
==== Front
BMC Cancer
BMC Cancer
BMC Cancer
1471-2407
BioMed Central London

39237890
12590
10.1186/s12885-024-12590-2
Systematic Review
High expression of small nucleolar host gene RNA may predict poor prognosis of Hepatocellular carcinoma, based on systematic reviews and meta-analyses
Du Sheng-qi 1
Liu Ya-Tong 2
Yang Fen 1
Wang Pei-xue 1
Zhang Jun 50101068@qq.com

1
1 https://ror.org/05ses6v92 grid.459509.4 Department of Gastroenterology, The First People’s Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, 434000 Hubei China
2 https://ror.org/05ses6v92 grid.459509.4 Department of Emergency, The First People’s Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, 434000 Hubei China
5 9 2024
5 9 2024
2024
24 11101 3 2024
2 7 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Background

The prognosis of patients with hepatocellular cancer is substantially correlated with the abnormal expression of growing long non-coding RNA small nucleolar host gene RNA (SNHG) families in liver cancer tissues. This study aimed to examine the relationship between SNHG expression and liver cancer prognosis.

Methods

After searching six internet databases, pertinent manuscripts were found based on inclusion and exclusion criteria. To determine whether SNHG expression levels affect liver cancer prognosis, raw data were collected and hazard ratios (HRs) and odds ratios (ORs) were calculated. The results were examined for potential publication bias using the sensitivity analysis and Beeg’s test.

Results

Most SNHG family members were up-regulated in liver cancer tissues. High SNHG expression predicts poor liver cancer outcomes of, including overall survival (OS) (HR: 1.697, 95% confidence interval [CI]: 1.373–2.021), especially SNHG5 (the HR of OS is 4.74, 95%CI range from 1.35 to 6.64), progression-free survival (HR: 1.85, 95% CI: 1.25–2.73), tumor, node, metastasis (TNM) stage (OR: 1.696, 95% CI: 1.436–2.005), lymph node metastasis (OR: 2.383, 95% CI: 1.098–5.173), and tumor size (OR: 1363, 95% CI: 1.165–1.595). The OS results were found to be reliable and robust, as indicated by the sensitivity analysis. Additionally, Beeg’s test demonstrated the absence of any potential publication bias for each result.

Conclusion

In liver cancer tissues, most SNHGs are highly expressed, which may signal poor prognosis. SNHG has the potential to be an intriguing predictive marker and a prospective therapeutic target for liver cancer.

Keywords

lncRNA
SNHG
Hepatocellular carcinoma
Prognosis
Meta-analysis
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcIntroduction

Cancer is the primary cause of human mortality, resulting in significant mental and physical suffering for individuals and an extensive financial strain on the global community on an annual basis [1]. Globally, approximately 10 million cancer-related fatalities and 19.8 million newly diagnosed cancer patients were reported in 2020, as per the 2021 global cancer statistics [2]. Among all newly diagnosed cancers, liver cancer ranks fifth among new cases among men and ninth among women [3, 4]. The 5-year survival rate remains unsatisfactory, although the application of targeted therapy, immunotherapy, radiotherapy, and chemotherapy has enhanced the survival benefits of patients with liver cancer to a certain extent [5, 6]. Several investigators are endeavoring to identify novel therapeutic targets and prognostic indicators [7, 8].

As molecular biology and high-throughput sequencing have advanced, long non-coding RNAs (lncRNAs), a family of small molecule nucleotides without protein-coding activities, are now recognized as a leading risk factor for liver cancer [9, 10]. LncRNAs influence coding RNA expression at the transcriptional, post-transcriptional translation, and post-translational modification levels and directly or indirectly interfere with the cell cycle, proliferation, immigration, invasion, and apoptosis of tumor cells by acting on downstream genes or signal cascades through sponging with microRNAs [11–13]. The prognosis of liver cancer was substantially correlated with the growth of aberrantly expressed long noncoding genes [14, 15]. Zhao et al. [16], for instance, demonstrated that small nucleolar host gene RNA (SNHG) 7 (SNHG7) may enhance the proliferation, immigration, and metastasis of hepatocellular carcinoma (HCC) cells by increasing forkhead box K2 expression through sponging and decreasing miR-122-5p. According to Kou et al. [17], NEAT1 regulates Bax, Bcl-2, and epidermal growth factor receptors to help HCC cells proliferate, invade, and suppress apoptosis.

The lncRNA family SNHG includes dozens of family members [18–20]. Additional studies show that SNHG regulates liver cancer cell proliferation, migration, metastasis, and apoptosis and is linked to liver cancer prognosis [21, 22]. Many researchers have examined the link between SNHG expression and liver cancer prognosis because SNHG may act as a promising target for treatment and prognostic marker [23, 24]. This study endeavors to conduct a meta-analysis to comprehensively investigate the association between the expression level of SNHG and the prognosis of liver cancer, in light of the limited sample size of a single study and the conflicting findings and conclusions of various studies.

Materials and methods

Literature search strategy

We thoroughly searched six electronic databases—China National Knowledge Infrastructure (CNKI), Web of Science, Google Scholar, Cochrane Library, Embase, and PubMed—to find pertinent literature. The following are the detailed search terms: “liver neoplasm” OR “liver cancer” OR “hepatocellular carcinoma” OR “hepatic carcinoma” OR “liver cell carcinoma” OR “liver tumor” OR “hepatoma” OR “hepatocarcinoma” OR “malignant hepatoma” OR “liver malignancy” OR “HCC” AND “small nucleolar RNA host lncRNA” OR “long non-coding RNA SNHG” OR “non-coding RNA SNHG” OR “small nucleolar RNA host gene” OR “snoRNA host gene” OR “lncRNA SNHG” OR “SNHG.” In addition to these search terms, we also checked reference lists of relevant studies to find other potentially relevant literature. The publication year of the literature is restricted to March 1, 2023, from database inception.

Inclusion and exclusion criteria

The original literature must satisfy the subsequent inclusion criteria for incorporation into this investigation: (1) The SNHG level was detected using clear detection methods. (2) The cancer patients were categorized into two groups: the SNHG low expression group and the SNHG high expression group, as determined using the SNHG level. (3) The primary goal of the initial investigation was to explore the relationship between SNHG expression and liver prognosis. (4) The thesis’s research quality must satisfy the established standards. (5) Provide sufficient and available data. Original documents meeting the following criteria will be excluded: (1) The research object was not a population. (2) The data were unavailable or insufficient. (3) Non-English literature. (4) Reviews, meta-analyses, case reports, and meeting abstracts.

Quality assessment of included literature

Two researchers independently assessed the quality of the included studies using the Newcastle-Ottawa Quality Assessment Scale (NOS). NOS is an evaluation tool widely used in non-randomized controlled studies [25]. This mainly includes the following three major projects: (1) Selection of research objects. (2) Comparability between groups. (3) Exposure/outcome evaluation. There are a total of 8 sub-items with a total of 9 points based on star ratings, and we will assign each study a quality score based on its performance in these areas. If two researchers are different regarding the scoring of the same original document, they may either engage in a discussion or request that a third researcher negotiate a resolution. A score between 6 and 9 was suggestive of excellent and appropriate for this study’s inclusion. A score between 0 and 5 was regarded as low quality and discarded.

Data extraction

This meta-analysis comprised original literature from which two researchers independently extracted the first author’s name, publication year, number of cases, SNHG level cut-off value, and detection method. The hazard ratio (HR) and 95% confidence interval (CI) were also retrieved to assess the relationship between SNHG expression and liver cancer prognosis. When survival curves are the sole information provided in the original literature, HR values with 95% CI were derived indirectly using the Engauge 4.0 version software [26]. To investigate the association between SNHG expression and HCC clinicopathological parameters, including distant metastasis (DM), lymph node metastasis (LNM), tumor, node, metastasis (TNM) stage, and tumor size, the number of occurrences and total number of events of each clinicopathological parameter were also extracted.

Statistical analysis

Stata SE 12.0 and Revman 5.4.0 were used in this investigation. The relationship between SNHG expression and the survival prognosis in HCC patients was evaluated using the pooled HR with 95% CI results. The correlation between the clinicopathological features (LNM, DM, and TNM stage) of patients with HCC and SNHG expression and, was investigated using the combined OR with 95% CI results. The significance of heterogeneity was evaluated using I-square (I2) and p-value (p). When I2 ≤ 50% and p ≥ 0.05, we do not anticipate significant heterogeneity in the results and would apply the fixed-effect model; when I2 > 50% and p < 0.05, we consider the results to be significantly heterogeneous and would apply subgroup analysis and the random-effect model. Sensitivity analysis was conducted to investigate the robustness and dependability of the findings. To ascertain whether publication bias or other types of bias affected the original study’s findings, the Beeg’s test was used.

Result

The included publications’ fundamental characteristics

A comprehensive search of six internet databases yielded 1326 original articles. In total, 639 duplicate publications and 609 original studies were eliminated because they did not examine the relationship between SNHG expression and liver cancer prognosis. Among the remaining 78 publications, 13 studies had insufficient data, 9 studies did not study the population, 11 literatures were not written in English, 5 articles were meta-analyzed, and the research quality of 5 studies was not up to standard. This study included 38 pieces of original evidence from 2917 patients [16, 23, 27–62]. These 38 publications included only Chinese patients. Except for decreased SNHG2 (also named as GAS5) expression in liver cancer tissues [30, 31, 63], most of the SNHG family members were highly expressed in liver cancer tissues (Fig. 1 and Table 1). Most studies examined the relationship between SNHG expression and liver cancer prognosis, with 30 to 160 patients and NOS scores of 6 to 9 (Table 2).

Fig. 1 The process for including appropriate articles

Table 1 Basic features of the publications included in this meta-analysis (n = 38)

Author and year	lncSNHG	sample size	expression level	detected method	cut-off value	refence gene	prognostic index	HR with 95%CI	HR extraction	follow-up-month	NOS score	
Meng FZ [21]	SNHG1	115	upregulation	qRT-PCR	median	β-actin	OS	1.999 (1.302–3.06)	paper	60	9	
Zhang M [28]	SNHG1	82	upregulation	qRT-PCR	median	GAPDH	OS	2.14 (1.12–4.11)	survival curve	60	8a	
DFS	2.22 (1.25–3.94)	
Hu LT 2015 [30]	GAS5 (SNHG2)	30	downregulation	qRT-PCR	mean	GAPDH	OS	0.48 (0.169–1.37)	survival curve	22	7a	
Chang L [29]	GAS5 (SNHG2)	60	downregulation	qRT-PCR	mean	GAPDH	OS	0.307 (0.113–0.415)	paper	60	9	
Tu ZQ [31]	GAS5 (SNHG2)	71	downregulation	qRT-PCR	mean	GAPDH	OS	0.417 (0.244–0.617)	paper	60	9	
Zhang T 2015 [39]	SNHG3	144	upregulation	qRT-PCR	not reported	β-actin	OS	3.464 (1.820–6.594)	paper	60	8d	
RFS	2.134 (1.311–3.474)	paper	60	8d	
DFS	1.97 (1.19–3.28)	survival curve	60	8d	
Zhang PF 2018 [38]	SNHG3	70	upregulation	qRT-PCR	mean	GAPDH	OS	1.94 (1.17–3.22)	survival curve	24	7a	
Li YR 2018 [34]	SNHG5	48	upregulation	qRT-PCR	median	β-actin	OS	4.74 (1.350–6.640)	paper	36	9	
RFS	3.690 (1.229–11.082)	paper	36	9	
Cao C 2016 [32]	SNHG6	160	upregulation	qRT-PCR	mean	β-actin	OS	1.832 (1.032–3.253)	paper	60	9	
Fan XX [23]	SNHG6	40	upregulation	qRT-PCR	mean	GAPDH	OS	1.71 (0.49–5.96)	survival curve	100	8a	
PFS	1.78 (0.69–4.62)	survival curve	100	8a	
Xie YT [36]	SNHG7	80	upregulation	qRT-PCR	mean	GAPDH	OS	1.89 (1.16–3.08)	survival curve	60	6be	
Shen A [35]	SNHG7	100	upregulation	qRT-PCR	median	GAPDH	OS	2.584 (1.621–3.880)	paper	60	9	
PFS	1.86 (1.21–2.86)	paper	60	9	
Yang X [37]	SNHG7	80	upregulation	qRT-PCR	median	GAPDH	OS	2.87 (1.53–5.39)	survival curve	60	7ab	
Zhao ZB 2021 [16]	SNHG7	30	upregulation	qRT-PCR	mean	GAPDH	OS	3.25 (0.8-13.21)	survival curve	60	8a	
Feng SG [33]	SNHG9	40	upregulation	qRT-PCR	mean	GAPDH	OS	2.44 (0.44–13.39)	survival curve	60	8a	
Lan T [45]	SNHG10	64	upregulation	qRT-PCR	mean	GAPDH	OS	1.144 (1.042–1.256)	paper	60	9	
Huang W [42]	SNHG11	57	upregulation	qRT-PCR	mean	GAPDH	OS	1.68 (0.85–3.30)	survival curve	60	7ab	
Lan T [44]	SNHG12	48	upregulation	qRT-PCR	median	GAPDH	OS	2.28 (0.88–5.88)	survival curve	48	7ab	
RFS	2.24 (1.01-5)	survival curve	48	7ab	
Wang X [50]	SNHG13	62	upregulation	qRT-PCR	mean	GAPDH	OS	3.63 (2.01–6.58)	survival curve	44	7ab	
Liu Y [48]	SNHG13	66	upregulation	qRT-PCR	median	GAPDH	OS	2.3 (1.02–5.18)	survival curve	60	7ab	
Ma X [49]	SNHG13	52	upregulation	qRT-PCR	mean	β-actin	not reported	NA	NA	NA	7ab	
Yuan SX [54]	DANCR

(SNHG13)

	135	upregulation	qRT-PCR	median	GAPDH	OS	2.757 (1.379–5.514)	paper	48	9	
RFS	2.228 (1.359–3.653)	paper	48	9	
Zhang H [56]	SNHG14	40	upregulation	qRT-PCR	mean	GAPDH	OS	1.77 (0.61–5.1)	survival curve	36	8a	
Xu XY [52]	SNHG14	55	upregulation	qRT-PCR	mean	GAPDH	not reported	NA	NA	NA	7c	
Liao ZB [46]	SNHG14	66	upregulation	qRT-PCR	mean	GAPDH	OS	1.3 (0.67–2.5)	survival curve	120	8	
Zhang JH [57]	SNHG15	152	upregulation	qRT-PCR	median	GAPDH	OS	2.247 (1.331–6.255)	paper	66	9	
Chen W 2020 [40]	SNHG15	58	upregulation	qRT-PCR	mean	GAPDH	OS	1.64 (0.56–4.83)	survival curve	60	8a	
Dai W [41]	SNHG15	101	upregulation	qRT-PCR	mean	GAPDH	not reported	NA	NA	NA	7c	
Jing Z [43]	SNHG16	40	upregulation	qRT-PCR	mean	GAPDH	OS	1.48 (0.71–3.1)	survival curve	96	7	
Lin Q 2018 [47]	SNHG16	88	upregulation	qRT-PCR	mean	GAPDH	OS	2.34 (1.04–5.28)	survival curve	60	8a	
Zhong JH 2019 [58]	SNHG16	108	upregulation	qRT-PCR	median	GAPDH	OS	1.94 (1.07–3.52)	survival curve	60	8a	
DFS	1.69 (1.07–2.66)	survival curve	60	
Ye JF [53]	SNHG16	103	upregulation	qRT-PCR	mean	GAPDH	not reported	NA	NA	NA	7c	
Xie XH [51]	SNHG16	40	upregulation	qRT-PCR	mean	β-actin	not reported	NA	NA	NA	7c	
Zhu XM [59]	SNHG17	58	upregulation	qRT-PCR	mean	GAPDH	OS	1.426 (0.796–3.434)	paper	60	9	
Zhang DY 2016 [55]	SNHG20	144	upregulation	qRT-PCR	median	GAPDH	OS	3.985 (1.981–8.017)		60	9	
Liu JX [60]	SNHG20	96	upregulation	qRT-PCR	median	GAPDH	OS	2.79 (1.74–4.48)	survival curve	60	8a	
Zhang YX [62]	SNHG22	60	upregulation	qRT-PCR	mean	GAPDH	not reported	NA	NA	NA	7c	
Luo J [61]	MEG8 (SNHG23)	74	upregulation	qRT-PCR	mean	GAPDH	OS	2.29 (1.22–4.32)	survival curve	60	8a	
DFS	1.89 (1.11–3.2)	survival curve	60	8a	
Note

anot multivariate analysis

bSurvival curves only, lack of clinicopathological parameters

cNo follow-up data, only clinicopathological parameters provided

dNot report the cutoff-value

ePossible other bias due to relatively low sample size

SNHG: small nucleotide host RNA; OS: overall survival; HR: hazard ratio; CI: confidence interval; PFS: progression-free survival; DFS: disease-free survival; RFS: Recurrence free survival. NA: not available. GAPDH: glyceraldehyde-3-phosphate dehydrogenase; qRT-PCR: Quantitative real time polymerase chain reaction; NOS: Newcastle-Ottawa Quality Assessment Scale; survival curve: The original literature only provides survival curves without HR values and 95% CI, and HR values with 95% CI were derived indirectly using the Engauge 4.0 version software; paper: The original literature directly provides HR values and 95% CI

Table 2 Quality assessment of eligible studies Newcastle-Ottawa scale (NOS) score

Author and Year	Country	Selection	Comparability	Outcome	Total	
Adequate of case definition	Representativeness of the cases	Selection of Controls	Definition of Controls	Comparability of cases and controls	Ascertainment of exposure	Same method of ascertainment	Non-Response rate	
Meng FZ 2021 [21]	China	*	*	*	*	**	*	*	*	9	
Zhang M 2016 [28]	China	*	*	*	*	*	*	*	*	8	
Hu LT 2015 [30]	China	*	*	*	*	*	*	*	-	7	
Chang L 2016 [29]	China	*	*	*	*	**	*	*	*	9	
Tu ZQ 2014 [31]	China	*	*	*	*	**	*	*	*	9	
Zhang T 2015 [39]	China	*	*	*	*	*	*	*	*	8	
Zhang PF 2018 [38]	China	*	*	*	*	*	*	*	-	7	
Li YR 2018 [34]	China	*	*	*	*	**	*	*	*	9	
Cao C 2016 [32]	China	*	*	*	*	**	*	*	*	9	
Fan XX 2021 [23]	China	*	*	*	*	*	*	*	*	8	
Xie YT 2020 [36]	China	-	*	*	*	*	*	*	-	6	
Shen A 2020 [35]	China	*	*	*	*	**	*	*	*	9	
Yang X 2019 [37]	China	*	*	*	*	*	*	*	-	7	
Zhao ZB 2021 [16]	China	*	*	*	*	*	*	*	*	8	
Feng SG 2021 [33]	China	*	*	*	*	*	*	*	*	8	
Lan T 2019 [45]	China	*	*	*	*	**	*	*	*	9	
Huang W 2020 [42]	China	*	*	*	*	*	*	*	-	7	
Lan T 2017 [44]	China	*	*	*	*	*	*	*	-	7	
Wang X 2020 [50]	China	*	*	*	*	*	*	*	-	7	
Liu Y 2020 [48]	China	*	*	*	*	*	*	*	-	7	
Ma X 2016 [49]	China	*	*	*	*	*	*	*	-	7	
Yuan SX 2016 [54]	China	*	*	*	*	**	*	*	*	9	
Zhang H 2020 [56]	China	*	*	*	*	*	*	*	*	8	
Xu XY 2020 [52]	China	*	*	*	*	*	*	*	-	7	
Liao ZB 2021 [46]	China	*	*	*	*	*	*	*	*	8	
Zhang JH 2016 [57]	China	*	*	*	*	**	*	*	*	9	
Chen W 2020 [40]	China	*	*	*	*	*	*	*	*	8	
Dai W 2019 [41]	China	*	*	*	*	*	*	*	-	7	
Jing Z 2020 [43]	China	*	*	*	*	*	*	*	-	7	
Lin Q 2018 [47]	China	*	*	*	*	*	*	*	*	8	
Zhong JH 2019 [58]	China	*	*	*	*	*	*	*	*	8	
Ye JF 2019 [53]	China	*	*	*	*	*	*	*	-	7	
Xie XH 2019 [51]	China	*	*	*	*	*	*	*	-	7	
Zhu XM 2021 [59]	China	*	*	*	*	**	*	*	*	9	
Zhang DY 2016 [55]	China	*	*	*	*	**	*	*	*	9	
Liu JX 2017 [60]	China	*	*	*	*	*	*	*	*	8	
Zhang YX 2021 [62]	China	*	*	*	*	*	*	*	-	7	
Luo J 2021 [61]	China	*	*	*	*	*	*	*	*	8	

Association between the survival prognosis of HCC and SNHG expression

To assess the correlation between HCC prognosis and SNHG expression, 32 studies were included in this meta-analysis, covering 2506 patients with HCC. The combination of HR and 95% CI demonstrates a positive and statistically significant relationship between increased SNHG expression and poor prognosis for HCC (HR: 1.697, 95% CI: 1.373–2.021). Subgroup analysis was used in this study because of the significant heterogeneity of the results (I2 = 83.5%, p < 0.0001), the inconsistent SNHG expression (increased and decreased level), the mean and median cut-off values, the multivariate and univariate analysis methods, the sample size (< 100 and not < 100), the follow-up month (< 60 and not < 60), and the study quality (NOS score) across different original studies. Pooling HR with 95% CI demonstrated similar favorable correlation between increasing SNHG expression and poor OS in the subgroup of patients with elevated SNHG expression (HR: 1.259, 95% CI: 1.159–1.359), < 9 of NOS score (HR: 1.613, 95% CI: 1.315–1.911), univariate analysis of analysis method (HR: 1.613, 95% CI: 1.315–1.911), median of cut-off value (HR: 2.412, 95% CI: 1.966–2.858), < 100 of sample size (HR: 1.484, 95% CI: 1.131–1.837), not < 100 of sample size (HR: 2.229, 95% CI: 1.744–2.715) (Fig. 2 and Table 3). Furthermore, combining HR and 95% CI reveals a significant positive link between high SNHG expression and poor progression-free survival (PFS) (HR: 1.85, 95% CI: 1.25–2.73) (Fig. 3A), disease-free survival (HR: 1.90, 95% CI: 1.47–2.46) (Fig. 3B), and relapse-free survival (HR: 2.22, 95% CI: 1.63–3.02) (Fig. 3C).

Fig. 2 Forest plot showed the correlation between SNHG expression and overall survival (OS) of Hepatocellular carcinoma (HCC). Note HR: hazard ratio CI: confidence interval

Table 3 Pooled HRs of overall survival of hepatocellular carcinoma patients with increased SNHG expression

Subgroup analysis	sample size	No. of studies	Pooled HR (95% CI)	P	Heterogeneity	
Fixed	Random	I2(%)	p − value	
OS	32	2506	0.870 (0.795–0.946)	1.697 (1.373–2.021)	< 0.0001	83.5	< 0.0001	
SNHG expression								
Upregulation	29	2345	1.259 (1.159–1.359)	2.021 (1.677–2.364)	< 0.0001	46.7	0.003	
Downregulation	3	161	0.355 (0.240–0.470)	0.355 (0.240–0.470)	< 0.0001	0	0.613	
NOS score								
9	11	1107	0.819 (0.741–0.897)	1.428 (0.959–2.760)	< 0.0001	92.6	< 0.0001	
less than 9	21	1399	1.613 (1.315–1.911)	1.834 (1.446–2.221)	< 0.0001	27.7	0.118	
Analytical method								
Multivariate analysis	11	1107	0.819 (0.741–0.897)	1.428 (0.959–1.898)	< 0.0001	92.6	< 0.0001	
Univariate analysis	21	1399	1.613 (1.315–1.911)	1.834 (1.446–2.221)	< 0.0001	27.7	0.118	
Cut-off value								
Mean	19	1188	0.822 (0.745–0.899)	1.263 (0.906–1.620)	< 0.0001	86.1	< 0.0001	
Median	12	1174	2.412 (1.966–2.858)	2.412 (1.966–2.858)	< 0.0001	0	0.852	
Not reported	1	144	3.464 (1.077–5.851)	3.464 (1.077–5.851)	0.004	NA	NA	
Sample size								
not less than 100	8	1058	2.229 (1.744–2.715)	2.229 (1.744–2.715)	< 0.0001	0	0.788	
less than 100	24	1448	0.837 (0.760–0.913)	1.484 (1.131–1.837)	< 0.0001	85	< 0.0001	
Follow-up month								
not less than 60	25	2073	0.859 (0.783–0.936)	1.643 (1.291–1.994)	< 0.0001	85.3	< 0.0001	
less than 60	7	433	1.271 (0.807–1.734)	2.241 (1.094–3.389)	< 0.0001	72.5	0.001	
Refence gene								
GAPDH	28	2039	0.852 (0.776–0.928)	1.581 (1.245–1.916)	< 0.0001	83.7	< 0.0001	
β-actin	4	467	2.211 (1.569–2.853)	2.443 (1.463–3.422)	< 0.0001	42.8	0.155	
Note OS: overall survival; Random: Random effects; Fixed: Fixed effects; directly: HR was extracted directly from the primary articles; indirectly: HR was extracted indirectly from the primary articles; CI: confidence interval

Fig. 3 Forest plot showed the correlation between SNHG expression and progress-free survival (PFS), disease-free survival (DFS) and Recurrence free survival (RFS) of Hepatocellular carcinoma (HCC). Note (A) PFS; (B) DFS; (C) RFS. HR: hazard ratio CI: confidence interval

The relationship between the TNM stage of HCC and SNHG expression

This study examined 28 publications with 2251 patients to determine whether TNM stage and SNHG expression are related. According to the pooled OR with 95% CI values (OR: 1.696, 95% CI: 1.436–2.005) (Fig. 4), an advanced TNM stage has been linked to high SNHG expression. We conducted a subgroup analysis although the overall results’ heterogeneity was negligible (I2 = 36.5%, p = 0.029), but heterogeneity is unavoidably caused by different main studies’ varying cut-off values, research quality, and analytical techniques. Subgroup analysis findings showed that elevated SNHG expression (SNHG1, SNHG3, SNHG8, SNHG15, and others) manifested advanced TNM stage (HR: 1.822, 95%CI: 1.534–2.164), while low SNHG expression (SNHG2) manifested advanced stage of TNM (HR: 0.29, 95%CI: 0.109–0.771) (Table 4).

Fig. 4 Forest plot showed the correlation between SNHG expression and TNM stage of Hepatocellular carcinoma (HCC). Note OR: odds ratio CI: confidence interval

Table 4 Pool effects of clinicopathologic characteristics in hepatocellular carcinoma patients with abnormal SNHG expression

Subgroup analysis	sample size	No. of studies	Pooled OR (95% CI)	P	Heterogeneity	
Fixed	Random	I2(%)	p-value	
TNM stage	28	2251	1.696 (1.436–2.005)	1.672 (1.335–2.094)	< 0.0001	36.5	0.029	
NOS_score								
9	9	912	1.743 (1.341–2.266)	1.698 (0.964–2.991)	0.067	72.6	< 0.0001	
less than 9	19	1339	1.665 (1.341–2.067)	1.657 (1.330–2.064)	< 0.0001	0	0.77	
SNHG expression								
increased	26	2148	1.822 (1.534–2.164)	1.777 (1.457–2.167)	< 0.0001	18.2	0.204	
decreased	2	103	0.290 (0.109–0.771)	0.306 (0.113–0.827)	0.02	0	0.447	
LNM	6	492	2.514 (1.747–3.620)	2.383 (1.098–5.173)	< 0.0001	72.7	0.003	
NOS_score								
9	3	286	2.293 (0.392–13.421)	2.293 (0.392–13.421)	0.357	88.7	< 0.0001	
less than 9	3	206	2.334 (1.351–4.030)	2.314 (1.336–4.008)	0.002	0	0.787	
SNHG expression								
increased	5	421	3.373 (2.238–5.085)	3.239 (1.831–5.730)	< 0.0001	43.1	0.135	
decreased	1	71	0.364 (0.113–1.172)	0.364 (0.113–1.172)	0.09	NA	NA	
DM	5	518	1.265 (0.846–1.892)	1.415 (0.658–3.042)	0.252	39.4	0.158	
NOS_score								
9	2	304	1.784 (0.539–5.904)	1.770 (0.070-45.067)	0.343	74.6	0.047	
less than 9	3	214	1.203 (0.783–1.849)	1.417 (0.668–3.005)	0.399	35.3	0.213	
Tumor size	25	2018	1.363 (1.165–1.595)	1.358 (1.093–1.687)	0.006	39.7	0.022	
NOS_score								
9	9	912	1.339 (1.060–1.690)	1.403 (0.918–2.143)	0.117	63	0.006	
less than 9	16	1106	1.384 (1.119–1.711)	1.353 (1.061–1.725)	0.015	17.3	0.255	
SNHG expression								
increased	23	1915	1.434 (1.221–1.685)	1.433 (1.168–1.758)	< 0.0001	31	0.079	
decreased	2	103	0.394 (0.167–0.933)	0.394 (0.167–0.933)	0.034	0	0.95	
Histological grade	17	1557	1.399 (1.140–1.717)	1.394 (1.135–1.713)	0.001	0	0.843	
NOS_score								
9	7	741	1.387 (1.030–1.868)	1.383 (1.026–1.864)	0.031	0	0.703	
less than 9	10	816	1.410 (1.064–1.870)	1.404 (1.056–1.866)	0.017	0	0.677	
Cut-off value								
mean	10	776	1.359 (1.019–1.810)	1.351 (1.011–1.805)	0.037	0	0.507	
median	6	637	1.482 (1.087–2.020)	1.479 (1.084–2.017)	0.013	0	0.874	
not reported	1	144	1.165 (0.489–2.777)	1.165 (0.489–2.777)	0.731	NA	NA	
Depth of invasion	11	1077	1.911 (1.476–2.475)	1.869 (1.439–2.427)	< 0.0001	0	0.9	
NOS_score								
9	4	434	1.965 (1.305–2.958)	1.910 (1.263–2.888)	0.001	0	0.601	
less than 9	7	643	1.876 (1.344–2.619)	1.842 (1.314–2.581)	< 0.0001	0	0.81	
Cut-off value								
mean	6	495	2.077 (1.400-3.082)	2.030 (1.364–3.021)	< 0.0001	0	0.899	
median	4	438	1.659 (1.159–2.376)	1.651 (1.150–2.369)	0.006	0	0.682	
not reported	1	144	3.689 (1.042–13.054)	3.689 (1.042–13.054)	0.043	NA	NA	
Age	26	2703	1.047 (0.894–1.226)	1.046 (0.893–1.226)	0.567	0	1	
Gender	27	2136	1 (0.832–1.203)	0.999 (0.829–1.205)	0.994	0	0.97	
Note TNM: Tumor Node Metastasis, LNM: lymph node metastasis, DM: distant metastasis, CI: confidence interval, No.: number, NA: not applicable

Association between HCC tumor size and SNHG expression

This research involved 25 publications that focused on 2018 patients with HCC and examined the relationship between SNHG expression and HCC tumor size. Pooling OR with 95% CI showed a strong substantial relationship between elevated SNHG expression and larger HCC tumors (OR: 1.363, 95% CI: 1.165–1.595) (Fig. 5). The results of subgroup analysis demonstrated that in the subgroup of elevated SNHGs expression (for example SNHG1, SNHG3, SNHG9, SNHG12, SNHG16 et al.), increasing SNHG expression manifesting bigger tumor size (HR: 1.434, 95%CI: 1.221–1.685), in the subgroup of low SNHGs expression (for example SNHG2), increasing SNHG expression manifesting smaller tumor size (HR: 0.394, 95%CI: 0.167–0.933) (Table 4).

Fig. 5 Forest plot showed the correlation between SNHG expression and tumor size of Hepatocellular carcinoma (HCC). Note OR: odds ratio CI: confidence interval

Association between various clinicopathological markers and SNHG expression

Pooling OR with 95% CI show a significant positive relationship between elevated SNHG expression and easier LNM (OR: 2.383, 95% CI: 1.098–5.173) (Fig. 6), poor histologic status (OR: 1.399, 95% CI: 1.140–1.717) (Fig. 7) and deeper HCC cell invasion (OR: 1.911, 95% CI: 1.476–2.475) (Fig. 8). Simultaneously, DM (OR: 1.265, 95% CI: 0.846–1.892) (Fig. 9), gender (OR: 1, 95% CI: 0.832–1.203), and age (OR: 1.047, 95% CI: 0.894–1.226) were found to have an insignificant relationship with SNHG expression (Table 4).

Fig. 6 Forest plot showed the correlation between SNHG expression and LNM of Hepatocellular carcinoma (HCC). Note OR: odds ratio CI: confidence interval

Fig. 7 Forest plot showed the correlation between SNHG expression and histological grade of Hepatocellular carcinoma (HCC). Note OR: odds ratio CI: confidence interval

Fig. 8 Forest plot showed the correlation between SNHG expression and depth of invasion of Hepatocellular carcinoma (HCC). Note OR: odds ratio CI: confidence interval

Fig. 9 Forest plot showed the correlation between SNHG expression and DM of Hepatocellular carcinoma (HCC). Note OR: odds ratio CI: confidence interval

Publication bias and sensitivity analysis

Outcomes of the sensitivity analysis indicated that, except for one paper (Lan T and Tu ZQ), the outcomes of the other papers did not have a considerable impact on the overall outcome. We used this information to conduct a subgroup analysis, and the findings demonstrated that the OS rate results had increased robustness and reliability after this paper was removed (Fig. 10). Begg’s test results indicated that except for invasion depth (Pr > |z| = 0.005), no OS publication bias was observed. (Pr > |z| = 0.206), TNM stage (Pr > |z| = 0.502), LNM (Pr > |z| = 0.851), DM (Pr > |z| = 0.086), tumor size (Pr > |z| = 0.051), histologic status (Pr > |z| = 0.650), age (Pr > |z| = 0.582), and gender (Pr > |z| = 0.269) (Fig. 11).

Fig. 10 Sensitivity analysis for SNHG expression with overall survival (OS) of Hepatocellular carcinoma (HCC). Note HR: hazard ratio, CI: confidence interval

Fig. 11 Funnel plot about the relationship between SNHG expression and survival outcome of Hepatocellular carcinoma (HCC). Note (A) OS; (B) TNM stage; (C) Tumor size; (D) LNM; (E) Histological grade; (F) Depth of invasion. (G) DM; (H) Age; (I) Gender

Discussion

Despite not directly encoding proteins, lncRNAs regulate many tumor cell behaviors, including cell proliferation, apoptosis, drug resistance, immigration, and invasion affecting the progression of breast cancer [64], pancreatic cancer [65], and head and neck squamous cell carcinoma [66]. Many lncRNA SNHGs were found to be abnormally expressed in liver cancer [41, 63]. SNHG that is abnormally expressed has the potential to directly affect downstream signaling cascades or to function as a competitive endogenous RNA, absorbing microRNAs in a sponge-like fashion. The drug resistance, immigration, proliferation, and invasion of liver cancer cells are influenced by the indirect regulation of downstream signaling pathways or genes. In patients with cancer, there is a significant correlation between the DM, LNM, tumor size, TNM stage, PFS, and OS [27, 33, 42, 43, 48]. Multiple studies have demonstrated that members of the SNHG family have the potential to serve as therapeutic targets and prognostic indicators for liver cancer. Because SNHG expression was inconsistent with liver cancer prognosis in multiple prior studies, this study used a meta-analysis to comprehensively and systematically analyze the link between liver cancer patients’ prognosis and SNHG expression.

This investigation analyzed 38 relevant publications and found that liver cancer tissues expressed more SNHG family members. The combined HR and its 95%CI results showed that increased SNHG expression predicted poor liver cancer outcomes, including shorter OS and DFS duration. Inconsistent cut-off values, number of cases, follow-up time, and expression differences between different original literatures were taken into account. In this study, subgroup analysis findings showed that the mean, median, subgroup with > 100 cases, and subgroup with < 100 cases had follow-up times of at least 60 months. High SNHG expression substantially anticipated poor OS in these categories. Furthermore, elevated SNHG expression was predictive of easier LNM, advanced TNM stage, worse histologic grade, easier DM, and greater tumor size according to the pooled OR and its 95% CI values.

Many researchers tried to determine how SNHG affects liver cancer progression molecularly (Table 5). First, by directly affecting downstream signaling cascades or genes, SNHG may alter liver cancer cell biology. Zhang et al. [28] found that SNHG1 down-regulates p53 to increase HCC cell growth and block apoptosis. Zhang et al. [67] proposed that DANCR (SNHG13) could facilitate the proliferation, immigration, and invasion of Hep3B and HepG2 cells by interacting with PTEN signaling. By up-regulating p62 expression, Zhong et al. [58] demonstrated that SNHG16 can promote the growth, immigration, and infiltration of HuH-7 and HepG2 cells while preventing apoptosis. Secondly, by functioning as a sponging microRNA and an endogenous RNA competitor, SNHG can control downstream genes or signaling cascades. According to Meng et al. [27], SNHG1 can activate the FOXK1/Snail axis via sponging and down-regulating miR-376a, which in turn can drive the proliferation, invasion, immigration, and suppression of apoptosis in HCC cells. Li et al. [34] showed that SNHG5 may enhance GSK3β expression through sponging and down-regulating miR-26a-5p, which may aid in the processes of epithelial-mesenchymal transition, proliferation, invasion, and migration. Xie et al. proved that SNHG7 may contribute to the proliferation and block apoptosis of Hep3B and HepG2 through the down-regulation of Bax and caspase-3 by down-regulating miR-9-5p [36]. Xie et al. [51] observed that SNHG16 may promote matrix metalloproteinase (MMP) 2 and MMP9 expression through sponging and reduce miR-195, which could aid in the proliferation and invasion of HCC cells. Third, certain members of the SNHG family may have a considerable impact on liver cancer cells’ resistance to medications related to tumors. According to Zhang et al. [38], SNHG3 may enhance HCC cell invasion, proliferation, and sorafenib resistance by down-regulating miR-128 and up-regulating CD151 expression. Liu et al. reported that DANCR facilitates Sorafenib resistance of HCC cells by activating interleukin 6/STAT3 signaling. Jing et al. [43] showed that SNHG16 may contribute to the sorafenib resistance through the interaction with early growth response 1 by sponging and down-regulating miR-23b-3p. Finally, SNHG family members may affect HCC cell autophagy to enhance liver cancer progression. To stimulate HCC cell proliferation, and migration, and prevent apoptosis and autophagy, Huang et al. [42] found that SNHG11 up-regulated argonaute-2 via down-regulating miR-184. SNHG family members that have reduced expression in liver cancer tissues may have a better survival outcome for patients with HCC. For example, Hu et al. [30] found that lncGAS5 up-regulates miR-21 to suppress HCC cell invasion and migration. Yang et al. [63] revealed that lncGAS5 inhibits cell invasion of HCC cells via up-regulating reversion-inducing cysteine-rich protein with Kazal motifs (RECK) by targeting miR-135b.

Table 5 Regulation mechanism of SNHG involved in hepatocellular carcinoma cancer cells

Author and year	lncSNHG	expression level	role	miR-RNA	Downstream genes or pathways	cell lines	function (high SNHG expression)	
Meng FZ [21]	SNHG1	upregulation	oncogene	miR-376a	FOXK1/Snail axis	HL7702,HepG2,SMMC-7721 and HuH-7	induce proliferation, invasion and migration, suppress apoptosis	
Zhang M [28]	SNHG1	upregulation	oncogene	-	p53	SMMC-7721, MHCC97H, HCCLM3 and HepG2	promotes cells proliferation, inhibits apoptosis	
Zhang PF 2018 [38]	SNHG3	upregulation	oncogene	miR-128	CD151	PLC/PRF/5, Hep3B, HepG2, MHCC97L, Huh7, SMMC-7721, and HCCLM3	promotes HCC cell invasion, induces EMT and sorafenib resistance	
Li YR 2018 [34]	SNHG5	upregulation	oncogene	miR-26a-5p	GSK3β	Hep3B, HepG2, SMCC-7721,

MHCC-97 L, MHCC-97 H, Huh7 and LO2

	induce proliferation, invasion and migration and EMT process	
Chen SY 2019 [68]	SNHG6	upregulation	oncogene	miR-let-7c-5p	c-Myc	MHCC-97 H and HCC-LM3	promotes proliferation	
Cao C 2016 [32]	SNHG6	upregulation	oncogene	miR-26a/b	TAK1	BEL-7402, SMMC-7721, MHCC-97 H, SK-Hep-1, Huh7 and

HCC-LM3

	promoting cellular

proliferation and inhibiting apoptosis

	
Fan XX [23]	SNHG6	upregulation	oncogene	miR-6509-5p	HIF1A	Hep3B and Huh7	promote proliferation, migration and invasion	
XieYT 2020 [36]	SNHG7	upregulation	oncogene	miR-9-5p	CNNM1,bcl-2, bax, caspase-3	THLE-3, BEL-7404, HCCLM3, Hep3B and HepG2	facilitated cell proliferation, suppressed cell apoptosis	
Yang X [37]	SNHG7	upregulation	oncogene	miR-122-5p	RPL4	Huh7, Hep3B, HCCLM3,

MHCC97 H

	induce cell proliferation, migration and invasion	
Dong JY 2018 [69]	SNHG8	upregulation	oncogene	miR-149-5p	E-cadherin, N-cadherin, and Vimentin	LO2, Huh6, Huh7, SK-hep1, HepG2, and PLC5	facilitated cell proliferation, invasion, and Migration	
Zhao ZB 2021 [16]	SNHG7	upregulation	oncogene	miR-122-5p	FOXK2,E-cadherin, N-cadherin and Vimentin	SNU449, Hep3B, and THLE-2	induce cell proliferation and migration	
Feng SG [33]	SNHG9	upregulation	oncogene	miR-23a-5p	miR-23a-5p/Wnt3a Axis	HUH6,HepG2, QSG7701	facilitated cell proliferation	
Lan T [45]	SNHG10	upregulation	oncogene	miR-150-5p	c-Myb	SNU-182, Huh-7, Hep3B, SK-Hep1, and SNU-387	promote cell proliferation, invasion, and migration and EMT process	
Huang W [42]	SNH11	upregulation	oncogene	miR-184	AGO2	HL-7702, SK-HEP-1, Hep G2, HuH-7, and Li-7	induce Proliferation, Migration, inhibit Apoptosis and Autophagy	
Lan T [44]	SNHG12	upregulation	oncogene	miR-199a/b-5p	MLK3,NF-κB pathway	SK-Hep1	induced cell proliferation

and suppress cell apoptosis

	
Wang X [50]	DANCR	upregulation	oncogene	miR-222-3p	ATG7	Bel7407, Hep3B,

HepG2, Huh7 and MHCC97H

	accelerate cell

proliferation and inhibit autophagy

	
Liu Y [48]	DANCR	upregulation	oncogene	-	IL-6/STAT3 Signaling	HEK-293T, Huh7, Huh7/sorafenib-resistant (SR) and Hep3B/

SR and Hep3B

	facilitate Sorafenib Resistance	
Yuan SX [54]	DANCR	upregulation	oncogene	miR-214, miR-

320a, miR-199a

	CTNNB1	293T, SMMC7721	increased stemness features of HCC cells	
Zhang H [56]	SNHG14	upregulation	oncogene	-	PTEN signaling	Hep3B and HepG2 cells	promoted cell proliferation, migration, and angiogenesis	
Xu XY [52]	SNHG14	upregulation	oncogene	miR-217	E2F3	THLE-2, Huh-7,

Hep3B

	induce cell proliferation and suppress cell apoptosis	
Liao ZB [46]	SNHG14	upregulation	oncogene	miR-876-5p	miR-876-5p/SSR2	HepG2 and Hep3B	promoted proliferation and

metastasis

	
Lin RX 2021 [71]	SNHG14	upregulation	oncogene	miR-206	SOX9	MHCC97-H, Bel-7404, HepG2, SMCC7721, and QGY-7703	contribute to the proliferation, invasion, and migration	
Chen W 2020 [40]	SNHG15	upregulation	oncogene	miR-18b-5p	LMO4	BEL-7402, HepG2, SMMC-7721, Hep3B	promote cell proliferation, invasion and migration and inhibit apoptosis	
Dai W [41]	SNHG15	upregulation	oncogene	miR-490-3p	HDAC2	HuH-1, HuH-7 and L-O2	facilitate cell proliferation, migration and invasion	
Jing Z [43]	SNHG16	upregulation	oncogene	miR-23b-3p	EGR1	ATCC HB-8064, ACTT	promote Sorafenib

Resistance

	
Lin Q 2018 [47]	SNHG16	upregulation	oncogene	miR-4500	STAT3	SMMC-7721, L02, MHCC‐97 H, HepG2	promoting cell proliferation,

migration, invasion, and EMT process as well as

inhibiting cell apoptosis

	
Zhong JH 2019 [58]	SNHG16	upregulation	oncogene	-	p62	HuH-7, HepG2, SMMC-7721, HL-7702	promoted proliferation, migration, and invasion, while inhibiting apoptosis	
Hu YL 2020 [70]	SNHG16	upregulation	oncogene	miR-605-3p	TRAF6/NF-κB feedback loop	HCCLM3, MHCC97L and MHCC-97 H	promoted proliferation, migration	
Ye JF [53]	SNHG16	upregulation	oncogene	miR-140-5p	-	HepG2/SOR	facilitate Sorafenib Resistance	
Xie XH [51]	SNHG16	upregulation	oncogene	miR-195	MMP-2, MMP-9	SMMC7721

and HepG2

	promote proliferation, invasion	
Zhu XM [59]	SNHG17	upregulation	oncogene	-	-	HepG2 and SNU-182	promotes cell proliferation and migration	
Zhang DY 2016 [55]	SNHG20	upregulation	oncogene	-	-	HL-7702, MHCC-97 H, HepG2, SK-Hep-1, SMMC-7721, and BEL-7402	promoted proliferation, migration and invasion	
Liu JX [60]	SNHG20	upregulation	oncogene	-	ZEB1, ZEB2, N-cadherin, E-cadherin and Vimentin	MHCC97L, SMCC-

7721, MHCC97H and Huh-7

	induce cell proliferation and invasion	
Zhang YX [62]	SNHG22	upregulation	oncogene	miR-16-5p	DNMT1	Huh7, HCCLM6, MHCC97H and SNU-398	promoted cell proliferation, invasion and migration	
Luo J [61]	MEG8 (SNHG23)	upregulation	oncogene	miR-367-3p	TGFβR1	HepG2, Huh7,

HCCLM3, and HMCC-97 H

	promoted cell proliferation, invasion and migration	

This study inevitably has certain limitations. Initially, the results of this research could only be relevant to Asian or Chinese populations, as all of the patients included in the study were from China. Second, the survival prognosis’s HR value and its 95% CI are explicitly provided in a few of the included studies, while others only provide the number of patients and survival curve. The software Engauge 4.0 version was applied to obtain the HR value indirectly, which is inevitable There may be some statistical bias or other bias. Third, the overall results may be subject to a certain level of bias due to inconsistent sample sizes, statistical analysis methods, follow-up duration, cut-off values, and other factors among different original studies. We conducted a subgroup analysis to mitigate these biases. Nevertheless, this research is the first meta-analysis to investigate the relationship between SNHG family member expression and HCC prognosis. Meanwhile, the molecular biological mechanism of SNHG affecting the progression of liver cancer was also comprehensively summarized.

Conclusion

Most SNHG family members have substantial expression in HCC tissues, and high expression is positively connected with poor OS, advanced TNM stage, easy LNM and DM, poorer histopathological grade, and greater tumor size. SNHG may be an effective HCC prognostic marker and potential therapeutic target.

Acknowledgements

Not applicable.

Author contributions

Jun Zhang design the project; Sheng-qi Du, Fen Yang, Pei-xue Wang and Ya-Tong Liu searched databases and performed literature screen; Pei-xue Wang and Fen Yang extracted and analyzed the data; Sheng-qi Du, Jun Zhang and Ya-Tong Liu evaluated the quality of included literature; Fen Yang, Sheng-qi Du, Ya-Tong Liu and Jun Zhang contributed to writing the manuscript. Final draft was approved by all the authors.

Funding

Not applicable.

Data availability

This manuscript contains all study data or may be obtained from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study does not require an ethics declaration and does not conduct any clinical patient studies. The writers bear full responsibility for the work, guaranteeing that any doubts about the precision or consistency of any portion are duly examined and settled.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Sheng-qi Du and Ya-tong Liu contributed equally to this article as co-author.
==== Refs
References

1. Siegel RL Miller KD Wagle NS Jemal A Cancer statistics, 2023 Cancer J Clin 2023 73 1 17 48 10.3322/caac.21763
Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. Cancer J Clin. 2023;73(1):17–48.10.3322/caac.21763
2. Sung H Ferlay J Siegel RL Laversanne M Soerjomataram I Jemal A Bray F Global Cancer statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries Cancer J Clin 2021 71 3 209 49 10.3322/caac.21660
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries. Cancer J Clin. 2021;71(3):209–49.10.3322/caac.21660
3. Miller KD Nogueira L Devasia T Mariotto AB Yabroff KR Jemal A Kramer J Siegel RL Cancer treatment and survivorship statistics, 2022 Cancer J Clin 2022 72 5 409 36 10.3322/caac.21731
Miller KD, Nogueira L, Devasia T, Mariotto AB, Yabroff KR, Jemal A, Kramer J, Siegel RL. Cancer treatment and survivorship statistics, 2022. Cancer J Clin. 2022;72(5):409–36.10.3322/caac.21731
4. Giaquinto AN Miller KD Tossas KY Winn RA Jemal A Siegel RL Cancer statistics for African American/Black people 2022 Cancer J Clin 2022 72 3 202 29 10.3322/caac.21718
Giaquinto AN, Miller KD, Tossas KY, Winn RA, Jemal A, Siegel RL. Cancer statistics for African American/Black people 2022. Cancer J Clin. 2022;72(3):202–29.10.3322/caac.21718
5. Harding-Theobald E Louissaint J Maraj B Cuaresma E Townsend W Mendiratta-Lala M Singal AG Su GL Lok AS Parikh ND Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma Aliment Pharmacol Ther 2021 54 7 890 901 10.1111/apt.16563 34390014
Harding-Theobald E, Louissaint J, Maraj B, Cuaresma E, Townsend W, Mendiratta-Lala M, Singal AG, Su GL, Lok AS, Parikh ND. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther. 2021;54(7):890–901.34390014 10.1111/apt.16563
6. Huo TI Liao JI Ho SY Prognostic prediction for patients with hepatocellular carcinoma receiving immunotherapy: are we there yet? J Hepatol 2022 76 4 987 8 10.1016/j.jhep.2021.11.007 34780876
Huo TI, Liao JI, Ho SY. Prognostic prediction for patients with hepatocellular carcinoma receiving immunotherapy: are we there yet? J Hepatol. 2022;76(4):987–8.34780876 10.1016/j.jhep.2021.11.007
7. Xu RH Wei W Krawczyk M Wang W Luo H Flagg K Yi S Shi W Quan Q Li K Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma Nat Mater 2017 16 11 1155 61 10.1038/nmat4997 29035356
Xu RH, Wei W, Krawczyk M, Wang W, Luo H, Flagg K, Yi S, Shi W, Quan Q, Li K, et al. Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nat Mater. 2017;16(11):1155–61.29035356 10.1038/nmat4997
8. Gu Y Li X Bi Y Zheng Y Wang J Li X Huang Z Chen L Huang Y Huang Y CCL14 is a prognostic biomarker and correlates with immune infiltrates in hepatocellular carcinoma Aging 2020 12 1 784 807 10.18632/aging.102656 31927532
Gu Y, Li X, Bi Y, Zheng Y, Wang J, Li X, Huang Z, Chen L, Huang Y, Huang Y. CCL14 is a prognostic biomarker and correlates with immune infiltrates in hepatocellular carcinoma. Aging. 2020;12(1):784–807.31927532 10.18632/aging.102656
9. Farzaneh M Ghasemian M Ghaedrahmati F Poodineh J Najafi S Masoodi T Kurniawan D Uddin S Azizidoost S Functional roles of lncRNA-TUG1 in hepatocellular carcinoma Life Sci 2022 308 120974 10.1016/j.lfs.2022.120974 36126725
Farzaneh M, Ghasemian M, Ghaedrahmati F, Poodineh J, Najafi S, Masoodi T, Kurniawan D, Uddin S, Azizidoost S. Functional roles of lncRNA-TUG1 in hepatocellular carcinoma. Life Sci. 2022;308:120974.36126725 10.1016/j.lfs.2022.120974
10. Sheng JQ Wang MR Fang D Liu L Huang WJ Tian DA He XX Li PY LncRNA NBR2 inhibits tumorigenesis by regulating autophagy in hepatocellular carcinoma Biomed Pharmacotherapy = Biomedecine Pharmacotherapie 2021 133 111023 10.1016/j.biopha.2020.111023
Sheng JQ, Wang MR, Fang D, Liu L, Huang WJ, Tian DA, He XX, Li PY. LncRNA NBR2 inhibits tumorigenesis by regulating autophagy in hepatocellular carcinoma. Biomed Pharmacotherapy = Biomedecine Pharmacotherapie. 2021;133:111023.10.1016/j.biopha.2020.111023
11. Statello L Guo CJ Chen LL Huarte M Gene regulation by long non-coding RNAs and its biological functions Nat Rev Mol Cell Biol 2021 22 2 96 118 10.1038/s41580-020-00315-9 33353982
Statello L, Guo CJ, Chen LL, Huarte M. Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol. 2021;22(2):96–118.33353982 10.1038/s41580-020-00315-9
12. Liu SJ Dang HX Lim DA Feng FY Maher CA Long noncoding RNAs in cancer metastasis Nat Rev Cancer 2021 21 7 446 60 10.1038/s41568-021-00353-1 33953369
Liu SJ, Dang HX, Lim DA, Feng FY, Maher CA. Long noncoding RNAs in cancer metastasis. Nat Rev Cancer. 2021;21(7):446–60.33953369 10.1038/s41568-021-00353-1
13. Chen S Shen X Long noncoding RNAs: functions and mechanisms in colon cancer Mol Cancer 2020 19 1 167 10.1186/s12943-020-01287-2 33246471
Chen S, Shen X. Long noncoding RNAs: functions and mechanisms in colon cancer. Mol Cancer. 2020;19(1):167.33246471 10.1186/s12943-020-01287-2
14. Zhang G Sun J Zhang X A novel cuproptosis-related LncRNA signature to predict prognosis in hepatocellular carcinoma Sci Rep 2022 12 1 11325 10.1038/s41598-022-15251-1 35790864
Zhang G, Sun J, Zhang X. A novel cuproptosis-related LncRNA signature to predict prognosis in hepatocellular carcinoma. Sci Rep. 2022;12(1):11325.35790864 10.1038/s41598-022-15251-1
15. Feng J Yang G Liu Y Gao Y Zhao M Bu Y Yuan H Yuan Y Yun H Sun M LncRNA PCNAP1 modulates hepatitis B virus replication and enhances tumor growth of liver cancer Theranostics 2019 9 18 5227 45 10.7150/thno.34273 31410212
Feng J, Yang G, Liu Y, Gao Y, Zhao M, Bu Y, Yuan H, Yuan Y, Yun H, Sun M, et al. LncRNA PCNAP1 modulates hepatitis B virus replication and enhances tumor growth of liver cancer. Theranostics. 2019;9(18):5227–45.31410212 10.7150/thno.34273
16. Zhao Z Gao J Huang S LncRNA SNHG7 promotes the HCC Progression through miR-122-5p/FOXK2 Axis Dig Dis Sci 2022 67 3 925 35 10.1007/s10620-021-06918-2 33738672
Zhao Z, Gao J, Huang S. LncRNA SNHG7 promotes the HCC Progression through miR-122-5p/FOXK2 Axis. Dig Dis Sci. 2022;67(3):925–35.33738672 10.1007/s10620-021-06918-2
17. Kou JT Ma J Zhu JQ Xu WL Liu Z Zhang XX Xu JM Li H Li XL He Q LncRNA NEAT1 regulates proliferation, apoptosis and invasion of liver cancer Eur Rev Med Pharmacol Sci 2020 24 8 4152 60 32373951
Kou JT, Ma J, Zhu JQ, Xu WL, Liu Z, Zhang XX, Xu JM, Li H, Li XL, He Q. LncRNA NEAT1 regulates proliferation, apoptosis and invasion of liver cancer. Eur Rev Med Pharmacol Sci. 2020;24(8):4152–60.32373951
18. Tan AQ, Zheng YF. The roles of SNHG Family in osteoblast differentiation. Genes 2022, 13(12).
19. Zhen N Zhu J Mao S Zhang Q Gu S Ma J Zhang Y Yin M Li H Huang N Alternative splicing of lncRNAs from SNHG Family alters snoRNA expression and induces Chemoresistance in Hepatoblastoma Cell Mol Gastroenterol Hepatol 2023 16 5 735 55 10.1016/j.jcmgh.2023.07.007 37478905
Zhen N, Zhu J, Mao S, Zhang Q, Gu S, Ma J, Zhang Y, Yin M, Li H, Huang N, et al. Alternative splicing of lncRNAs from SNHG Family alters snoRNA expression and induces Chemoresistance in Hepatoblastoma. Cell Mol Gastroenterol Hepatol. 2023;16(5):735–55.37478905 10.1016/j.jcmgh.2023.07.007
20. Shi J Ding W Lu H Identification of long non-coding RNA SNHG family as promising prognostic biomarkers in Acute myeloid leukemia OncoTargets Therapy 2020 13 8441 50 10.2147/OTT.S265853 32922034
Shi J, Ding W, Lu H. Identification of long non-coding RNA SNHG family as promising prognostic biomarkers in Acute myeloid leukemia. OncoTargets Therapy. 2020;13:8441–50.32922034 10.2147/OTT.S265853
21. Li J Gao J Kan A Hao T Huang L SNHG and UCA1 as prognostic molecular biomarkers in hepatocellular carcinoma: recent research and meta-analysis Minerva Med 2017 108 6 568 74 10.23736/S0026-4806.17.05094-7 28466631
Li J, Gao J, Kan A, Hao T, Huang L. SNHG and UCA1 as prognostic molecular biomarkers in hepatocellular carcinoma: recent research and meta-analysis. Minerva Med. 2017;108(6):568–74.28466631 10.23736/S0026-4806.17.05094-7
22. Li Y Wang X Chen S Wu B He Y Du X Yang X Long non-coding RNA small nucleolar RNA host genes: functions and mechanisms in hepatocellular carcinoma Mol Biol Rep 2022 49 3 2455 64 10.1007/s11033-021-07018-0 34989961
Li Y, Wang X, Chen S, Wu B, He Y, Du X, Yang X. Long non-coding RNA small nucleolar RNA host genes: functions and mechanisms in hepatocellular carcinoma. Mol Biol Rep. 2022;49(3):2455–64.34989961 10.1007/s11033-021-07018-0
23. Fan X Zhao Z Song J Zhang D Wu F Tu J Xu M Ji J LncRNA-SNHG6 promotes the progression of hepatocellular carcinoma by targeting mir-6509-5p and HIF1A Cancer Cell Int 2021 21 1 150 10.1186/s12935-021-01835-w 33663502
Fan X, Zhao Z, Song J, Zhang D, Wu F, Tu J, Xu M, Ji J. LncRNA-SNHG6 promotes the progression of hepatocellular carcinoma by targeting mir-6509-5p and HIF1A. Cancer Cell Int. 2021;21(1):150.33663502 10.1186/s12935-021-01835-w
24. Luo Y, Lin J, Zhang J, Song Z, Zheng D, Chen F, Zhuang X, Li A, Liu X. LncRNA SNHG17 Contributes to Proliferation, Migration, and Poor Prognosis of Hepatocellular Carcinoma. Canadian journal of gastroenterology & hepatology 2021, 2021:9990338.
25. Stang A Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses Eur J Epidemiol 2010 25 9 603 5 10.1007/s10654-010-9491-z 20652370
Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5.20652370 10.1007/s10654-010-9491-z
26. Tierney JF Stewart LA Ghersi D Burdett S Sydes MR Practical methods for incorporating summary time-to-event data into meta-analysis Trials 2007 8 16 10.1186/1745-6215-8-16 17555582
Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8:16.17555582 10.1186/1745-6215-8-16
27. Meng F Liu J Lu T Zang L Wang J He Q Zhou A SNHG1 knockdown upregulates miR-376a and downregulates FOXK1/Snail axis to prevent tumor growth and metastasis in HCC Mol Therapy Oncolytics 2021 21 264 77 10.1016/j.omto.2021.02.002
Meng F, Liu J, Lu T, Zang L, Wang J, He Q, Zhou A. SNHG1 knockdown upregulates miR-376a and downregulates FOXK1/Snail axis to prevent tumor growth and metastasis in HCC. Mol Therapy Oncolytics. 2021;21:264–77.10.1016/j.omto.2021.02.002
28. Zhang M Wang W Li T Yu X Zhu Y Ding F Li D Yang T Long noncoding RNA SNHG1 predicts a poor prognosis and promotes hepatocellular carcinoma tumorigenesis Biomed Pharmacotherapy = Biomedecine Pharmacotherapie 2016 80 73 9 10.1016/j.biopha.2016.02.036
Zhang M, Wang W, Li T, Yu X, Zhu Y, Ding F, Li D, Yang T. Long noncoding RNA SNHG1 predicts a poor prognosis and promotes hepatocellular carcinoma tumorigenesis. Biomed Pharmacotherapy = Biomedecine Pharmacotherapie. 2016;80:73–9.10.1016/j.biopha.2016.02.036
29. Chang L Li C Lan T Wu L Yuan Y Liu Q Liu Z Decreased expression of long non-coding RNA GAS5 indicates a poor prognosis and promotes cell proliferation and invasion in hepatocellular carcinoma by regulating vimentin Mol Med Rep 2016 13 2 1541 50 10.3892/mmr.2015.4716 26707238
Chang L, Li C, Lan T, Wu L, Yuan Y, Liu Q, Liu Z. Decreased expression of long non-coding RNA GAS5 indicates a poor prognosis and promotes cell proliferation and invasion in hepatocellular carcinoma by regulating vimentin. Mol Med Rep. 2016;13(2):1541–50.26707238 10.3892/mmr.2015.4716
30. Hu L Ye H Huang G Luo F Liu Y Liu Y Yang X Shen J Liu Q Zhang J Long noncoding RNA GAS5 suppresses the migration and invasion of hepatocellular carcinoma cells via miR-21 Tumour Biology: J Int Soc Oncodevelopmental Biology Med 2016 37 2 2691 702 10.1007/s13277-015-4111-x
Hu L, Ye H, Huang G, Luo F, Liu Y, Liu Y, Yang X, Shen J, Liu Q, Zhang J. Long noncoding RNA GAS5 suppresses the migration and invasion of hepatocellular carcinoma cells via miR-21. Tumour Biology: J Int Soc Oncodevelopmental Biology Med. 2016;37(2):2691–702.10.1007/s13277-015-4111-x
31. Tu ZQ Li RJ Mei JZ Li XH Down-regulation of long non-coding RNA GAS5 is associated with the prognosis of hepatocellular carcinoma Int J Clin Exp Pathol 2014 7 7 4303 9 25120813
Tu ZQ, Li RJ, Mei JZ, Li XH. Down-regulation of long non-coding RNA GAS5 is associated with the prognosis of hepatocellular carcinoma. Int J Clin Exp Pathol. 2014;7(7):4303–9.25120813
32. Cao C Zhang T Zhang D Xie L Zou X Lei L Wu D Liu L The long non-coding RNA, SNHG6-003, functions as a competing endogenous RNA to promote the progression of hepatocellular carcinoma Oncogene 2017 36 8 1112 22 10.1038/onc.2016.278 27530352
Cao C, Zhang T, Zhang D, Xie L, Zou X, Lei L, Wu D, Liu L. The long non-coding RNA, SNHG6-003, functions as a competing endogenous RNA to promote the progression of hepatocellular carcinoma. Oncogene. 2017;36(8):1112–22.27530352 10.1038/onc.2016.278
33. Feng SG Bhandari R Ya L Zhixuan B Qiuhui P Jiabei Z Sewi M Ni Z Jing W Fenyong S SNHG9 promotes Hepatoblastoma Tumorigenesis via miR-23a-5p/Wnt3a Axis J Cancer 2021 12 20 6031 49 10.7150/jca.60748 34539877
Feng SG, Bhandari R, Ya L, Zhixuan B, Qiuhui P, Jiabei Z, Sewi M, Ni Z, Jing W, Fenyong S, et al. SNHG9 promotes Hepatoblastoma Tumorigenesis via miR-23a-5p/Wnt3a Axis. J Cancer. 2021;12(20):6031–49.34539877 10.7150/jca.60748
34. Li Y Guo D Zhao Y Ren M Lu G Wang Y Zhang J Mi C He S Lu X Long non-coding RNA SNHG5 promotes human hepatocellular carcinoma progression by regulating miR-26a-5p/GSK3β signal pathway Cell Death Dis 2018 9 9 888 10.1038/s41419-018-0882-5 30166525
Li Y, Guo D, Zhao Y, Ren M, Lu G, Wang Y, Zhang J, Mi C, He S, Lu X. Long non-coding RNA SNHG5 promotes human hepatocellular carcinoma progression by regulating miR-26a-5p/GSK3β signal pathway. Cell Death Dis. 2018;9(9):888.30166525 10.1038/s41419-018-0882-5
35. Shen A Ma J Hu X Cui X High expression of lncRNA-SNHG7 is associated with poor prognosis in hepatocellular carcinoma Oncol Lett 2020 19 6 3959 63 32382340
Shen A, Ma J, Hu X, Cui X. High expression of lncRNA-SNHG7 is associated with poor prognosis in hepatocellular carcinoma. Oncol Lett. 2020;19(6):3959–63.32382340
36. Xie Y Wang Y Gong R Lin J Li X Ma J Huo L SNHG7 facilitates Hepatocellular Carcinoma occurrence by sequestering mir-9-5p to Upregulate CNNM1 expression Cancer Biother Radiopharm 2020 35 10 731 40 32397799
Xie Y, Wang Y, Gong R, Lin J, Li X, Ma J, Huo L. SNHG7 facilitates Hepatocellular Carcinoma occurrence by sequestering mir-9-5p to Upregulate CNNM1 expression. Cancer Biother Radiopharm. 2020;35(10):731–40.32397799
37. Yang X Sun L Wang L Yao B Mo H Yang W LncRNA SNHG7 accelerates the proliferation, migration and invasion of hepatocellular carcinoma cells via regulating mir-122-5p and RPL4 Biomed Pharmacotherapy = Biomedecine Pharmacotherapie 2019 118 109386 10.1016/j.biopha.2019.109386
Yang X, Sun L, Wang L, Yao B, Mo H, Yang W. LncRNA SNHG7 accelerates the proliferation, migration and invasion of hepatocellular carcinoma cells via regulating mir-122-5p and RPL4. Biomed Pharmacotherapy = Biomedecine Pharmacotherapie. 2019;118:109386.10.1016/j.biopha.2019.109386
38. Zhang PF Wang F Wu J Wu Y Huang W Liu D Huang XY Zhang XM Ke AW LncRNA SNHG3 induces EMT and sorafenib resistance by modulating the miR-128/CD151 pathway in hepatocellular carcinoma J Cell Physiol 2019 234 3 2788 94 10.1002/jcp.27095 30132868
Zhang PF, Wang F, Wu J, Wu Y, Huang W, Liu D, Huang XY, Zhang XM, Ke AW. LncRNA SNHG3 induces EMT and sorafenib resistance by modulating the miR-128/CD151 pathway in hepatocellular carcinoma. J Cell Physiol. 2019;234(3):2788–94.30132868 10.1002/jcp.27095
39. Zhang T Cao C Wu D Liu L SNHG3 correlates with malignant status and poor prognosis in hepatocellular carcinoma Tumour Biology: J Int Soc Oncodevelopmental Biology Med 2016 37 2 2379 85 10.1007/s13277-015-4052-4
Zhang T, Cao C, Wu D, Liu L. SNHG3 correlates with malignant status and poor prognosis in hepatocellular carcinoma. Tumour Biology: J Int Soc Oncodevelopmental Biology Med. 2016;37(2):2379–85.10.1007/s13277-015-4052-4
40. Chen W Huang L Liang J Ye Y Yu S Zhang Y Long noncoding RNA small nucleolar RNA host gene 15 deteriorates liver cancer via microRNA-18b-5p/LIM-only 4 axis IUBMB Life 2021 73 2 349 61 10.1002/iub.2431 33372376
Chen W, Huang L, Liang J, Ye Y, Yu S, Zhang Y. Long noncoding RNA small nucleolar RNA host gene 15 deteriorates liver cancer via microRNA-18b-5p/LIM-only 4 axis. IUBMB Life. 2021;73(2):349–61.33372376 10.1002/iub.2431
41. Dai W Dai JL Tang MH Ye MS Fang S lncRNA-SNHG15 accelerates the development of hepatocellular carcinoma by targeting miR-490-3p/ histone deacetylase 2 axis World J Gastroenterol 2019 25 38 5789 99 10.3748/wjg.v25.i38.5789 31636472
Dai W, Dai JL, Tang MH, Ye MS, Fang S. lncRNA-SNHG15 accelerates the development of hepatocellular carcinoma by targeting miR-490-3p/ histone deacetylase 2 axis. World J Gastroenterol. 2019;25(38):5789–99.31636472 10.3748/wjg.v25.i38.5789
42. Huang W Huang F Lei Z Luo H LncRNA SNHG11 promotes Proliferation, Migration, apoptosis, and Autophagy by regulating hsa-miR-184/AGO2 in HCC OncoTargets Therapy 2020 13 413 21 10.2147/OTT.S237161 32021286
Huang W, Huang F, Lei Z, Luo H. LncRNA SNHG11 promotes Proliferation, Migration, apoptosis, and Autophagy by regulating hsa-miR-184/AGO2 in HCC. OncoTargets Therapy. 2020;13:413–21.32021286 10.2147/OTT.S237161
43. Jing Z Ye X Ma X Hu X Yang W Shi J Chen G Gong L SNGH16 regulates cell autophagy to promote Sorafenib Resistance through suppressing miR-23b-3p via sponging EGR1 in hepatocellular carcinoma Cancer Med 2020 9 12 4324 38 10.1002/cam4.3020 32324343
Jing Z, Ye X, Ma X, Hu X, Yang W, Shi J, Chen G, Gong L. SNGH16 regulates cell autophagy to promote Sorafenib Resistance through suppressing miR-23b-3p via sponging EGR1 in hepatocellular carcinoma. Cancer Med. 2020;9(12):4324–38.32324343 10.1002/cam4.3020
44. Lan T Ma W Hong Z Wu L Chen X Yuan Y Long non-coding RNA small nucleolar RNA host gene 12 (SNHG12) promotes tumorigenesis and metastasis by targeting miR-199a/b-5p in hepatocellular carcinoma J Experimental Clin cancer Research: CR 2017 36 1 11 10.1186/s13046-016-0486-9
Lan T, Ma W, Hong Z, Wu L, Chen X, Yuan Y. Long non-coding RNA small nucleolar RNA host gene 12 (SNHG12) promotes tumorigenesis and metastasis by targeting miR-199a/b-5p in hepatocellular carcinoma. J Experimental Clin cancer Research: CR. 2017;36(1):11.10.1186/s13046-016-0486-9
45. Lan T Yuan K Yan X Xu L Liao H Hao X Wang J Liu H Chen X Xie K LncRNA SNHG10 facilitates Hepatocarcinogenesis and Metastasis by modulating its Homolog SCARNA13 via a positive feedback Loop Cancer Res 2019 79 13 3220 34 10.1158/0008-5472.CAN-18-4044 31101763
Lan T, Yuan K, Yan X, Xu L, Liao H, Hao X, Wang J, Liu H, Chen X, Xie K, et al. LncRNA SNHG10 facilitates Hepatocarcinogenesis and Metastasis by modulating its Homolog SCARNA13 via a positive feedback Loop. Cancer Res. 2019;79(13):3220–34.31101763 10.1158/0008-5472.CAN-18-4044
46. Liao Z Zhang H Su C Liu F Liu Y Song J Zhu H Fan Y Zhang X Dong W Long noncoding RNA SNHG14 promotes hepatocellular carcinoma progression by regulating miR-876-5p/SSR2 axis J Experimental Clin cancer Research: CR 2021 40 1 36 10.1186/s13046-021-01838-5
Liao Z, Zhang H, Su C, Liu F, Liu Y, Song J, Zhu H, Fan Y, Zhang X, Dong W, et al. Long noncoding RNA SNHG14 promotes hepatocellular carcinoma progression by regulating miR-876-5p/SSR2 axis. J Experimental Clin cancer Research: CR. 2021;40(1):36.10.1186/s13046-021-01838-5
47. Lin Q Zheng H Xu J Zhang F Pan H LncRNA SNHG16 aggravates tumorigenesis and development of hepatocellular carcinoma by sponging miR-4500 and targeting STAT3 J Cell Biochem 2019 120 7 11604 15 10.1002/jcb.28440 30779219
Lin Q, Zheng H, Xu J, Zhang F, Pan H. LncRNA SNHG16 aggravates tumorigenesis and development of hepatocellular carcinoma by sponging miR-4500 and targeting STAT3. J Cell Biochem. 2019;120(7):11604–15.30779219 10.1002/jcb.28440
48. Liu Y Chen L Yuan H Guo S Wu G LncRNA DANCR promotes Sorafenib Resistance via activation of IL-6/STAT3 signaling in Hepatocellular Carcinoma cells OncoTargets Therapy 2020 13 1145 57 10.2147/OTT.S229957 32103983
Liu Y, Chen L, Yuan H, Guo S, Wu G. LncRNA DANCR promotes Sorafenib Resistance via activation of IL-6/STAT3 signaling in Hepatocellular Carcinoma cells. OncoTargets Therapy. 2020;13:1145–57.32103983 10.2147/OTT.S229957
49. Ma X Wang X Yang C Wang Z Han B Wu L Zhuang L DANCR acts as a diagnostic biomarker and promotes Tumor Growth and Metastasis in Hepatocellular Carcinoma Anticancer Res 2016 36 12 6389 98 10.21873/anticanres.11236 27919960
Ma X, Wang X, Yang C, Wang Z, Han B, Wu L, Zhuang L. DANCR acts as a diagnostic biomarker and promotes Tumor Growth and Metastasis in Hepatocellular Carcinoma. Anticancer Res. 2016;36(12):6389–98.27919960 10.21873/anticanres.11236
50. Wang X Cheng ML Gong Y Ma WJ Li B Jiang YZ LncRNA DANCR promotes ATG7 expression to accelerate hepatocellular carcinoma cell proliferation and autophagy by sponging miR-222-3p Eur Rev Med Pharmacol Sci 2020 24 17 8778 87 32964966
Wang X, Cheng ML, Gong Y, Ma WJ, Li B, Jiang YZ. LncRNA DANCR promotes ATG7 expression to accelerate hepatocellular carcinoma cell proliferation and autophagy by sponging miR-222-3p. Eur Rev Med Pharmacol Sci. 2020;24(17):8778–87.32964966
51. Xie X Xu X Sun C Yu Z Long intergenic noncoding RNA SNHG16 interacts with miR-195 to promote proliferation, invasion and tumorigenesis in hepatocellular carcinoma Exp Cell Res 2019 383 1 111501 10.1016/j.yexcr.2019.111501 31306653
Xie X, Xu X, Sun C, Yu Z. Long intergenic noncoding RNA SNHG16 interacts with miR-195 to promote proliferation, invasion and tumorigenesis in hepatocellular carcinoma. Exp Cell Res. 2019;383(1):111501.31306653 10.1016/j.yexcr.2019.111501
52. Xu X Song F Jiang X Hong H Fei Q Jin Z Zhu X Dai B Yang J Sui C Long non-coding RNA SNHG14 contributes to the Development of Hepatocellular Carcinoma via sponging miR-217 OncoTargets Therapy 2020 13 4865 76 10.2147/OTT.S244530 32581548
Xu X, Song F, Jiang X, Hong H, Fei Q, Jin Z, Zhu X, Dai B, Yang J, Sui C, et al. Long non-coding RNA SNHG14 contributes to the Development of Hepatocellular Carcinoma via sponging miR-217. OncoTargets Therapy. 2020;13:4865–76.32581548 10.2147/OTT.S244530
53. Ye J Zhang R Du X Chai W Zhou Q Long noncoding RNA SNHG16 induces sorafenib resistance in hepatocellular carcinoma cells through sponging miR-140-5p OncoTargets Therapy 2019 12 415 22 10.2147/OTT.S175176 30655679
Ye J, Zhang R, Du X, Chai W, Zhou Q. Long noncoding RNA SNHG16 induces sorafenib resistance in hepatocellular carcinoma cells through sponging miR-140-5p. OncoTargets Therapy. 2019;12:415–22.30655679 10.2147/OTT.S175176
54. Yuan SX Wang J Yang F Tao QF Zhang J Wang LL Yang Y Liu H Wang ZG Xu QG Long noncoding RNA DANCR increases stemness features of hepatocellular carcinoma by derepression of CTNNB1 Hepatology (Baltimore MD) 2016 63 2 499 511 10.1002/hep.27893 25964079
Yuan SX, Wang J, Yang F, Tao QF, Zhang J, Wang LL, Yang Y, Liu H, Wang ZG, Xu QG, et al. Long noncoding RNA DANCR increases stemness features of hepatocellular carcinoma by derepression of CTNNB1. Hepatology (Baltimore MD). 2016;63(2):499–511.25964079 10.1002/hep.27893
55. Zhang D Cao C Liu L Wu D Up-regulation of LncRNA SNHG20 predicts poor prognosis in Hepatocellular Carcinoma J Cancer 2016 7 5 608 17 10.7150/jca.13822 27053960
Zhang D, Cao C, Liu L, Wu D. Up-regulation of LncRNA SNHG20 predicts poor prognosis in Hepatocellular Carcinoma. J Cancer. 2016;7(5):608–17.27053960 10.7150/jca.13822
56. Zhang H Xu HB Kurban E Luo HW LncRNA SNHG14 promotes hepatocellular carcinoma progression via H3K27 acetylation activated PABPC1 by PTEN signaling Cell Death Dis 2020 11 8 646 10.1038/s41419-020-02808-z 32811821
Zhang H, Xu HB, Kurban E, Luo HW. LncRNA SNHG14 promotes hepatocellular carcinoma progression via H3K27 acetylation activated PABPC1 by PTEN signaling. Cell Death Dis. 2020;11(8):646.32811821 10.1038/s41419-020-02808-z
57. Zhang JH Wei HW Yang HG Long noncoding RNA SNHG15, a potential prognostic biomarker for hepatocellular carcinoma Eur Rev Med Pharmacol Sci 2016 20 9 1720 4 27212162
Zhang JH, Wei HW, Yang HG. Long noncoding RNA SNHG15, a potential prognostic biomarker for hepatocellular carcinoma. Eur Rev Med Pharmacol Sci. 2016;20(9):1720–4.27212162
58. Zhong JH Xiang X Wang YY Liu X Qi LN Luo CP Wei WE You XM Ma L Xiang BD The lncRNA SNHG16 affects prognosis in hepatocellular carcinoma by regulating p62 expression J Cell Physiol 2020 235 2 1090 102 10.1002/jcp.29023 31256427
Zhong JH, Xiang X, Wang YY, Liu X, Qi LN, Luo CP, Wei WE, You XM, Ma L, Xiang BD, et al. The lncRNA SNHG16 affects prognosis in hepatocellular carcinoma by regulating p62 expression. J Cell Physiol. 2020;235(2):1090–102.31256427 10.1002/jcp.29023
59. Zhu XM Li L Ren LL Du L Wang YM LncRNA SNHG17 predicts poor prognosis and promotes cell proliferation and migration in hepatocellular carcinoma Eur Rev Med Pharmacol Sci 2021 25 12 4219 27 34227089
Zhu XM, Li L, Ren LL, Du L, Wang YM. LncRNA SNHG17 predicts poor prognosis and promotes cell proliferation and migration in hepatocellular carcinoma. Eur Rev Med Pharmacol Sci. 2021;25(12):4219–27.34227089
60. Liu J Lu C Xiao M Jiang F Qu L Ni R Long non-coding RNA SNHG20 predicts a poor prognosis for HCC and promotes cell invasion by regulating the epithelial-to-mesenchymal transition Biomed Pharmacotherapy = Biomedecine Pharmacotherapie 2017 89 857 63 10.1016/j.biopha.2017.01.011
Liu J, Lu C, Xiao M, Jiang F, Qu L, Ni R. Long non-coding RNA SNHG20 predicts a poor prognosis for HCC and promotes cell invasion by regulating the epithelial-to-mesenchymal transition. Biomed Pharmacotherapy = Biomedecine Pharmacotherapie. 2017;89:857–63.10.1016/j.biopha.2017.01.011
61. Lou J Yan W Li QY Zhu AK Tan BQ Dong R Zou XZ Liu T LncRNA MEG8 plays an oncogenic role in hepatocellular carcinoma progression through miR-367-3p/14-3-3ζ/TGFβR1 axis Neoplasma 2021 68 2 273 82 10.4149/neo_2020_200730N785 33147050
Lou J, Yan W, Li QY, Zhu AK, Tan BQ, Dong R, Zou XZ, Liu T. LncRNA MEG8 plays an oncogenic role in hepatocellular carcinoma progression through miR-367-3p/14-3-3ζ/TGFβR1 axis. Neoplasma. 2021;68(2):273–82.33147050 10.4149/neo_2020_200730N785
62. Zhang Y Lu C Cui H Long non-coding RNA SNHG22 facilitates hepatocellular carcinoma tumorigenesis and angiogenesis via DNA methylation of microRNA miR-16-5p Bioengineered 2021 12 1 7446 58 10.1080/21655979.2021.1975969 34652260
Zhang Y, Lu C, Cui H. Long non-coding RNA SNHG22 facilitates hepatocellular carcinoma tumorigenesis and angiogenesis via DNA methylation of microRNA miR-16-5p. Bioengineered. 2021;12(1):7446–58.34652260 10.1080/21655979.2021.1975969
63. Yang L, Jiang J. GAS5 Regulates RECK Expression and Inhibits Invasion Potential of HCC Cells by Sponging miR-135b. BioMed research international 2019, 2019:2973289.
64. Kansara S Singh A Badal AK Rani R Baligar P Garg M Pandey AK The emerging regulatory roles of non-coding RNAs associated with glucose metabolism in breast cancer Sem Cancer Biol 2023 95 1 12 10.1016/j.semcancer.2023.06.007
Kansara S, Singh A, Badal AK, Rani R, Baligar P, Garg M, Pandey AK. The emerging regulatory roles of non-coding RNAs associated with glucose metabolism in breast cancer. Sem Cancer Biol. 2023;95:1–12.10.1016/j.semcancer.2023.06.007
65. Pandya G Kirtonia A Sethi G Pandey AK Garg M The implication of long non-coding RNAs in the diagnosis, pathogenesis and drug resistance of pancreatic ductal adenocarcinoma and their possible therapeutic potential Biochim et Biophys acta Reviews cancer 2020 1874 2 188423 10.1016/j.bbcan.2020.188423
Pandya G, Kirtonia A, Sethi G, Pandey AK, Garg M. The implication of long non-coding RNAs in the diagnosis, pathogenesis and drug resistance of pancreatic ductal adenocarcinoma and their possible therapeutic potential. Biochim et Biophys acta Reviews cancer. 2020;1874(2):188423.10.1016/j.bbcan.2020.188423
66. Sharma A Kansara S Mahajan M Yadav B Garg M Pandey AK Long non-coding RNAs orchestrate various molecular and cellular processes by modulating epithelial-mesenchymal transition in head and neck squamous cell carcinoma Biochim et Biophys acta Mol Basis Disease 2021 1867 11 166240 10.1016/j.bbadis.2021.166240
Sharma A, Kansara S, Mahajan M, Yadav B, Garg M, Pandey AK. Long non-coding RNAs orchestrate various molecular and cellular processes by modulating epithelial-mesenchymal transition in head and neck squamous cell carcinoma. Biochim et Biophys acta Mol Basis Disease. 2021;1867(11):166240.10.1016/j.bbadis.2021.166240
67. Zhang J Jin X Zhou C Zhao H He P Hao Y Dong Q Resveratrol suppresses human nasopharyngeal Carcinoma Cell Growth Via inhibiting differentiation antagonizing Non-protein Coding RNA (DANCR) expression Med Sci Monitor: Int Med J Experimental Clin Res 2020 26 e923622
Zhang J, Jin X, Zhou C, Zhao H, He P, Hao Y, Dong Q. Resveratrol suppresses human nasopharyngeal Carcinoma Cell Growth Via inhibiting differentiation antagonizing Non-protein Coding RNA (DANCR) expression. Med Sci Monitor: Int Med J Experimental Clin Res. 2020;26:e923622.
68. Chen S, Xie C, Hu X. lncRNA SNHG6 functions as a ceRNA to up-regulate c-Myc expression via sponging let-7c-5p in hepatocellular carcinoma. Biochem Biophys Res Commun. 2019;519(4):901–908.
69. Dong J, Teng F, Guo W, Yang J, Ding G, Fu Z. lncRNA SNHG8 promotes the tumorigenesis and metastasis by sponging miR-149-5p and predicts tumor recurrence in hepatocellular carcinoma. Cell Physiol Biochem Internat J Exper Cell Physiol Biochem Pharmacology 2018;51(5):2262–2274.
70. Hu YL, Feng Y, Chen YY, Liu JZ, Su Y, Li P, Huang H, Mao QS, Xue WJ. SNHG16/miR-605-3p/TRAF6/NF-κB feedback loop regulates hepatocellular carcinoma metastasis. J Cell Mol Med. 2020;24(13):7637–7651.
71. Lin RX, Zhan GF, Wu JC, Fang H, Yang SL. LncRNA SNHG14 sponges miR-206 to affect proliferation, apoptosis, and metastasis of hepatocellular carcinoma cells by regulating SOX9. Digest Dis Sci. 2022;67(3):936–946.
