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Review

Prognostic Role of miR-221 and miR-222 Expression in Cancer Patients: A Systematic Review and Meta-Analysis

1
Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy
2
Department of Pharmaceutical Sciences and Interdepartmental Research Center of Pharmacogenetics and Pharmacogenomics (CRIFF), University of Piemonte Orientale, 28100 Novara, Italy
3
Institute of Medical Biometry and Informatics, University of Heidelberg, 69120 Heidelberg, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to the work.
These authors jointly supervised the work.
Submission received: 3 July 2019 / Accepted: 8 July 2019 / Published: 11 July 2019

Abstract

:
Background: A wealth of evidence has shown that microRNAs (miRNAs) can modulate specific genes, increasing our knowledge on the fine-tuning regulation of protein expression. miR-221 and miR-222 have been frequently identified as deregulated across different cancer types; however, their prognostic significance in cancer remains controversial. In view of these considerations, we performed an updated systematic review and meta-analysis of published data investigating the effects of miR-221/222 on overall survival (OS) and other secondary outcomes among cancer patients. A systematic search of PubMed, Web of Knowledge, and Cochrane Library databases was performed. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were used to assess the strength of association. Results: Fifty studies, analyzing 6086 patients, were included in the systematic review. Twenty-five studies for miR-221 and 17 studies for miR-222 which assessed OS were included in the meta-analysis. High expression of miR-221 and miR-222 significantly predicted poor OS (HR: 1.48, 95% CI: 1.14–1.93, p = 0.003 and HR: 1.90, 95% CI: 1.43–2.54, p < 0.001, respectively). Subgroup analysis revealed that the finding on miR-221 was not as robust as the one on miR-222. Furthermore, high miR-222 expression was also associated with worse progression-free survival and disease-free survival pooled with recurrence-free survival. Conclusions: The meta-analysis demonstrated that high expression of miR-222 is associated with poor prognosis in cancer patients, whereas the significance of miR-221 remains unclear. More work is required to fully elucidate the role of miR-221 and miR-222 in cancer prognosis, particularly in view of the limitations of existing results, including the significant heterogeneity and limited number of studies for some cancers.

1. Introduction

MicroRNAs (miRNAs) are a wide family of small non-coding RNAs that have emerged as key players in regulating the expression of target genes and are therefore important in many biological processes [1]. In the last two decades, a wealth of evidence has clarified the mechanisms through which miRNAs can modulate specific genes, increasing our knowledge on the fine-tuning regulation of protein expression [1,2,3,4]. miRNAs have been shown to be involved in complex regulatory networks, basically playing a role in every aspect of the biology of cells and organs [5,6]. Therefore, it is clear that miRNA deregulation may promote the disruption of a fine-tuned equilibrium within a cell, altering physiologically relevant functions [7,8,9]. Consequently, it is not surprising that miRNA expression has been found to be dysregulated in the majority of human diseases, including cancer [10,11]. miRNA deregulation takes place via different mechanisms such as (i) deletion or amplification of miRNA genes [12,13]; (ii) aberrant expression of transcription factors [14]; (iii) dysregulation of the miRNA biogenesis process [15]; and (iv) miRNA sequestration (miRNA sponging) [16,17,18]. miRNA deregulation can affect the hallmarks of cancer, including sustaining proliferation, evading apoptosis and resisting cell death, promoting invasion and metastasis, and inducing angiogenesis [19]. In particular, miRNAs may act as oncogenes, tumor suppressors, or, depending on the cellular context, may have both functions [20]. In addition to the cancerogenetic process, compelling evidence has shown that miRNAs can be involved in tumor sensitivity to treatment, showing a correlation with patients’ clinical outcomes [21,22,23,24].
miR-221 and miR-222 have been frequently identified as deregulated across different cancer types [25,26,27,28,29,30]. These two miRNAs, encoded on chromosome X (Xp11.3), are highly homologous, share seed sequences, and usually act as a cluster (miR-221/222) [31,32]. In particular, this cluster might promote the overcoming of the status of cell quiescence and increase proliferation, survival, and metastatic potential, acting as an oncogene [31,33,34]. In addition, it is well documented that miR-221 and miR-222 play a key role in modulating the clinical outcome in cancer patients in both solid and hematological malignancies [30,35]. In this regard, the prognostic value of miR-221 and miR-222 expression in cancer has been extensively investigated in the last decade, with controversial results. A number of studies support the hypothesis that their overexpression may be predictive for poor cancer survival [36,37], while others reported a better prognosis [38,39] or failed to find a significant association [40,41]. Few meta-analyses have investigated the association between miR-221/222 [42,43,44] and malignancies; however, their prognostic role in cancer patients has not been fully analyzed. In addition, single studies may be underpowered to achieve a comprehensive and reliable conclusion. Given these premises, it is imperative and timely to perform an exhaustive meta-analysis to evaluate the prognostic value of miR-221/222 expression in cancer patients. Therefore, we systematically performed a meta-analysis based on all eligible studies to evaluate the association between miR-221/222 expression and the prognosis of patients with malignant tumors.

2. Methods

2.1. Search Strategy

The present systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Statement principles [45]. The PubMed, Web of Knowledge, and Cochrane Library databases were systematically searched for original articles analyzing the prognostic value of miR-221/222 in different cancer patients (last updated search 28 September 2018). Relevant studies were selected using Boolean combinations of the following key terms variably combined: “miR-221 OR miR-222 OR miRNA-221 OR miRNA-222” OR miR-221/222 AND “cancer OR tumor OR tumour OR neoplasia OR tumors OR tumours OR cancers” AND “survival OR prognosis OR progression”. Additionally, the reference list of review papers, meta-analyses, and all original studies were hand searched to acquire further relevant studies missed by the initial electronic quest.

2.2. Inclusion and Exclusion Criteria

Eligible studies were required to meet the following inclusion criteria: (i) miR-221/222 expression measured in the tumor tissue; (ii) investigating the correlation of miR-221 or miR-222 expression level with human cancer prognosis; and (iii) reporting hazard ratios (HR) with corresponding 95% confidence intervals (CI) or sufficient information to estimate them. Exclusion criteria were (i) circulating miRNA, case reports, review articles, and editorials; (ii) non-human and in vitro studies; and (iii) non-English articles. When more than one study had been published sharing part of the same patient population, only the most recent and complete study was selected in this meta-analysis.

2.3. Data Extraction

After removing duplicated studies, two independent investigators (G.S. and F.Z.) carefully read the titles and abstracts of the relevant articles and judged their eligibility. Then, the entire text of potential eligible studies was evaluated to assess appropriateness for inclusion in the meta-analysis. Disagreements were resolved by discussion with another investigator (S.T. or S.A.) for consensus. Two authors (G.R. and S.C.) independently extracted the data from all included studies. The following data were sought and recorded: (1) first author, publication year, and nationality of studied population; (2) miRNA analyzed, cancer type, sample type, and miRNA assay method; and (3) sample size, cut-off value, HR, and corresponding 95% CI. When a study reported the survival results of both univariate and multivariate analyses, only the latter was extracted, as it accounts for confounding factors and is therefore more accurate. For studies only providing Kaplan–Meier curves, variables were read from the graphical survival plots using PlotDigitizer software (version 2.6.8, freely available at http://plotdigitizer.sourceforge.net); after that the HR value and 95% CI were estimated via the method described by Parmar et al. [46]. Finally, extracted data were crosschecked by two investigators (G.R. and S.C.), and any divergences were resolved by discussion with a third author (S.T. or S.A.).

2.4. Outcomes and Definition

In the present study, all survival outcomes were extracted. Overall survival (OS) was the primary outcome of interest selected to calculate the association with miR-221 and/or miR-222 expression in cancer patients. OS was defined as the length of time from when the sample was obtained to the date of death from any cause or the date of last follow-up. All the others were considered secondary survival outcomes.

2.5. Quality Assessment

The scientific quality of the included studies was evaluated according to the Newcastle–Ottawa Scale (NOS) for nonrandomized studies [47]. The methodological quality of included studies was firstly evaluated by two investigators (G.S. and F.Z.) and then by two other investigators (G.R and S.C.) in a second round of study quality evaluation; any disagreement was resolved through consensus. The NOS, assigning up to a maximum of 9 points, consists of three components: (I) selection and definition of the study groups (0–4 points); (II) comparability of the cohorts (0–2 points); and (III) ascertainment of outcomes (0–3 points). A possible score of 0–9 was assigned to each study. Studies with a NOS score greater than 7 were considered of high quality.

2.6. Statistical Analysis

Analyses were performed using ProMeta software Version 2 (INTERNOVI di Scarpellini Daniele s.a.s., Cesena FC, Italy). The pooled HR and corresponding 95% CI were used to evaluate the prognostic value of miR-221 and/or miR-222 for different malignancies. All analyses were performed using the low miR-221/222 expression group as the reference group (HR = 1). An observed HR of >1 implies a worse prognosis for the group with higher miR-221/222 expression. Heterogeneity between studies was estimated using the χ2-based Cochran’s Q statistic (significant for p < 0.10) [48], and the I2 index (range 0%–100%) which quantifies heterogeneity irrespective of the number of studies (an I2 of over 50% is interpreted as the presence of large degree of heterogeneity) [49]. Survival outcomes were pooled using the random effects model which takes into account both within-study variance and between-study variance; the random effects model coincides with the fixed effect model in the absence of heterogeneity (I2 = 0) [34]. To assess the robustness of the overall findings, subgroup analyses were conducted based on multiple criteria, including patient ethnicity, tumor type, biological specimen, sample size, survival analysis, and NOS. Additionally, the presence of small-study effects and publication bias was evaluated graphically by funnel plots and statistically by means of Egger’s test [50]. An Egger’s test p value of <0.10 was considered statistically significant. Moreover, leave-one-out meta-analysis was performed to identify studies that had a crucial influence on the pooled HR by removing one study at a time.

3. Results

3.1. Study Characteristics and Quality Assessment

The study selection process on the PubMed, Web of Knowledge, and Cochrane databases yielded 852 studies. After removing 242 duplicated publications, the remaining 610 records were evaluated by carefully reading titles and abstracts, after which 248 studies were excluded for the following reasons: only in vitro studies, reviews or meta-analyses, non-human studies, not in English, and case reports. Then, the entire text of the remaining 362 studies was assessed, determining the removal of 312 further studies. Finally, 50 articles, published between 2009 and 2018, were included in the systematic review [35,36,37,38,39,40,41,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93]. A detailed flow-chart illustrating the literature selection process is presented in Figure 1.
The main general information on the included studies are reported in Table 1 and Table 2. Among them, 27 focused on miR-221 [36,38,40,52,54,57,60,61,62,64,69,70,72,73,74,75,79,80,81,82,83,84,85,88,89,91,92], 11 on miR-222 [37,39,53,56,59,63,67,68,86,87,93], and the remaining 12 investigated the cluster miR-221/miR-222 [35,41,51,55,58,65,66,71,76,77,78,90]. Overall, 6086 patients suffering from different tumors were included in the systematic review, with the sample size in each study ranging from 20 to 393 patients. With regard to the cancer type, three studies focused on hematological tumors [56,62,92], whereas all the others evaluated solid tumors. Overall, the most evaluated neoplasms in the present meta-analysis were hepatocellular carcinoma (HCC; 7 studies) [54,64,71,80,83,91,93], non-small cell lung cancer (NSCLC; 5 studies) [37,67,79,84,86], prostate cancer (PCa; 5 studies) [51,52,60,66,76], breast cancer (BC; 4 studies) [61,75,81,87], and colorectal cancer (CRC; 4 studies) [38,40,72,90]. The majority of the studies were performed in Asia (32/50, 64%; 25 in China) and Europe (10/50, 20%). All the studies were retrospective in design; qRT-PCR was mostly used to measure the expression of miR-221/222 (45/50 studies, 90%). Twenty-four out of 50 studies (48%) measured the expression in formalin-fixed, paraffin-embedded (FFPE) samples, 21 (42%) in fresh/frozen tissue, 2 studies used both specimens, another 2 studies (4%) did not specifically report the tissue type, and 1 used bone marrow. The cut-off value stratifying patients into high and low expression groups varied among the different studies, with the median value being the most broadly used value (26/50 studies, 52%). The length of follow-up ranged from 24 to 300 months. Regarding the quality of included studies, the NOS scores ranged from 5 to 9 (median 7). Thirty-six studies that had scores of ≥7 were categorized as high-quality studies [35,36,37,38,39,40,41,51,52,54,56,57,59,60,61,62,65,66,67,68,69,70,71,72,73,75,77,78,79,80,84,85,87,89,90,92], while the remaining 14, which had scores of <7, were categorized as low-quality studies [53,55,58,63,64,74,76,81,82,83,86,88,91,93].

3.2. Impact of miR-221 Expression on Survival Outcomes

With regard to miR-221, 25 [35,36,38,40,41,55,57,58,62,64,69,71,72,73,78,79,80,82,83,84,85,88,89,90,92] out of the 50 studies included in the systematic review evaluated the relationship between miR-221 expression and OS in patients suffering from different tumors. Three studies reported cancer-specific survival (CSS) [61,70,91] and one reported the disease-specific survival (DSS) [77]. Concerning secondary outcomes, six studies reported disease-free survival (DFS) [35,38,62,74,80,81]; five reported recurrence-free survival (RFS) [51,52,60,66,75]; four reported progression-free survival (PFS) [35,71,76,77]; one reported the distant metastasis rate (DMR) and local recurrence rate (LRR) [40]; and three studies reported the time to local recurrence (TTLR) [54], time to recurrence (TTR) [91], and metastasis-free survival (MFS) [65], respectively.

3.2.1. miR-221 Expression and OS

We observed a high degree of heterogeneity among the 25 studies reporting OS and included in the meta-analysis (I2 = 75%, p < 0.001). Despite this, the pooled HR indicated a significantly short OS in patients with higher expression of miR-221 (Npatients = 2240; HR: 1.48, 95% CI: 1.14–1.93, p = 0.003; Figure 2A). No evidence of publication bias or small-study effects was observed in either the funnel plot (Figure 2B) or in Egger’s test (p = 0.770).
In order to assess the robustness of the overall finding, subgroup analyses were further performed based on seven subcategories: ethnicity, tumor type, pre-operative treatment, specimen, sample size, survival analysis (univariate or multivariate), and NOS score (Table 3). Statistically significant heterogeneity was detected in all the subgroup analyses except for studies performed in patients with liver cancer (I2 = 0%, p = 0.698) or NSCLC (I2 = 0%, p = 0.602) or with no pre-operative anticancer treatment (I2 = 0%, p = 0.813). When stratified by ethnicity, miR-221 overexpression was significantly correlated with poor OS in Asians with the combined HR being 1.67 (95% CI: 1.30–2.14, p < 0.001). When stratified by cancer types, high miR-221 expression was associated with poor OS in patients with liver cancer (pooled HR: 1.98, 95% CI: 1.51–2.60, p < 0.001) and NSCLC (pooled HR: 1.92, 95% CI: 1.31–2.81, p = 0.001). When stratified by pre-operative anticancer treatment, high miR-221 expression was associated with poor OS in patients with no pre-treatment (pooled HR: 1.84, 95% CI: 1.56–2.17, p < 0.001). In the subgroup analysis stratified by type of biological specimen, high miR-221 expression showed a significant relationship with poor OS in both FFPE samples (pooled HR: 1.68, 95% CI: 1.20–2.36, p = 0.003) and fresh/frozen tissue (pooled HR: 1.54, 95% CI: 1.05–2.28, p = 0.028). For the subgroup analysis of sample size, high expression of miR-221 in samples of <100 patients was significantly associated with poor OS (pooled HR: 1.62, 95% CI: 1.21–2.18, p < 0.001), whereas no association was observed in samples of equal to or greater than 100 patients. The analysis also showed that high expression of miR-221 was significantly associated with poor OS in both the survival methods. Finally, in subgroup analysis stratified by quality assessment, high miR-221 expression was associated with poor OS in high-quality studies, with the combined HR being 1.61 (95% CI 1.20–2.16, p = 0.002).
The four studies reporting CSS [61,70,91] and DSS [77] available for the meta-analysis were characterized by significant heterogeneity (I2 = 88%, p < 0.001). No significant association was found between CSS/DSS and high expression of miR-221, with the pooled HR being 0.95 (95% CI: 0.52–1.76, p = 0.878; Table 4). The forest plot is shown in Supplementary Figure S1. We also analyzed OS along with CSS and DSS. Despite significant heterogeneity (I2 = 77%; p < 0.001), high miR-221 expression was significantly associated with poor OS/CSS/DSS (pooled HR: 1.38, 95% CI: 1.09–1.74, p = 0.007; Supplementary Figure S2).

3.2.2. miR-221 Expression and Secondary Outcomes

DFS was analyzed along with RFS by pooling together 11 studies [35,38,51,52,60,62,66,74,75,80,81] encompassing 913 cancer patients. We observed a large degree of heterogeneity (I2 = 84%, p < 0.001) and no significant association between disease progression and high expression of miR-221 (HR: 0.90, 95% CI: 0.46–1.75, p = 0.753; Supplementary Figure S3A). No evidence of publication bias or small-study effects was observed in either the funnel plot (Supplementary Figure S3B) or in Egger’s test (p = 0.901). When assessing the association of miR-221 with PFS, no significant results were found (Table 4).

3.3. Impact of miR-222 on Survival Outcomes

Among the 23 studies focusing on the prognostic value of miR-222 in cancer patients, 17 studies investigated the effect of miR-222 overexpression on OS [35,37,39,41,53,55,58,59,63,65,67,68,71,78,86,90,93]. Only one study reported DSS [77] in place of OS. Concerning secondary outcomes, seven studies were available for the meta-analysis of miR-222 and PFS [35,55,56,71,76,77,86], three studies investigated its correlation with DFS [35,87,93], two with RFS [51,66], and one study reported MFS [65].

3.3.1. miR-222 Expression and OS

We observed a high degree of heterogeneity among the 17 studies reporting OS and included in the meta-analysis (I2 = 77%, p < 0.001). Despite this, the pooled results of miR-222 and OS provided evidence of significantly poorer prognosis in patients expressing higher levels of miR-222 (Npatients = 1782; HR: 1.90, 95% CI: 1.43–2.54, p < 0.001; Figure 3A). There was no evidence of publication bias either from the funnel plot (Figure 3B) or from Egger’s test (p = 0.299). Subgroup analyses were further performed to evaluate the robustness of the overall finding on the association between miR-222 expression and OS, based on the same seven categories used for miR-221; the data are presented in Table 5. Statistically significant heterogeneity was detected in most of the subgroups except for studies performed in patients with liver cancer (I2 = 0%, p = 0.441), gastro-intestinal malignancies (I2 = 25%, p = 0.256), or NSCLC (I2 = 0%, p = 0.470) or studies with a NOS score of <7 (I2 = 0%, p = 0.602). The results confirmed the significant association between high miR-222 expression and poor OS in all the subgroup analyses conducted, with the exception of studies including patients with urogenital cancer (HR: 1.22, 95% CI: 0.29–5.07, p = 0.781) and those conducted on FFPE tissues (HR: 1.22, 95% CI: 0.79–1.88, p = 0.360).
We also analyzed OS along with DSS; despite significant heterogeneity (I2 = 75%, p < 0.001), high miR-221 expression was significantly associated with poor OS/DSS (pooled HR: 1.88, 95% CI: 1.46–2.42, p < 0.001, Supplementary Figure S4).

3.3.2. miR-222 Expression and Secondary Outcomes

Despite a large degree of heterogeneity (I2 = 73%, p = 0.001), the results showed a significantly shorter PFS in patients with high expression of miR-222 (HR: 1.67, 95% CI: 1.10–2.51, p < 0.015; Supplementary Figure S5A). No evidence of publication bias or small-study effects was observed in either the funnel plot (Supplementary Figure S5B) or in Egger’s test (p = 0.914). When assessing the association of miR-222 with DFS pooled with RFS, high expression was associated with poor prognosis (Table 4; Supplementary Figure S6).

3.4. Sensitivity Analysis

For both miR-221 and 222, sensitivity analysis of the OS was performed to investigate the influence of each individual study on the pooled HRs. The analysis (Supplementary Figure S7A,B, respectively) showed that the pooled results were not significantly altered by omitting any single data set. This result suggests that no single study significantly influenced the pooled HRs or the 95% CIs, highlighting that the pooled results of OS were robust.

4. Discussion

In the last two decades, compelling evidence has repeatedly demonstrated the key involvement of miRNAs in cancer [19]. miRNAs minutely regulate a plethora of biological mechanisms at the post-transcriptional level, and an increasing number of studies have provided clues that aberrantly expressed miRNAs are involved in diagnosis and prognosis in numerous chronic diseases, including cancer [94,95]. Specifically, miRNAs may have both oncogenic and tumor-suppressive roles in any step of cancerogenesis, depending on the cellular context and tumor type. Among the miRNAs frequently dysregulated in cancer, miR-221 and miR-222 are considered of great importance. Indeed, the miR-221/222 cluster acts as onco-miR in the majority of epithelial tumors [96] while acting as oncosuppressor-miR in erythroleukemic cells [97]. Recently, diverse reports have shown their prognostic importance as circulating molecules in plasma, blood, and other biological fluids (for a review on circulating molecules, see ref [98]). Furthermore, one of the emerging relevant aspects is the contribution of miR-221 and miR-222 to the clinical outcome in cancer patients. To the best of our knowledge, to date, only two meta-analyses have assessed the association of miR-221 and miR-222 with the prognosis of patients with malignant tumors, respectively encompassing 1204 [44] and 2693 [42] patients. Results from both the meta-analyses agree on the association between miR-221/222 overexpression and poor OS and their possible translation into clinical practice. However, both meta-analyses were based on a limited number of studies. In particular, with regard to the primary outcome, few studies were published at the time of the first meta-analysis [40], which included nine studies for miR-221 and four studies for miR-222. In the meta-analysis performed by Zhang and coworkers [42], the authors pooled together miR-221 and miR-222; therefore, they suggested the development of biomarkers in cancer prognosis based on the cluster. However, some studies highlighted opposite results on survival outcomes between the two miRNAs; thus, a different prognostic role for miR-221 and miR-222 cannot be ruled out. In addition, Zhang et al. [42]., combined different sample sources (i.e., tissue and blood), adding a degree of uncertainty to the conclusive result. In view of this evidence, we performed an updated meta-analysis to corroborate the previous conclusions and possibly uncover some novel findings on secondary outcomes, separately analyzing the prognostic roles of these two miRNAs. We identified 50 studies, published between 2009 and 2018, including a total of 6086 patients, covering 21 different tumor types, and representing the widest meta-analysis so far performed. The current study demonstrated that high expression of both miR-221 and miR-222 significantly predicted poor OS in cancer, confirming the findings of the two previous meta-analyses [42,44]. Subgroup analysis revealed that the finding on miR-221 was not as robust as the one on miR-222. Indeed, high miR-222 expression was associated with poor OS, irrespectively of ethnicity, sample size, and quality score. On the contrary, miR-221 was a predictor of poor OS only in Asian (not in Caucasian) patients, in small cohorts of patients (<100), and in high-quality studies (NOS ≥ 7). With regard to ethnicity, differences in the genetic background, dietary habits, and environmental exposure might in part explain the findings. However, the fact that significance is retained only in studies of small sample size makes the result unconvincing, and we cannot exclude the possibility that the significance of miR-221 is due to chance. Additionally, subgroup analysis based on tissue type did not reveal differences in the prognostic value of miR-221. On the contrary, high miR-222 expression was significantly associated with poor OS in fresh/frozen tissue but not in FFPE samples. The finding is consistent with the knowledge of a higher speed of RNA degradation in FFPE tissues than in the other two specimen types [99]. With regard to secondary outcome, high expression of miR-222 was significantly associated with both worse PFS and worse DFS/RFS, whereas no association was observed with miR-221. Unfortunately, due to the limited number of studies investigating secondary outcomes, we could not perform a subgroup analysis to verify the robustness of the findings on miR-222. Despite this, the results on miR-222 are convincing, as they are consistent with OS. Overall, the findings have important clinical implications, as they may provide a rationale for the study and the development of a molecular sponge to regulate miR-222 expression [18,100]. In this context, intensive research is currently ongoing to understand the role of miRNA sponges that could be used to control miRNA expression and therefore function for therapeutic purposes [101,102].
Although the present meta-analysis provides evidence of the prognostic value of miR-222, several limitations should be highlighted. Primarily, although 50 relevant studies were identified, the number of studies for some cancer types was insufficient to be evaluated. Therefore, when analyzing OS, subgroup analysis was carried out by considering the anatomic site instead of the specific type of cancer. In this context, high miR-222 expression was associated with poor OS in all the anatomic sites of cancer with the exception of urogenital cancer. In view of this consideration, we should not underestimate the limitations associated with differences in patients’ baseline characteristics, treatments received, duration of follow-up, and cancer stage as reported also for other miRNAs [81,85,89,103,104,105]. Additional bias could arise from differences in the biological properties of the specific cancer types, particularly regarding the tumor microenvironment and the diversity of signals provided by the tumor cells. Further sources of micro statistical errors could be (i) the variety of cut-off values used to define miR-221/222 expression among studies; (ii) the calculation of HR and 95% CI from the survival curve in some studies; and (iii) the statistical methodology, i.e., the use of univariate in place of multivariate analysis in some investigations. Therefore, better-designed clinical studies are needed to gain new insights into the prognostic role of miR-222 in different human malignancies. In particular, to reduce the source of heterogeneity and make meta-analysis a powerful tool, we should also consider reaching an agreement on the clinical outcomes. Indeed, of the 23 studies investigating the prognostic role of miR-222, only 17 investigated OS, while the others explored the association with different secondary outcomes; these could not be analyzed together, reducing the number of studies eligible for the meta-analysis.

5. Conclusions

In conclusion, to the best of our knowledge, our study represents the widest meta-analysis so far performed on the prognostic value of miR-221 and miR-222. These findings should bring the attention of scientists to the importance of high miR-222 expression as a biomarker of poor prognosis in terms of both OS and secondary outcomes, including DFS and RFS. On the contrary, the results on miR-221 remain controversial. Despite the fact that no obvious publication bias was detected in the analysis, we cannot exclude that what we observed is a chance finding. Therefore, further large-scale prospective studies are warranted to validate the appropriateness of miR-222’s poor prognosis prediction capability and to exclude the involvement of miR-221.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/2072-6694/11/7/970/s1, Figure S1: Forest plots for the effect of miR-221 overexpression and CSS and DSS, Figure S2: Forest plots for the effect of miR-221 overexpression and OS/CSS/DSS, Figure S3: Forest (A) and Funnel (B) plots for the effect of miR-221 overexpression on DFS/RFS. (B), Figure S4: Forest plots for the effect of miR-222 overexpression and OS/DSS, Figure S5: Forest (A) and Funnel (B) plots for the effect of miR-222 overexpression on PFS, Figure S6: Forest plots for the effect of miR-222 overexpression and RFS/DFS, Figure S7: Sensitivity analysis of the meta-analysis for miR-221 (A) and miR-222 (B).

Author Contributions

G.R., S.C., G.S., F.Z., S.T., S.A. extracted the data; S.T. and S.C. analyzed the data; G.R., S.C., G.S., F.Z., J.L.B., P.H., S.T., S.A. wrote and revised the manuscript.

Funding

This work was partly supported by grants from the Ministry of Education, University and Research of Italy (MIUR, grant number 2015Y3C5KP_002 to SA) and Fondazione Cassa di Risparmio di Bologna (CARISBO to SA).

Acknowledgments

G.R. is supported by L’Oréal-UNESCO for Women in Science. S.C. holds a temporary research fellowship (Bando Formazione CRT, ID 393), supported by University of Piemonte Orientale. G.S. was supported by Fondazione Famiglia Parmiani.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ha, M.; Kim, V.N. Regulation of microRNA biogenesis. Nat. Rev. Mol. Cell Biol. 2014, 15, 509–524. [Google Scholar] [CrossRef] [PubMed]
  2. Di Leva, G.; Garofalo, M.; Croce, C.M. MicroRNAs in cancer. Annu. Rev. Pathol. Mech. Dis. 2014, 9, 287–314. [Google Scholar] [CrossRef] [PubMed]
  3. Garzon, R.; Calin, G.A.; Croce, C.M. MicroRNAs in cancer. Annu. Rev. Med. 2009, 60, 167–179. [Google Scholar] [CrossRef]
  4. Choi, J.D.; Lee, J.-S. Interplay between epigenetics and genetics in cancer. Genomics Inform. 2013, 11, 164. [Google Scholar] [CrossRef]
  5. Pritchard, C.C.; Cheng, H.H.; Tewari, M. MicroRNA profiling: Approaches and considerations. Nat. Rev. Genet. 2012, 13, 358–369. [Google Scholar] [CrossRef] [PubMed]
  6. Sevignani, C.; Calin, G.A.; Siracusa, L.D.; Croce, C.M. Mammalian microRNAs: A small world for fine-tuning gene expression. Mamm. Genome 2006, 17, 189–202. [Google Scholar] [CrossRef]
  7. Hrovatin, K.; Kunej, T. Classification of MiRNA-related sequence variations. Epigenomics 2018, 10, 463–481. [Google Scholar] [CrossRef] [PubMed]
  8. Calin, G.A.; Sevignani, C.; Dumitru, C.D.; Hyslop, T.; Noch, E.; Yendamuri, S.; Shimizu, M.; Rattan, S.; Bullrich, F.; Negrini, M.; et al. Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc. Natl. Acad. Sci. USA 2004, 101, 2999–3004. [Google Scholar] [CrossRef] [Green Version]
  9. Calin, G.A.; Croce, C.M. MicroRNA-cancer connection: The beginning of a new tale. Cancer Res. 2006, 66, 7390–7394. [Google Scholar] [CrossRef]
  10. Rupaimoole, R.; Slack, F.J. MicroRNA therapeutics: Towards a new era for the management of cancer and other diseases. Nat. Rev. Drug Discov. 2017, 16, 203–222. [Google Scholar] [CrossRef]
  11. Alamoudi, A.A.; Alnoury, A.; Gad, H. MiRNA in tumour metabolism and why could it be the preferred pathway for energy reprograming. Brief. Funct. Genomics 2018, 17, 157–169. [Google Scholar] [CrossRef] [PubMed]
  12. Ventura, A.; Young, A.G.; Winslow, M.M.; Lintault, L.; Meissner, A.; Erkeland, S.J.; Newman, J.; Bronson, R.T.; Crowley, D.; Stone, J.R.; et al. Targeted deletion reveals essential and overlapping functions of the MiR-17 through 92 family of MiRNA clusters. Cell 2008, 132, 875–886. [Google Scholar] [CrossRef] [PubMed]
  13. Lu, Y.; Ryan, S.L.; Elliott, D.J.; Bignell, G.R.; Futreal, P.A.; Ellison, D.W.; Bailey, S.; Clifford, S.C. Amplification and overexpression of Hsa-MiR-30b, Hsa-MiR-30d and KHDRBS3 at 8q24.22-Q24.23 in medulloblastoma. PLoS ONE 2009, 4, e6159. [Google Scholar] [CrossRef] [PubMed]
  14. Moutinho, C.; Esteller, M. MicroRNAs and epigenetics. Adv. Cancer Res. 2017, 135, 189–220. [Google Scholar] [CrossRef] [PubMed]
  15. Hata, A.; Kashima, R. Dysregulation of microRNA biogenesis machinery in cancer. Crit. Rev. Biochem. Mol. Biol. 2016, 51, 121–134. [Google Scholar] [CrossRef] [PubMed]
  16. Patel, S. Exploring novel therapeutic targets in GIST: Focus on the PI3K/Akt/MTOR Pathway. Curr. Oncol. Rep. 2013, 15, 386–395. [Google Scholar] [CrossRef] [PubMed]
  17. Bak, R.O.; Mikkelsen, J.G. MiRNA sponges: Soaking up MiRNAs for regulation of gene expression. Wiley Interdiscip. Rev. RNA 2014, 5, 317–333. [Google Scholar] [CrossRef]
  18. Ravegnini, G.; Serrano, C.; Simeon, V.; Sammarini, G.G.; Nannini, M.; Roversi, E.; Urbini, M.; Ferrè, F.; Ricci, R.; Tarantino, G.; et al. The Rs17084733 variant in the KIT 3’ UTR disrupts a MiR-221/222 binding site in gastrointestinal stromal tumor: A sponge-like mechanism conferring disease susceptibility. Epigenetics 2019, 545–557. [Google Scholar] [CrossRef]
  19. Calin, G.A.; Croce, C.M. MicroRNA signatures in human cancers. Nat. Rev. Cancer 2006, 6, 857–866. [Google Scholar] [CrossRef]
  20. Svoronos, A.A.; Engelman, D.M.; Slack, F.J. OncomiR or tumor suppressor? The duplicity of microRNAs in cancer. Cancer Res. 2016, 76, 3666–3670. [Google Scholar] [CrossRef]
  21. Wu, Q.-B.; Sheng, X.; Zhang, N.; Yang, M.-W.; Wang, F. Role of microRNAs in the resistance of colorectal cancer to chemoradiotherapy (review). Mol. Clin. Oncol. 2018, 8, 528–532. [Google Scholar] [CrossRef] [PubMed]
  22. Hahne, J.C.; Valeri, N. Non-coding RNAs and resistance to anticancer drugs in gastrointestinal tumors. Front. Oncol. 2018, 8, 226. [Google Scholar] [CrossRef] [PubMed]
  23. Ghasabi, M.; Mansoori, B.; Mohammadi, A.; Duijf, P.H.; Shomali, N.; Shirafkan, N.; Mokhtarzadeh, A.; Baradaran, B. MicroRNAs in cancer drug resistance: Basic evidence and clinical applications. J. Cell. Physiol. 2019, 234, 2152–2168. [Google Scholar] [CrossRef] [PubMed]
  24. Mohajeri, M.; Banach, M.; Atkin, S.L.; Butler, A.E.; Ruscica, M.; Watts, G.F.; Sahebkar, A. MicroRNAs: Novel molecular targets and response modulators of statin therapy. Trends Pharmacol. Sci. 2018, 39, 967–981. [Google Scholar] [CrossRef] [PubMed]
  25. Gits, C.M.M.; van Kuijk, P.F.; Jonkers, M.B.E.; Boersma, A.W.M.; van IJcken, W.F.; Wozniak, A.; Sciot, R.; Rutkowski, P.; Schöffski, P.; Taguchi, T.; et al. MiR-17-92 and MiR-221/222 cluster members target KIT and ETV1 in human gastrointestinal stromal tumours. Br. J. Cancer 2013, 109, 1625–1635. [Google Scholar] [CrossRef] [PubMed]
  26. Shaker, O.; Alhelf, M.; Morcos, G.; Elsharkawy, A. MiRNA-101-1 and MiRNA-221 Expressions and their polymorphisms as biomarkers for early diagnosis of hepatocellular carcinoma. Infect. Genet. Evol. 2017, 51, 173–181. [Google Scholar] [CrossRef] [PubMed]
  27. Nannini, M.; Ravegnini, G.; Angelini, S.; Astolfi, A.; Biasco, G.; Pantaleo, M.A. MiRNA Profiling in gastrointestinal stromal tumors: Implication as diagnostic and prognostic markers. Epigenomics 2015, 7, 1033–1049. [Google Scholar] [CrossRef] [PubMed]
  28. He, H.; Jazdzewski, K.; Li, W.; Liyanarachchi, S.; Nagy, R.; Volinia, S.; Calin, G.A.; Liu, C.-G.; Franssila, K.; Suster, S.; et al. The role of microRNA genes in papillary thyroid carcinoma. Proc. Natl. Acad. Sci. USA 2005, 102, 19075–19080. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Song, J.; Ouyang, Y.; Che, J.; Li, X.; Zhao, Y.; Yang, K.; Zhao, X.; Chen, Y.; Fan, C.; Yuan, W. Potential value of MiR-221/222 as diagnostic, prognostic, and therapeutic biomarkers for diseases. Front. Immunol. 2017, 8, 56. [Google Scholar] [CrossRef] [PubMed]
  30. Garofalo, M.; Quintavalle, C.; Romano, G.; Croce, C.M.; Condorelli, G. MiR221/222 in cancer: Their role in tumor progression and response to therapy. Curr. Mol. Med. 2012, 12, 27–33. [Google Scholar] [CrossRef]
  31. Zhao, J.-J.; Chu, Z.-B.; Hu, Y.; Lin, J.; Wang, Z.; Jiang, M.; Chen, M.; Wang, X.; Kang, Y.; Zhou, Y.; et al. Targeting the MiR-221-222/PUMA/BAK/BAX pathway abrogates dexamethasone resistance in multiple myeloma. Cancer Res. 2015, 75, 4384–4397. [Google Scholar] [CrossRef] [PubMed]
  32. Kim, Y.-K.; Yu, J.; Han, T.S.; Park, S.-Y.; Namkoong, B.; Kim, D.H.; Hur, K.; Yoo, M.-W.; Lee, H.-J.; Yang, H.-K.; et al. Functional links between clustered microRNAs: Suppression of cell-cycle inhibitors by microRNA clusters in gastric cancer. Nucleic Acids Res. 2009, 37, 1672–1681. [Google Scholar] [CrossRef] [PubMed]
  33. Medina, R.; Zaidi, S.K.; Liu, C.-G.; Stein, J.L.; Croce, C.M.; Stein, G.S. MicroRNAs 221 and 222 bypass quiescence and compromise cell survival. Cancer Res. 2008, 68, 2773–2780. [Google Scholar] [CrossRef] [PubMed]
  34. Yang, C.-J.; Shen, W.G.; Liu, C.-J.; Chen, Y.-W.; Lu, H.-H.; Tsai, M.-M.; Lin, S.-C. MiR-221 and MiR-222 Expression increased the growth and tumorigenesis of oral carcinoma cells. J. Oral Pathol. Med. 2011, 40, 560–566. [Google Scholar] [CrossRef] [PubMed]
  35. Tsikrika, F.D.; Avgeris, M.; Levis, P.K.; Tokas, T.; Stravodimos, K.; Scorilas, A. MiR-221/222 cluster expression improves clinical stratification of non-muscle invasive bladder cancer (TaT1) patients’ risk for short-term relapse and progression. Genes Chromosom. Cancer 2018, 57, 150–161. [Google Scholar] [CrossRef] [PubMed]
  36. Xue, L.; Wang, Y.; Yue, S.; Zhang, J. The expression of MiRNA-221 and MiRNA-222 in gliomas patients and their prognosis. Neurol. Sci. 2017, 38, 67–73. [Google Scholar] [CrossRef]
  37. Lei, Y.; Liu, Z.; Yang, W. Negative correlation of cytoplasm TIMP3 with MiR-222 indicates a good prognosis for NSCLC. Onco. Targets Ther. 2018, 11, 5551–5557. [Google Scholar] [CrossRef]
  38. Yuan, K.; Xie, K.; Fox, J.; Zeng, H.; Gao, H.; Huang, C.; Wu, M. Decreased levels of Mir-224 and the passenger strand of MiR-221 increase MBD2, suppressing maspin and promoting colorectal tumor growth and metastasis in mice. Gastroenterology 2013, 145, 853–864. [Google Scholar] [CrossRef]
  39. Fu, X.; Li, Y.; Alvero, A.; Li, J.; Wu, Q.; Xiao, Q.; Peng, Y.; Hu, Y.; Li, X.; Yan, W.; et al. MicroRNA-222-3p/GNAI2/AKT axis inhibits epithelial ovarian cancer cell growth and associates with good overall survival. Oncotarget 2016, 7, 80633–80654. [Google Scholar] [CrossRef] [Green Version]
  40. Liu, L.; Meng, T.; Yang, X.H.; Sayim, P.; Lei, C.; Jin, B.; Ge, L.; Wang, H.J. Prognostic and predictive value of long non-coding RNA GAS5 and mircoRNA-221 in colorectal cancer and their effects on colorectal cancer cell proliferation, migration and invasion. Cancer Biomarkers 2018, 22, 283–299. [Google Scholar] [CrossRef]
  41. Chen, Y.Y.; Ho, H.L.; Lin, S.C.; Ho, T.D.H.; Hsu, C.Y. Upregulation of MiR-125b, MiR-181d, and MiR-221 predicts poor prognosis in MGMT promoter-unmethylated glioblastoma patients. Am. J. Clin. Pathol. 2018, 149, 412–417. [Google Scholar] [CrossRef] [PubMed]
  42. Zhang, P.; Zhang, M.; Han, R.; Zhang, K.; Ding, H.; Liang, C.; Zhang, L. The correlation between microRNA-221/222 cluster overexpression and malignancy: An updated meta-analysis including 2693 patients. Cancer Manag. Res. 2018, 10, 3371–3381. [Google Scholar] [CrossRef] [PubMed]
  43. Liang, L.; Zheng, X.; Hu, M.; Cui, Y.; Zhong, Q.; Wang, S.; Huang, F. MiRNA-221/222 in thyroid cancer: A meta-analysis. Clin. Chim. Acta 2018, 484, 284–292. [Google Scholar] [CrossRef]
  44. Wang, J.; Liu, S.; Sun, G.P.; Wang, F.; Zou, Y.F.; Jiao, Y.; Ning, J.; Xu, J. Prognostic significance of microRNA-221/222 expression in cancers: Evidence from 1204 subjects. Int. J. Biol. Markers 2014, 29, 129–141. [Google Scholar] [CrossRef] [PubMed]
  45. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef]
  46. Parmar, M.K.; Torri, V.; Stewart, L. Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat. Med. 1998, 17, 2815–2834. [Google Scholar] [CrossRef]
  47. Wells, G.; Shea, B.; O’Connell, D.; Peterson, J.; Welch, V.; Losos, M.; Tugwell, P. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. Available online: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (accessed on 29 January 2019).
  48. Lau, J.; Ioannidis, J.P.; Schmid, C.H. Quantitative synthesis in systematic reviews. Ann. Intern. Med. 1997, 127, 820–826. [Google Scholar] [CrossRef] [PubMed]
  49. Higgins, J.P.T.; Thompson, S.G.; Deeks, J.J.; Altman, D.G. Measuring inconsistency in meta-analyses. BMJ 2003, 327, 557–560. [Google Scholar] [CrossRef] [Green Version]
  50. Egger, M.; Davey Smith, G.; Schneider, M.; Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997, 315, 629–634. [Google Scholar] [CrossRef] [Green Version]
  51. Schaefer, A.; Jung, M.; Mollenkopf, H.J.; Wagner, I.; Stephan, C.; Jentzmik, F.; Miller, K.; Lein, M.; Kristiansen, G.; Jung, K. Diagnostic and prognostic implications of microRNA profiling in prostate carcinoma. Int. J. Cancer 2010, 126, 1166–1176. [Google Scholar] [CrossRef]
  52. Spahn, M.; Kneitz, S.; Scholz, C.J.; Stenger, N.; Rüdiger, T.; Ströbel, P.; Riedmiller, H.; Kneitz, B. Expression of microRNA-221 is progressively reduced in aggressive prostate cancer and metastasis and predicts clinical recurrence. Int. J. Cancer 2010, 127, 394–403. [Google Scholar] [CrossRef] [PubMed]
  53. Greither, T.; Grochola, L.F.; Udelnow, A.; Lautenschläger, C.; Würl, P.; Taubert, H. Elevated expression of microRNAs 155, 203, 210 and 222 in pancreatic tumors is associated with poorer survival. Int. J. Cancer 2010, 126, 73–80. [Google Scholar] [CrossRef] [PubMed]
  54. Yoon, S.O.; Chun, S.M.; Han, E.H.; Choi, J.; Jang, S.J.; Koh, S.A.; Hwang, S.; Yu, E. Deregulated expression of microRNA-221 with the potential for prognostic biomarkers in surgically resected hepatocellular carcinoma. Hum. Pathol. 2011, 42, 1391–1400. [Google Scholar] [CrossRef] [PubMed]
  55. Delfino, K.R.; Serão, N.V.L.; Southey, B.R.; Rodriguez-Zas, S.L. Therapy-, gender- and race-specific microRNA markers, target genes and networks related to glioblastoma recurrence and survival. Cancer Genomics Proteomics 2011, 8, 173–183. [Google Scholar] [PubMed]
  56. Alencar, A.J.; Malumbres, R.; Kozloski, G.A.; Advani, R.; Gascoyne, D.; Tibshirani, R.; Lossos, I.S. MicroRNAs are independent predictors of outcome in diffuse large B-Cell lymphoma patients treated with R-CHOP. Clin Cancer Res 2011, 17, 4125–4135. [Google Scholar] [CrossRef] [PubMed]
  57. Liu, K.; Li, G.; Fan, C.; Diao, Y.; Wu, B.; Li, J. Increased expression of microRNA-221 in gastric cancer and its clinical significance. J. Int. Med. Res. 2012, 40, 467–474. [Google Scholar] [CrossRef] [PubMed]
  58. Zhang, C.; Zhang, J.; Hao, J.; Shi, Z.; Wang, Y.; Han, L.; Yu, S.; You, Y.; Jiang, T.; Wang, J.; et al. High level of MiR-221/222 confers increased cell invasion and poor prognosis in glioma. J. Transl. Med. 2012, 10, 1–11. [Google Scholar] [CrossRef]
  59. Schultz, N.A.; Andersen, K.K.; Roslind, A.; Willenbrock, H.; Wøjdemann, M.; Johansen, J.S. Prognostic microRNAs in cancer tissue from patients operated for pancreatic cancer—five micrornas in a prognostic index. World J. Surg. 2012, 36, 2699–2707. [Google Scholar] [CrossRef]
  60. Kang, S.G.; Ha, Y.R.; Kim, S.J.; Kang, S.H.; Park, H.S.; Lee, J.G.; Cheon, J.; Kim, C.H. Do microRNA 96, 145 and 221 expressions really aid in the prognosis of prostate carcinoma? Asian J. Androl. 2012, 14, 752–757. [Google Scholar] [CrossRef] [Green Version]
  61. Hanna, J.; Wimberly, H.; Kumar, S.; Slack, F.; Agarwal, S.; Rimm, D. Quantitative analysis of microRNAs in tissue microarrays by in situ hybridization. Biotechniques 2012, 52, 235–245. [Google Scholar] [CrossRef]
  62. Gimenes-Teixeira, H.L.; Lucena-Araujo, A.R.; dos Santos, G.A.; Zanette, D.L.; Scheucher, P.S.; Oliveira, L.C.; Dalmazzo, L.F.; Silva-Júnior, W.A.; Falcão, R.P.; Rego, E.M. Increased expression of MiR-221 Is associated with shorter overall survival in T-cell acute lymphoid leukemia. Exp. Hematol. Oncol. 2013, 2, 1–8. [Google Scholar] [CrossRef]
  63. Lee, C.; He, H.; Jiang, Y.; Di, Y.; Yang, F.; Li, J.; Jin, C.; Fu, D. Elevated expression of tumor MiR-222 in pancreatic cancer is associated with Ki67 and poor prognosis. Med. Oncol. 2013, 30, 700. [Google Scholar] [CrossRef] [PubMed]
  64. Karakatsanis, A.; Papaconstantinou, I.; Gazouli, M.; Lyberopoulou, A.; Polymeneas, G.; Voros, D. Expression of microRNAs, MiR-21, MiR-31, MiR-122, MiR-145, MiR-146a, MiR-200c, MiR-221, MiR-222, and MiR-223 in patients with hepatocellular carcinoma or intrahepatic cholangiocarcinoma and its prognostic significance. Mol. Carcinog. 2013, 52, 297–303. [Google Scholar] [CrossRef] [PubMed]
  65. Kim, B.-H.H.; Hong, S.W.; Kim, A.; Choi, S.H.; Yoon, S.O. Prognostic implications for high expression of oncogenic microRNAs in advanced gastric carcinoma. J. Surg. Oncol. 2013, 107, 505–510. [Google Scholar] [CrossRef] [PubMed]
  66. Amankwah, E.K.; Anegbe, E.; Park, H.; Pow-Sang, J.; Hakam, A.; Park, J.Y. MiR-21, MiR-221 and MiR-222 expression and prostate cancer recurrence among obese and non-obese cases. Asian J. Androl. 2013, 15, 226–230. [Google Scholar] [CrossRef] [PubMed]
  67. Mao, K.P.; Zhang, W.N.; Liang, X.M.; Ma, Y.R. MicroRNA-222 expression and its prognostic potential in non-small cell lung cancer. Sci. World J. 2014, 2014, 1–4. [Google Scholar] [CrossRef] [PubMed]
  68. Zhang, D.Q.; Zhou, C.K.; Jiang, X.W.; Chen, J.; Shi, B.K. Increased expression of MiR-222 is associated with poor prognosis in bladder cancer. World J. Surg. Oncol. 2014, 12, 1–5. [Google Scholar] [CrossRef] [PubMed]
  69. Tao, K.; Yang, J.; Guo, Z.; Hu, Y.; Sheng, H.; Gao, H.; Yu, H. Prognostic value of MiR-221-3p, MiR-342-3p and MiR-491-5p expression in colon cancer. Am. J. Transl. Res. 2014, 6, 391–401. [Google Scholar] [CrossRef] [PubMed]
  70. Vergho, D.C.; Kneitz, S.; Kalogirou, C.; Burger, M.; Krebs, M.; Rosenwald, A.; Spahn, M.; Löser, A.; Kocot, A.; Riedmiller, H.; et al. Impact of MiR-21, MiR-126 and MiR-221 as prognostic factors of clear cell renal cell carcinoma with tumor thrombus of the inferior vena cava. PLoS ONE 2014, 9, 1–11. [Google Scholar] [CrossRef]
  71. Gyöngyösi, B.; Végh, É.; Járay, B.; Székely, E.; Fassan, M.; Bodoky, G.; Schaff, Z.; Kiss, A. Pretreatment microRNA level and outcome in sorafenib-treated hepatocellular carcinoma. J. Histochem. Cytochem. 2014, 62, 547–555. [Google Scholar] [CrossRef]
  72. Hur, K.; Toiyama, Y.; Schetter, A.J.; Okugawa, Y.; Harris, C.C.; Boland, C.R.; Goel, A. Identification of a metastasis-specific microRNA signature in human colorectal cancer. J. Natl. Cancer Inst. 2015, 107, 1–11. [Google Scholar] [CrossRef] [PubMed]
  73. Cai, K.; Shen, F.; Cui, J.H.; Yu, Y.; Pan, H.Q. Expression of MiR-221 in colon cancer correlates with prognosis. Int. J. Clin. Exp. Med. 2015, 8, 2794–2798. [Google Scholar] [PubMed]
  74. Li, J.; Yao, L.; Li, G.; Ma, D.; Sun, C.; Gao, S.; Zhang, P.; Gao, F. MiR-221 Promotes epithelial-mesenchymal transition through targeting PTEN and forms a positive feedback loop with β-catenin/c-Jun signaling pathway in extra-hepatic cholangiocarcinoma. PLoS ONE 2015, 10, 1–16. [Google Scholar] [CrossRef] [PubMed]
  75. Eissa, S.; Matboli, M.; Sharawy, A.; El-Sharkawi, F. Prognostic and biological significance of microRNA-221 in breast cancer. Gene 2015, 574, 163–167. [Google Scholar] [CrossRef] [PubMed]
  76. Goto, Y.; Kojima, S.; Nishikawa, R.; Kurozumi, A.; Kato, M.; Enokida, H.; Matsushita, R.; Yamazaki, K.; Ishida, Y.; Nakagawa, M.; et al. MicroRNA expression signature of castration-resistant prostate cancer: The microRNA-221/222 cluster functions as a tumour suppressor and disease progression marker. Br. J. Cancer 2015, 113, 1055–1065. [Google Scholar] [CrossRef]
  77. García-Donas, J.; Beuselinck, B.; Inglada-Pérez, L.; Graña, O.; Schöffski, P.; Wozniak, A.; Bechter, O.; Apellániz-Ruiz, M.; Leandro-García, L.J.; Esteban, E.; et al. Deep sequencing reveals microRNAs predictive of antiangiogenic drug response. JCI Insight 2016, 1, 1–10. [Google Scholar] [CrossRef] [PubMed]
  78. Li, X.; Zheng, J.; Chen, L.; Diao, H.; Liu, Y. Predictive and prognostic roles of abnormal expression of tissue MiR-125b, MiR-221, and MiR-222 in glioma. Mol. Neurobiol. 2016, 53, 577–583. [Google Scholar] [CrossRef]
  79. Zhang, Y.; Zhao, Y.; Sun, S.; Liu, Z.; Zhang, Y.; Jiao, S. Overexpression of microRNA-221 Is associated with poor prognosis in non-small cell lung cancer patients. Tumor Biol. 2016, 37, 10155–10160. [Google Scholar] [CrossRef]
  80. Chen, F.; Li, X.-F.F.; Fu, D.-S.S.; Huang, J.-G.G.; Yang, S.-E.E. Clinical potential of MiRNA-221 as a novel prognostic biomarker for hepatocellular carcinoma. Cancer Biomarkers 2017, 18, 209–214. [Google Scholar] [CrossRef]
  81. Deng, L.; Lei, Q.; Wang, Y.; Wang, Z.; Xie, G.; Zhong, X.; Wang, Y.; Chen, N.; Qiu, Y.; Pu, T.; et al. Downregulation of MiR-221-3p and upregulation of its target gene PARP1 are prognostic biomarkers for triple negative breast cancer patients and associated with poor prognosis. Oncotarget 2017, 8, 108712–108725. [Google Scholar] [CrossRef]
  82. He, X.-Y.; Tan, Z.-L.; Mou, Q.; Liu, F.-J.; Liu, S.; Yu, C.-W.; Zhu, J.; Lv, L.-Y.; Zhang, J.; Wang, S.; et al. MicroRNA-221 Enhances MYCN via targeting nemo-like kinase and functions as an oncogene related to poor prognosis in neuroblastoma. Clin. Cancer Res. 2017, 23, 2905–2918. [Google Scholar] [CrossRef] [PubMed]
  83. Li, T.; Li, M.; Hu, S.; Cheng, X.; Gao, Y.; Jiang, S.; Yu, Q.; Zhang, C.; Sun, P.; Xian, W.; et al. MiR-221 Mediates the epithelial-mesenchymal transition of hepatocellular carcinoma by targeting AdipoR1. Int. J. Biol. Macromol. 2017, 103, 1054–1061. [Google Scholar] [CrossRef] [PubMed]
  84. Li, Y.; Zhang, H.; Dong, Y.; Fan, Y.; Li, Y.; Zhao, C.; Wang, C.; Liu, J.; Li, X.; Dong, M.; et al. MiR-146b-5p Functions as a suppressor MiRNA and prognosis predictor in non-small cell lung cancer. J. Cancer 2017, 8, 1704–1716. [Google Scholar] [CrossRef] [PubMed]
  85. Xie, D.; Yuan, P.; Wang, D.; Jin, H.; Chen, H. Expression and prognostic significance of MiR-375 and MiR-221 in liver cancer. Oncol. Lett. 2017, 14, 2305–2309. [Google Scholar] [CrossRef] [PubMed]
  86. Xu, Y.-M.; Wei, L.; Ding, S.-G.; Hai-Tao, H.; Zhong, C.-J. Up-regulation of MiRNA-222 predicts poor prognosis and tumor progression in patients with advanced stage non-small cell lung cancer. Acta Med. Mediterr. 2017, 33, 231–236. [Google Scholar]
  87. Han, S.-H.; Kim, H.J.; Gwak, J.M.; Kim, M.; Chung, Y.R.; Park, S.Y. MicroRNA-222 Expression as a predictive marker for tumor progression in hormone receptor-positive breast cancer. J. Breast Cancer 2017, 20, 35–44. [Google Scholar] [CrossRef] [PubMed]
  88. Wu, Q.; Ren, X.; Zhang, Y.; Fu, X.; Li, Y.; Peng, Y.; Xiao, Q.; Li, T.; Ouyang, C.; Hu, Y.; et al. MiR-221-3p Targets ARF4 and inhibits the proliferation and migration of epithelial ovarian cancer cells. Biochem. Biophys. Res. Commun. 2018, 497, 1162–1170. [Google Scholar] [CrossRef] [PubMed]
  89. Gong, N.; Gong, M. MiRNA-221 from tissue may predict the prognosis of patients with osteosarcoma. Medicine 2018, 97. [Google Scholar] [CrossRef] [PubMed]
  90. Iida, M.; Hazama, S.; Tsunedomi, R.; Tanaka, H.; Takenouchi, H.; Kanekiyo, S.; Tokumitsu, Y.; Tomochika, S.; Tokuhisa, Y.; Sakamoto, K.; et al. Overexpression of MiR-221 and MiR-222 in the cancer stroma is associated with malignant potential in colorectal cancer. Oncol. Rep. 2018, 40, 1621–1631. [Google Scholar] [CrossRef]
  91. Gramantieri, L.; Fornari, F.; Ferracin, M.; Veronese, A.; Sabbioni, S.; Calin, G.A.; Grazi, G.L.; Croce, C.M.; Bolondi, L.; Negrini, M. MicroRNA-221 targets Bmf in hepatocellular carcinoma and correlates with tumor multifocality. Clin. Cancer Res. 2009, 15, 5073–5081. [Google Scholar] [CrossRef] [PubMed]
  92. Wang, Y.; Li, Z.; He, C.; Wang, D.; Yuan, X.; Chen, J.; Jin, J. MicroRNAs expression signatures are associated with lineage and survival in acute leukemias. Blood Cells Mol. Dis. 2010, 44, 191–197. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Wong, Q.W.L.; Ching, A.K.K.; Chan, A.W.H.; Choy, K.W.; To, K.F.; Lai, P.B.S.; Wong, N. MiR-222 Overexpression confers cell migratory advantages in hepatocellular carcinoma through enhancing AKT signaling. Clin. Cancer Res. 2010, 16, 867–875. [Google Scholar] [CrossRef] [PubMed]
  94. Hayes, J.; Peruzzi, P.P.; Lawler, S. MicroRNAs in cancer: Biomarkers, functions and therapy. Trends Mol. Med. 2014, 20, 460–469. [Google Scholar] [CrossRef] [PubMed]
  95. Tutar, L.; Özgür, A.; Tutar, Y. Involvement of MiRNAs and pseudogenes in cancer. Methods Mol. Biol. 2018, 1699, 45–66. [Google Scholar] [CrossRef] [PubMed]
  96. Volinia, S.; Calin, G.A.; Liu, C.-G.; Ambs, S.; Cimmino, A.; Petrocca, F.; Visone, R.; Iorio, M.; Roldo, C.; Ferracin, M.; et al. A MicroRNA expression signature of human solid tumors defines cancer gene targets. Proc. Natl. Acad. Sci. USA 2006, 103, 2257–2261. [Google Scholar] [CrossRef] [PubMed]
  97. Felli, N.; Fontana, L.; Pelosi, E.; Botta, R.; Bonci, D.; Facchiano, F.; Liuzzi, F.; Lulli, V.; Morsilli, O.; Santoro, S.; et al. MicroRNAs 221 and 222 inhibit normal erythropoiesis and erythroleukemic cell growth via Kit receptor down-modulation. Proc. Natl. Acad. Sci. USA 2005, 102, 18081–18086. [Google Scholar] [CrossRef] [PubMed]
  98. Ravegnini, G.; Sammarini, G.; Serrano, C.; Nannini, M.; Pantaleo, M.A.; Hrelia, P.; Angelini, S. Clinical relevance of circulating molecules in cancer: Focus on gastrointestinal stromal tumors. Ther. Adv. Med. Oncol. 2019, 11, 175883591983190. [Google Scholar] [CrossRef]
  99. Abdueva, D.; Wing, M.; Schaub, B.; Triche, T.; Davicioni, E. Quantitative expression profiling in formalin-fixed paraffin-embedded samples by affymetrix microarrays. J. Mol. Diagn. 2010, 12, 409–417. [Google Scholar] [CrossRef]
  100. Gu, J.; Wang, Y.; Wang, X.; Zhou, D.; Shao, C.; Zhou, M.; He, Z. Downregulation of LncRNA GAS5 confers tamoxifen resistance by activating MiR-222 in breast cancer. Cancer Lett. 2018, 434, 1–10. [Google Scholar] [CrossRef]
  101. Militello, G.; Weirick, T.; John, D.; Döring, C.; Dimmeler, S.; Uchida, S. Screening and validation of LncRNAs and CircRNAs as MiRNA sponges. Brief. Bioinform. 2017, 18, 780–788. [Google Scholar] [CrossRef]
  102. Jalali, S.; Bhartiya, D.; Lalwani, M.K.; Sivasubbu, S.; Scaria, V. Systematic transcriptome wide analysis of LncRNA-MiRNA interactions. PLoS ONE 2013, 8, e53823. [Google Scholar] [CrossRef] [PubMed]
  103. Link, J.C.; Hasin-Brumshtein, Y.; Cantor, R.M.; Chen, X.; Arnold, A.P.; Lusis, A.J.; Reue, K. Diet, gonadal sex, and sex chromosome complement influence white adipose tissue MiRNA expression. BMC Genomics 2017, 18, 89. [Google Scholar] [CrossRef] [PubMed]
  104. Hussein, S.; Mosaad, H.; Rashed, H.E.; El-Anwar, M.W. Up-regulated MiR-221 expression as a molecular diagnostic marker in laryngeal squamous cell carcinoma and its correlation with Apaf-1 expression. Cancer Biomarkers 2017, 19, 279–287. [Google Scholar] [CrossRef] [PubMed]
  105. Zhao, H.; Yan, P.; Wang, J.; Zhang, Y.; Zhang, M.; Wang, Z.; Fu, Q.; Liang, W. Clinical significance of tumor MiR-21, MiR-221, MiR-143, and MiR-106a as biomarkers in patients with osteosarcoma. Int. J. Biol. Markers 2019, 34, 184–193. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Flowchart of the literature search and selection process of eligible studies.
Figure 1. Flowchart of the literature search and selection process of eligible studies.
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Figure 2. Forest (A) and Funnel (B) plots for the effect of miR-221 overexpression on overall survival. ES, effect size (i.e., hazard ratio); W, weight; Sig, statistical significance; N, total number of cancer patients included in the survival analysis.
Figure 2. Forest (A) and Funnel (B) plots for the effect of miR-221 overexpression on overall survival. ES, effect size (i.e., hazard ratio); W, weight; Sig, statistical significance; N, total number of cancer patients included in the survival analysis.
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Figure 3. Forest (A) and Funnel (B) plots for the effect of miR-222 overexpression on overall survival. ES, effect size (i.e., hazard ratio); W, weight; Sig, statistical significance; N, total number of cancer patients included in the survival analysis.
Figure 3. Forest (A) and Funnel (B) plots for the effect of miR-222 overexpression on overall survival. ES, effect size (i.e., hazard ratio); W, weight; Sig, statistical significance; N, total number of cancer patients included in the survival analysis.
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Table 1. Main characteristics of studies evaluating the impact of higher expression of miR-221 or miR-222 on survival outcomes in cancer patients.
Table 1. Main characteristics of studies evaluating the impact of higher expression of miR-221 or miR-222 on survival outcomes in cancer patients.
First Author [Ref] YearCountrySample Size (Pts Analyzed #)Gender (M/F)Age Median (Range) or Mean ± SD or Age CategoryCancer TypeClinical ClassificationPre-Operative (Localized or Systemic) TreatmentType of SpecimenNOS Score
Gramantieri et al. [91] 2009Italy(51) 4540/1168 (49–82)HCCES stage: G2–G4 Nofrozen tissue/FFPE5
Wang et al. [92] 2010China85 (32 ALL)42/4343 (14–82)ALL/AMLFAB classification; ALL: L1–L3, Tcell; AML: M0–M6, mast cell NRbone marrow7
Wong et al. [93] 2010China99 (76)80/1957 (48–66.5)HCCT stage: T1–T3No (NAT in 3 pts)frozen tissue5
Schaefer et al. [51] 2010Germany76 (75)76/063 (49–74)PCaTumor stage: pT2–pT3Nofrozen tissue8
Spahn et al. [52] 2010Germany9292/067 (52–78)PCaTumor stage: pT2–pT4 NoFFPE9
Greither et al. [53] 2010Germany; UK56 (52)34/2261.7 (34–80)PCNRNRfrozen tissue6
Yoon et al. [54] 2011Korea11591/2451 (26–76)HCCTNM stage: I–III NoFFPE7
Delfino et al. [55] 2011USA253162/9155.7GBMWHO glioma grade 4NSNR6
Alencar et al. [56] 2011USA; Canada; Spain176NR59.5 (16–92)Lymphoma AAS: I–IV NoFFPE9
Liu et al. [57] 2012China9250/4259.3 ± 5.6GCTNM stage: I–III Nofrozen tissue8
Zhang et al. [58] 2012China72 (36)NRNRGMGrade: I–IV NRFFPE/frozen tissue6
Schultz et al. [59] 2012Denmark328 (225)111/11464 (31–85)PCWHO stage: I–IINRFFPE8
Kang et al. [60] 2012Korea 92 (73)92/064.7 (50–76)PCaTNM stage: T2–T3NSFFPE8
Hanna et al. [61] 2012USA 473NRNRBC NRNRFFPE8
Gimenes-Teixeira et al. [62] 2013Brazil 48 (42)39/920.1 (1–66)T-ALLNRNRFresh/frozen tissue 7
Lee et al. [63] 2013China60 (36)38/22≤60 y (n = 18);
>60 y (n = 42)
PCStage: I–IV NRfrozen tissue6
Yuan et al. [38] 2013USA10868/40<65 y (n = 63)
≥65 y (n = 45)
CRCT stage: T2–T4 NRfrozen tissue7
Karakatsanis et al. [64] 2013Greece81 (60 HCC)60/21≤60 y (n = 21)
>60 y (n = 60)
HCC/ICCT stage: T1–T4NoFFPE6
Kim et al. [65] 2013Korea91 (MFS: 79; OS: 88)57/3461 (25–80)GCTumor stage: pT2–pT4 NoFFPE7
Amankwah et al. [66] 2013USA65 (miR-221: 63; miR-222: 60)65/059.5 (46–75)PCaStage: I–IVNRFFPE7
Mao et al. [67] 2014China10059/41<60 y (n = 34)
≥60 y (n = 66)
NSCLCTNM stage: I–III NRfrozen tissue8
Zhang et al. [68] 2014China9751/46<60 y (n = 59);
≥60 y (n = 38)
Bladder T stage: Ta–T1 (n = 82);
≥T2 (n = 15)
Nofresh/frozen tissue8
Tao et al. [69] 2014China9047/43<70 y (n = 42);
≥70 y (n = 46); unknown (n = 2)
CC TNM stage: I–IVNoFFPE7
Vergho et al. [70] 2014Germany74 (37)48/2666.8RCCT stage: T1–T3; Tumor grade: G1–G4NRFFPE8
Gyöngyösi et al. [71] 2014Hungary2016/468 (52–82)HCCAdvanced stageNoFFPE7
Hur et al. [72] 2015USA393 (84)66/1864.6 ± 10.7 (n = 84)CRC TNM stage: I–IV NRfrozen tissue 8
Cai et al. [73] 2015China18273/10952 ± 6.25CCTNM stage: I–IIINoFFPE8
Li et al. [74] 2015China2514/11<60 y (n = 13);
≥60 y (n = 12)
EHCCStage: I–IV Nofrozen tissue5
Eissa et al. [75] 2015Egypt78 (76)0/7852.28 ± 12BC Stage: I-III; grade: I–IIINofrozen tissue 9
Goto et al. [76] 2015Japan 54 (52)54/073.5 (58–88)PCaTNM stage: III–IV NRNR6
García-Donas et al. [77] 2016Belgium145 (74)90/4861.0 (35–82)RCCMetastaticimmuno- or chemotherapyFFPE8
Li et al. [78] 2016China4530/15<50 y (n = 18);
≥50 y (n = 27)
GMTNM stage: I–IV; T classification: T1–T4NRfresh tissue8
Zhang et al. [79] 2016China10458/46<55 y (n = 44);
≥55 y (n = 60)
NSCLCTNM stage: I–IV Nofrozen tissue7
Fu et al. [39] 2016China740/74<50 y (n = 37);
≥50 y (n = 37)
OCFIGO stage: I–IV NRFFPE8
Chen et al. [80] 2017China13576/5953.3 ± 9.1HCCTumor stage: I–II NoFFPE7
Deng et al. [81] 2017China1250/12551.6 (25–81)BCT stage: T1–T4NoFFPE6
He et al. [82] 2017China3116/152.9 (0.1–12)NeuroBTumor stage IIb–IV NoFFPE5
Xue et al. [36] 2017China165114/51<60 y (n = 132)
≥60 y (n = 33)
GMTumor stage: I–IV NRfrozen tissue7
Li et al. [83] 2017China4018/22<60 y (n = 17);
>60 y (n = 23)
HCCNRNofrozen tissue5
Li et al. [84] 2017China7852/26<63 y (n = 38);
≥63 y (n = 40)
NSCLCTNM stage: I–IV Nofrozen tissue8
Xie et al. [85] 2017China7038/32<50 y (n = 29);
≥50 y (n = 41)
LiverTumor stage: I–IV Nofrozen tissue 8
Xu et al. [86] 2017China17893/85<60 y: N = 90;
≥60 y: N = 88
NSCLCGrade: Low: Ia–IIa; high: IIb–IIIaNofrozen tissue 6
Han et al. [87] 2017Korea1970/19749 (27–85)BCT stage: T1–T3NRFFPE8
Wu et al. [88] 2018China107 (74)0/107<50 y (n = 37);
≥50 y (n = 37); unknown (n = 74)
OCFIGO stage: I–IVNRFFPE6
Gong et al. [89] 2018China6938/31<40 y (n = 51);
≥40 y (n = 18)
OsteoSTumor stage: II–III NRFFPE8
Lei et al. [37] 2018China230113/117<55 y (n = 114);
≥55 y (n = 116)
NSCLCTNM stage: I–IV NoFFPE 7
Liu et al. [40] 2018China15895/6356.9 ± 7.4CRCTNM stage: I–IV NoFFPE9
Tsikrika et al. [35] 2018Greece182 (DSF/PFS: 101; OS: 57)152/3070BladderTumor stage: pTa, pT1–pT4
WHO grade: 1–3
Nofrozen/fresh tissue 9
Iida et al. [90] 2018Japan113 (101)49/52; unknown (n = 12)68.0 ± 11.6CRCTumor stage: I–II e IV Nofrozen tissue 7
Chen et al. [41] 2018Taiwan11468/4654 (38–65)GBMWHO glioma grade 4NRFFPE8
# Patients analyzed. Abbreviations: AAS, Ann Arbor System of staging; ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; BC, breast cancer; CC, colon cancer; CRC, colorectal cancer; DFS, disease-free survival; EHCC, extrahepatic cholangiocarcinoma; ES; Edmondson and Steiner’s criteria; FFPE, formalin-fixed, paraffin-embedded tissue; FIGO: International Federation of Obstetricians and Gynecologists; GC, gastric cancer; GBM, glioblastoma; GM, glioma; HCC, hepatocellular carcinoma; ICC, intrahepatic cholangiocarcinoma; MFS, metastasis-free survival; NA, not applicable; NAT, neoadjuvant therapy; NeuroB: Neuroblastoma; NOS, Newcastle–Ottawa Scale; NR, not reported; NS, not specified; NSCLC, non-small cell lung cancer; OC, ovarian cancer; OS, overall survival; OsteoS: Osteosarcoma; PC, pancreatic cancer; PCa, prostate cancer; PFS, progression-free survival; RCC, renal cell carcinoma; SD, standard deviation; T-ALL, T-cell acute lymphoblastic leukemia; TNM, tumor nodes and metastases staging system.
Table 2. Characteristics and prognostic information extracted from the studies included in the systematic review.
Table 2. Characteristics and prognostic information extracted from the studies included in the systematic review.
First Author [Ref] YearmiRNA Analyzed and Clinical Outcome ExtractedDetection MethodCut-Off ValueClinical Outcome: HR (95% CI)
Gramantieri et al. [91] 2009miR-221: TTR u/CSS uqRT-PCRmedianTTR: 1.73 (1.15–2.60) *; CSS: 1.27 (0.64–2.52) *
Wang et al. [92] 2010miR-221: OS mqRT-PCRmedianOS: 0.54 (0.30–0.97)
Wong et al. [93] 2010miR-222 OS u/DFS uqRT-PCRfold change ≥ 2OS: 3.12 (1.42–5.56); DFS: 2.21 (1.19–3.83)
Schaefer et al. [51] 2010miR-221: RFS m;miR-222: RFS mqRT-PCRmedianRFS: 0.50 (0.01–39.1); RFS: 5.04 (0.03–9.40)
Spahn et al. [52] 2010miR-221: RFS mqRT-PCRmedianRFS: 0.53 (0.29–0.95)
Greither et al. [53] 2010miR-222: OS mqRT-PCRmedianOS: 2.05 (1.05–4.00)
Yoon et al. [54] 2011miR-221: TTLR m qRT-PCRmean TTLR: 3.07 (1.56–6.07)
Delfino et al. [55] 2011miR-221: OS m
miR-222: OS m/PFS m
Microarray NROS: 0.55 (0.34–0.88); OS: 2.14 (1.51–3.03); PFS: 1.44 (1.11–1.86)
Alencar et al. [56] 2011miR-222: PFS mqRT-PCRmedianPFS: 2.26 (1.24–4.14)
Liu et al. [57] 2012miR-221: OS mqRT-PCRmeanOS: 2.32 (1.11–4.85)
Zhang et al. [58] 2012miR-221: OS u
miR-222: OS u
IHCfinal score ≤ 3OS: 2.53 (1.24–5.18) *; OS: 2.97 (1.19–7.74) *
Schultz et al. [59] 2012miR-222: OS mmicroarray10th percentileOS: 1.39 (1.06–1.84)
Kang et al. [60] 2012miR-221: RFS uqRT-PCRmedianRFS: 0.36 (0.17–1.90)
Hanna et al. [61] 2012miR-221: CSS mqRT-PCR75th percentile CSS: 0.70 (0.51–0.97)
Gimenes-Teixeira et al. [62] 2013miR-221: OS m/DFS mqRT-PCRmedianOS: 2.31 (0.92–5.81); DFS: 1.54 (0.57–4.17)
Lee et al. [63] 2013miR-222: OS mqRT-PCRmedianOS: 5.16 (1.16–22.91)
Yuan et al. [38] 2013miR-221: OS m/DFS mqRT-PCRNROS: 0.18 (0.09–0.16); DFS: 0.04 (0.01–0.16)
Karakatsanis et al. [64] 2013miR-221: OS mqRT-PCRmeanOS: 1.79 (1.24–2.58)
Kim et al. [65] 2013miR-221: MFS u
miR-222 OS m/MFS u
qRT-PCRfold change ≥ 3MFS: 1.72 (1.03–2.89) *; OS: 1.50 (0.70–2.90); MFS: 1.92 (1.05–3.50) *
Amankwah et al. [66] 2013miR-221: RFS m
miR-222: RFS m
qRT-PCRmedianRFS: 1.79 (0.67–4.76); RFS: 2.56 (0.87–7.14)
Mao et al. [67] 2014miR-222: OS mqRT-PCRmedianOS: 3.31 (1.97–5.58)
Zhang et al. [68] 2014miR-222: OS mqRT-PCRmedianOS: 6.17 (2.33–10.39)
Tao et al. [69] 2014miR-221: OS mqRT-PCRmedianOS: 2.04 (1.09–3.81)
Vergho et al. [70] 2014miR-221: CSS mqRT-PCR1.84 of ROC curveCSS: 0.47 (0.22–1.00)
Gyöngyösi et al. [71] 2014miR-221: OS u/PFS u
miR-222: OS u/PFS u
qRT-PCRmedianOS: 1.92 (0.61–6.10); PFS: 1.32 (0.47–3.66); OS: 2.04 (0.64–6.46); PFS: 1.43 (0.51–3.99)
Hur et al. [72] 2015miR-221: OS mMicroarray 1.68 of ROC curveOS: 1.64 (0.73–3.71)
Cai et al. [73] 2015miR-221: OS mqRT-PCRmedianOS: 2.39 (1.21–4.91)
Li et al. [74] 2015miR-221: DFS uqRT-PCRmedianDFS: 2.19 (1.07–4.47) *
Eissa et al. [75] 2015miR-221: RFS mqRT-PCR1.03 of ROC curveRFS: 14.84 (1.89–116.25)
Goto et al. [76] 2015miR-221: PFS u
miR-222: PFS m
qRT-PCRNRPFS: 0.69 (0.42–1.14) *; PFS: 0.21 (0.07–0.64)
García-Donas et al. [77] 2016miR-221: DSS m/PFS m
miR-222: DSS m/PFS m
NGS and qRT-PCRmedianDSS: 1.71 (1.35–2.16); PFS: 2.25 (1.67–3.02); DSS: 1.77 (1.41–2.22); PFS: 2.02 (1.51–2.71)
Li et al. [78] 2016miR-221: OS u
miR-222: OS u
qRT-PCRmeanOS: 2.18 (1.02–4.65); OS: 2.13 (1.01–4.48)
Zhang et al. [79] 2016miR-221: OS mqRT-PCRmeanOS: 1.87 (1.27–2.77)
Fu et al. [39] 2016miR-222: OS mqRT-PCRmedianOS: 0.35 (0.16–0.73)
Chen et al. [80] 2017miR-221: OS m/DFS mqRT-PCRmedianOS: 2.97 (1.63–5.41); DFS: 2.85 (1.56–5.18)
Deng et al. [81] 2017miR-221: DFS mqRT-PCRmedianDFS: 0.48 (0.26–0.88)
He et al. [82] 2017miR-221: OS uqRT-PCRmeanOS: 1.65 (1.02–2.68)
Xue et al. [36] 2017miR-221: OS uqRT-PCRmedianOS: 1.66 (1.13–2.49)
Li et al. [83] 2017miR-221: OS uqRT-PCRNROS: 1.87 (0.47–7.43) *
Li et al. [84] 2017miR-221: OS mqRT-PCRmedianOS: 3.01 (0.53–17.07)
Xie et al. [85] 2017miR-221: OS mqRT-PCRNROS: 1.74 (1.00–3.77)
Xu et al. [86] 2017miR-222: OS m/PFS uqRT-PCRmeanOS: 3.23 (2.12–5.47); PFS: 2.62 (1.56–4.39) *
Han et al. [87] 2017miR-222: DFS mqRT-PCRmedianDFS: 5.67 (1.08–29.76)
Wu et al. [88] 2018miR-221: OS u qRT-PCRmedianOS: 0.39 (0.20–0.80)
Gong et al. [89] 2018miR-221: OS mqRT-PCR medianOS: 3.24 (1.17–9.01)
Lei et al. [37] 2018miR-222: OS mIHCfinal score ≥ 5OS: 2.23 (1.35–3.67)
Liu et al. [40] 2018miR-221: OS m/LRR m/DMR mqRT-PCR Youden index of ROC curveOS: 1.01 (0.57–1.77); DMR: 1.21 (0.66–2.20); LRR: 1.17 (0.65–2.11)
Tsikrika et al. [35] 2018miR-221: OS u/DFS u/PFS u miR-222: OS u/DFS m/PFS mqRT-PCR 30th percentileOS: 1.69 (0.41–6.84) *; DFS: 0.71 (0.38–1.33); PFS: 1.37 (0.54–3.62); OS: 0.87 (0.57–1.33) *; DFS: 2.59 (1.15–5.83); PFS: 8.98 (1.10–73.54)
Iida et al. [90] 2018miR-221: OS u
miR-222: OS u
qRT-PCR medianOS: 1.70 (0.87–3.32); OS: 1.51 (0.78–2.94)
Chen et al. [41] 2018miR-221: OS m
miR-222: OS m
qRT-PCR fold change > 1OS: 2.11 (1.12–3.97); OS: 1.01 (0.64–1.58)
m Multivariate; u univariate; * data extracted from the Kaplan–Meier curve. Abbreviations: CSS, cancer-specific survival; DFS, disease-free survival; DMR, distant metastasis rate; DSS, disease-specific survival; HR, hazard ratio; IHC, immunohistochemistry; LRR, local recurrence rate; MFS, metastasis-free survival; NGS, next-generation sequencing; NR, not reported; OS, overall survival; PFS, progression-free survival; qRT-PCR, quantitative reverse transcription polymerase chain reaction; RFS, recurrence-free survival; ROC, receiver operating characteristic; TTLR, time to local recurrence; TTR, time to recurrence.
Table 3. Summary of random effect meta-analyses for the association between miR-221 overexpression and overall survival of cancer patients.
Table 3. Summary of random effect meta-analyses for the association between miR-221 overexpression and overall survival of cancer patients.
Study GroupsStudies IncludedTest of AssociationTest of Heterogeneity
HR (95% CI)p-ValueI2%p-Value
All studies251.48 (1.14–1.93)0.00375<0.001
Ethnicity
Asian181.67 (1.30–2.14)<0.00162<0.001
Mostly Caucasian51.30 (0.69–2.44)0.410750.003
Mixed20.64 (0.05–7.72)0.72394<0.001
Type of cancer
Liver51.98 (1.51–2.60)<0.00100.698
Gastro-intestinal71.29 (0.68–2.43)0.43483<0.001
Neurological61.56 (0.98–2.48)0.06277<0.001
Urogenital20.72 (0.18–2.90)0.638700.069
NSCLC21.92 (1.31–2.81)0.00100.602
Other31.51 (0.45–5.01)0.504840.002
No pre-treatment #141.84 (1.56–2.17)<0.00100.813
Sample detected
Fresh/frozen tissue121.54 (1.05–2.28)0.02870<0.001
FFPE101.68 (1.20–2.36)0.003660.002
Sample size *
≥10091.27 (0.78–2.08)0.33286<0.001
<100161.62 (1.21–2.18)0.001580.002
Survival analysis
Univariate91.54 (1.07–2.21)0.020550.024
Multivariate161.46 (1.02–2.09)0.03981<0.001
NOS score
≥7191.61 (1.20–2.16)0.00271<0.001
<761.17 (0.65–2.11)0.60484<0.001
* Number of cancer patients; # Patients without pre-operative anticancer treatment; FFPE, formalin-fixed, paraffin-embedded tissue; HR, hazard ratio; NOS, Newcastle–Ottawa Scale; NSCLC, non-small cell lung cancer.
Table 4. Summary of meta-analyses for the association between high miR-221/222 expression and survival outcomes in cancer patients.
Table 4. Summary of meta-analyses for the association between high miR-221/222 expression and survival outcomes in cancer patients.
Survival OutcomeStudies IncludedTest of AssociationTest of HeterogeneityEgger’s p-Value
HR (95% CI)p-Valuep-Value Q-TestI2%
miR-221
  CSS30.75 (0.48–1.17)0.2050.148480.913
  CSS/DSS40.95 (052–1.76)0.878<0.001880.448
  DFS60.79 (0.31–1.98)0.614<0.001890.338
  RFS51.09 (0.37–3.17)0.8810.007710.443
  PFS41.31 (0.65–2.63)0.4520.001820.504
miR-222
  PFS71.67 (1.10–2.51)0.0150.001730.914
  DFS32.50 (1.58–3.94)<0.0010.57300.053
  DFS/RFS52.52 (1.66–3.82)<0.0010.57300.143
Abbreviations: CSS, cancer-specific survival; DFS, disease-free survival; DSS, disease-specific survival; HR, hazard ratio; OS, overall survival; PFS, progression-free survival; RFS, recurrence-free survival.
Table 5. Summary of random effect meta-analyses for the association between miR-222 overexpression and OS in cancer patients.
Table 5. Summary of random effect meta-analyses for the association between miR-222 overexpression and OS in cancer patients.
Study GroupsStudies IncludedTest of AssociationTest of Heterogeneity
HR (95% CI)p-ValueI2%p-Value
All studies171.90 (1.43–2.54)<0.00177<0.001
Ethnicity
Asian122.11 (1.40–3.17)<0.00179<0.001
Mostly Caucasian51.52 (1.07–2.18)0.021660.019
Type of cancer
Liver22.80 (1.55–5.04)0.00100.441
Gastro-intestinal51.52 (1.22–1.90)<0.001250.256
Neurological41.80 (1.12–2.90)0.015660.033
Urogenital31.22 (0.29–5.07)0.78193<0.001
NSCLC32.88 (2.16–3.83)<0.00100.470
No pre-treatment #82.16 (1.37–3.42)0.00177<0.001
Sample detected
Fresh/frozen tissue92.48 (1.60–3.85)<0.00177<0.001
FFPE61.22 (0.79–1.88)0.360730.002
Sample size *
≥10071.93 (1.40–2.66)<0.001740.001
<10051.92(1.13–3.28)0.01681<0.001
Survival analysis
Univariate61.82 (1.12–2.97)0.016640.015
Multivariate111.94 (1.34–2.80)<0.00181<0.001
NOS score
≥7111.58 (1.09–2.30)0.01680<0.001
<762.56 (2.03–3.22)<0.00100.602
* Number of cancer patients; # Patients without pre-operative anticancer treatment; FFPE, formalin-fixed, paraffin-embedded tissue; HR, hazard ratio; NOS, Newcastle–Ottawa Scale; NSCLC, non-small cell lung cancer.

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Ravegnini, G.; Cargnin, S.; Sammarini, G.; Zanotti, F.; Bermejo, J.L.; Hrelia, P.; Terrazzino, S.; Angelini, S. Prognostic Role of miR-221 and miR-222 Expression in Cancer Patients: A Systematic Review and Meta-Analysis. Cancers 2019, 11, 970. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers11070970

AMA Style

Ravegnini G, Cargnin S, Sammarini G, Zanotti F, Bermejo JL, Hrelia P, Terrazzino S, Angelini S. Prognostic Role of miR-221 and miR-222 Expression in Cancer Patients: A Systematic Review and Meta-Analysis. Cancers. 2019; 11(7):970. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers11070970

Chicago/Turabian Style

Ravegnini, Gloria, Sarah Cargnin, Giulia Sammarini, Federica Zanotti, Justo Lorenzo Bermejo, Patrizia Hrelia, Salvatore Terrazzino, and Sabrina Angelini. 2019. "Prognostic Role of miR-221 and miR-222 Expression in Cancer Patients: A Systematic Review and Meta-Analysis" Cancers 11, no. 7: 970. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers11070970

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