Introduction
============

miRNAs are an abundant class of endogenous small non-coding RNAs. Since the discovery of miRNAs, emerging studies have demonstrated that miRNAs could serve as oncogenes or tumor suppressors participating in initiation and progression of various cancers.[@b1-cmar-10-3371] It is well documented that miRNAs can inhibit translation and/or induce degradation of target mRNAs, thereby negatively regulated gene expression. The misexpression of miRNAs has a great impact on extensive biological functions consisting of cell differentiation, apoptosis, tumorigenesis, and metabolism.[@b2-cmar-10-3371] Thus, aberrantly expressed miRNAs can be viewed as a new type of biomarkers for monitoring therapeutic efficacy and predicting prognosis.

Two highly homologous miRNAs (miR-221, miR-222), commonly acting as a gene cluster (miR-221/222 cluster), have been extensively studied in human malignancies.[@b3-cmar-10-3371] miR-221/222 cluster overexpression was observed in hepatocellular cancer, pancreatic cancer (PC), breast cancer (BC), etc[@b4-cmar-10-3371]--[@b6-cmar-10-3371]; while the lowly expressed miR-221/222 cluster was found in tongue squamous cell carcinoma and prostate cancer (PCa).[@b7-cmar-10-3371],[@b8-cmar-10-3371] Several studies have shown that the expression levels of miR-221/222 cluster have close connections with occurrence, progression, and prognosis in several tumors. For instance, a receiver operating characteristic curve analysis conducted by Eissa et al revealed that the higher miR-221 expression, the worse 5-year relapse-free survival (RFS) in patients with BC (*P*=0.0124)[@b6-cmar-10-3371]; a recent study indicated that overall survival (OS) of higher miR-221 expression group was obviously lower than that of the lower miR-221 expression group (*P*\<0.05).[@b9-cmar-10-3371] Whereas, according to the log-rank test, including 125 triple-negative BC (TNBC) patients, Deng et al found that patients with high miR-221 expression have a better 5-year disease-free survival (DFS).[@b10-cmar-10-3371] The aforementioned studies showed that miR-221/222 cluster could be potentially used in predicting the prognostic value of cancer. Meanwhile, the details of over-expressed miR-221/222 cluster's prognostic value in various cancer types are still controversial.

Meta-analysis is capable to obtain relatively accurate estimation by integrating all available evidence to explore authentic and comprehensive results.[@b11-cmar-10-3371] The only meta-analysis regarding the prognostic value of miRNA-221/222 cluster in cancers was published in 2013 by Wang et al.[@b12-cmar-10-3371] Thus, we carried out an updated meta-analysis with larger sample size, more cancer types, more ethnicities, and different analysis models to verify the previous conclusions and especially uncover some novel findings, which may contribute to the exploration of new promising biomarkers for assessing the therapeutic efficacy and prognostic value of different types of cancers.

Methods
=======

Search strategy
---------------

To search eligible studies, we explored literature resources consisting of PubMed, Cochrane Library, Embase, Chinese National Knowledge Infrastructure, and Chinese Biomedical Literature database, with the terms ("microRNA OR miRNA OR miR-221 OR miR-222 OR miR-221/222 cluster OR miR-221/222 family"), ("survival OR prognosis OR prognostic") and ("cancer OR tumor OR tumor OR neoplasm OR neo-plasma OR neoplasia OR carcinoma OR Malignancy"). The deadline for current investigation was on January 30, 2018. Meanwhile, the language of publication was only limited to English and Chinese. To avoid omissions as much as possible, we checked through the references list in search of potentially relevant researches.

Inclusion and exclusion
-----------------------

Available studies must obey the following criteria: 1) Clinical study about the correlation of miR-221/222 family with human cancer prognosis; 2) Study with relevant data of hazard ratios (HRs) with corresponding 95% CIs. Besides, studies met one of the criteria should be excluded as follows: 1) Studies with deficient data; 2) Duplicate studies (we select the study with relatively complete data); 3) Articles with other types, such as reviews and abstracts; and 4) Cell lines or animal models as research objects.

Data extraction
---------------

At the criteria of inclusion and exclusion, the corresponding data of qualified studies were extracted. If we noticed disagreements, a discussion would be conducted by PZ and MZ, or further reviewed by RH. The data, including first author, publication year, research country, test method, cancer type, miRNAs category, sample source, survival outcome, HR (95% CI), sample size, and the cutoff value were extracted to assess the tumor prognosis. Moreover, patient sources came from Asia, Africa, Europe, and North America. In situ hybridization (ISH) and reverse transcription-polymerase chain reaction (RT-PCR) were included in test methods; sample sources were divided into tissue, formalin-fixed and paraffin-embedded (FFPE), and blood; sample sizes included ≥100 and \<100 groups; we just divided cancer types into 3 groups of hepatocellular carcinoma (HCC), non-small cell lung cancer (NSCLC), and others due to insufficient data; analyses methods included univariate and multivariate analyses. Patients' prognostic outcomes consisted of OS, DFS, RFS, and progression-free survival (PFS).

Data analysis
-------------

STATA (Version 12.0) and Review Manager (RevMan 5) soft-wares were utilized in this study. HR and corresponding 95% CI were extracted to evaluate the potential prognostic value of miR-221/222 cluster overexpression in human malignancy. Moreover, chi-square-based Q and I^2^ tests have been applied in this study to assess the heterogeneity. (I[@b2-cmar-10-3371]\<25% means no heterogeneity, I^2^\>50% means extreme heterogeneity).[@b13-cmar-10-3371] Generally, we used fixed-effects model in studies with no or moderate heterogeneity. When I^2^\>50% or *P*\<0.01 for Q test occurred in studies, we always used random-effects model to avoid obvious heterogeneity.[@b14-cmar-10-3371] Publications were individually deleted to assess the stability of the results and investigated the effect of each study on pooled HR. We evaluated the publication bias and attached the corresponding Begg's funnel plot. *P*\<0.05 indicated a bias of study.[@b15-cmar-10-3371] Correspondingly, we also carried out similar statistical analysis in different subgroups included country category, sample source, test method, sample size, miR-221/222 component, and cancer type.

Results
=======

Summary of included studies
---------------------------

Thirty-two studies consisted of 2,693 samples satisfied the eligible studies[@b4-cmar-10-3371]--[@b6-cmar-10-3371],[@b8-cmar-10-3371]--[@b10-cmar-10-3371],[@b16-cmar-10-3371]--[@b41-cmar-10-3371] ([Figure 1](#f1-cmar-10-3371){ref-type="fig"}). [Table 1](#t1-cmar-10-3371){ref-type="table"} listed the main features of those available studies. Among those studies, Amankwah et al's[@b27-cmar-10-3371] study involved in tumor samples of 37 non-recurrent and 28 recurrent PCa patients after radical prostatectomy aimed to explore whether the expression levels of miR-221 and miR-222 were related to PCa recurrence. Gyöngyösi et al[@b29-cmar-10-3371] analyzed the association between the various miRNA of HCC and survival rate of patients treated with sorafenib after fine-needle aspiration biopsy. Mao et al[@b33-cmar-10-3371] compared 2 different cohorts of regional lymph node involvement and no regional lymph node involvement to explore the prognostic potential of miR-222 expression in NSCLC. Cai et al[@b34-cmar-10-3371] evaluated the prognostic value of miR-221 from 182 colon cancer (CC) patients after surgical resection both in the squamous cell carcinoma and adenocarcinoma cohorts. Finally, we integrated 32 studies independently into meta-analysis and established in [Table 1](#t1-cmar-10-3371){ref-type="table"}.

There are 31 studies published in English and 1 study written in Chinese in current study. The maximum sample size was 180 and the minimum was 20. Tumor types were as follows: 1 triple-negative BC, 1 osteosarcoma, 1 PC, 4 gastric cancer (GC), 1 NK/T-cell lymphoma, 1 glioma, 4 NSCLC, 1 cutaneous malignant melanoma, 1 acute lymphoblastic leukemia, 2 colorectal cancer, 1 CC, 1 BC, 8 HCC, 1 glioblastoma (GBM), and 4 PCa. Besides, 1 ISH and 31 RT-PCR were applied in this study. Meanwhile, there were 8 FFPE; 7 blood; and 17 tissue. As for the survival outcomes, we split 32 eligible studies into 50 datasets: 27 for OS, 13 for DFS, 6 for PFS, and 4 for RFS. However, the cutoff value of included studies was inconsistent partly ([Table 1](#t1-cmar-10-3371){ref-type="table"}).

Meta-analysis in OS
-------------------

Twelve studies were included in univariate analysis to evaluate the prognostic value of overexpression of miR-221/222 cluster in human malignancy. Highly expressed miR-221/222 cluster of tumors was connected with poor OS (HR =1.69, 95% CI: 1.18--2.44, *P*\<0.01) ([Figure 2A](#f2-cmar-10-3371){ref-type="fig"}). In addition, sub-analyses results revealed that there were certain correlations ([Table 2](#t2-cmar-10-3371){ref-type="table"}). 15 studies were involved in multivariate analysis to conduct an evaluation regarding the prognostic value of miR-221/222 cluster. Meanwhile, tumor-associated miR-221/222 cluster overexpression also connected with poor OS (HR =2.10, 95% CI: 1.63--2.69, *P*\<0.01) ([Figure 2B](#f2-cmar-10-3371){ref-type="fig"}). Similarly, several subgroups have an analogous result ([Table 2](#t2-cmar-10-3371){ref-type="table"}).

Meta-analysis in DFS/PFS/RFS
----------------------------

There were 9, 5, and zero studies about DFS/PFS/RFS involved in univariate analysis. Correspondingly, 5, 1, and 4 studies about DFS/PFS/RFS were collected in multivariate analysis, respectively. Ultimately, we detected no association between highly expressed miR-221/222 cluster with DFS (univariate: HR =1.45, 95% CI: 0.94--2.25, *P*=0.10; multivariate: HR =1.40, 95% CI: 0.59--3.34, *P*=0.45) ([Figure 3](#f3-cmar-10-3371){ref-type="fig"}), PFS (univariate: HR =1.41, 95% CI: 0.65--3.05, *P*=0.38; multivariate: HR =0.21, 95% CI: 0.07--0.63, *P*=0.006) ([Figure S1](#SD1-cmar-10-3371){ref-type="supplementary-material"}), and RFS (multivariate: HR =2.18, 95% CI:0.34--13.89, *P*=0.41) ([Figure S2](#SD2-cmar-10-3371){ref-type="supplementary-material"}).

Sensitivity analysis
--------------------

We removed each single study respectively to evaluate its specific effect on the mixed HRs, and sensitivity analysis indicated a relatively stable mixed result. Univariate and multivariate analyses of the pooled results (OS and DFS) were performed in [Figure 4A, B](#f4-cmar-10-3371){ref-type="fig"} and [Figure S3A, B](#SD3-cmar-10-3371){ref-type="supplementary-material"}, but the result of PFS and RFS only in single-variate analysis was not shown.

Publication bias
----------------

According to Begg's funnel plot, there was no publication bias of OS (*P*=0.784\>0.05) ([Figure 5A](#f5-cmar-10-3371){ref-type="fig"}), DFS (*P*=0.322\>0.05), and PFS (*P*=0.624\>0.05) among univariate analysis, and no publication bias of OS (*P*=0.324\>0.05) ([Figure 5B](#f5-cmar-10-3371){ref-type="fig"}), DFS (*P*=0.624\>0.05), and RFS (*P*=0.497\>0.05) among multivariate analysis. Similar to that, we also performed evaluations of subgroup of OS. There was no evidence for publication bias in any subgroup. However, the publication bias of PFS or RFS was not evaluated due to the fewer or no datasets for meta-analysis.

Discussion
==========

Currently, exploring the clinically available cancer signatures is still the hotspot of researches due to the complexity of tumor. Dysregulations of distinct miRNA fingerprints often occur in specific types of human malignant tumors and obviously associated with cancer diagnosis, therapy, and even prognosis.[@b42-cmar-10-3371],[@b43-cmar-10-3371] Numerous studies have demonstrated that miR-221/222 cluster plays a significantly regulatory role in the prognosis of several types of tumors. For instance, miR-222 overexpression promoted HCC cell migratory through activating AKT phosphorylation assisted by the regulation of protein phosphatase 2A subunit B, contributing to both the development and poor prognosis of HCC.[@b16-cmar-10-3371] Besides, it is reported that miR-221 negatively regulates poly ADP-Ribose polymerase 1 expression levels by 3′-untranslated region binding, thereby affecting TNBC patients' prognoses.[@b10-cmar-10-3371] Meanwhile, overexpression of miRNA-221 enhanced cell proliferation via downregulating the target gene apoptotic protease activating factor-1 (APAF-1), while APAF-1 expression was associated with a poor prognosis.[@b44-cmar-10-3371] It is reported that the upregulation of miR-221/222 cluster in GBM may be a key factor in the decrease of p27Kip1 expression levels and as such, will correlate with adverse prognosis.[@b45-cmar-10-3371] Similarly, miRNA-222 could target HIPK2 to promote GC cell proliferation, invasion, and inhibited apoptosis, revealing a poor survival outcome for GC patients.[@b46-cmar-10-3371] Moreover, the expression of miR-221 exhibited modulatory effect on BIM-Bax/Bak axis, upregulated target gene BIM, induced by miR-221 knockdown, can promote cisplatin-eliciting apoptosis through inducing mitochondrial dysfunction, revealing a poor survival outcome for BC patients.[@b47-cmar-10-3371] Hence, we proposed that activation or inhibition of multiple pathways might play synergistic roles in the impact of miRNA-221/222 cluster expression on prognosis. However, the clinically prognostic value of dysregulated miR-221/222 expression often affected by small sample-size studies with insufficient data and thus inconsistent and unconvincing.

Meta-analysis is a useful tool, which can get a relatively precise result and provide convincing evidence through integrating and assessing inconsistent outcomes from different studies. We have explored the potential associations in overall population and the corresponding subgroups via combining univariate and multivariate analyses. Likewise, several subgroups have similar statistical results. However, no association of miR-221/222 family was detected with DFS/PFS/RFS.

Generally, we only explored the potential associations in corresponding subgroups of OS due to the sufficient data. When stratified by sample source, given the result in multivariate analysis, blood miR-221/222 expression was unlikely as a promising marker on tumor prognosis. Meanwhile, the result was also not convincing in univariate analysis based on only one study. However, the miR-221/222 expression in FFPE or tissue might be associated with poor OS of carcinoma. When stratified by the sample size, we discovered that high expression of miR-221/222 cluster connected with unfavorable OS significantly in both groups with different sample sizes. When stratified by miRNA, the result indicated that the over expression of miR-221 and miR-222 both can predict poor OS apparently in multivariate analysis. However, we proposed overexpression miRNA-221/222 cluster could be viewed as a protection factor in PCa after integrating 4 studies on PCa[@b48-cmar-10-3371] ([Figure S4](#SD4-cmar-10-3371){ref-type="supplementary-material"}). It is supposed that overexpression miRNA-221/222 cluster could repress PCa cell invasion[@b37-cmar-10-3371] and decrease prostate-specific antigen expression mediated by androgen receptor,[@b49-cmar-10-3371] which may participate in the protection process. Besides, lack of sufficient data indeed has a certain impact on the intriguing result. Future studies from multicenter comprising larger cohort size are needed to verify the current findings.

Actually, there were some limitations in our study that we should notice in the current meta-analysis. First, as for OS, part of the outcome failed to be analyzed separately due to the small sample size and no publication bias could be assessed in the current meta-analysis. In consideration of the limitations of qualified studies quantities, we did not process subgroup analysis for DFS/PFS/RFS. But the relative DFS/PFS/RFS data presented in this study may improve our understanding of cancer progression and relapse. Moreover, the design of studies, cutoff values, and measure methods being distinct in different studies, we could not consolidate those inconsistent objective elements, which were influential factors to the results. In addition, we did not compare the difference of ethnicities because of the limited number of different countries or races.

Conclusion
==========

A significant correlation was explored in overall population and corresponding subgroups. Concretely, it presented that miR-221/222 family overexpression was significantly linked with poor OS, while no relationship was found between higher miR-221/222 family expression and DFS/PFS/RFS. Besides, sample sizes had no effect on our results, majority of subgroup analysis result was consistent with overall conclusion. Besides, miRNA-221/222 cluster from tissue or FFPE was more convincing in prediction of OS of tumor patients compared with blood-derived miRNA-221/222 cluster. Moreover, miRNA-221/222 cluster overexpression could be viewed as a protection factor in PCa to some extent. The elucidation of deregulated miR-221/222 cluster is expected to improve the understanding on tumor and promote the further development of biomarkers in cancer prognosis.

Supplementary materials
=======================

###### 

Forest plot of the association between high expression of miR-221/222 family in various tumors and PFS under different types of analysis. (**A**: univariate analysis; **B**: multivariate analysis).

**Abbreviations:** HR, hazard ratio; PFS, progression-free survival; SE, standard error.

###### 

Forest plot of the association between high expression of miR-221/222 family in various tumors and RFS under multivariate analysis.

**Abbreviations:** HR, hazard ratio; RFS, relapse free survival; SE, standard error.

###### 

One-way sensitivity analysis of high expression of miR-221/222 family in various tumors with DFS under different types of analysis.

**Note:** (**A**) univariate analysis; (**B**) multivariate analysis.

**Abbreviation:** DFS, disease-free survival.

###### 

Forest plot of the association between high expression of miR-221/222 family in prostate cancer and DFS/RFS.

**Abbreviations:** HR, hazard ratio; DFS, disease-free survival; RFS, relapse free survival; SE, standard error.

This work was supported by the National Natural Science Foundation of China (Grant nos. 81401518, 81630019), Anhui Provincial Institutes for Translational Medicine (Grant no. 2017ZHYX02), and Cultivation Project of Young Top-Notch Talent Support from Anhui Medical University (AHMU). Received Funding for Distinguished Young Scientists of the First Affiliated Hospital of AHMU.

**Disclosure**

The authors report no conflicts of interest in this work.

![Flow diagram of the study selection process in the meta-analysis.\
**Abbreviations:** CBM, Chinese Biomedical Literature Database; CNKI, Chinese National Knowledge Infrastructure.](cmar-10-3371Fig1){#f1-cmar-10-3371}

![Forest plot of the association between high expression of miR-221/222 family in various tumors and OS under different types of analysis.\
**Note:** (**A)** univariate analysis; (**B**) multivariate analysis.\
**Abbreviations:** HR, hazard ratio; OS, overall survival; SE, standard error.](cmar-10-3371Fig2){#f2-cmar-10-3371}

![Forest plot of the association between high expression of miR-221/222 family in various tumors and DFS under different types of analysis.\
**Note:** (**A)** univariate analysis; (**B)** multivariate analysis.\
**Abbreviations:** DFS, disease-free survival; HR, hazard ratio; SE, standard error.](cmar-10-3371Fig3){#f3-cmar-10-3371}

![One-way sensitivity analysis of high expression of miR-221/222 family in various tumors with OS under different types of analysis.\
**Note:** (**A)** univariate analysis; (**B)** multivariate analysis.\
**Abbreviation:** OS, overall survival.](cmar-10-3371Fig4){#f4-cmar-10-3371}

![Funnel plot analysis of publication bias of high expression of miR-221/222 family in various tumors with OS under different types of analysis.\
**Notes:** (**A)** univariate analysis; (**B)** multivariate analysis.\
**Abbreviations:** HR, hazard ratio; OS, overall survival.](cmar-10-3371Fig5){#f5-cmar-10-3371}

###### 

The main characteristics and survival data of included 32 studies

  First author                        Year     Country                                         Test method   Cancer type   miRNA             Sample source   Outcome   HR (95% CI)                                       Sample size   Cutoff value
  ----------------------------------- -------- ----------------------------------------------- ------------- ------------- ----------------- --------------- --------- ------------------------------------------------- ------------- ---------------------
  Gramantieri L[@b4-cmar-10-3371]     2009     Italy                                           qRT-PCR       HCC           miR-221           Tissue          OS/DFS    (U)1.12 (0.81--1.56)/(U)1.56 (1.20--2.03)         51            Median
  Greither T[@b5-cmar-10-3371]        2010     Germany                                         qRT-PCR       PC            miR-222           Tissue          OS        (M)2.05 (1.05--4.00)                              56            Median
  Schaefer A[@b8-cmar-10-3371]        2010     Germany                                         qRT-PCR       PCa           miR-221/miR-222   Tissue          DFS       (U)0.93 (0.30--2.89)/(U)0.69 (0.22--2.17)         76            Median
  Wong QW[@b16-cmar-10-3371]          2010     China                                           qRT-PCR       HCC           miR-222           Tissue          OS/DFS    (U)2.73 (1.41--5.30)/(U)2.18 (1.20--3.98)         76            Median
  Wang Y[@b17-cmar-10-3371]           2010     China                                           qRT-PCR       ALL           miR-221           Tissue          OS        (U)0.54 (0.30--0.97)                              32            Median
  Spahn M[@b18-cmar-10-3371]          2010     Germany                                         qRT-PCR       PCa           miR-221           FFPE            DFS       \(M\) 0.53 (0.29--0.95)                           92            Median
  Pu XX[@b19-cmar-10-3371]            2010     China                                           qRT-PCR       CRC           miR-221           Blood           OS        \(M\) 3.48 (1.04--11.65)                          103           Youden index
  Guo HQ[@b20-cmar-10-3371]           2010     China                                           qRT-PCR       N/T L         miR-221           Blood           OS        \(U\) 0.40 (0.17--0.95)/(M) 0.18 (0.06--0.56)     79            Youden index
  Wang RM[@b21-cmar-10-3371]          2011     China                                           qRT-PCR       GC            miR-221           Tissue          OS        \(U\) 5.85 (2.32--14.72)                          96            Median
  Li JP[@b9-cmar-10-3371]             2011     China                                           qRT-PCR       HCC           miR-221           Blood           OS        \(M\) 1.90 (1.24--2.98)                           46            Average fold change
  Yoon SO[@b22-cmar-10-3371]          2011     Korea                                           qRT-PCR       HCC           miR-221           FFPE            DFS       \(M\) 3.07 (1.56--6.07)                           115           Fold change =1
  Karakatsanis A[@b23-cmar-10-3371]   2011     Greece                                          qRT-PCR       HCC           miR-221           FFPE            OS        \(M\) 1.79 (1.24--2.58)                           60            Average fold change
  Liu K[@b24-cmar-10-3371]            2012     China                                           qRT-PCR       GC            miR-221           Tissue          OS        \(M\) 2.32 (1.11--4.85)                           92            Mean
  Zhang C[@b25-cmar-10-3371]          2012     China                                           ISH           GM            miR-221/miR-222   Tissue          OS        \(U\) 2.03 (1.18--3.50)/(U) 2.56 (1.16--5.65)     50            Final score =3
  Rong M[@b26-cmar-10-3371]           2013     China                                           qRT-PCR       HCC           miR-221           FFPE            DFS       \(U\) 1.40 (0.91--2.15)                           48            Median
  Amankwah EK[@b27-cmar-10-3371]      2013     USA                                             qRT-PCR       PCa           miR-221           FFPE            DFS       \(U\) 1.79 (0.67--4.77)                           65            Median
  Kim BH[@b28-cmar-10-3371]           2013     Korea                                           qRT-PCR       GC            miR-221           FFPE            OS        \(M\) 1.50 (0.70--2.90)                           91            Fold change =3
  Gyöngyösi B[@b29-cmar-10-3371]      2014     Italy                                           qRT-PCR       HCC           miR-221           FFPE            OS/PFS    \(U\) 1.92 (0.61--6.10)/(U) 1.32 (0.47--3.66)     20            Median
  miR-222                             OS/PFS   \(U\) 2.04 (0.64--6.46)/(U) 1.43 (0.51--3.99)                                                                                                                                           
  Tao K[@b30-cmar-10-3371]            2014     China                                           qRT-PCR       CRC           miR-221           FFPE            OS        \(U\) 2.42 (1.31--4.45)/(M) 2.04 (1.10--3.81)     90            Median
  Fu ZC[@b31-cmar-10-3371]            2014     China                                           qRT-PCR       GC            miR-222           Blood           DFS       \(U\) 4.49 (2.68--7.49)/(M) 3.41 (1.84--6.16)     114           2.23
  Li P[@b32-cmar-10-3371]             2014     China                                           qRT-PCR       CMM           miR-221           Blood           OS/DFS    \(M\) 3.19 (1.78--6.78)/(M) 2.12 (1.96--8.55)     72            2.95
  Mao KP[@b33-cmar-10-3371]           2014     China                                           qRT-PCR       NSCLC         miR-222           Tissue          OS        \(M\) 3.31 (1.97--5.58)                           100           Median
  Cai K[@b34-cmar-10-3371]            2015     China                                           qRT-PCR       CC            miR-221           Tissue          OS        \(U\) 2.19 (1.11--4.43)/(M) 2.39 (1.21--4.91)     182           Median
  Zhang YH[@b35-cmar-10-3371]         2015     China                                           qRT-PCR       NSCLC         miR-221           Tissue          OS        \(U\) 1.87 (1.27--2.76)/(M) 1.87 (1.27--2.77)     104           Mean
  Yang Z[@b36-cmar-10-3371]           2015     China                                           qRT-PCR       OSM           miR-221           Blood           OS/RFS    \(M\) 7.66 (1.83--15.92)/(M) 6.82 (1.33--13.69)   108           Median
  Goto Y[@b37-cmar-10-3371]           2015     Japan                                           qRT-PCR       PCa           miR-222           Tissue          PFS       \(U\) 0.35 (0.14--0.92)/(M) 0.21 (0.07--0.64)     92            NM
  Eissa S[@b6-cmar-10-3371]           2015     Egypt                                           qRT-PCR       BC            miR-221           Tissue          RFS       \(M\) 14.84 (1.80--9.50)                          76            1.03
  Liao L[@b38-cmar-10-3371]           2016     China                                           qRT-PCR       NSCLC         miR-221/miR-222   Tissue          PFS       \(U\) 2.52 (1.07--5.92)/(U) 3.15 (1.30--7.65)     55            Median
  Xie DF[@b39-cmar-10-3371]           2017     China                                           qRT-PCR       HCC           miR-221           Tissue          OS        \(M\) 1.74 (1.00--3.77)                           70            ≥1.795
  Liu Y[@b40-cmar-10-3371]            2017     China                                           qRT-PCR       NSCLC         miR-221           Tissue          OS        \(M\) 2.58 (1.41--4.85)                           151           Median
  Deng L[@b10-cmar-10-3371]           2017     China                                           qRT-PCR       TNBC          miR-221           Tissue          DFS       \(U\) 0.49 (0.27--0.88)/(M) 0.48 (0.26--0.88)     125           NM
  Zhao H[@b41-cmar-10-3371]           2017     USA                                             qRT-PCR       GBM           miR-222           Blood           DFS       \(U\) 1.71 (1.07--3.63)                           106           Median

**Abbreviations:** TNBC, triple-negative breast cancer; BC, breast cancer; NSCLC, non-small cell lung cancer; HCC, hepatocellular carcinoma; GC, gastric cancer; CRC, colorectal cancer; ALL, acute lymphoblastic leukemia; CMM, cutaneous malignant melanoma; CC, colon cancer; N/T L, NK/T-cell lymphoma; GBM, glioblastoma; GM, glioma; OSM, osteosarcoma; PC, pancreatic cancer; PCa, prostate cancer; NM, not mentioned; qRT-PCR, quantitative reverse transcription-polymerase chain reaction; OS, overall survival; DFS, disease-free survival; ISH, in situ hybridization; PFS, progression-free survival; RFS, relapse-free survival.

###### 

Stratified analysis of the high expression of miR-221/222 family and overall survival

  Categories      Subgroups   Univariate analyses         multivariate analyses                                                                                                       
  --------------- ----------- --------------------------- --------------------------- ------- ------- ------- --------------------------- --------------------------- ------- ------- -------
  All                         12                          **1.26** (1.07**--**1.47)   0.004   75%     0.000   15                          **2.10** (1.63**--**2.69)   0.000   56%     0.004
  Country         China       9                           **1.28** (1.05**--**1.56)   0.02    79%     0.000   12                          **2.19** (1.60**--**3.01)   0.000   64%     0.001
  Others          3           1.09 (0.95--1.24)           0.21                        0%      0.45    3       **1.28** (1.12**--**1.48)   0.000                       0%      0.82    
  Test method     qRT-PCR     10                          **1.23** (1.02**--**1.48)   0.03    78%     0.000   15                          **2.10** (1.63**--**2.69)   0.000   56%     0.004
  ISH             2           **1.40** (1.16**--**1.71)   0.000                       0%      0.64    /       /                           /                           /       /       
  Sample source   FFPE        3                           **1.42** (1.15**--**1.76)   0.001   0%      0.93    3                           **1.29** (1.12**--**1.48)   0.000   0%      0.82
  Tissue          8           **1.30** (1.08**--**1.58)   0.005                       78%     0.000   7       **1.37** (1.24**--**1.52)   0.000                       0%      0.94    
  Blood           1           **0.67** (0.46**--**0.98)   0.04                        /       /       5       1.35 (0.90--2.03)           0.15                        85%     0.000   
  miR221/222      miR221      9                           1.20 (1.00**--**1.45)       0.05    79%     0.000   13                          **2.01** (1.52**--**2.65)   0.000   58%     0.004
  Component       miR222      3                           **1.50** (1.23**--**1.84)   0.000   0%      0.91    2                           **2.73** (1.72**--**4.33)   0.000   19%     0.27
  Sample size     ≥100        2                           **1.33** (1.15**--**1.55)   0.000   0%      0.70    6                           **2.72** (1.96**--**3.79)   0.000   35%     0.18
  \<100           10          **1.24** (1.02**--**1.51)   0.03                        78%     0.000   9       **1.27** (1.10**--**1.47)   0.001                       60%     0.01    
  Cancer type     NSCLC       1                           **1.87** (1.27**--**2.76)   0.002   /       /       3                           **2.42** (1.69**--**3.45)   0.000   35%     0.22
  HCC             4           1.71 (1.00**--**2.90)       0.05                        53%     0.09    3       **1.83** (1.38**--**2.42)   0.000                       0%      0.97    
  others          7           1.64 (0.87--3.10)           0.13                        83%     0.000   9       **2.05** (1.26**--**3.33)   0.004                       71%     0.000   

**Note:** The bold font indicated *P*-value of the HR was \<0.05.

**Abbreviations:** NSCLC, non-small cell lung cancer; HCC, hepatocellular carcinoma; qRT-PCR, quantitative reverse transcription-polymerase chain reaction; ISH, in situ hybridization; FFPE, formalin-fixed and paraffin-embedded; HR, hazard ratio; CI, confidence interval; P^h^, *P*-value of heterogeneity test.
