBF2-AS1,” “SET-binding factor 2 antisense RNA1,” “tumor,” “cancer,” “carcinoma,” and “prognosis.” In case of any discrepancy, a third reviewer would join in the discussion.

Inclusion and Exclusion Criteria

The studies were included based on all of the following criteria: (1) the SBF2-AS1 expression must be detected in human cancer tissues; (2) the correlation between the prognostic and clinicopathologic features of lncRNA SBF2-AS1 in patients with malignant tumor was described; (3) there was sufficient information to extract the pooled hazard risk (HR) and 95% confidence interval (CI).

The studies were excluded based on the following criteria: (1) studies without the clinical outcomes and prognosis of cancers; (2) duplicate publications; (3) non-human studies, letters, case reports, letters, review articles and other studies without survival data.

Data Extraction and Quality Assessment

The data extraction was independently completed by investigators, and reached a consensus. For all the included studies, the following information was successively collected: (1) generally necessary elements, including author, region, year of publication, tumor size, type of cancers, follow-up time, detection method and cut-off value, and total number of patients; (2) the clinicopathological characteristics, including lymph node metastasis (LNM), distant metastasis (DM), differentiation, histological grade and tumor stage; (3) the relationship between the expression level of SBF2-AS1 and overall survival; (4) survival curves.

If only the Kaplan-Meier survival curves are available, the investigators obtained the information from the graph survival plot using Engauge Digitizer V4.1 (http://digitizer.sourceforge.net/), in order to estimate the survival time and HR. If the HRs, 95% CIs and P-values from the multivariate for OS were reported, there were directly collected. The Newcastle–Ottawa Quality Assessment Scale (NOS) score, which ranged within 0-9 points, was used to evaluate the quality of each eligible study. A study with a NOS score over 6 was considered of high quality.

Statistical Analysis

The meta-analysis was performed using the Review Manager (RevMan) 5.3 software and Stata SE 12.0 software. The pooled HRs with the corresponding 95%CIs were used to estimate the link between the SBF2-AS1 expression and clinical prognosis of cancer patients. The lncRNA SBF2-AS1 expression with the clinicopathological features were assessed by calculating the aggregated odds ratios (ORs) with the 95% CIs. The significance of heterogeneity among the studies was examined using the I ² statistics. The fixed-effects model was selected for the data analysis when I ² < 50%. Otherwise, the random-effects model was applied for the data analysis. A subgroup and sensitivity analysis were used when the heterogeneity between studies was significant and the source of heterogeneity failed to be identified (I ² > 50%). The assessment for publication bias was utilized by using Begg’s test. A P < 0.05 was considered statistically significant.

Results

Characteristics and Basic Information of the Included Literature

After the preliminary online search, the investigators retrieved a total of 103 relevant literatures from the electronic databases. After the removal of duplicates, a total of 49 studies were excluded. Then, after thoroughly screening the titles and abstracts, 31 publications were subjected for inclusion. After carefully assessing the full texts, 11 literatures published between 2015 and 2020 were enrolled for the present meta-analysis. The screening process for the literature presented in Figure 1. These eligible literatures included a total of 984 patients. In the present meta-analysis, a variety of tumors types was reported, which included pancreatic cancer,¹⁷ hepatocellular carcinoma,^18,19 esophageal squamous cell carcinoma,^20,21 colorectal cancer,²² non-small cell lung cancer,^16,23 gastric cancer,²⁴ papillary thyroid cancer,²⁵ and breast cancer.²⁶ The expression of SBF2-AS1 in these included studies was quantified using real-time fluorescent PCR (qRT-PCR). The median was selected as the cut-off value to distinguish between the high expression and low expression of SBF2-AS1. A total of 9 eligible literatures used OS to estimate the patient survival. The detailed clinical characteristics of the 11 published literatures are summarized in Table 1. The NOS scores for all included studies were ≥6.

Figure 1.

Flow diagram of the literatures selection procedure in this meta-analysis.

Table 1.

The Main Characteristics of the Eligible Literatures Included in the Meta-Analysis.

Study	Region	Tumor type	Sample size	TNM stage	SBF2-AS1 expression		Cut-off value	Detection method	Outcome measure	NOS
Study	Region	Tumor type	Sample size	TNM stage	High	Low	Cut-off value	Detection method	Outcome measure	NOS
Hua, et al 2019¹⁷	China	PC	82	I-IV	39	43	Median	qRT-PCR	OS	6
Zhang, et al 2018¹⁸	China	HCC	134	I-IV	68	66	Median	qRT-PCR	OS	7
Chen, et al 2017²⁰	China	ESCC	60	NA	30	30	Median	qRT-PCR	OS	7
Chen, et al 2019²²	China	CRC	61	I-IV	35	26	Median	qRT-PCR	OS	8
Li, et al 2018¹⁹	China	HCC	184	NA	92	92	Median	qRT-PCR	OS	6
Zhao, et al 2016²³	China	NSCLC	174	NA	87	87	Median	qRT-PCR	OS	7
Chen, et al 2020¹⁶	China	NSCLC	56	NA	28	28	Median	qRT-PCR	OS	8
He, et al 2019²⁴	China	GC	60	I-IV	30	30	Median	qRT-PCR	NA	8
Zhang, et al 2020²¹	China	ESCC	50	NA	25	25	Median	qRT-PCR	OS	8
Wen, et al 2020²⁵	China	PTC	73	NA	36	37	Median	qRT-PCR	OS	7
Xia, et al 2020²⁶	China	BRCA	50	I-IV	39	11	Median	qRT-PCR	NA	6

Abbreviations: NA, not available; OS, overall survival; NOS: Newcastle-Ottawa quality assessment scale; GC, gastric cancer; CRC, colorectal cancer; HCC, hepatocellular carcinoma; BRCA, breast cancer; LC, liver cancer; PTC, Papillary thyroid cancer; PC, pancreatic cancer; NSCLC, non-small cell lung cancer; ESCC, esophageal squamous cell carcinoma.

The Expression of SBF2-AS1 Was Significantly Correlated With OS

Figure 2 presents the forest plot results for the lncRNA SBF2-AS1 expression and patient outcomes. A total of 9 datasets of SBF2-AS1 expression for OS, which included 874 cancer patients, with the reported HR and 95% CI, were included for the present meta-analysis. The fix-effect model was adopted to calculate the pooled HR due to no significant heterogeneous among the literatures (I² = 46%, P = 0.07). Obviously, the aggregated data indicated that the elevated SBF2-AS1 expression levels were significantly correlated with poor overall survival (OS) (HR = 1.60, 95%CI: 1.37-1.88, P < 0.00001). In order to investigate the pooled HR among the different cancer types, the investigators conducted 2 subgroups (digestive system tumors and non-digestive system tumors). The subgroup for the different of cancer types indicated that the high SBF2-AS1 expression was strongly correlated to poor OS in both subgroups (digestive system tumors: pooled HR = 1.65 95%CI: 1.32-2.07, I² = 56%, P < 0.00001; non-digestive system tumors: pooled HR = 1.70 95%CI: 1.20-2.41, I² = 39%, P = 0.003) (Figure 2). This means that the cancer patients with low SBF2-AS1 expression may have a better prognostic outcome. Furthermore, the investigators performed a subgroup meta-analysis stratified by the analysis method and sample size, and this suggested that the high SBF2-AS1expression is obviously correlated with poor OS (Figure 3).

Figure 2.

Forest plots of the included literatures evaluating the association between SBF2-AS1 expression with overall survival (OS).

Figure 3.

Forest plots of the included literatures evaluating the association between SBF2-AS1 expression with overall survival: (A) stratified by analysis type, (B) stratified by sample size.

The Association Between SBF2-AS1 and the Clinicopathologic Characteristics

According to the evaluation of the 11 included eligible literatures that contained detailed clinicopathologic characteristics, it was observed that the elevated expression of SBF2-AS1 positively correlated to the tumor size ((larger vs. smaller) OR = 2.34, 95%CI = 1.47-3.70, P = 0.0003), advanced TNM stage ((III/IV vs. I/II) OR = 2.78, 95%CI = 1.75-4.41, P < 0.0001), lymph node metastasis ((Positive vs. Negative) OR = 3.06, 95%CI = 1.93-4.86, P < 0.00001), and histological grade ((poorly vs. well/moderately) OR = 2.58, 95%CI = 1.47-4.52, P = 0.001) (Figure 4). Furthermore, for the other clinical parameters, including age, gender, and differentiation, no significant correlation was found between the SBF2-AS1 expression and the parameters (Figure 5). The details are presented in Table 2.

Figure 4.

Forest plots of the included literatures evaluating the correlation between SBF2-AS1 expression and clinicopathological characteristics: (A) tumor size; (B) TNM stage; (C) lymph node metastasis; (D) histological grade.

Figure 5.

Forest plots of the included literatures evaluating the correlation between SBF2-AS1 expression and clinicopathological characteristics: (A) age; (B) gender; (C) distant metastasis.

Table 2.

Summary of Correlation Between SBF2-AS1 Expression and Clinicopathological Characteristics of Cancers.

Clinicopathological parameters	Studies	Patients	OR (95% CI)	P-value	Heterogeneity
Clinicopathological parameters	Studies	Patients	OR (95% CI)	P-value	I²	P-value	Model
Age (older vs. younger)	6	561	1.00 (0.71, 1.40)	0.99	0%	0.69	Fixed
Gender (male vs. female)	5	511	0.99 (0.70, 1.41)	0.97	6%	0.38	Fixed
Tumor size (larger vs. smaller)	4	345	2.34 (1.47, 3.70)	0.0003	47%	0.13	Fixed
Differentiation (poor vs. well)	2	216	1.35 (0.26, 6.85)	0.72	85%	0.01	Random
TNM stage (III+IV vs. I+II)	5	377	2.78 (1.75, 4.41)	<0.0001	0%	0.50	Fixed
LNM (Positive vs. Negative)	4	356	3.06 (1.93, 4.86)	<0.00001	0%	0.83	Fixed
Histological grade (Poorlyvs. Well/moderately)	2	224	2.58 (1.47, 4.52)	0.001	0%	0.51	Fixed

Abbreviations: LNM, Lymph node metastasis; DM, Distant metastasis.

Boldface values indicate significant results if P < 0.01.

Sensitivity Analysis and Publication Bias

In order to determine the impact of a single study to the overall results for OS, a sensitivity analysis was performed. The results revealed that the pooled HR was not altered by removing one study at a time, illustrating that these results are considerably stable and reasonable (Figure 6(a)). In additional, according to the Begg’s test, the publication bias of the included studies in the present meta-analysis for OS was not obvious (P = 0.108) (Figure 6(b)).

Figure 6.

Sensitivity analysis and publication bias for OS in this meta-analysis: (A) sensitivity analysis; (B) Begg’s funnel plots.

Validation of the Results in the TCGA Dataset

Furthermore, in order to validate these results, the investigators took full advantage of The Cancer Genome Atlas (TCGA) dataset, analyzed the expression of SBF2-AS1 in the different types of cancers. As shown in Figure 7, it was found that SBF2-AS1 was significantly overexpressed in tumor tissues, especially in patients with Cholangio carcinoma, Lymphoid neoplasm diffuse large Bcell lymphoma, Kidney chromophobe, and Thymoma (Figure 7(a)). Furthermore, the information in the violin plot elucidated that the SBF2-AS1 expression was also correlated with the clinical stage of human tumors (P < 0.05, Figure 7(b)). In addition, the investigators utilized the survival plots in the TCGA dataset to explore the association between the SBF2-AS1 expression and prognosis of cancer patients. The results revealed that the upregulated SBF2-AS1 expression exhibited negative effects on OS (Figure 7(c)) and DFS (Figure 7(d)), and these were in accordance with the consequences in the present meta-analysis.

Figure 7.

Validation of the role of lncRNA SBF2-AS1 in human cancers in the TCGA dataset: (A) the expression of SBF2-AS1 in cancers and normal tissues; (B) violin plot of clinical stage of SBF2-AS1 expression in human cancers; (C) overall survival plot of SBF2-AS1; (D) disease-free survival plot ofSBF2-AS1.

Discussion

At present, with the rapid development of molecular genetics and molecular biology, researchers have focused on long non-coding RNAs, which have previously been regarded as the “transcriptional noise,” due to its potential role in predicting the prognosis of cancer patients in recent years.^27,28 Previous studies have demonstrated that lncRNA participates in a variety of biological processes, and enables tumor cells to take part in the course of epigenetic regulation and immune response, cell cycle control, apoptosis and induced pluripotent stem cell reprogramming and so on.^29,30 It is noteworthy that an abnormal lncRNA expression may be involved in various biological processes of tumorigenesis and have an effect on its progression.^31
-33 For instance, the high expression of MALAT1 was markedly associated with pathological grade, LNM, and OS in cancer patients and is engaged in tumor proliferation, and drug resistance.^34
-36 Studies have shown that lncRNA can be used as a target for the new therapy and prognosis of cancer, providing a novel strategy for the prognosis assessment of cancer.^37,38

The lncRNA SET-binding factor 2 (SBF2) antisense RNA1 (lncRNA-SBF2-AS1), located at the 11p15.1 locus, is a novel carcinogenic lncRNA.³⁹ Increasing studies have indicated that SBF2-AS1 is notable for its upregulated expression and plays a vital role in different types of malignant tumors that commonly imply the worse clinical outcomes. In 2020, the study conducted by Wang suggested that the elevated SBF2-AS1 expression was correlated with advanced TNM stage and poor overall survival in non-small cell lung cancer (NSCLC) and could accelerate the growth and migration of tumor cells by targeting miR-362-3p to enhance the GRB2 expression, which could promote lung cancer cell proliferation, migration and invasion.⁴⁰ Meanwhile, Yang’s study revealed that SBF2-AS1 was described as a key regulator in the proliferation and migration of clear cell renal cell carcinoma cells via sponging miR-338-3p and suppressing E26 transformation specific-1 (ETS1).⁴¹ Zhang’s study found that the overexpression of SBF2-AS1 led to the promotion of temozolomide-resistance in glioblastoma patients, which may provide treatment strategies for TMZ-resistant GBM patients.⁴² Another research demonstrated that the high SBF2-AS1 expression could downregulate the miR-122-5p expression and then promoted the X-linked inhibitor of apoptosis protein (XIAP) expression, which in turn, led to the proliferation of pancreatic cancer cells.⁴³ The majority of other mechanism studies demonstrated that SBF2-AS1may be functioned as a ceRNA to modulate target genes through the miRNA sponge, and regulate specific classical signaling pathways in different cancers, including miR-338-3p availability in glioblastoma,⁴⁴ miR-361-5o in cervical cancer,¹³ miR-30a in osteosarcoma.⁴⁵ Radiotherapy-resistance is a hard nut to crack in the treatment of tumors. However, Previous studies have confirmed that muscleblind-like 3 (MBNL3) is closely correlated to macrophage-associated organs, such as the lung.⁴⁶ The research conducted by Yu noted that the inhibition of SBF2-AS1 coule induce NSCLC to be sensitive to radiotherapy via the modulation of the MBNL3 expression, provided that SBF2-AS1 could be regarded as a therapeutic target for NSCLC.⁴⁷ Taken together, it is essential to further identify the relationship between lncRNA SBF2-AS1 and cancer clinical outcomes.

Until now, this is the first meta-analysis conducted to comprehensively explore the correlation between the expression and clinical outcomes of tumors, and determine the predictive value of SBF2-AS1 in cancers. In the present study, the combined results revealed that the hoisted SBF2-AS1 expression can be regarded as a sign of poor OS in cancer patients. The present analysis demonstrated that the high expression of SBF2-AS1 might be an essential predictive factor for OS in various cancers. What’s more, the subsequent pooled results demonstrated that cancer patients with an elevated expression level of SBF2-AS1 had a significant property to metastasize to lymph nodes and exhibit malignant biological behaviors, further indicating that the high SBF2-AS1 expression could serve as a predictable hallmark for clinical outcomes. Besides, based on the TCGA analysis data, the results verified that the high SBF2-AS1 expression was link to unfavorable prognosis. This was observed in multiple tumor tissues.

Of note, it is noteworthy that the present study had some limitations. First, few of the subjects were from countries other than China. This may make the results best suitable for the clinical characteristics of Asian patients with tumors. Second, due to the relatively limitation in sample size, a single type of tumor stills need to be investigated in a larger number of samples, and more literatures are needed to evaluate the DFS and PFS. Finally, the HRs and 95%CIs were extracted using the indirect method, which would inevitably lead to some bias. Hence, there is a need to increase of the number of follow-up studies, in order to avoid these various factors in the compound.

In conclusion, SBF2-AS1 can be an effective independent predictive biomarker for estimating the overall survival of cancer patients. However, more prospective researches are warranted to further confirm the value of SBF2-AS1 in cancer.

Supplemental Material

Supplemental Material, sj-pdf-1-tct-10.1177_15330338211004915 - The Clinical Prognostic Value of lncRNA SBF2-AS1 in Cancer Patients: A Meta-Analysis

Supplemental Material, sj-pdf-1-tct-10.1177_15330338211004915 for The Clinical Prognostic Value of lncRNA SBF2-AS1 in Cancer Patients: A Meta-Analysis by Jie Wang, Pingyong Zhong and Hao Hua in Technology in Cancer Research & Treatment

Footnotes

Abbreviations

Authors’ Note

Conceptualization: Jie Wang. Data curation: Hao Hua. Formal analysis: Jie Wang, Pingyong Zhong, Hao Hua. Funding acquisition: Hao Hua. Investigation: Jie Wang, Pingyong Zhong, Hao Hua. Project administration: Pingyong Zhong. Software: Jie Wang. Supervision: Hao Hua. Writing—original draft: Jie Wang. Writing—review & editing: Hao Hua. All authors agree to publish. All data supporting this meta-analysis are from previously reported studies and datasets, which have been cited. The processed data are available from the corresponding author upon request. Our study did not require an ethical board approval because it did not contain human or animal trials.

Acknowledgment

The authors would like to thank Dr. Hua for his guidance on this article and for his editing and proofreading of this English manuscript. The authors would also like to thank the authors of the primary studies.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Hao Hua, MD

Supplemental Material

Supplemental material for this article is available online.

References

Ferlay

Soerjomataram

Dikshit

, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359–E386. doi:10.1002/ijc.29210

Siegel

Miller

Jemal

. Cancer statistics, 2018. CA Cancer J Clin. 2018;68(1):7–30. doi:10.3322/caac.21442

Simionescu-Bankston

Kumar

. Noncoding RNAs in the regulation of skeletal muscle biology in health and disease. J Mol Med (Berl). 2016;94(8):853–66. doi:10.1007/s00109-016-1443-y

Spizzo

Almeida

Colombatti

Calin

. Long non-coding RNAs and cancer: a new frontier of translational research? Oncogene. 2012;31(43):4577–4587. doi:10.1038/onc.2011.621

Guttman

Rinn

. Modular regulatory principles of large non-coding RNAs. Nature. 2012;482(7385):339–346. doi:10.1038/nature10887

Ding

Wang

, et al. Long noncoding RNA lncCAMTA1 promotes proliferation and cancer stem cell-like properties of liver cancer by inhibiting CAMTA1. Int J Mol Sci. 2016;17(10):1617. doi:10.3390/ijms17101617

Peng

Shan

, et al. LINC81507 act as a competing endogenous RNA of miR-199b-5p to facilitate NSCLC proliferation and metastasis via regulating the CAV1/STAT3 pathway. Cell Death Disease. 2019;10(7):533. doi:10.1038/s41419-019-1740-9

Huo

Tian

Wang

. Long non-coding RNA NORAD upregulate SIP1 expression to promote cell proliferation and invasion in cervical cancer. Biomed Pharmacother. 2018;106:1454–1460. doi:10.1016/j.biopha.2018.07.101

Huarte

. The emerging role of lncRNAs in cancer. Nat Med. 2015;21(11):1253–1261. doi:10.1038/nm.3981

10.

Liu

Lin

Zhang

Chen

Prognostic role of lncRNA TUG1 for cancer outcome: evidence from 840 cancer patients. Oncotarget. 2017;8(30):50051–50060. doi:10.18632/oncotarget.17844

11.

Chen

Lun

Hou

Tian

Zhang

The value of lncRNA HULC as a Prognostic Factor for survival of cancer outcome: a meta-analysis. Cell Physiol Biochem. 2017;41(4):1424–1434. doi:10.1159/000468005

12.

Zhang

Lun

, et al. The value of lncRNA NEAT1 as a prognostic factor for survival of cancer outcome: a meta-analysis. Sci Rep. 2017;7(1):13080. doi:10.1038/s41598-017-10001-0

13.

Gao

Feng

Yao

Yang

. LncRNA SBF2-AS1 promotes the progression of cervical cancer by regulating miR-361-5p/FOXM1 axis. Artif cells Nanomed Biotechnol. 2019;47(1):776–782. doi:10.1080/21691401.2019.1577883

14.

Wang

Zhang

Sun

Liu

. LncRNA SBF2-AS1 inhibits apoptosis and promotes proliferation in lung cancer cell via regulating FOXM1. J BUON. 2020;25(4):1761–1770.

15.

Chen

Xia

Wang

, et al. Long noncoding RNA SBF2-AS1 is critical for tumorigenesis of early-stage lung adenocarcinoma. Mol Ther Nucleic Acids. 2019;16:543–553. doi:10.1016/j.omtn.2019.04.004

16.

Chen

Guo

Huang

, et al. Long noncoding RNA SBF2-AS1 contributes to the growth and metastatic phenotypes of NSCLC via regulating miR-338-3p/ADAM17 axis. Aging. 2020;12(18):17902–17920. doi:10.18632/aging.103332

17.

Hua

Zhu

Xie

, et al. Long non-coding SBF2-AS1 acting as a competing endogenous RNA to sponge microRNA-142-3p to participate in gemcitabine resistance in pancreatic cancer via upregulating TWF1. Aging. 2019;11(20):8860–8878. doi:10.18632/aging.102307

18.

Zhang

, et al. LncRNA SBF2-AS1 promotes hepatocellular carcinoma metastasis by regulating EMT and predicts unfavorable prognosis. Eur Rev Med Pharmacol Sci. 2018;22(19):6333–6341. doi:10.26355/eurrev_201810_16044

19.

Liu

Dong

Xiao

. Long non-coding RNA SBF2-AS1 promotes hepatocellular carcinoma progression through regulation of miR-140-5p-TGFBR1 pathway. Biochem Biophys Res Commun. 2018;503(4):2826–2832. doi:10.1016/j.bbrc.2018.08.047

20.

Chen

Xia

Wang

, et al. Upregulated long non-coding RNA SBF2-AS1 promotes proliferation in esophageal squamous cell carcinoma. Oncol Lett. 2018;15(4):5071–5080. doi:10.3892/ol.2018.7968

21.

Zhang

Pan

You

. Overexpression of long non-coding RNA SBF2-AS1 promotes cell progression in esophageal squamous cell carcinoma (ESCC) by repressing miR-494 to up-regulate PFN2 expression. Biol Open. 2020. doi:10.1242/bio.048793

22.

Chen

Han

Zhao

Gao

. Long noncoding RNA SBF2-AS1 promotes colorectal cancer proliferation and invasion by inhibiting miR-619-5p activity and facilitating HDAC3 expression. J Cell Physiol. 2019;234(10):18688–18696. doi:10.1002/jcp.28509

23.

Zhao

Zhang

, et al. Over-expression of lncRNA SBF2-AS1 is associated with advanced tumor progression and poor prognosis in patients with non-small cell lung cancer. Eur Rev Med Pharmacol Sci. 2016;20(14):3031–3034.

24.

Feng

Rao

Zhu

. Long noncoding RNASBF2-AS1 promotes gastric cancer progression via regulating miR-545/EMS1 axis. BioMed Res Int. 2020;2020:6590303. doi:10.1155/2020/6590303

25.

Wen

Lin

Liang

Xie

. Long noncoding RNAs SET-binding factor 2-antisense RNA1 promotes cell growth through targeting miR-431-5p/CDK14 axis in human papillary thyroid cancer. Kaohsiung J Med Sci. 2020;36(10):808–816. doi:10.1002/kjm2.12259

26.

Xia

Liu

Cheng

Dong

. Down-regulated lncRNA SBF2-AS1 inhibits tumorigenesis and progression of breast cancer by sponging microRNA-143 and repressing RRS1. J Exp Clin Cancer Res. 2020;39(1):18. doi:10.1186/s13046-020-1520-5

27.

Müller

Raulefs

Bruns

, et al. Next-generation sequencing reveals novel differentially regulated mRNAs, lncRNAs, miRNAs, sdRNAs and a piRNA in pancreatic cancer. Mol Cancer. 2015;14:94. doi:10.1186/s12943-015-0358-5

28.

Bin

Hongjian

Xiping

Shifeng

Binbin

. Research progresses in roles of LncRNA and its relationships with breast cancer. Cancer cell Int. 2018;18:179. doi:10.1186/s12935-018-0674-0

29.

Qiu

Chen

, et al. Long noncoding RNA, tissue differentiation-inducing nonprotein coding RNA is upregulated and promotes development of esophageal squamous cell carcinoma. Dis Esophagus. 2016;29(8):950–958. doi:10.1111/dote.12436

30.

Serratì

De Summa

Pilato

, et al. Next-generation sequencing: advances and applications in cancer diagnosis. Onco Targets Ther. 2016;9:7355–7365. doi:10.2147/ott.S99807

31.

Serghiou

Kyriakopoulou

Ioannidis

. Long noncoding RNAs as novel predictors of survival in human cancer: a systematic review and meta-analysis. Mol Cancer. 2016;15(1):50. doi:10.1186/s12943-016-0535-1

32.

Yan

Tang

Chen

, et al. Long noncoding RNA MIR31HG inhibits hepatocellular carcinoma proliferation and metastasis by sponging microRNA-575 to modulate ST7 L expression. J Exp Clin Cancer Res. 2018;37(1):214. doi:10.1186/s13046-018-0853-9

33.

Wang

Feng

, et al. LncRNA MIR31HG targets HIF1A and P21 to facilitate head and neck cancer cell proliferation and tumorigenesis by promoting cell-cycle progression. Mol cancer. 2018;17(1):162. doi:10.1186/s12943-018-0916-8

34.

YiRen

YingCong

Sunwu

, et al. Long noncoding RNA MALAT1 regulates autophagy associated chemoresistance via miR-23b-3p sequestration in gastric cancer. Mol Cancer. 2017;16(1):174. doi:10.1186/s12943-017-0743-3

35.

Feng

Zhao

Zhang

Liu

. LncRNA MALAT1 promotes lung cancer proliferation and gefitinib resistance by acting as a miR-200a sponge. Arch Bronconeumol. 2019;55(12):627–633. doi:10.1016/j.arbres.2019.03.026

36.

Liao

Lin

Gao

, et al. Blocking lncRNA MALAT1/miR-199a/ZHX1 axis inhibits glioblastoma proliferation and progression. Mol Ther Nucleic Acids. 2019;18:388–399. doi:10.1016/j.omtn.2019.09.005

37.

Song

Wang

. LncRNA SNHG16 promotes cell proliferation through miR-302a-3p/FGF19 axis in hepatocellular carcinoma. Neoplasma. 2019;66(3):397–404. doi:10.4149/neo_2018_180720N504

38.

Liu

Zhou

Wang

Song

Zhou

Ren

. Long non-coding RNA SNHG12 promotes proliferation and invasion of colorectal cancer cells by acting as a molecular sponge of microRNA-16. Exp Ther Med. 2019;18(2):1212–1220. doi:10.3892/etm.2019.7650

39.

Qiu

Xia

, et al. High expression of long non-coding RNA SBF2-AS1 promotes proliferation in non-small cell lung cancer. J Exp Clin Cancer Res. 2016;35:75. doi:10.1186/s13046-016-0352-9

40.

Wang

. E2F1-induced overexpression of long noncoding RNA SBF2-AS1 promotes non-small-cell lung cancer metastasis through regulating miR-362-3p/GRB2 Axis. DNA Cell Biol. 2020;39(7):1290–1298. doi:10.1089/dna.2020.5426

41.

Yang

Zhang

Fan

. Downregulation of SBF2-AS1 functions as a tumor suppressor in clear cell renal cell carcinoma by inhibiting miR-338-3p-targeted ETS1. Cancer Gene Ther. 2020. doi:10.1038/s41417-020-0197-4

42.

Zhang

Yin

Wei

Zeng

You

. Exosomal transfer of long non-coding RNA SBF2-AS1 enhances chemoresistance to temozolomide in glioblastoma. J Exp Clin Cancer Res. 2019;38(1):166. doi:10.1186/s13046-019-1139-6

43.

Yin

Zhou

, et al. Down-regulated lncRNA SBF2-AS1 in M2 macrophage-derived exosomes elevates miR-122-5p to restrict XIAP, thereby limiting pancreatic cancer development. J Cell Mol Med. 2020;24(9):5028–5038. doi:10.1111/jcmm.15125

44.

Zheng

Liu

, et al. Transcription Factor NFAT5 promotes glioblastoma cell-driven angiogenesis via SBF2-AS1/miR-338-3p-mediated EGFL7 expression change. Front Mol Neurosci. 2017;10:301. doi:10.3389/fnmol.2017.00301

45.

Dai

Huang

Deng

Lin

Luo

. Silencing of long noncoding RNA SBF2-AS1 inhibits proliferation, migration and invasion and contributes to apoptosis in osteosarcoma cells by upregulating microRNA-30a to suppress FOXA1 expression. Cell cycle. Oct 2019;18(20):2727–2741. doi:10.1080/15384101.2019.1656478

46.

Liang

Song

Tromp

Kolattukudy

. Genome-wide survey and expression profiling of CCCH-zinc finger family reveals a functional module in macrophage activation. Plos One. 2008;3(8):e2880. doi:10.1371/journal.pone.0002880

47.

Wang

Zhang

, et al. LncRNA SBF2-AS1 affects the radiosensitivity of non-small cell lung cancer via modulating microRNA-302a/MBNL3 axis. Cell cycle. 2020;19(3):300–316. doi:10.1080/15384101.2019.1708016

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