Abstract

Background: Long non-coding RNA associated with poor prognosis of hepatocellular carcinoma (AWPPH) is dysregulated in a variety of human cancers. However, the prognostic value of AWPPH in various cancers remains unclear.

Methods: Comprehensive literature search was performed in PubMed, Web of Science, CNKI and Wangfang databases, and eligible studies were obtained according to the inclusion and exclusion criteria. The pooled hazard ratios (HRs) and odds ratios (ORs) were applied to assess the clinical value of AWPPH expression for overall survival (OS) and clinicopathological features.

Results: A total of 19 articles including 1699 cancer patients were included in the study. The pooled results demonstrated that evaluated AWPPH expression was positively related to a poorer overall survival of patients with cancers (HR = 1.79, 95%CI: 1.44–2.14, P<0.001). Subgroup analysis revealed that tumor type and sample size affect the predictive value of AWPPH on OS, whereas cut-off value and HR estimation method have no impact on it. In addition, the pooled data also showed that AWPPH was positively linked to advanced TNM stage (OR = 2.50, 95%CI: 1.94–3.22, P<0.001), bigger tumor size (OR = 2.64, 95%CI: 1.47–4.73, P=0.001), macro-vascular invasion (OR = 2.08, 95%CI: 1.04–4.16, P=0.04) and lymph node metastasis (OR = 2.68, 95%CI: 1.82–3.96, P<0.001). Moreover, the results of the trim and fill analysis confirmed the reliability of our finding.

Conclusions: Up-regulation of AWPPH was associated with advanced TNM stage, bigger tumor size, worse lymph node metastasis, macro-vascular invasion and shorter overall survival, suggesting that AWPPH may serve as a biomarker for prognosis and clinicopathological characteristics in human cancers among the Chinese population.

Background

Cancer is a major public health problem worldwide, and it has been the leading cause of death in China since 2010 [1]. Cancer is a highly complex disease involving numerous molecular changes, including chromosomal translocations, deletions and amplification, epigenetic alterations and genetic mutations [2–4], which make it more difficult to be cured than ordinary diseases. Although great advances have been achieved in diagnoses and treatments, the clinical prognosis remains undesirable in most cancer patients. Therefore, the exploration of effective molecular biomarkers that can be used to guide clinical prevention, treatment and prognosis prediction of cancer is becoming imminent.

LncRNA is a typical kind of non-coding RNA without meaningful open reading frame, which also possesses many significant functions and plays important roles in tumorigenesis and tumor progression. Most lncRNA transcripts involved in the epigenetic, transcriptional, and posttranscriptional regulation of cancer cells [5]. Furthermore, a variety of lncRNA could function as enhancers [6], splicing regulators [7], as well as chromatin remodelers [8]. Notably, accumulating evidence demonstrated that dysregulated lncRNA occurred in a broad spectrum of human cancers [9,10]. These cancer-related lncRNAs have been proved to participate in cancer initiation and progression, which may have potential value as clinical biomarkers and therapeutic targets. Recently, the long non-coding RNA associated with poor prognosis of hepatocellular carcinoma (AWPPH) attracted increasing attention.

AWPPH, also well-known as AK001796, MIR4435-2HG, LINC00978 and other names, was localized at 2q13 and found to be dysregulated in many human cancers. Growing evidence showed that AWPPH was associated with tumorigenesis and prognostic outcome [11–13]. However, abundant studies reported the prognostic value of AWPPH for human cancers were constrained by sample size and discrete outcome so far. Consequently, we performed this systematic review and meta-analysis on the basis of eligible retrospective studies to investigate the potential prognostic value of AWPPH for cancer patients.

Methods

Literature collection

This meta-analysis was performed in accordance with the PRISMA 2009 guidelines (Supplementary S1) [14]. We performed literature search using PubMed, Web of Science, CNKI and Wangfang database for eligible studies which reported the relationship between lncRNA AWPPH and prognosis of human cancers before October 5, 2020. Search terms used as follows: (‘carcinoma’ OR ‘cancer’ OR ‘tumor’ OR ‘neoplasm’) AND (‘prognosis’ OR ‘outcome’ OR ‘diagnosis’ OR ‘survival’) AND (‘AWPPH’ OR ‘LINC00978’ OR ‘MIR4435-1HG’ OR ‘MORRBID’ OR ‘AGD2’ OR ‘MIR4435-12HG’ OR ‘AK001796’ OR ‘MIR4435-2HG’). The reference lists of primary publications were also manually searched to obtain potential eligible studies. There is no requirement for patient consent or ethical approval due to all the analyses were conducted on the basis of the prior published researches.

Inclusion and exclusion criteria

Eligible studies should meet the following inclusion criteria : (1) Studies evaluated the association between AWPPH and cancer patient samples; (2) Available prognosis outcomes or clinicopathologic features data; (3) sufficient information to obtain hazard ratio (HR) or odds ratio (OR) with 95% confidence interval (95% CI). The following articles were excluded from the study: (1) reviews, letters or case reports; (2) non-human studies; (3) duplicated publication; (4) studies with insufficient data for HR/OR/95%CI extraction.

Data extraction and quality assessment

Eligible articles were reviewed by two reviewers (Li and Rui) independently according to the inclusion and exclusion criteria. Disagreement was resolved during a consensus with a third reviewer (Chen). The essential information was screened and extracted from each eligible study, including the name of first author, year of publication, origin country, cancer type, sample size, detection method of AWPPH, HR and corresponding 95%CI for OS, as well as clinicopathological features. The HRs with 95%CIs were obtained directly from studies which performed the multivariate analysis, and the Kaplan–Meier curves were used for the extraction of the survival information if the 95% CIs and HRs have not been directly reported from the researches according to the method described in the previous publication [15]. The Newcastle–Ottawa Scale (NOS) was applied to evaluate the quality of the included study. The NOS scores ranged from 0 to 9 and studies with a NOS score >6 were considered to be high quality.

Statistical analysis

The present meta-analysis was performed with STATA SE 15.0 (Stata Corporation). HR and corresponding 95%CI for OS were applied to determine the pooled effect for clinical outcomes, and the odds ratio (OR) with 95%CI were used to evaluate the correlation between LncRNA AWPPH and clinicopathological parameters. Statistical heterogeneity was assessed using the I2 test as well as the chi-based Q-test, to determine heterogeneity between several studies. Heterogeneity was considered as statistically significant with I2 < 50%. The fixed-effect model was used if heterogeneity exists (I2 > 50% and P<0.05), otherwise, the random-effect model was applied. Publication bias was assessed using Begg’s funnel plot and Egger’s regression test. The sensitivity analysis was used to check the stability of the combined results and to determine the source of any heterogeneity. The P-value <0.05 was considered to be statistically significant.

Results

Summary of eligible studies

As shown in Figure 1, a total of 143 potentially relevant articles were obtained from the first attempt to search by using the keywords. There are 57 duplicate articles and 60 irrelevant articles excluded after screening the titles and abstracts. Finally, 7 studies with insufficient data were excluded and the remain 19 studies were included in the subsequent meta-analysis. The main characteristics of the included 19 studies have been summarized in Table 1. A total of 1699 patients from 19 studies between 2016 and 2020 were included [11–13,16–31]. The respective sample sizes ranged from 36 to 195 patients. 19 studies had addressed 12 different types of cancer: including hepatocellular carcinoma (HCC, n=3), colorectal adenocarcinoma (CRC, n=3), ovarian carcinoma (OC, n=2), triple-negative breast cancer (TNBC, n=1), non-small cell lung cancer (NSCLC, n=2), osteosarcoma (n=1), cervical cancer (CC, n=1), oral squamous cell carcinom (OSCC, n=1), clear cell renal cell carcinoma (CCRCC, n=1), GC (n=1), prostate carcinoma (PC, n=1), breast cancer (BC, n=1), esophageal squamous cell carcinoma (ESCC, n=1). Clinical outcomes were recorded including 19 studies for overall survival (OS), 3 for recurrence-free survival (RFS), 1 for progression-free survival (PFS), and 1 for disease-free survival (DFS). HRs with corresponding 95% CIs were obtained from the original data in 4 studies, and calculated from Kaplan–Meier curves for the rest 15 studies. In addition, for the quality assessment, the Newcastle–Ottawa Scale (NOS) score of individual cohort studies was ranged from 6 to 8, which indicated that the methodological quality of included studies was medium or high. The clinicopathological features of the included studies were summarized in Table 2.

Flow chart of literature search

Figure 1
Flow chart of literature search
Figure 1
Flow chart of literature search
Table 1
Characteristics of the included eligible studies
AuthorYearCountryTumorSample sizeCut-off valueDetection methodOutcomesHR estimation methodHR (95%CI)NOS
Zhao, X.D. 2017 China HCC 88 Median qRT-PCR OS/RFS U/M OS: 3.509 (1.574–7.820)
RFS: 2.579 (1.425–4.668) 
Liu, C.C. 2018 China CRC 86 Median qRT-PCR OS Indirectly 1.51 (0.74,3.07) 
Yu, G.Y. 2019 China OC 58 Median qRT-PCR OS Indirectly 2.05 (1.01,4.14) 
Wang, K.N. 2018 China TNBC 68 Median qRT-PCR OS Indirectly 1.79 (0.90,3.59) 
Song, Z. 2018 China NSCLC 88 Median qRT-PCR OS Indirectly Tissue: 1.78 (0.99,3.20)
Serum: 1.66 (0.91,3.05) 
Li, H. 2019 China Osteosarcoma 36 Median qRT-PCR OS/RFS Indirectly OS: 0.53 (0.14,2.00)
RFS: 0.56 (0.14,2.29) 
Wu, D. 2020 China NSCLC 56 Median qRT-PCR OS Indirectly 2.861 (1.439–5.686) 
Chen, X.H. 2020 China CC 75 Mean qRT-PCR OS U/M 2.104 (1.221–3.626) 
Ma, X.D. 2020 China OSCC 82 Mean qRT-PCR OS Indirectly 7.24 (1.58,33.10) 
Dong, X.H. 2020 China CRC 90 Median qRT-PCR OS Indirectly 1.30 (0.44,3.80) 
Ho, J.Q. 2020 China CCRCC 118 Median qRT-PCR OS/RFS Indirectly OS: 2.98 (0.52,17.17)
RFS: 2.17 (0.65,7.18) 
Bu, J.Y. 2018 China GC 150 Median qRT-PCR OS Indirectly 1.97 (1.24,3.14) 
Zhu, L.J. 2020 China OC 42 Median qRT-PCR OS Indirectly 1.85 (0.65,5.26) 
Zhang, H. 2019 China PC 68 Mean qRT-PCR OS Indirectly 1.83 (0.83,4.03) 
Shen, M.Y. 2020 China CRC 102 Mean qRT-PCR OS/PFS Indirectly OS: 2.57 (0.98,6.74)
PFS: 3.18 (1.20,8.39) 
Zhang, Q. 2020 China HCC 49 Mean qRT-PCR OS Indirectly 1.96 (0.66,5.84) 
Deng, L.L. 2016 China BC 195 Mean qRT-PCR OS U/M 2.27 (1.237,4.165) 
Han, Q.L. 2019 China HCC 73 Median qRT-PCR OS Indirectly 2.02 (1.04,3.92) 
Zong, M.Z. 2019 China ESCC 175 Median qRT-PCR OS/DFS U/M OS: 3.347 (1.423,5.457)
DFS: 3.568 (1.537,5.778) 
AuthorYearCountryTumorSample sizeCut-off valueDetection methodOutcomesHR estimation methodHR (95%CI)NOS
Zhao, X.D. 2017 China HCC 88 Median qRT-PCR OS/RFS U/M OS: 3.509 (1.574–7.820)
RFS: 2.579 (1.425–4.668) 
Liu, C.C. 2018 China CRC 86 Median qRT-PCR OS Indirectly 1.51 (0.74,3.07) 
Yu, G.Y. 2019 China OC 58 Median qRT-PCR OS Indirectly 2.05 (1.01,4.14) 
Wang, K.N. 2018 China TNBC 68 Median qRT-PCR OS Indirectly 1.79 (0.90,3.59) 
Song, Z. 2018 China NSCLC 88 Median qRT-PCR OS Indirectly Tissue: 1.78 (0.99,3.20)
Serum: 1.66 (0.91,3.05) 
Li, H. 2019 China Osteosarcoma 36 Median qRT-PCR OS/RFS Indirectly OS: 0.53 (0.14,2.00)
RFS: 0.56 (0.14,2.29) 
Wu, D. 2020 China NSCLC 56 Median qRT-PCR OS Indirectly 2.861 (1.439–5.686) 
Chen, X.H. 2020 China CC 75 Mean qRT-PCR OS U/M 2.104 (1.221–3.626) 
Ma, X.D. 2020 China OSCC 82 Mean qRT-PCR OS Indirectly 7.24 (1.58,33.10) 
Dong, X.H. 2020 China CRC 90 Median qRT-PCR OS Indirectly 1.30 (0.44,3.80) 
Ho, J.Q. 2020 China CCRCC 118 Median qRT-PCR OS/RFS Indirectly OS: 2.98 (0.52,17.17)
RFS: 2.17 (0.65,7.18) 
Bu, J.Y. 2018 China GC 150 Median qRT-PCR OS Indirectly 1.97 (1.24,3.14) 
Zhu, L.J. 2020 China OC 42 Median qRT-PCR OS Indirectly 1.85 (0.65,5.26) 
Zhang, H. 2019 China PC 68 Mean qRT-PCR OS Indirectly 1.83 (0.83,4.03) 
Shen, M.Y. 2020 China CRC 102 Mean qRT-PCR OS/PFS Indirectly OS: 2.57 (0.98,6.74)
PFS: 3.18 (1.20,8.39) 
Zhang, Q. 2020 China HCC 49 Mean qRT-PCR OS Indirectly 1.96 (0.66,5.84) 
Deng, L.L. 2016 China BC 195 Mean qRT-PCR OS U/M 2.27 (1.237,4.165) 
Han, Q.L. 2019 China HCC 73 Median qRT-PCR OS Indirectly 2.02 (1.04,3.92) 
Zong, M.Z. 2019 China ESCC 175 Median qRT-PCR OS/DFS U/M OS: 3.347 (1.423,5.457)
DFS: 3.568 (1.537,5.778) 

Abbreviations: BC, breast cancer; CC, cervical cancer; CCRCC, clear cell renal cell carcinoma; CRC, colorectal adenocarcinoma; DFS, disease-free survival; ESCC, esophageal squamous cell carcinoma; GC, gastric cancer; HCC, hepatocellular carcinoma; NOS, Newcastle-Ottawa Scale; NSCLC, non-small cell lung cancer; OC, ovarian carcinoma; OS, overall survival; OSCC, oral squamous cell carcinoma; PC, prostate carcinoma; PFS, progression-free survival; RFS, recurrence-free survival; TNBC, triple-negative breast cancer; U/M, univariate/multivariate analysis.

Table 2
The clinicopathological features of the included studies
AuthorYearAWPPH expressionTNMTumor sizeMacro-vascular invasionLymph node metastasis
highlow>I stage in HG>I stage in LG>50 in HG>50 in LGYes in HGYES in LGYes in HGYES in LG
Zhang, Q. 2020 26 23 16 16 14 14 10   
Zhu, L.J. 2020 21 21   11   13 
Ma, X.D. 2020 20 62 12 22     
Dong, X.H. 2020 45 45 24 10 29 17   32 30 
Han, Q.L. 2019 37 36 19 25     
Shen, M.Y. 2020 55 47 30 15 40 15   38 14 
Zhao, X.D. 2017 44 44 33 24 26 24 28 18   
Zong, M.Z. 2019 87 88 39 24     35 22 
Wang, K.N. 2018 34 34 26 14 14     
Ho, J.Q. 2020 59 59 32 11       
Deng, L.L. 2016 49 146 18 37       
Zhang, H. 2019 31 37 17 21       
Li, H. 2019 19 17 15       
Yu, G.Y. 2019 29 29         
Song, Z. 2018 44 44         
AuthorYearAWPPH expressionTNMTumor sizeMacro-vascular invasionLymph node metastasis
highlow>I stage in HG>I stage in LG>50 in HG>50 in LGYes in HGYES in LGYes in HGYES in LG
Zhang, Q. 2020 26 23 16 16 14 14 10   
Zhu, L.J. 2020 21 21   11   13 
Ma, X.D. 2020 20 62 12 22     
Dong, X.H. 2020 45 45 24 10 29 17   32 30 
Han, Q.L. 2019 37 36 19 25     
Shen, M.Y. 2020 55 47 30 15 40 15   38 14 
Zhao, X.D. 2017 44 44 33 24 26 24 28 18   
Zong, M.Z. 2019 87 88 39 24     35 22 
Wang, K.N. 2018 34 34 26 14 14     
Ho, J.Q. 2020 59 59 32 11       
Deng, L.L. 2016 49 146 18 37       
Zhang, H. 2019 31 37 17 21       
Li, H. 2019 19 17 15       
Yu, G.Y. 2019 29 29         
Song, Z. 2018 44 44         

Note: HG represented the group with high AWPPH expression, LG represented the group with low AWPPH expression.

Prognostic value of AWPPH

A total of 19 studies with 1699 patients reported the relationship between OS and AWPPH in human cancers. As shown in Figure 2A, a significant correlation was observed between elevated AWPPH expression and poor OS (HR = 1.79, 95%CI: 1.44–2.14, P<0.001) with non-significant heterogeneity (I2 = 0%, P=0.737). Furthermore, subgroup analysis across several different variables, including cancer type, sample size, HR estimation method, and cut-off value, were further performed to explore the association between HRs and OS. The results showed that cancer type and sample size influence the prognostic value of AWPPH on OS (Figures 3 and 4), whereas the HE estimation methods and cut-off value have no impact on it (Figures 5 and 6). There was a negatively relationship between AWPPH expression and OS in the patients HCC (HR = 2.22, 95%CI: 1.05–3.38), NSCLC (HR = 2.01, 95%CI: 1.03–2.99), BC (HR = 2.01, 95%CI: 1.02–3.00), and other cancers (HR = 1.62, 95%CI: 1.10–2.15) (Figure 3). Moreover, the effect of AWPPH over-expression on predicting short OS occurred in the studies with sample size >70 (HR = 1.99, 95%CI: 1.55–2.44) (Figure 4).

Meta-analysis of the association between AWPPH expression and prognosis index

Figure 2
Meta-analysis of the association between AWPPH expression and prognosis index

(A and B) Forest plot and of studies evaluating the association between AWPPH expression and OS and RFS. (C) Begg’s publication bias plots of OS, and (D) sensitivity analysis for OS.

Figure 2
Meta-analysis of the association between AWPPH expression and prognosis index

(A and B) Forest plot and of studies evaluating the association between AWPPH expression and OS and RFS. (C) Begg’s publication bias plots of OS, and (D) sensitivity analysis for OS.

Forest plots of subgroup analysis for the HRs of OS by tumor type

Figure 3
Forest plots of subgroup analysis for the HRs of OS by tumor type
Figure 3
Forest plots of subgroup analysis for the HRs of OS by tumor type

Forest plots of subgroup analysis for the HRs of OS by sample size

Figure 4
Forest plots of subgroup analysis for the HRs of OS by sample size
Figure 4
Forest plots of subgroup analysis for the HRs of OS by sample size

Forest plots of subgroup analysis for the HRs of OS by cut-off value

Figure 5
Forest plots of subgroup analysis for the HRs of OS by cut-off value
Figure 5
Forest plots of subgroup analysis for the HRs of OS by cut-off value

Forest plots of subgroup analysis for the HRs of OS by HR estimation method

Figure 6
Forest plots of subgroup analysis for the HRs of OS by HR estimation method
Figure 6
Forest plots of subgroup analysis for the HRs of OS by HR estimation method

Association between AWPPH and clinicopathological features

The correlation between AWPPH expression and clinicopathological characteristics were examined with OR analysis in 15 studies with 1332 cancer patients (Figure 7 and Table 3). About 12 studies with 1143 patients were included to analysis the link between AWPPH and TNM stage, and the pooled data found an obvious association between AWPPH overexpression and advanced TNM stage (OR = 2.50, 95%CI: 1.94–3.22, P<0.001) (Figure 7B). The results also showed that over-expression of AWPPH predicts larger tumor size (OR = 2.64, 95%CI:1.47–4.73, P=0.001, Figure 7D). In addition, 2 studies with 137 patients were included to analyze the link between AWPPH and macro-vascular invasion, the results revealed an obvious association between AWPPH expression and MVI (OR = 2.08, 95%CI: 1.04–4.16, P=0.039, Figure 7E). As shown in Figure 7F, 491 cancer patients from 5 studies were included to evaluate the correlation between AWPPH and LNM, and the results indicated that the patients with elevated AWPPH expression were more susceptibility to develop LNM (OR = 2.68, 95%CI: 1.82–3.96, P<0.001).

Meta-analysis for the association between AWPPH expression with clinicopathological parameters

Figure 7
Meta-analysis for the association between AWPPH expression with clinicopathological parameters

The investigated clinicopathological parameters are: (A) differentiation status, (B) TNM stage, (C) distant metastasis, (D) tumor size, (E) macro-vascular invasion and (F) lymph node metastasis.

Figure 7
Meta-analysis for the association between AWPPH expression with clinicopathological parameters

The investigated clinicopathological parameters are: (A) differentiation status, (B) TNM stage, (C) distant metastasis, (D) tumor size, (E) macro-vascular invasion and (F) lymph node metastasis.

Table 3
The P-values obtained from either the fixed or random model for the risk association analyses
Risk factorsModelsP value
Differentiation Random effect 0.45 
Distant metastasis Random effect 0.854 
Lymph node metastasis Fixed model <0.001 
Macro-vascular invasion Fixed model 0.039 
TNM stage Fixed model <0.001 
Tumor size Random effect 0.001 
Risk factorsModelsP value
Differentiation Random effect 0.45 
Distant metastasis Random effect 0.854 
Lymph node metastasis Fixed model <0.001 
Macro-vascular invasion Fixed model 0.039 
TNM stage Fixed model <0.001 
Tumor size Random effect 0.001 

Publication bias and sensitivity analysis

Begg’s funnel plot and Egger’s linear regression tests were introduced to evaluate potential publication bias in our present meta-analysis. In the analysis of evaluating the association between AWPPH expression and OS, visual inspection of the Begg’s funnel plot did not reveal asymmetry (Figure 2C), and Egger’s test also suggested the absence of publication bias (t = 0.06, P=0.953). Sensitivity analyses were performed to evaluate whether individual study influenced pooled HRs by excluding one study by turns. The results showed that the pooled HR was not significantly changed after removing each study, suggested that the results were stable (Figure 2D).

Discussion

Evidence from multiple publications demonstrated that lncRNA AWPPH is closely associated with cancers. AWPPH was first discovered in breast caner as the name of LINC00978 [26]. In breast cancer patients, the expression of AWPPH was negatively associated with hormone receptor status, and high AWPPH expression predicted poor DFS. In recent years, It has been shown from prior studies that AWPPH serves as a dysregulated oncogene in several cancers, such as GC [24], CRC [32] and NSCLC [30]. AWPPH can promote cell proliferation, migration and invasion in a variety of human cancers, and played an crucial role in tumor progression, metastasis and prognosis. However, a persuasive support of the AWPPH in clinical practice is still controversial. In order to combine previous research results about AWPPH and cancers to arrive at a summary conclusion, a comprehensive study was carried out.

In this meta-analysis, we pooled data from a total of 19 retrospective eligible studies with 1699 cancer patients to systematically explore the relationship between AWPPH and prognosis. We found that elevated AWPPH expression was an unfavorable prognostic factor in multiple cancer patients. Furthermore, the results also demonstrated that high AWPPH expression level was positively related to advanced TNM stage, higher risk of LNM and MVI, and bigger tumor size.

The exact mechanisms underlying the association between aberrant AWPPH expression and poor clinical prognosis remains elusive. The molecular mechanism of AWPPH in various cancers from prior studies were illustrated in Figure 8. Previous study reported that AWPPH regulates cell proliferation and cell cycle via modulating MDM2/p53 signaling in ESCC [33]. AWPPH acted as an oncogene to interact with YBX1 to activate the expression of SNAIL1 and PI3K/AKT pathway in the HCC [11]. Wnt/β-catenin signal pathway involved in the regulation of cell proliferation, migration and invasion in certain cancers [34,35], and AWPPH could promote the proliferation, migration and invasion of BC, OC and NSCLC by activating this pathway [12,30,36]. Several important pathways were also conformed to be modulated by AWPPH in cancers, including MDM2-p53 pathway esophageal squamous cell carcinoma [33] and MEK/ERK pathway in HCC [22]. Furthermore, AWPPH could inhibit colon cancer cell proliferation by down-regulating GLUT-1 [37] and mediate the metastasis and postoperative distant recurrence by up-regulating TGF-β1 [29,38,39]. Liu et al. demonstrated that AWPPH contributes to cisplatin resistance by inducing the expression of CDK1 and GTSE5, and suppressing the expression of CCNC and BIRC5. This provided a brand new insight for the cisplatin resistance of gastric cancer NSCLC [40].

Schematic diagrams of various molecules and signaling pathways associated with AWPPH in human cancers

Figure 8
Schematic diagrams of various molecules and signaling pathways associated with AWPPH in human cancers
Figure 8
Schematic diagrams of various molecules and signaling pathways associated with AWPPH in human cancers

Additionally, a number of studies revealed that AWPPH could act as a key competing endogenous RNA (ceRNA) or sponge for miRNAs to regulate the initiation, development, and chemoresistance of cancer. For example, in gastric cancer, Bu et al. demonstrated that AWPPH promotes cell proliferation and tumorigenesis by regulating miR-497/NTRK3 axis [24]. Recently, miR-128-3p was confirmed as a target of AWPPH in ovarian cancer by Zhu et al. [23]. In NSCLC, Wu et al. found that AWPPH could directly interacted with miR-204 and functioned as a ceRNA, thus regulating the expression of CDK6 [13]. Furthermore, AWPPH functioned as a ceRNA to promote malignant progression of human cancers through competitive sponging of miR-93-3p in osteosarcoma [28], miR-802 in melanoma [41], miR-206 in CRC [18], miR-1224-5p in glioblastoma [42] and miR-513a-5p in CCRCC [43].

Nevertheless, several limitations to this meta-analysis should be taken into account. First, all included studies were performed in the population from China, which may limit the applicability of our results for other ethnic population. Second, the cut-off values were lack of uniform standard in different types of cancer, which may result in some heterogeneity and affect the results of the study. Third, some of the HRs were calculated based on data extracted from the survival curves, which may not be very accurate and result in a calculation bias.

Conclusion

To conclude, this meta-analysis revealed that AWPPH expression level served as a prognostic indicator in multiple cancers in the Chinese population. Higher expression of AWPPH was significantly associated with poorer overall survival in patients with cancers and correlated with advanced TNM stage, higher risk of LNM and MVI, and bigger tumor size. Ultimately, more high-quality studies were required to certify clinical utility of AWPPH in cancers.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

This work was supported by the Natural Science Foundation of Zhejiang Province [grant number LY13H160029]; the Natural Science Foundation of Zhejiang Province [grant number LQ17H160013]; and the Zhejiang Province Health Department Foundation [grant number 2018ky284] participated by Y.F.L. Y.F.L. not only conceived and designed this study, but also read and approved the final manuscript. The funding bodies had no influences on the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

CRediT Author Contribution

Yongfeng Li: Conceptualization, Data curation, Writing—original draft, Writing—review and editing. Xinmiao Rui: Data curation. Daobao Chen: Conceptualization, Data curation. Haojun Xuan: Formal analysis. Hongjian Yang: Formal analysis. Xuli Meng: Writing—review and editing.

Acknowledgements

We are grateful to all researchers of enrolled studies.

Abbreviations

     
  • AWPPH

    long non-coding RNA associated with poor prognosis of hepatocellular carcinoma

  •  
  • BC

    breast cancer

  •  
  • CC

    cervical cancer

  •  
  • CCRCC

    clear cell renal cell carcinoma

  •  
  • ceRNA

    competitive endogenous RNA

  •  
  • CRC

    colorectal adenocarcinoma

  •  
  • DFS

    disease-free survival

  •  
  • DM

    distant metastasis

  •  
  • ESCC

    esophageal squamous cell carcinoma

  •  
  • GC

    gastric cancer

  •  
  • HCC

    hepatocellular carcinoma

  •  
  • LNM

    lymph node metastasis

  •  
  • MVI

    macro-vascular invasion

  •  
  • NOS

    Newcastle–Ottawa Scale

  •  
  • NSCLC

    non-small cell lung cancer

  •  
  • OC

    ovarian carcinoma

  •  
  • ORs

    odds ratios

  •  
  • OS

    overall survival

  •  
  • OSCC

    oral squamous cell carcinom

  •  
  • PC

    prostate carcinoma

  •  
  • PFS

    progression-free survival

  •  
  • qRT-PCR

    quantitative real-time polymerase chain reaction

  •  
  • RFS

    recurrence-free survival

  •  
  • TNBC

    triple-negative breast cancer

References

1.
Chen
W.
,
Zheng
R.
,
Baade
P.D.
,
Zhang
S.
,
Zeng
H.
,
Bray
F.
et al.
(
2016
)
Cancer statistics in China, 2015
.
CA Cancer J. Clin.
66
,
115
132
[PubMed]
2.
Arenz
A.
,
Patze
J.
,
Kornmann
E.
,
Wilhelm
J.
,
Ziemann
F.
,
Wagner
S.
et al.
(
2019
)
HPV-negative and HPV-positive HNSCC cell lines show similar numerical but different structural chromosomal aberrations
.
Head Neck
41
,
3869
3879
[PubMed]
3.
Helleux
A.
,
Debien
V.
,
Fadloun
A.
,
Rippinger
M.
,
Lebedinsky
S.
,
Davidson
I.
et al.
(
2019
)
Epigenetic alterations in kidney cancers
.
Bull. Cancer
106
,
839
841
[PubMed]
4.
Johansson
P.A.
,
Nathan
V.
,
Bourke
L.M.
,
Palmer
J.M.
,
Zhang
T.
,
Symmons
J.
et al.
(
2019
)
Evaluation of the contribution of germline variants in BRCA1 and BRCA2 to uveal and cutaneous melanoma
.
Melanoma Res.
29
,
483
490
[PubMed]
5.
Chen
Y.
,
Wang
J.
,
Fan
Y.
,
Qin
C.
,
Xia
X.
,
Johnson
J.
et al.
(
2019
)
Absence of the long noncoding RNA H19 results in aberrant ovarian STAR and progesterone production
.
Mol. Cell. Endocrinol.
490
,
15
20
[PubMed]
6.
Fico
A.
,
Fiorenzano
A.
,
Pascale
E.
,
Patriarca
E.J.
and
Minchiotti
G.
(
2019
)
Long non-coding RNA in stem cell pluripotency and lineage commitment: functions and evolutionary conservation
.
Cell. Mol. Life Sci.
76
,
1459
1471
[PubMed]
7.
Porto
F.W.
,
Daulatabad
S.V.
and
Janga
S.C.
(
2019
)
Long Non-Coding RNA Expression Levels Modulate Cell-Type-Specific Splicing Patterns by Altering Their Interaction Landscape with RNA-Binding Proteins
.
Genes (Basel)
10
,
593
8.
Tang
Y.
,
Wang
J.
,
Lian
Y.
,
Fan
C.
,
Zhang
P.
,
Wu
Y.
et al.
(
2017
)
Linking long non-coding RNAs and SWI/SNF complexes to chromatin remodeling in cancer
.
Mol. Cancer
16
,
42
[PubMed]
9.
Cui
R.J.
,
Fan
J.L.
,
Lin
Y.C.
,
Pan
Y.J.
,
Liu
C.
,
Wan
J.H.
et al.
(
2019
)
miR-124-3p availability is antagonized by LncRNA-MALAT1 for Slug-induced tumor metastasis in hepatocellular carcinoma
.
Cancer Med.
8
,
6358
6369
[PubMed]
10.
Xu
Y.
,
Deng
J.
,
Wang
G.
and
Zhu
Y.
(
2019
)
Long non-coding RNAs in prostate cancer: functional roles and clinical implications
.
Cancer Lett.
464
,
37
55
[PubMed]
11.
Zhao
X.
,
Liu
Y.
and
Yu
S.
(
2017
)
Long noncoding RNA AWPPH promotes hepatocellular carcinoma progression through YBX1 and serves as a prognostic biomarker
.
Bba.-Mol. Basis Dis.
1863
,
1805
1816
12.
Yu
G.
,
Wang
W.
,
Deng
J.
and
Dong
S.
(
2019
)
LncRNA AWPPH promotes the proliferation, migration and invasion of ovarian carcinoma cells via activation of the Wnt/-catenin signaling pathway
.
Mol. Med. Rep.
19
,
3615
3621
[PubMed]
13.
Wu
D.
,
Qin
B.Y.
,
Qi
X.G.
,
Hong
L.L.
,
Zhong
H.B.
and
Huang
J.Y.
(
2020
)
LncRNA AWPPH accelerates the progression of non-small cell lung cancer by sponging miRNA-204 to upregulate CDK6
.
Eur. Rev. Med. Pharmaco.
24
,
4281
4287
14.
Liberati
A.
,
Altman
D.G.
,
Tetzlaff
J.
,
Mulrow
C.
,
Gøtzsche
P.C.
,
Ioannidis
J.P.
et al.
(
2009
)
The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration
.
Ann. Intern. Med.
151
,
W65
W94
15.
Tierney
J.F.
,
Stewart
L.A.
,
Ghersi
D.
,
Burdett
S.
and
Sydes
M.R.
(
2007
)
Practical methods for incorporating summary time-to-event data into meta-analysis
.
8
,
16
[PubMed]
16.
Han
Q.L.
,
Chen
B.T.
,
Zhang
K.J.
,
Xia
S.T.
,
Zhong
W.W.
and
Zhao
Z.M.
(
2019
)
The long non-coding RNA AK001796 contributes to poor prognosis and tumor progression in hepatocellular carcinoma
.
Eur. Rev. Med. Pharmacol. Sci.
23
,
2013
2019
[PubMed]
17.
Zong
M.Z.
and
Shao
Q.
(
2019
)
An X.S. Expression and prognostic significance of long noncoding RNA AK001796 in esophageal squamous cell carcinoma
.
Eur. Rev. Med. Pharmacol. Sci.
23
,
181
186
[PubMed]
18.
Dong
X.
,
Yang
Z.
,
Yang
H.
,
Li
D.
and
Qiu
X.
(
2020
)
Long non-coding RNA MIR4435-2HG promotes colorectal cancer proliferation and metastasis through miR-206/YAP1 axis
.
Front. Ooncol.
10
,
160
19.
Ma
X.
and
Sheng
M.
(
2020
)
Prognostic value of serum MIR4435-2HG in oral squamous cell carcinoma
.
Chin. J. Stomatol.
55
,
15
19
20.
Shen
M.Y.
,
Zhou
G.R.
and
Zhang
Z.Y.
(
2020
)
LncRNA MIR4435-2HG contributes into colorectal cancer development and predicts poor prognosis
.
Eur. Rev. Med. Pharmacol. Sci.
24
,
1771
1777
[PubMed]
21.
Wu
K.
,
Hu
L.
,
Lv
X.
,
Chen
J.
,
Yan
Z.
,
Jiang
J.
et al.
(
2020
)
Long non-coding RNA MIR4435-1HG promotes cancer growth in clear cell renal cell carcinoma
.
Cancer Biomarkers: Section A Dis. Markers
29
,
39
50
22.
Zhang
Q.
,
Cheng
S.
,
Cao
L.
,
Yang
J.
,
Wang
Y.
and
Chen
Y.
(
2020
)
LINC00978 promotes hepatocellular carcinoma carcinogenesis partly via activating the MAPK/ERK pathway
.
Biosci. Rep.
40
,
BSR20192790
[PubMed]
23.
Zhu
L.
,
Wang
A.
,
Gao
M.
,
Duan
X.
and
Li
Z.
(
2020
)
LncRNA MIR4435-2HG triggers ovarian cancer progression by regulating miR-128-3p/CKD14 axis
.
Cancer Cell Int.
20
,
145
[PubMed]
24.
Bu
J.
,
Lv
W.
,
Liao
Y.
,
Xiao
X.
and
Lv
B.
(
2019
)
Long non-coding RNA LINC00978 promotes cell proliferation and tumorigenesis via regulating microRNA-497/NTRK3 axis in gastric cancer
.
Int. J. Biol. Macromol.
123
,
1106
1114
[PubMed]
25.
Zhang
H.
,
Meng
H.
,
Huang
X.
,
Tong
W.
,
Liang
X.
,
Li
J.
et al.
(
2019
)
lncRNA MIR4435-2HG promotes cancer cell migration and invasion in prostate carcinoma by upregulating TGF-beta 1
.
Oncol. Lett.
18
,
4016
4021
[PubMed]
26.
Deng
L.
,
Chi
Y.
,
Liu
L.
,
Huang
N.
,
Wang
L.
and
Wu
J.
(
2016
)
LINC00978 predicts poor prognosis in breast cancer patients
.
Sci. Rep.-UK
6
,
37936
27.
Chen
X.
,
Qu
J.
,
Yao
L.
and
Chen
X.
(
2020
)
Expression and clinicopathological significance of lncRNA AWPPH and miR-203a in cervical cancer[In Chinese]
.
J. Clin. Exp. Pathol.
9
,
1052
1057
28.
Li
C.
,
Wang
F.
,
Wei
B.
,
Wang
L.
and
Kong
D.
(
2019
)
LncRNA AWPPH promotes osteosarcoma progression via activation of Wnt/β-catenin pathway through modulating miR-93-3p/FZD7 axis
.
Biochem. Biophys. Res. Commun.
514
,
1017
1022
[PubMed]
29.
Liu
C.
,
Han
B.
,
Xin
J.
and
Yang
C.
(
2019
)
LncRNA-AWPPH activates TGF-β1 in colorectal adenocarcinoma
.
Oncol. Lett.
18
,
4719
4725
[PubMed]
30.
Song
Z.
,
Du
J.
,
Zhou
L.
and
Sun
B.
(
2019
)
lncRNA AWPPH promotes proliferation and inhibits apoptosis of non-small cell lung cancer cells by activating the Wnt/-catenin signaling pathway
.
Mol. Med. Rep.
19
,
4425
4432
[PubMed]
31.
Wang
K.
,
Li
X.
,
Song
C.
and
Li
M.
(
2018
)
LncRNA AWPPH promotes the growth of triple-negative breast cancer by up-regulating frizzled homolog 7 (FZD7)
.
Biosci. Rep.
38
,
BSR20181223
[PubMed]
32.
Liu
C.
,
Han
B.
,
Xin
J.
and
Yang
C.
(
2019
)
LncRNA-AWPPH activates TGF-β1 in colorectal adenocarcinoma
.
Oncol. Lett.
18
,
4719
4725
[PubMed]
33.
Liu
B.
,
Pan
C.F.
,
Yao
G.L.
,
Wei
K.
,
Xia
Y.
and
Chen
Y.J.
(
2018
)
The long non-coding RNA AK001796 contributes to tumor growth via regulating expression of p53 in esophageal squamous cell carcinoma
.
Cancer Cell Int.
18
,
38
[PubMed]
34.
Hseu
Y.C.
,
Lin
Y.C.
,
Rajendran
P.
,
Thigarajan
V.
,
Mathew
D.C.
,
Lin
K.Y.
et al.
(
2019
)
Antrodia salmonea suppresses invasion and metastasis in triple-negative breast cancer cells by reversing EMT through the NF-kappaB and Wnt/beta-catenin signaling pathway
.
Food Chem. Toxicol.
124
,
219
230
[PubMed]
35.
Liu
M.
,
Sun
X.
and
Shi
S.
(
2018
)
MORC2 enhances tumor growth by promoting angiogenesis and tumor-associated macrophage recruitment via Wnt/beta-catenin in lung cancer
.
Cell. Physiol. Biochem.
51
,
1679
1694
[PubMed]
36.
Xiu
D.
,
Liu
G.
,
Yu
S.
,
Li
L.
,
Zhao
G.
,
Liu
L.
et al.
(
2019
)
Long non-coding RNA LINC00968 attenuates drug resistance of breast cancer cells through inhibiting the Wnt2/-catenin signaling pathway by regulating WNT2
.
J. Exp. Clin. Canc. Res.
38
,
94
37.
Bai
J.
,
Xu
J.
,
Zhao
J.
and
Zhang
R.
(
2019
)
Downregulation of lncRNA AWPPH inhibits colon cancer cell proliferation by downregulating GLUT-1
.
Oncol. Lett.
18
,
2007
2012
[PubMed]
38.
Yanxia
H.
,
Aimin
L.
and
Zhihua
W.
(
2019
)
LncRNA AWPPH participates in the metastasis of non-small cell lung cancer by upregulating TGF-β1 expression
.
Oncol. Lett.
18
,
4246
4252
[PubMed]
39.
Tang
L.
,
Wang
T.
,
Zhang
Y.
,
Zhang
J.
,
Zhao
H.
,
Wang
H.
et al.
(
2019
)
Long non-coding RNA AWPPH promotes postoperative distant recurrence in resected non-small cell Lung cancer by upregulating transforming growth factor beta 1 (TGF-β1)
.
Med. Sci. Monit.
25
,
2535
2541
[PubMed]
40.
Liu
B.
,
Pan
C.
,
Ma
T.
,
Wang
J.
,
Yao
G.
,
Wei
K.
et al.
(
2017
)
Long non-coding RNA AK001796 contributes to cisplatin resistance of non-small cell lung cancer
.
Mol. Med. Rep.
16
,
4107
4112
[PubMed]
41.
Ma
D.
,
Sun
D.
,
Wang
J.
,
Jin
D.
,
Li
Y.
and
Han
Y.
(
2020
)
Long non-coding RNA MIR4435-2HG recruits miR-802 from FLOT2 to promote melanoma progression
.
Eur. Rev. Med. Pharmaco.
24
,
2616
2624
42.
Xu
H.
,
Zhang
B.
,
Yang
Y.
,
Li
Z.
,
Zhao
P.
,
Wu
W.
et al.
(
2020
)
LncRNA MIR4435-2HG potentiates the proliferation and invasion of glioblastoma cells via modulating miR-1224-5p/TGFBR2 axis
.
J. Cell. Mol. Med.
24
,
6362
6372
[PubMed]
43.
Zhu
K.
,
Miao
C.
,
Tian
Y.
,
Qin
Z.
,
Xue
J.
,
Xia
J.
et al.
(
2020
)
lncRNA MIR4435-2HG promoted clear cell renal cell carcinoma malignant progression via miR-513a-5p/KLF6 axis
.
J. Cell. Mol. Med.
24
,
10013
10026
[PubMed]
This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).