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. 2017 Jun 6;10:2849–2863. doi: 10.2147/OTT.S128810

Prognostic value of high IMP3 expression in solid tumors: a meta-analysis

Luyao Chen 1,2,*, Yongpeng Xie 3,*, Xintao Li 1, Liangyou Gu 1, Yu Gao 1, Lu Tang 1, Jianwen Chen 1, Xu Zhang 1,✉
PMCID: PMC5476767  PMID: 28652767

Abstract

Background

Accumulated studies have investigated the prognostic role of insulin-like growth factor II mRNA-binding protein 3 (IMP3) in various cancers, but inconsistent and controversial results were obtained. Therefore, we performed a systematic review and meta-analysis to investigate the potential value of IMP3 in the prognostic prediction of human solid tumors.

Materials and methods

A systematic literature search in the electronic databases PubMed, Embase, Web of Science, and Cochrane library (updated to April 2016) was conducted to identify eligible studies. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) for survival outcomes were calculated and gathered using STATA 12.0 software.

Results

A total of 53 studies containing 8,937 patients with solid tumors were included in this meta-analysis. High IMP3 expression was significantly associated with worse overall survival (OS) of solid tumors (HR =2.08, 95% CI: 1.80–2.42, P<0.001). Similar results were observed in cancer-specific survival (CSS), disease-free survival (DFS), recurrence-free survival (RFS), progression-free survival (PFS), and metastasis-free survival (MFS). Further subgroup analysis stratified by tumor type showed that elevated IMP3 expression was associated with poor OS in renal cell carcinoma (RCC), lung cancer, oral cancer, urothelial carcinoma, hepatocellular carcinoma (HCC), colorectal cancer, pancreatic cancer, gastric cancer, and intrahepatic cholangiocarcinoma (ICC).

Conclusion

The current evidence suggests that high IMP3 expression is associated with poor prognosis in most solid tumors. IMP3 is a potential valuable prognostic factor and might serve as a promising biomarker to guide clinical decisions in human solid tumors.

Keywords: IMP3, prognosis, solid tumor, biomarker, meta-analysis

Introduction

Insulin-like growth factor II mRNA-binding protein 3 (IMP3 or IGF2BP3) is a member of the RNA-binding protein family, which plays an important role in RNA trafficking and stabilization, cell growth, and cell migration during the early stages of embryogenesis.1 IMP3 was proposed to control the translation or turnover of various candidate target genes, including IGF2, CD44, HMGA2, and MMP9.2–5 This oncofetal protein has been reported to promote tumor cell survival, proliferation, chemoresistance, and tumor cell invasiveness in vitro. In recent years, accumulating studies have shown that IMP3 is specifically expressed in malignant tumors and acts as an important cancer-specific gene involved in many aggressive and advanced cancers.6,7

Numerous studies have reported that upregulated IMP3 expression in tumor tissues is correlated with poor patient survival and can be used as a prognostic factor to guide clinical decisions and distinguish different prognoses in various solid tumors, such as renal cell carcinoma (RCC), lung cancer, oral cancer, bladder cancer, gastrointestinal tumors, and gynecological tumors.8–13 However, some other studies have reported the absence of association between IMP3 expression and cancer prognosis.14,15 Some investigators have also replayed completely opposite results in ovarian cancer. For instance, Kobel et al16 proposed that IMP3 expression is a marker of unfavorable prognosis, whereas Noske et al17 asserted that IMP3 expression is associated with improved survival. Hence, the prognostic role of IMP3 expression in solid tumors remains unclear and controversial.

Therefore, we conducted a systematic review of published studies, with a standard meta-analysis combining available evidence, to evaluate the prognostic value of IMP3 expression in solid tumors.

Materials and methods

This meta-analysis was conducted according to the guideline of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)18 (Table S1). Because the data included in this study were retrieved from published articles, ethical approval from ethics committees was not needed.

Literature search

A comprehensive literature search was performed in PubMed, Embase, Web of Science, and Cochrane Library to identify studies evaluating IMP3 expression and clinical prognosis in solid tumors up to April 2016. The search strategy included the following terms through MeSH headings, keywords, and text words: “IMP3” or “Insulin-like growth factor 2 mRNA binding protein 3” or “IGF2BP3” combined with “cancer” or “carcinoma” or “neoplasm”. The references cited in the identified articles were also screened for possible inclusions. The database search and preliminary evaluation of identified studies were performed independently by two investigators (LC and YX). No language limitation existed in the process.

Study selection

The inclusion criteria for selecting articles in our analysis are listed as follows: 1) studies that reported IMP3 expression in cancer tissues, 2) studies analyzing the relationship between IMP3 expression level and clinical cancer outcomes, 3) studies that directly reported survival outcomes with hazard ratio (HR) and corresponding 95% confidence interval (CI) or studies that provided sufficient data for estimating HR and 95% CI by using the methods described by Tierney et al,19 and 4) studies with a median follow-up of at least 6 months. Studies were excluded if they were 1) case reports, letters, conference abstracts, or reviews, 2) non-human research, 3) investigations on the diagnostic role, but not the prognostic role, of IMP3, and 4) studies with insufficient data for calculating the HR and 95% CI. If duplicate publications by the same authors were retrieved, we included only the most informative and recent study. Two independent reviewers (LC and YX) evaluated the full articles for study eligibility, and any disagreement was resolved by consensus.

Data extraction and quality assessment

Two authors (LC and YX) independently extracted data from each eligible study by using predefined item forms. The following information, if available, was recorded: first author’s name, year of publication, study country or region, type of cancer, cancer stage, number of patients, detected method, cutoff definition, percentage of high IMP3 expression, follow-up period, and survival outcomes with their HRs and corresponding 95% CIs. If univariate and multivariate analyses were reported to obtain the HRs, the results of multivariate analysis were preferentially selected. If HRs and 95% CIs were not provided directly, we attempted to estimate these points with Kaplan–Meier curve or other required data in the original study by using Tierney et al’s methods.19 Study quality was scored by two investigators (LC and YX) using the Newcastle–Ottawa Scale, which involves three main categories: selection, comparability, and outcome ascertainment. We defined studies with scores no less than 6 as qualified to be included in the meta-analysis. Discrepancies between investigators were resolved through discussion.

Statistical analysis

Pooled HRs and corresponding 95% CIs were calculated to evaluate the prognostic role of high IMP3 expression in the clinical outcomes of solid tumors. An observed HR greater than 1 implied a worse prognosis in patients with high IMP3 expression, and an HR less than 1 indicated a better prognosis. Statistical heterogeneity of combined HR was assessed using Cochrane Q-test and Higgins I2 metrics. I2>50% was considered a measure of obvious heterogeneity.20 If no evident heterogeneity existed, the fixed-effect model (Mantel–Haenszel method) was used to pool the results.21 Otherwise, the randomeffect model (DerSimonian and Laird method) was selected.22 The potential sources for heterogeneity, if significant, were further explored using a predefined subgroup analysis and meta-regression analysis (based on cancer type, ethnicity, case number, cutoff, cancer stage, HR obtained method, and analysis method). To assess the stability of the pooled results, sensitivity analysis was performed by sequential omission of each single study. Publication bias was also estimated by visually assessing the asymmetry of the funnel plot and then quantitatively evaluated by Begg’s and Egger’s tests.23,24 All the abovementioned analyses were performed using STATA version 12.0 (Stata Corporation, College Station, TX, USA). All statistical tests were two sided, and statistical significance was defined as a P-value less than 0.05.

Results

Search results and study characteristics

The flowchart of the literature search is shown in Figure 1. A total of 420 potentially relevant studies were retrieved from the initial literature search in the aforementioned electronic databases. A total of 144 duplicated records were excluded by a literature manager software. After carefully screening titles and abstracts of the remaining 120 records, 46 studies were excluded and 74 studies were selected for full-text assessment. Given the inclusion and exclusion criteria, 21 studies that belonged to duplicate publication or failed to offer sufficient prognostic information were excluded. Finally, 53 studies satisfied our eligibility criteria and were included in this meta-analysis.

Figure 1.

Figure 1

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Flowchart of the study selection process.

The characteristics of these enrolled studies are summarized in Table 1. The 53 studies involved 8,937 patients with different cancer types, including 6 studies of RCC,8,25–29 6 lung cancer,9,30–34 4 oral cancer,10,35–37 4 urothelial carcinoma,38–41 4 ovarian cancer,16,17,42,43 3 hepatocellular carcinoma (HCC),44–46 4 colorectal cancer,12,47–49 3 prostate cancer,14,15,50 3 pancreatic cancer,51–53 2 gastric cancer,11,54 2 intrahepatic cholangiocarcinoma (ICC),55,56 and one study each of tongue cancer,57 thyroid carcinoma,58 sacral chordoma,59 pilocytic astrocytoma and pilomyxoid astrocytoma (PA/PMA),60 neuroblastoma,61 meningioma,62 melanoma,63 breast cancer,64 giant cell tumor,65 bile duct carcinoma,66 esophageal carcinoma,67 and cervical cancer.13 A total of 25 studies involved Caucasians and 28 involved Asians. The survival outcomes in these studies, including overall survival (OS), cancer-specific survival (CSS), disease-free survival (DFS), recurrence-free survival (RFS), progression-free survival (PFS), and metastasis-free survival (MFS), were investigated in 40, 10, 8, 7, 4, and 5 studies, respectively. HRs were reported directly in most of these studies (43/53) and were estimated indirectly in the 10 other studies. Multivariate Cox analysis was performed to evaluate the prognostic role of IMP3 in 38 studies; and univariate analysis was conducted in the other 15 studies. Immunohistochemistry (IHC) staining and quantitative polymerase chain reaction (qPCR) were used to test the IMP3 expression in cancer tissues. Notably, the definition and cutoff of high IMP3 expression were heterogeneous among these studies. The majority of included studies used the percentage of positive staining cells (0%, 10%, 25%, or 50%) as the criteria, whereas in some other studies, staining scores with the percentage and intensity score were obtained as cutoff values for high IMP3 expression. The percentage of high expression in the cohort population varied in different cancer types and ranged from 6.5% to 83.3%. Quality score assessment suggested that the scores of enrolled studies ranged from 6 to 9, which were considered adequate for quantitative meta-analysis.

Table 1.

Characteristics of studies included in the meta-analysis

Author Year Country or region Cancer type Case number Method Cutoff High expression Follow-up Outcomes Analysis HR obtained NOS score
Jiang et al8 2006 USA RCC 371 IHC Positive vs negative* 71 (19.1%) Median 63 months OS MFS Multi Report 9
Pei et al26 2015 USA RCC 346 IHC Positive vs negative 73 (21.1%) >10 years OS RFS Multi Report 8
Hoffmann et al25 2008 USA RCC 716 IHC Positive vs negative 213 (29.7%) 9.5 years CSS MFS Multi Report 8
Park et al27 2014 Korea RCC 148 IHC >5% of cells stained 43 (29.1%) Median 55.5 months CSS Multi Report 7
Jiang et al28 2008 USA RCC 317 IHC Positive vs negative 40 (12.6%) 8.8 years OS MFS Multi Report 9
Tantravahi et al29 2015 USA RCC 27 IHC >20% of cells stained 14 (51.9%) >2 years OS Multi Report 6
Del Gobbo et al34 2014 Italy Lung cancer 74 IHC Positive vs negative 24 (32.4%) Mean 65.6 months OS DFS Uni Report 7
Sun et al32 2015 China Lung cancer 196 IHC H-score >100 (0–300) 83 (42.3%) Range (16.5–69.0) months OS DFS Multi Report 8
Yan et al9 2016 China Lung cancer 95 IHC >25% of cells stained 39 (41.1%) >5 years OS Multi Report 7
Zhang et al33 2015 China Lung cancer 186 IHC >5% of cells stained 139 (74.7%) >5 years OS Multi Report 8
Lin et al30 2015 China Lung cancer 92 IHC Positive vs negative 62 (67.4%) >5 years OS Multi Report 8
Beljan Perak et al31 2012 Croatia Lung cancer 90 IHC >10% of cells stained 61 (67.8%) >5 years OS Uni SC 6
Clauditz et al35 2013 Germany Oral cancer 145 IHC >10% of cells stained 79 (54.5%) Mean 41.3 months OS Multi Report 8
Lin et al37 2011 Taiwan Oral cancer 93 IHC >25% of cells stained 51 (54.8%) Mean 44.8 months OS Multi Report 9
Li et al36 2010 Korea Oral cancer 96 IHC Positive vs negative 65 (67.7%) Median 73 months OS Multi Report 9
Kim and Cha10 2011 Korea Oral cancer 95 IHC Positive vs negative 67 (70.5%) >5 years OS Multi Report 7
Szarvas et al40 2012 Germany Urothelial carcinoma 106 IHC Staining index >7 (0–9) 17 (16.0%) Median 15 months OS CSS MFS Multi Report 7
Sitnikova et al39 2008 USA Urothelial carcinoma 214 IHC Positive vs negative 42 (19.6%) Median 35 months PFS DFS Multi Report 8
Lee et al41 2013 Multicenter Urothelial carcinoma 622 IHC Positive vs negative 76 (12.2%) Median 27 months OS CSS RFS Multi Report 9
Niedworok et al38 2015 Germany Urothelial carcinoma 26 IHC H-score >100 (0–300) 7 (26.9%) Median 50 months OS PFS Uni Report 7
Bi et al43 2016 China Ovarian cancer 73 IHC >10% of cells stained 46 (63.0%) >5 years OS Uni SC 7
Kobel et al16 2009 British and North America Ovarian cancer 278 IHC >5% of cells stained 147 (52.9%) >4.6 years CSS Multi Report 8
Hus et al42 2015 Taiwan Ovarian cancer 140 IHC The median value (IRS: 0–9) NR Median 39 months PFS Multi Report 6
Noske et al17 2009 Germany Ovarian cancer 68 IHC IRS >6 32 (47.1%) Median 37 months OS Uni SC 7
Hu et al44 2014 China HCC 160 IHC Staining score (2–7 vs 0–1) 97 (60.6%) Median 36 months OS RFS Uni SC 8
Wachter et al45 2011 Germany HCC 365 IHC Staining group (2–3 vs 0–1) 67 (18.4%) Mean 23.3 months OS Multi Report 7
Chen et al46 2013 China HCC 92 IHC Positive vs negative 65 (70.7%) >3 years OS Multi Report 7
Yuan et al48 2009 Taiwan Colorectal cancer 186 IHC >50% of cells stained 66 (35.5%) Median >5 years OS Multi Report 8
Li et al49 2009 China Colorectal cancer 203 IHC Staining score (2–7 vs 0–1) 132 (65.0%) Median 61 months OS DFS Multi Report 9
Lochhead et al12 2012 USA Colorectal cancer 671 IHC Intense or moderate vs weak or absent 234 (34.9%) Median 160 months OS CSS Multi Report 8
Lin et al30 2013 China Colorectal cancer 186 IHC Positive vs negative 143 (76.9%) >2 years OS Multi Report 7
Ikenberg et al15 2010 Switzerland Prostate cancer 425 IHC Positive vs negative 354 (83.3%) Median 63 months RFS Uni Report 9
Chromecki et al14 2011 USA Prostate cancer 232 IHC >10% of cells stained 42 (18.1%) Median 69.8 months RFS Multi Report 9
Szarvas et al50 2014 Germany Prostate cancer 124 IHC >10% of cells stained 30 (24.2%) Median 155 months OS CSS Uni Report 8
Wang et al52 2014 China Pancreatic cancer 50 qPCR Cutoff value based on the ROC curve 30 (60.0%) >2 years OS Multi Report 7
Schaeffer et al51 2010 Canada Pancreatic cancer 127 IHC IHC score >5 80 (63.0%) Mean 13 months OS Multi Report 8
Morimatsu et al53 2012 Japan Pancreatic cancer 32 IHC >50% of cells stained 17 (53.1%) Median 33.6 months CSS Uni SC 6
Wang et al54 2010 China Gastric cancer 92 IHC Positive vs negative 75 (81.5%) >2 years OS Uni SC 7
Okada et al11 2011 Japan Gastric cancer 96 IHC >10% of cells stained 71 (74.0%) Median 5.5 years OS DFS Multi Report 8
Chen et al46 2013 Taiwan ICC 61 IHC >10% of cells stained 25 (41.0%) Mean 33.5 months OS DFS Uni SC 7
Gao et al56 2014 China ICC 72 IHC Positive vs negative 59 (81.9%) Median 14.9 months OS Multi Report 8
Li et al57 2011 China Tongue carcinoma 65 IHC Positive vs negative 50 (76.9%) Median 36 months CSS Uni SC 8
Asioli et al58 2010 USA Thyroid carcinoma 103 IHC Final score >2 (0–6) 61 (59.2%) >5 years OS DFS MFS Multi Report 9
Zhou et al59 2014 China Sacral chordoma 32 IHC Staining score (2–7 vs 0–1) 20 (62.5%) Median 110 months DFS Uni SC 8
Barton et al60 2013 USA PA/PMA 77 IHC Three groups (1–2 vs 0) 24 (31.2%) Mean 8.8 years PFS Uni Report 7
Chen et al61 2011 Taiwan Neuroblastoma 90 IHC >10% of cells stained 52 (57.8%) Median 39.5 months OS Multi Report 8
Hao et al62 2011 USA Meningioma 107 IHC Positive vs negative 7 (6.5%) Median 53 months OS RFS Multi Report 7
Sheen et al63 2014 Taiwan Melanoma 97 IHC >10% of cells stained 72 (74.2%) Median 5.2 years OS Multi Report 7
Walter et al64 2009 USA Breast cancer 138 IHC >10% of cells stained 45 (32.6%) Median 71.5 months OS Multi Report 7
Zhang et al33 2015 China Giant cell tumor 38 IHC Staining score (3–7 vs 0–2) 13 (34.2%) Median 88.0 months RFS Uni SC 6
Riener et al66 2009 Switzerland Bile duct carcinoma 115 IHC Intense or moderate vs weak or absent 67 (58.3%) Median 9 months CSS Multi Report 8
Takata et al67 2014 Japan Esophageal carcinoma 191 IHC >10% of cells stained 113 (59.2%) Mean 41 months OS Multi Report 9
Wei et al13 2014 China Cervical carcinoma 96 IHC >10% of cells stained 54 (56.3%) Median 58.1 months OS Multi Report 8
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Note:

*

Positive vs negative: tumor cells with any detectable staining were considered positive.

Abbreviations: CSS, cancer-specific survival; DFS, disease-free survival; HCC, hepatocellular carcinoma; HR, hazard ratio; ICC, intrahepatic cholangiocarcinoma; IHC, immunohistochemistry; IRS, immunoreactivity score; MFS, metastasis-free survival; NOS, Newcastle–Ottawa Scale; NR, not reported; OS, overall survival; PA/PMA, pilocytic astrocytoma and pilomyxoid astrocytoma; PFS, progression-free survival; qPCR, quantitative polymerase chain reaction; RFS, recurrence-free survival; RCC, renal cell carcinoma; SC, survival curve.

Association of IMP3 with OS

The association of IMP3 expression and OS was investigated in 40 studies containing 6,425 patients with different cancer types. A random-effect model was selected because of the evident interstudy heterogeneity (I2=59.1%, P=0.005). Combined analysis revealed that high IMP3 expression was associated with the worse OS of solid tumors (HR =2.08, 95% CI: 1.80–2.42, P<0.001, Figure 2). The effect of IMP3 expression on OS was further analyzed by tumor types, and the results are presented in Figure 3A. High IMP3 expression was significantly associated with poor OS in RCC (HR =2.80, 95% CI: 1.59–4.93, P<0.001), lung cancer (HR =1.87, 95% CI: 1.22–2.84, P=0.004), oral cancer (HR =1.66, 95% CI: 1.27–2.18, P<0.001), urothelial carcinoma (HR =1.92, 95% CI: 1.42–2.59, P<0.001), HCC (HR =2.25, 95% CI: 1.65–3.06, P<0.001), colorectal cancer (HR =1.52, 95% CI: 1.23–1.90, P<0.001), pancreatic cancer (HR =3.54, 95% CI: 2.06–6.09, P<0.001), gastric cancer (HR =2.67, 95% CI: 1.38–5.17, P=0.003), and ICC (HR =2.10, 95% CI: 1.52–2.92, P<0.001) but not in ovarian cancer (HR =1.05, 95% CI: 0.18–6.15, P=0.957). To explore the source of heterogeneity, subgroup analysis and meta-regression were performed by the following stratification: patient ethnicity, study number, cutoff value, cancer stage, HR obtained method, and analysis style (Table 2). The results indicated that the combined HR estimates for OS in Caucasians and Asians were 2.08 (95% CI: 1.54–2.81, P<0.001) and 1.96 (95% CI: 1.73–2.22, P<0.001), respectively. Differences in the case number, cutoff value, cancer stage, HR obtained method, and analysis method did not influence the effect of IMP3 expression on the OS of solid tumors. Further meta-regression analysis revealed that cancer stage is a potential significant contributor to heterogeneity (P=0.017), unlike other factors (P>0.05).

Figure 2.

Figure 2

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Forest plot of studies evaluating HR of high IMP3 expression in solid tumors for OS.

Notes: A pooled analysis showed that high IMP3 expression was associated with poor OS in solid tumors (HR =2.08, 95% CI: 1.80–2.42, P<0.001). Weights are from random-effects analysis.

Abbreviations: CI, confidence interval; HRs, hazard ratios; IMP3, insulin-like growth factor II mRNA-binding protein 3; OS, overall survival.

Figure 3.

Figure 3

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Subgroup analysis of OS stratified by tumor types, funnel plot of OS for publication bias, and sensitive analysis of OS.

Notes: (A) High IMP3 expression was significantly associated with poor OS in RCC, lung cancer, oral cancer, urothelial carcinoma, HCC, colorectal cancer, pancreatic cancer, gastric cancer, and ICC but not in ovarian cancer. (B) The funnel plot for OS was asymmetric, which indicated the probability of publication bias. (C) Sensitivity analysis by sequential omission of individual studies did not alter the significance, which confirmed the credibility of outcomes.

Abbreviations: CI, confidence interval; HCC, hepatocellular carcinoma; HR, hazard ratio; ICC, intrahepatic cholangiocarcinoma; In, natural logarithm; IMP3, insulin-like growth factor II mRNA-binding protein 3; OS, overall survival; RCC, renal cell carcinoma; SE, standard error.

Table 2.

Subgroup analysis and meta-regression of the studies regarding overall survival

Subgroups Studies Patients Pooled HR and 95% CI P-value Heterogeneity (I2) Meta-regression P-value
Ethnicity 0.748
 Caucasian 18 3,827 2.08 (1.54–2.81) <0.001 76.3%
 Asian 22 2,598 1.96 (1.73–2.22) <0.001 9.6%
No of patients 0.659
 >100 20 4,850 2.08 (1.71–2.53) <0.001 62.3%
 <100 20 1,575 2.11 (1.66–2.67) <0.001 57.6%
Cutoff 0.421
 Positive vs negative 13 2,562 2.50 (1.96–3.19) <0.001 53.9%
 >10% of cells stained 11 1,201 1.95 (1.50–2.53) <0.001 29.7%
 >25% of cells stained 2 188 1.63 (1.06–2.52) 0.027 46.5%
 Others 14 2,474 1.87 (1.42–2.46) <0.001 65.6%
Cancer stage 0.017
 Nonmetastatic 14 2,918 2.01 (1.77–2.29) <0.001 23.4%
 Mixed (metastatic and nonmetastatic) 26 3,507 1.77 (1.58–1.97) <0.001 16.8%
HR obtain method 0.326
 Reported 34 5,881 2.14 (1.84–2.50) <0.001 55.5%
 Extracted 6 544 1.70 (1.03–2.82) 0.040 76.2%
Analysis 0.319
 Univariable analysis 9 768 1.76 (1.09–2.85) 0.020 74.7%
 Multivariable analysis 31 5,657 2.14 (1.84–2.48) <0.001 52.9%
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Abbreviations: CI, confidence interval; HR, hazard ratio.

To assess the credibility of the pooled outcomes, we performed a sensitivity analysis through the sequential omission of individual studies. The results were not obviously influenced by any single study (Figure 3C). The publication bias of all included studies was evaluated using a vertical funnel plot, Begg’s, and Egger’s tests. However, the funnel plot in Figure 3B appears asymmetrical, and the Begg’s (P=0.015) and Egger’s tests (P=0.002) revealed existing evidence of publication bias, which may be attributed to only seven studies that reported negative results among all the enrolled studies.

Association of IMP3 with CSS, DFS, RFS, PFS, and MFS

Ten studies that involved a total of 2,877 patients provided sufficient data for CSS analysis. No heterogeneity was observed among these studies (I2=31.3%, P=0.158). Thus, a fixed model was applied to pool the results. The combined HR was 1.75 (95% CI, 1.50–2.05, P<0.001), indicating that high IMP3 expression was associated with worse CSS in the patients with solid tumors (Figure 4A). The subgroup analysis stratified by cancer types showed that high IMP3 expression significantly affected the RCC (HR =1.49, 95% CI: 1.11–2.01, P=0.008) and urothelial carcinoma (HR =2.17, 95% CI: 1.54–3.07, P<0.001). Further sensitivity analysis did not alter the significance of combined HR, which validated the outcome credibility. Eight studies that involved 979 patients reported HRs for DFS, and the effect of high IMP3 expression is presented in Figure 4B. A combined analysis showed that high IMP3 expression was associated with poor DFS in solid tumors (HR =3.30, 95% CI: 1.82–5.99, P<0.001).

Figure 4.

Figure 4

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Forest plot of studies evaluating HRs of high IMP3 expression in solid tumors for CSS and DFS.

Notes: (A) High IMP3 expression was associated with poor CSS in solid tumors (HR =1.75, 95% CI: 1.50–2.05, P<0.001). (B) High IMP3 expression was associated with poor DFS in solid tumors (HR =3.30, 95% CI: 1.82–5.99, P<0.001). Weights are from random-effects analysis.

Abbreviations: CI, confidence interval; CSS, cancer-specific survival; DFS, disease-free survival; HRs, hazard ratios; IMP3, insulin-like growth factor II mRNA-binding protein 3; OS, overall survival.

Seven studies with 1,930 patients investigated the prognostic role of IMP3 expression in the RFS of solid tumors. Pooled results demonstrated that high IMP3 adversely influenced the RFS in patients with solid tumors (HR =2.11, 95% CI: 1.43–3.12, P<0.001, Figure 5A). For PFS, four studies with 457 patients were included in the analysis. A forest plot of study-specific HRs for PFS is presented in Figure 5B. The combined results indicated that high IMP3 expression was significantly associated with worse PFS in solid tumors (HR =2.18, 95% CI: 1.11–4.29, P=0.023). In addition, five studies, including 1,613 patients, focused on the influence of IMP3 on solid tumor metastasis. Meta-analysis of these studies suggested that IMP3 expression was also associated with poor MFS (HR =4.91, 95% CI: 2.05–11.73, P<0.001, Figure 5C).

Figure 5.

Figure 5

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Forest plot of studies evaluating HRs of high IMP3 expression in solid tumors for RFS, PFS, and MFS.

Notes: (A) High IMP3 expression was associated with poor RFS in solid tumors (HR =2.11, 95% CI: 1.43–3.12, P<0.001). (B) High IMP3 expression was associated with poor PFS in solid tumors (HR =2.18, 95% CI: 1.11–4.29, P=0.023). (C) High IMP3 expression was associated with poor MFS in solid tumors (HR =4.91, 95% CI: 2.05–11.73, P<0.001). Weights are from random-effects analysis.

Abbreviations: CI, confidence interval; HRs, hazard ratios; IMP3, insulin-like growth factor II mRNA-binding protein 3; MFS, metastasis-free survival; PFS, progression-free survival; RFS, recurrence-free survival.

Discussion

Over the past decades, increasing correlative studies describe the elevated IMP3 expression in human cancers, and various functional in vitro or in vivo studies provide strong evidence indicating that this oncofetal protein serves an essential role in modulating tumor cell fate.6 As a molecular biomarker, IMP3 has attracted extensive attention and can be used to distinguish different prognoses, improve prediction accuracy, and better guide clinical decisions in different tumor types.7 Nevertheless, the relationship between IMP3 expression and oncological outcome remains controversial and requires a consensus. Consequently, we attempted to perform a systematic review of published relevant studies and conduct a meta-analysis to clarify the prognostic value of IMP3 expression in patients with solid tumors.

In the present research, given the inclusion criteria, 53 studies involving 8,937 patients were eligible, and the HRs of cumulative survival rates were summarized quantitatively by standard meta-analysis techniques. Our results suggested that high IMP3 expression was associated with worse OS of the solid tumors. Further subgroup analysis stratified by tumor type presented detailed results as follows. The negative prognostic effects of IMP3 on OS were specifically observed in RCC, lung cancer, oral cancer, urothelial carcinoma, HCC, colorectal cancer, pancreatic cancer, gastric cancer, and ICC. Besides OS, we also investigated other frequently used survival outcomes, including CSS, DFS, RFS, PFS, and MFS. Similar influences were found for high IMP3 expression regarding the abovementioned end points, which provide a relatively comprehensive assessment of the value of IMP3 acting as a prognostic biomarker in solid tumors.

Accumulated literature suggests that IMP3 contributes to various aspects of cancer by promoting target genes expression by either preventing mRNA decay or stimulating mRNA translation. IMP3 knockdown in vitro can significantly inhibit the translation of IGF2 mRNA resulting in the marked inhibition of cell proliferation.2 By using solid cancer transcriptome data, IMP3 was also found to be correlated with HMGA2 mRNA expression in a dose-dependent manner. Additional assay for elucidating the mechanism indicated that IMP3 may function as a cytoplasmic safe house and prevents miRNA-directed mRNA decay of HMGA2 during tumor progression.4 Another recent study identified IMP3 as capable of directly binding the mRNAs of cyclins D1, D3, and G1 in vivo and in vitro. The study also found that IMP3 can regulate the expression of these cyclins depending on their protein partner HNRNPM in six human cancer cell lines of different origins.68 In addition, IMP3 promotes tumor cell invasion and migration by targeting the epithelial–mesenchymal transition-associated molecular makers, including E-cadherin, Slug, and vimentin.69 Overall, IMP3 plays an essential and multifaceted role in human cancers. Hence, targeting IMP3 may serve as a potential strategy for anticancer therapy.

To our knowledge, our study is the first meta-analysis that comprehensively evaluated the association between IMP3 expression and prognosis in patients with solid tumors. However, several limitations of our study must be acknowledged. First, we only extracted summarized population-level data rather than individual subject data from published literature. Second, different cutoff values and definitions of high IMP3 expression were used in these included studies. Third, a marked study heterogeneity existed in some analyses. The subgroup analyses and meta-regression revealed that cancer stage might be a significant contributor to heterogeneity. Moreover, several potential factors such as cancer type, cutoff value, baseline characteristics (sample size, sex, age, and pathological subtype), and duration of follow-up may partially contribute to the heterogeneity. Among the enrolled studies, 10 works did not directly report the HRs. The calculated HRs, which were estimated using the methods of Tierney et al, might not be as dependable as those retrieved directly from the reported results. As such, the HRs inevitably introduced some statistical errors and may have influenced the pooled analysis. Furthermore, some studies only provided univariate analysis results, which may have introduced a bias toward overestimation of the prognostic value compared with multivariate analysis. The funnel plot and Egger’s test suggested the probability of publication bias because of fewer studies reporting negative results. However, the greater difficulty in publishing studies with insignificant results than those with significant results may be unavoidable. Finally, despite the well-recognized advantages of systematic review and meta-analysis, the results were based on the quality of the included studies. Thus, further high-quality studies with larger samples and a unified detection method are entailed to achieve a consensus on this matter.

Conclusion

The current evidence suggests that high IMP3 expression in tumor tissues is associated with adverse survival in various cancers. Hence, IMP3 might be a potential and promising biomarker that can be used to improve prognosis stratification and guide decision making in the treatment of solid tumors. Further well-designed studies are needed to confirm our findings and obtain more precise evaluations of the prognostic value of IMP3 in cancers.

Supplementary material

Table S1.

Checklist of PRISMA 2009

Section/topic # Checklist item Reported on page #
Title
Title 1 Identify the report as a systematic review, meta-analysis, or both. 1
Abstract
Structured summary 2 Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number. 2
Introduction
Rationale 3 Describe the rationale for the review in the context of what is already known. 3
Objectives 4 Provide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS). 3,4
Methods
Protocol and registration 5 Indicate if a review protocol exists, if and where it can be accessed (eg, Web address), and, if available, provide registration information including registration number. No
Eligibility criteria 6 Specify study characteristics (eg, PICOS, length of follow-up) and report characteristics (eg, years considered, language, publication status) used as criteria for eligibility, giving rationale. 4,5
Information sources 7 Describe all information sources (eg, databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched. 4
Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated. 4
Study selection 9 State the process for selecting studies (ie, screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis). 5
Data collection process 10 Describe method of data extraction from reports (eg, piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators. 5
Data items 11 List and define all variables for which data were sought (eg, PICOS, funding sources) and any assumptions and simplifications made. 5,6
Risk of bias in individual studies 12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis. 5,6
Summary measures 13 State the principal summary measures (eg, risk ratio, difference in means). 5,6
Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (eg, I2) for each meta-analysis. 6
Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (eg, publication bias, selective reporting within studies). 6
Additional analyses 16 Describe methods of additional analyses (eg, sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified. 6
Results
Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram. 7
Study characteristics 18 For each study, present characteristics for which data were extracted (eg, study size, PICOS, follow-up period) and provide the citations. 7
Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). 7–14
Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group; (b) effect estimates and confidence intervals, ideally with a forest plot. 7–14
Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency. 7–14
Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see item 15). 7–14
Additional analysis 23 Give results of additional analyses, if done (eg, sensitivity or subgroup analyses, meta-regression [see Item 16]). 7–14
Discussion
Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (eg, healthcare providers, users, and policy makers). 14,15
Limitations 25 Discuss limitations at study and outcome level (eg, risk of bias), and at review-level (eg, incomplete retrieval of identified research, reporting bias). 15,16
Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research. 17
Funding
Funding 27 Describe sources of funding for the systematic review and other support (eg, supply of data); role of funders for the systematic review. None
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Notes: Reproduced from Moher D, Liberati A, Tetzlaff J, et al, Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. PLoS Med. 2009:6(7): e1000097.1

Reference

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Acknowledgments

This work was supported by the National High Technology Research and Development Program of China (2014AA020607).

Footnotes

Disclosure

The authors report no conflicts of interest in this work.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1.

Checklist of PRISMA 2009

Section/topic # Checklist item Reported on page #
Title
Title 1 Identify the report as a systematic review, meta-analysis, or both. 1
Abstract
Structured summary 2 Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number. 2
Introduction
Rationale 3 Describe the rationale for the review in the context of what is already known. 3
Objectives 4 Provide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS). 3,4
Methods
Protocol and registration 5 Indicate if a review protocol exists, if and where it can be accessed (eg, Web address), and, if available, provide registration information including registration number. No
Eligibility criteria 6 Specify study characteristics (eg, PICOS, length of follow-up) and report characteristics (eg, years considered, language, publication status) used as criteria for eligibility, giving rationale. 4,5
Information sources 7 Describe all information sources (eg, databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched. 4
Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated. 4
Study selection 9 State the process for selecting studies (ie, screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis). 5
Data collection process 10 Describe method of data extraction from reports (eg, piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators. 5
Data items 11 List and define all variables for which data were sought (eg, PICOS, funding sources) and any assumptions and simplifications made. 5,6
Risk of bias in individual studies 12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis. 5,6
Summary measures 13 State the principal summary measures (eg, risk ratio, difference in means). 5,6
Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (eg, I2) for each meta-analysis. 6
Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (eg, publication bias, selective reporting within studies). 6
Additional analyses 16 Describe methods of additional analyses (eg, sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified. 6
Results
Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram. 7
Study characteristics 18 For each study, present characteristics for which data were extracted (eg, study size, PICOS, follow-up period) and provide the citations. 7
Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). 7–14
Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group; (b) effect estimates and confidence intervals, ideally with a forest plot. 7–14
Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency. 7–14
Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see item 15). 7–14
Additional analysis 23 Give results of additional analyses, if done (eg, sensitivity or subgroup analyses, meta-regression [see Item 16]). 7–14
Discussion
Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (eg, healthcare providers, users, and policy makers). 14,15
Limitations 25 Discuss limitations at study and outcome level (eg, risk of bias), and at review-level (eg, incomplete retrieval of identified research, reporting bias). 15,16
Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research. 17
Funding
Funding 27 Describe sources of funding for the systematic review and other support (eg, supply of data); role of funders for the systematic review. None
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Notes: Reproduced from Moher D, Liberati A, Tetzlaff J, et al, Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. PLoS Med. 2009:6(7): e1000097.1

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