BioMed Research International
Volume 2020 (2020), Article ID 4689689, 7 pages
https://doi.org/10.1155/2020/4689689

Review Article

Prognostic Role of MicroRNA 222 in Patients with Glioma: A Meta-analysis

Yanlin Song,1,2 Jing Zhang,¹ Min He,³ and Jianguo Xu¹

¹Department of Neurosurgery and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
²Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
³Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China

Correspondence should be addressed to Jianguo Xu; jianguo_1229@126.com

Received 6 May 2020; Revised 10 September 2020; Accepted 15 September 2020; Published 25 September 2020

Academic Editor: Taiyoun Rhim

Copyright © 2020 Yanlin Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Several studies have focused on the prognostic role of microRNA 222 in glioma. But different conclusions were drawn by these studies. We aimed to systematically evaluate the role of microRNA 222 in glioma by conducting a meta-analysis. Methods. A systematic literature search until January 2020 was conducted in Web of Science, EMBASE, Cochrane Library, PubMed, and China National Knowledge Infrastructure. The general characteristics and relevant data of nine articles were extracted. Hazard ratios (HRs) with 95% confidence intervals (CIs) were applied to evaluate the prognostic role of microRNA 222 in glioma. The primary outcomes were overall survival (OS) and disease-free survival (DFS). Results. Nine articles (11 data sets) with 1564 patients were included. We systematically evaluated the role of microRNA 222 for OS and DFS in glioma patients (HR for ; 95% CI, 1.31-2.26; ; HR for ; 95% CI, 0.86-1.22; ). Subgroup analyses were performed according to the sources of patients, the types of the samples, the stages of the tumors, the methods for detecting the microRNA 222, and the sample size. No significant publication bias was found. Conclusion. In conclusion, our study provided evidence that a high expression of microRNA 222 was related to worse overall survival in glioma patients. However, given the limited study number, more high-quality studies are warranted in the future.

1. Introduction

Glioma is the most common cancer in the central nervous system with high mortality and recurrence [1]. Although significant advances have been achieved in the treatment, the prognosis of glioma patients is still very poor [2]. So, it is urgent to explore a novel biomarker to predict the prognosis of glioma.

MicroRNAs are a class of single-strand RNAs which silence the genes by targeting the mRNAs [3]. It is proven that aberrant expressions of microRNAs are related to the proliferation, migration, and invasion of tumor cells [4–6]. In addition, some microRNAs have been proved to play oncogenic roles [7–12]. Therefore, many studies focused on microRNAs. For example, microRNA 373, 15, 107, 133, and 211 were reported to be related to the prognosis of glioma [13–17].

MicroRNA 222 is reported to be related to the proliferation, invasion, and migration of several types of tumor cells [18–20]. Downregulation of microRNA 222 inhibited the growth and angiogenesis of glioma and sensitized glioma cells to temozolomide according to the previous studies [21–23]. Moreover, a high level of microRNA 222 was proven to promote the proliferation, invasion, migration, angiogenesis, radioresistance, and chemoresistance of glioma cells [24–27]. Considering the relationship between microRNA 222 and glioma, several studies investigated the role of microRNA 222 on the prognosis of glioma [11, 17, 26, 28–33]. But different conclusions were drawn by these studies. In order to reach an agreement, we evaluated the prognostic role of microRNA 222 in glioma patients by conducting a meta-analysis.

2. Method

2.1. Search Strategy

A systematic literature search until January 2020, was conducted in Web of Science, EMBASE, Cochrane Library, PubMed, and China National Knowledge Infrastructure. The key words, microRNA 222 (microRNA 222-3p or hsa-miR-222), glioma (astrocytoma or glioblastoma or ependymoma or subependymal or ganglioglioma or gliosarcoma or medulloblastoma or oligodendroglioma), and prognosis (survival) and all possible combinations were included. Moreover, the article was organized based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist (see available here).

2.2. Inclusion and Exclusion Criteria

All randomized control trials (RCT) and cohort studies were included in our study. Included studies met the following inclusion criteria: (1) the diagnosis of glioma was proven by pathological examination, (2) the prognostic role of microRNA 222 on glioma was studied in the study, and (3) hazard ratio (HR) and 95% CI were shown or can be calculated in the article. In addition, the studies that met the following criteria were excluded: (1) reviews, letters, case reports, and opinions from experts; (2) no available data for HR was shown in the article; and (3) duplicate data or figures.

2.3. Data Extraction

The general information, HR, and 95% CI were extracted from the selected articles. The general information included the name of author, the country of study, type of sample, number of samples, stage of glioma, the cut-off value, and the methods for detecting microRNA 222. The data which can be used to calculate the HR was also extracted according to Tierney’s report [34]. If multivariate analyses and univariate analyses were both applied to calculate HR, the results of multivariate analyses were extracted.

2.4. Statistical Analysis

The and stand error (SE) calculated from pooled HRs with 95% CIs were used to analyze the association between the expression of microRNA 222 and survival of glioma. The primary outcomes were overall survival (OS) and disease-free survival (DFS). Review Manager 5.3.5 (Cochrane Collaboration, Oxford, UK) was used to calculate and SE from HRs and values. HRs and values were not available in studies performed by Li and Zhang; and SE were extracted from the KM curve by Engauge Digitizer 10.0 (free software downloaded from https://sourceforge.net/projects/digitizer/). A random effects model was applied to calculate the pooled HR whether the heterogeneity was significant (% or ). Subgroup analyses were performed according to the sources of patients, the types of the samples, the stages of the tumors, and the methods for detecting the microRNA 222. The stability of our study was assessed with sensitivity analysis. The pooled HR was calculated by STATA 11.0 (StataCorp, College Station, TX, USA). The publication bias was analyzed by Begg’s test.

3. Results

3.1. Study Selection

As shown in Figure 1, the studies were selected following the PRISMA flow diagram (see available here). At the end, 64 articles by literature search and 2 articles found by other sources were included in the first round of research. No duplicated articles were found. After that, 48 unrelated articles were excluded by screening the title and abstract of these articles. At last, the full text of the remaining18 articles were carefully screened and only 9 articles (11 data sets) were included in our study [11, 26, 28–33, 35]. Among these studies, 7 data sets were related to overall survival (OS) and 4 data sets were related to the disease-free survival (DFS). All of the data sets were evaluated by the Newcastle–Ottawa scale criteria.

Figure 1: Selection process of studies.

3.2. Basic Characteristics of the Included Studies

The basic characteristics of the included studies were presented in Table 1. A total of 1564 patients were included in these studies from 2011 to 2018. The patients were from China or the USA. The samples which were used for detecting microRNA 222 were mostly from tumor tissues. The cut-off value and methods for detecting microRNA 222 were also extracted from these studies. The median value of the microRNA 222 level was commonly defined as the cut-off value according to the included studies.

Table 1: Characteristics of the included articles.

Author
Country
Sample
Number
Stage
Cut-off
Method
Results
HR (95% CI)
value
Quality score (NOS)

Chen Y
China
Tissue
114
IV
None
Q-PCR
OS
1.01 (0.65-1.58)
0.965
9

Zhang R
China
Blood
51
I-IV
None
Q-PCR
OS
2.81 (1.70-4.65)
0.0001
8

Li X
China
Tissue
45
I-IV
Mean
Q-PCR
OS
2.13 (1.01-4.48)
0.043
8

Zhang C
China
Tissue
36
I-IV
—
IHC
OS
2.39 (1.15-4.96)
0.02
8

Sun C
USA
Tissue
548
IV
Median
—
OS
1.68 (1.23-2.29)
0.001
8

Srinivasan S
USA
Tissue
111
IV
60%
—
OS
1.26 (1.11-1.43)
0.0004
7

Delfino K.R.
USA
Tissue
253
IV
—
—
OS
2.14 (1.51-3.03)
<0.0001
7

Zhao H
USA
Blood
106
IV
Median
—
DFS
1.71 (1.07-3.63)
0.038
7

Chen W
USA
Tissue
89
IV
Median
—
DFS
0.71 (0.46-1.09)
0.12
7

102
IV
Median
—
DFS
1.11 (0.99-1.23)
0.07
7

109
IV
Median
—
DFS
0.96 (0.86-1.08)
0.53
7

HR: hazard ratio; CI: confidence interval; Q-PCR: quantitative polymerase chain reaction; IHC: immunohistochemistry; NOS: Newcastle–Ottawa scale.

3.3. Overall Survival and Disease-Free Survival

The pooled HR for OS was provided by 7 data sets. As shown in Figure 2(a), a high expression of microRNA 222 was related to worse overall survival (; 95% CI, 1.31-2.26; ). But the HR for DFS implied that the high expression of microRNA 222 might not be related to the worse disease-free survival (; 95% CI, 0.86-1.22; ; Figure 2(b)). In order to explore the stability of our results, sensitivity analyses were performed by deleting one study at a time and recalculating the pooled HR at the same time. As expected, the recalculated HRs still proved that the high expression of microRNA 222 was related to the poor prognosis of glioma (Table 2).

(a)

(b)

Figure 2: Pooled hazard ratio of higher microRNA 222 for overall survival (a) and disease-free survival (b) in patients with glioma.

Table 2: Sensitivity analysis.

Excluding article
,
Pooled HR (95% CI)

Chen Y
74.7%, 0.001
1.88 (1.39-2.54)

Zhang R
66.5%, 0.011
1.58 (1.22-2.04)

Li X
76.3%, 0.001
1.69 (1.26-2.25)

Zhang C
75.4%, 0.001
1.67 (1.25-2.21)

Srinivasan S
56.1%, <0.001
1.87 (1.40-2.49)

Delfino K.R.
69.7%, 0.006
1.64 (1.22-2.19)

Sun C
76.2%, 0.001
1.75 (1.25-2.45)

HR: hazard ratio; CI: confidence interval.

3.4. Subgroup Analysis for Overall Survival

To further explore the effects of other factors, like sources of patients, types of samples, tumor grades, and methods for detecting microRNA 222, on the prognosis of glioma patients, we performed subgroup analysis.

3.4.1. Sources of Patients

Among the 7 data sets, 4 data sets were performed in China and 3 data sets were performed in the USA (Table 3). The pooled HR of the China and USA groups were 1.90 (95% CI, 1.10-3.26; ) and 1.61 (95% CI, 1.16-2.22; ), respectively, which indicated that the high expression of microRNA 222 was related the poor prognosis of glioma both in China and the USA.

Table 3: Summary of meta-analysis results.

Stratified study
Data sets
Pooled HR (95% CI)
value
Heterogeneity

value

OS
7
1.72 (1.31-2.26)
<0.001
73.00%
0.001

DFS
4
1.02 (0.86-1.22)
0.807
65.90%
0.032

Country

    China
4
1.90 (1.10-3.26)
0.021
70.70%
0.017

    USA
3
1.61 (1.16-2.22)
0.004
79.20%
0.008

Material

    Tissue
6
1.58 (1.22-2.04)
0.001
66.50%
0.011

    Blood
1
2.81 (1.7-4.65)
0.0001
—
—

Stage

    IV
4
1.47 (1.11-1.94)
0.007
73.70%
0.01

    I-IV
3
2.53 (1.76-3.63)
<0.001
0.00%
0.82

Method

    Q-PCR
3
1.79 (0.90-3.57)
0.099
78.80%
0.009

Immunohistochemistry scoring
1
2.39 (1.15-4.96)
0.02
—
—

Sample size

    >100
4
1.47 (1.12-1.94)
0.01
73.70%
0.007

    <100
3
2.53 (1.76-3.63)
<0.001
0.00%
0.82

HR: hazard ratio; CI: confidence interval; Q-PCR: quantitative polymerase chain reaction; IHC: immunohistochemistry.

3.4.2. Types of Samples

Only 1 data set tested the microRNA 222 through the blood of patients. So, we calculated the pooled HR by excluding this study (Table 3). The pooled HR changed slightly to 1.58 (95% CI, 1.22-2.04; ), which further proved the role of microRNA 222 on glioma.

3.4.3. Tumor Grades

The tumor grade was also a potential factor for the prognosis of glioma. Four data sets only included stage IV patients of glioma while 3 data sets included stage I-IV patients of glioma (Table 3). So, the pooled HR of these studies were analyzed, respectively. As expected, the high expression of microRNA 222 was still related to the poor prognosis of glioma according to the pooled HR of the different stage patients (; 95% CI, 1.11-1.94; ; ; 95% CI, 1.76-3.63; ).

3.4.4. Methods for Detecting MicroRNA 222

Three data sets detected the microRNA 222 by Q-PCR and 1 data set by immunohistochemistry scoring. The remaining 3 data sets did not mention the method for detecting the microRNA 222. So, we calculated the pooled HR of the Q-PCR group (; 95% CI, 0.90-3.57; ).

3.4.5. Sample Size

The pooled HR of three data sets which included patients less than 100 was 2.53 (95% CI, 1.76-3.63; ). As to the studies which included patients more than 100, the pooled HR was 1.47 (95% CI, 1.12-1.94; ).

3.5. Publication Bias

The publication bias were evaluated by Begg’s test () and Egger’s test (). The funnel plot was shown in Figure 3. No obvious publication bias was presented in these studies.

(a)

(b)

Figure 3: Begg’s (a) and Egger’s(b) publication bias plot of the included studies.

4. Discussion

MicroRNA 222 which acts as a good biomarker for the prognosis of patients is found in several cancers [17, 36, 37]. Numbers of clinical studies have explored the relationship between microRNA 222 and prognosis of glioma patients [11, 17, 26, 28–32, 38–45]. But different conclusions were drawn by these studies. To reach an agreement, we conducted a systematic meta-analysis. In this study, the pooled HR was calculated from the extracted data (HR for ; 95% CI, 1.31-2.26; HR for 95% CI, 0.86-1.22). It indicated that a high expression of microRNA 222 predicted the worse overall survival other than the recurrence or progression in glioma. But only four data sets were used for the analysis of DFS in our study. In addition, three data sets were from the same study. So, more studies focusing on DFS are needed to draw a convincing conclusion.

In order to explore the stability of our results, sensitivity analyses were performed by deleting one study at a time and recalculating the pooled HR at the same time. As expected, the recalculated HRs still proved that the high expression of microRNA 222 was related to the poor prognosis of glioma (Table 2). We then explored the potential role of the sources of patients, types of samples, tumor grades, methods for detecting microRNA 222, and sample size by subgroup analysis. Finally, the publication bias was analyzed by Begg’s and Egger’s tests. All of the subsequent analyses further proved the role of microRNA 222 in glioma.

Glioma characterized with rapid proliferation, high malignancy, and resistant to conventional therapy was reported to have poor prognosis in previous studies [46]. Intriguingly, microRNA 222 was proven to be related to the proliferation, migration, and invasion of glioma cells [18–20]. An obvious activation of AKT was found in microRNA 222 overexpression glioma cells. And the significant changes of AKT related genes were also observed in these cells [27]. Besides, PTPμ was found as a new target in this process [25]. An inverse correlation has been observed between PTPμ and microRNA 222 in vivo and in vitro. MicroRNA 222 promoted the migration and proliferation of glioma cells with downexpression of PTPμ. And the reexpression of PTPμ was able to revert the effects. The overexpression of microRNA 222 was also verified to be associated with radioresisitance and chemoresistance [21, 24]. MicroRNA 222 was identified as a key molecular in temozolomide-resistant glioma patients, and knockdown of microRNA 222 sensitized glioma cells to temozolomide by regulating the expression of apoptosis-independent p53. Another study revealed that microRNA 222 was mediated in the radio-induced DNA damage repair, which implied the potential of microRNA 222 on the increasing radiosensitivity of glioma cells. The results mentioned above showed potential reasons why microRNA 222 might be used as a good marker for predicting the prognosis of glioma patients.

Although our results were robust, some limitations of the issue should be emphasized. Firstly, the patients included in our study only from China and the USA limited the representativeness of the prognostic role in the world. Secondly, significant heterogeneity was observed in this study. Sensitivity analysis and subgroup analysis did not eliminate heterogeneity, which might be due to the different characteristics of included patients across the studies. The limitations implied the need for more studies that focused on microRNA 222 and glioma.

In conclusion, our study provided evidence that a high expression of microRNA 222 was related to worse overall survival of glioma patients. However, given the limited study number, more high-quality studies are warranted in the future.

Conflicts of Interest

The authors declare no conflict of interests.

Authors’ Contributions

Yanlin Song, Jing Zhang, and Min He contributed equally to this work.

Acknowledgments

This work was supported by the China Postdoctoral Science Foundation (2019M650244), Post-Doctor Research Project, West China Hospital, Sichuan University (2019HXBH094), 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC18007), and Key research and development project of Science and Technology Department of Sichuan Province (2019YFS0392).

Supplementary Materials

Figure 1: PRISMA 2009 flowchart. Table: PRISMA checklist. (Supplementary Materials)

References

X. Chen, Y. Yan, J. Zhou et al., “Clinical prognostic value of isocitrate dehydrogenase mutation, O-6-methylguanine-DNA methyltransferase promoter methylation, and 1p19q co-deletion in glioma patients,” Annals of Translational Medicine, vol. 7, no. 20, p. 541, 2019.

Q. Y. Zhong, E. X. Fan, G. Y. Feng et al., “A gene expression-based study on immune cell subtypes and glioma prognosis,” BMC Cancer, vol. 19, no. 1, p. 1116, 2019.

D. Sun, Y. Mu, and H. Piao, “MicroRNA-153-3p enhances cell radiosensitivity by targeting BCL2 in human glioma,” Biological Research, vol. 51, no. 1, p. 56, 2018.

Q. Ma, Y. Wang, H. Zhang, and F. Wang, “miR-1290 contributes to colorectal cancer cell proliferation by targeting INPP4B,” Oncology Research, vol. 26, no. 8, pp. 1167–1174, 2018.

H. Wang, Z. Tan, H. Hu et al., “MicroRNA-21 promotes breast cancer proliferation and metastasis by targeting LZTFL1,” Bmc Cancer, vol. 19, no. 1, p. 738, 2019.

Q. Zhang, K. Zhang, C. Zhang et al., “MicroRNAs as big regulators of neural stem/progenitor cell proliferation, differentiation and migration: a potential treatment for stroke,” Current pharmaceutical design, vol. 23, no. 15, pp. 2252–2257, 2017.

Y. Dang, X. Wei, L. Xue, F. Wen, J. Gu, and H. Zheng, “Long non-coding RNA in glioma: target miRNA and signaling pathways,” Clinical Laboratory, vol. 64, no. 6, pp. 887–894, 2018.

B. C. Wang and J. Ma, “Role of microRNAs in malignant glioma,” Chinese Medical Journal, vol. 128, no. 9, pp. 1238–1244, 2015.

D. Yan, C. Hao, L. Xiao-Feng, L. Yu-Chen, F. Yu-Bin, and Z. Lei, “Molecular mechanism of Notch signaling with special emphasis on microRNAs: implications for glioma,” Journal of Cellular Physiology, vol. 234, no. 1, pp. 158–170, 2019.

Y. Zhang, J. Chen, Q. Xue et al., “Prognostic significance of microRNAs in glioma: a systematic review and meta-analysis,” BioMed Research international., vol. 2019, article 4015969, 2019.

H. Zhao, J. Shen, T. R. Hodges, R. Song, G. N. Fuller, and A. B. Heimberger, “Serum microRNA profiling in patients with glioblastoma: a survival analysis,” Molecular Cancer, vol. 16, no. 1, p. 59, 2017.

Q. Zhou, J. Liu, J. Quan, W. Liu, H. Tan, and W. Li, “MicroRNAs as potential biomarkers for the diagnosis of glioma: a systematic review and meta-analysis,” Cancer Science, vol. 109, no. 9, pp. 2651–2659, 2018.

T. Tokudome, A. Sasaki, M. Tsuji et al., “Reduced PTEN expression and overexpression of miR-17-5p, -19a-3p, -19b-3p, -21-5p, -130b-3p, -221-3p and -222-3p by glioblastoma stem-like cells following irradiation,” Oncology Letters, vol. 10, no. 4, pp. 2269–2272, 2015.

S. Y. Jing, S. Q. Jing, L. L. Liu, L. F. Xu, F. Zhang, and J. L. Gao, “Down-expression of miR-373 predicts poor prognosis of glioma and could be a potential therapeutic target,” European review for medical and pharmacological sciences., vol. 21, no. 10, pp. 2421–2425, 2017.

C. Pang, Y. Guan, K. Zhao et al., “Up-regulation of microRNA-15b correlates with unfavorable prognosis and malignant progression of human glioma,” International journal of clinical and experimental pathology, vol. 8, no. 5, pp. 4943–4952, 2015.

K. Xue, J. Yang, J. Hu, J. Liu, and X. Li, “MicroRNA-133b expression associates with clinicopathological features and prognosis in glioma,” Artificial cells, nanomedicine, and biotechnology, vol. 46, no. 4, pp. 815–818, 2018.

L. Xue, Y. Wang, S. Yue, and J. Zhang, “The expression of miRNA-221 and miRNA-222 in gliomas patients and their prognosis,” Neurological Sciences, vol. 38, no. 1, pp. 67–73, 2017.

Y. W. Chu, C. R. Wang, F. B. Weng, Z. J. Yan, and C. Wang, “MicroRNA-222 contributed to cell proliferation, invasion and migration via regulating YWHAG in osteosarcoma,” European Review for Medical and Pharmacological Sciences, vol. 22, no. 9, pp. 2588–2597, 2018.

X. Tan, H. Tang, J. Bi, N. Li, and Y. Jia, “MicroRNA-222-3p associated with Helicobacter pylori targets HIPK2 to promote cell proliferation, invasion, and inhibits apoptosis in gastric cancer,” Journal of Cellular Biochemistry, vol. 119, no. 7, pp. 5153–5162, 2018.

L. Wang, C. Liu, C. Li et al., “Effects of microRNA-221/222 on cell proliferation and apoptosis in prostate cancer cells,” Gene, vol. 572, no. 2, pp. 252–258, 2015.

L. Chen, J. Zhang, L. Han et al., “Downregulation of miR-221/222 sensitizes glioma cells to temozolomide by regulating apoptosis independently of p53 status,” Oncology Reports, vol. 27, no. 3, pp. 854–860, 2012.

C. H. Xu, Y. Liu, L. M. Xiao et al., “Silencing microRNA-221/222 cluster suppresses glioblastoma angiogenesis by suppressor of cytokine signaling-3-dependent JAK/STAT pathway,” Journal of Cellular Physiology, vol. 234, no. 12, pp. 22272–22284, 2019.

C.-z. Zhang, C.-s. Kang, P.-y. Pu et al., “Inhibitory effect of knocking down microRNA-221 and microRNA-222 on glioma cell growth in vitro and in vivo,” Zhonghua zhong liu za zhi, vol. 31, no. 10, pp. 721–726, 2009.

W. Li, F. Guo, P. Wang, S. Hong, and C. Zhang, “miR-221/222 confers radioresistance in glioblastoma cells through activating Akt independent of PTEN status,” Current Molecular Medicine, vol. 14, no. 1, pp. 185–195, 2014.

C. Quintavalle, M. Garofalo, C. Zanca et al., “miR-221/222 overexpession in human glioblastoma increases invasiveness by targeting the protein phosphate PTPμ,” Oncogene, vol. 31, no. 7, pp. 858–868, 2012.

C. Zhang, J. Zhang, J. Hao et al., “High level of miR-221/222 confers increased cell invasion and poor prognosis in glioma,” Journal of Translational Medicine, vol. 10, no. 1, p. 119, 2012.

J. Zhang, L. Han, Y. Ge et al., “miR-221/222 promote malignant progression of glioma through activation of the Akt pathway,” International Journal of Oncology, vol. 36, no. 4, pp. 913–920, 2010.

Y. Y. Chen, H. L. Ho, S. C. Lin, T. D. Ho, and C. Y. Hsu, “Upregulation of miR-125b, miR-181d, and miR-221 predicts poor prognosis in MGMT promoter-unmethylated glioblastoma patients,” American Journal of Clinical Pathology, vol. 149, no. 5, pp. 412–417, 2018.

K. R. Delfino, N. V. Serao, B. R. Southey, and S. L. Rodriguez-Zas, “Therapy-, gender- and race-specific microRNA markers, target genes and networks related to glioblastoma recurrence and survival,” Cancer Genomics & Proteomics, vol. 8, no. 4, pp. 173–183, 2011.

X. Li, J. Zheng, L. Chen, H. Diao, and Y. Liu, “Predictive and prognostic roles of abnormal expression of tissue miR-125b, miR-221, and miR-222 in glioma,” Molecular Neurobiology, vol. 53, no. 1, pp. 577–583, 2016.

S. Srinivasan, I. R. Patric, and K. Somasundaram, “A ten-microRNA expression signature predicts survival in glioblastoma,” PLoS One, vol. 6, no. 3, article e17438, 2011.

R. Zhang, B. Pang, T. Xin et al., “Plasma miR-221/222 family as novel descriptive and prognostic biomarkers for glioma,” Molecular Neurobiology, vol. 53, no. 3, pp. 1452–1460, 2016.

W. Chen, Q. Yu, B. Chen, X. Lu, and Q. Li, “The prognostic value of a seven-microRNA classifier as a novel biomarker for the prediction and detection of recurrence in glioma patients,” Oncotarget, vol. 7, no. 33, pp. 53392–53413, 2016.

G. Troiano, F. Mastrangelo, V. C. A. Caponio, L. Laino, N. Cirillo, and L. L. Muzio, “Predictive prognostic value of tissue-based microRNA expression in oral squamous cell carcinoma: a systematic review and meta-analysis,” Journal of Dental Research, vol. 97, no. 7, pp. 759–766, 2018.

C. Sun and X. Zhao, “Joint covariate detection on expression profiles for selecting prognostic miRNAs in glioblastoma,” BioMed Research International, vol. 2017, Article ID 3017948, 10 pages, 2017.

S. Liu, Z. Wang, Z. Liu et al., “miR-221/222 activate the Wnt/β-catenin signaling to promote triple-negative breast cancer,” Journal of Molecular Cell Biology, vol. 10, no. 4, pp. 302–315, 2018.

F. Wei, C. Ma, T. Zhou et al., “Exosomes derived from gemcitabine-resistant cells transfer malignant phenotypic traits via delivery of miRNA-222-3p,” Molecular Cancer, vol. 16, no. 1, p. 132, 2017.

M. Swellam, L. E. El Arab, A. S. Al-Posttany, and S. B. Said, “Clinical impact of circulating oncogenic miRNA-221 and miRNA-222 in glioblastoma multiform,” Journal of Neuro-Oncology, vol. 144, no. 3, pp. 545–551, 2019.

B. Sun, X. Zhao, J. Ming, X. Liu, D. Liu, and C. Jiang, “Stepwise detection and evaluation reveal miR-10b and miR-222 as a remarkable prognostic pair for glioblastoma,” Oncogene, vol. 38, no. 33, pp. 6142–6157, 2019.

M. Tantawy, M. G. Elzayat, D. Yehia, and H. Taha, “Identification of microRNA signature in different pediatric brain tumors,” Genetics and Molecular Biology, vol. 41, no. 1, pp. 27–34, 2018.

A. Santangelo, P. Imbrucè, B. Gardenghi et al., “A microRNA signature from serum exosomes of patients with glioma as complementary diagnostic biomarker,” Journal of Neuro-Oncology, vol. 136, no. 1, pp. 51–62, 2018.

Z. Ai, L. Li, R. Fu, J. M. Lu, J. D. He, and S. Li, “Integrated Cox's model for predicting survival time of glioblastoma multiforme,” Tumour Biology, vol. 39, no. 4, 2017.

S. Yerukala Sathipati, H. L. Huang, and S. Y. Ho, “Estimating survival time of patients with glioblastoma multiforme and characterization of the identified microRNA signatures,” BMC Genomics, vol. 17, Supplement 13, p. 1022, 2016.

M. Visani, D. de Biase, G. Marucci et al., “Expression of 19 microRNAs in glioblastoma and comparison with other brain neoplasia of grades I-III,” Molecular Oncology, vol. 8, no. 2, pp. 417–430, 2014.

R. Lakomy, J. Sana, S. Hankeova et al., “MiR-195, miR-196b, miR-181c, miR-21 expression levels and O-6-methylguanine-DNA methyltransferase methylation status are associated with clinical outcome in glioblastoma patients,” Cancer Science, vol. 102, no. 12, pp. 2186–2190, 2011.

Z. Peng, C. Liu, and M. Wu, “New insights into long noncoding RNAs and their roles in glioma,” Molecular Cancer, vol. 17, no. 1, p. 61, 2018.