BioMed Research International

BioMed Research International / 2020 / Article

Review Article | Open Access

Volume 2020 |Article ID 4689689 | https://doi.org/10.1155/2020/4689689

Yanlin Song, Jing Zhang, Min He, Jianguo Xu, "Prognostic Role of MicroRNA 222 in Patients with Glioma: A Meta-analysis", BioMed Research International, vol. 2020, Article ID 4689689, 7 pages, 2020. https://doi.org/10.1155/2020/4689689

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

Academic Editor: Taiyoun Rhim
Received06 May 2020
Revised10 Sep 2020
Accepted15 Sep 2020
Published25 Sep 2020

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 [46]. In addition, some microRNAs have been proved to play oncogenic roles [712]. 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 [1317].

MicroRNA 222 is reported to be related to the proliferation, invasion, and migration of several types of tumor cells [1820]. Downregulation of microRNA 222 inhibited the growth and angiogenesis of glioma and sensitized glioma cells to temozolomide according to the previous studies [2123]. Moreover, a high level of microRNA 222 was proven to promote the proliferation, invasion, migration, angiogenesis, radioresistance, and chemoresistance of glioma cells [2427]. Considering the relationship between microRNA 222 and glioma, several studies investigated the role of microRNA 222 on the prognosis of glioma [11, 17, 26, 2833]. 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, 2833, 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.

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.


AuthorCountrySampleNumberStageCut-offMethodResultsHR (95% CI) valueQuality score (NOS)

Chen YChinaTissue114IVNoneQ-PCROS1.01 (0.65-1.58)0.9659
Zhang RChinaBlood51I-IVNoneQ-PCROS2.81 (1.70-4.65)0.00018
Li XChinaTissue45I-IVMeanQ-PCROS2.13 (1.01-4.48)0.0438
Zhang CChinaTissue36I-IVIHCOS2.39 (1.15-4.96)0.028
Sun CUSATissue548IVMedianOS1.68 (1.23-2.29)0.0018
Srinivasan SUSATissue111IV60%OS1.26 (1.11-1.43)0.00047
Delfino K.R.USATissue253IVOS2.14 (1.51-3.03)<0.00017
Zhao HUSABlood106IVMedianDFS1.71 (1.07-3.63)0.0387
Chen WUSATissue89IVMedianDFS0.71 (0.46-1.09)0.127
102IVMedianDFS1.11 (0.99-1.23)0.077
109IVMedianDFS0.96 (0.86-1.08)0.537

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).


Excluding article, Pooled HR (95% CI)

Chen Y74.7%, 0.0011.88 (1.39-2.54)
Zhang R66.5%, 0.0111.58 (1.22-2.04)
Li X76.3%, 0.0011.69 (1.26-2.25)
Zhang C75.4%, 0.0011.67 (1.25-2.21)
Srinivasan S56.1%, <0.0011.87 (1.40-2.49)
Delfino K.R.69.7%, 0.0061.64 (1.22-2.19)
Sun C76.2%, 0.0011.75 (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.


Stratified studyData setsPooled HR (95% CI) valueHeterogeneity
value

OS71.72 (1.31-2.26)<0.00173.00%0.001
DFS41.02 (0.86-1.22)0.80765.90%0.032
Country
 China41.90 (1.10-3.26)0.02170.70%0.017
 USA31.61 (1.16-2.22)0.00479.20%0.008
Material
 Tissue61.58 (1.22-2.04)0.00166.50%0.011
 Blood12.81 (1.7-4.65)0.0001
Stage
 IV41.47 (1.11-1.94)0.00773.70%0.01
 I-IV32.53 (1.76-3.63)<0.0010.00%0.82
Method
 Q-PCR31.79 (0.90-3.57)0.09978.80%0.009
Immunohistochemistry scoring12.39 (1.15-4.96)0.02
Sample size
 >10041.47 (1.12-1.94)0.0173.70%0.007
 <10032.53 (1.76-3.63)<0.0010.00%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.

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, 2832, 3845]. 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 [1820]. 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

  1. 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. View at: Publisher Site | Google Scholar
  2. 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. View at: Publisher Site | Google Scholar
  3. 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. View at: Publisher Site | Google Scholar
  4. 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. View at: Publisher Site | Google Scholar
  5. 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. View at: Publisher Site | Google Scholar
  6. 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. View at: Publisher Site | Google Scholar
  7. 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. View at: Publisher Site | Google Scholar
  8. B. C. Wang and J. Ma, “Role of microRNAs in malignant glioma,” Chinese Medical Journal, vol. 128, no. 9, pp. 1238–1244, 2015. View at: Publisher Site | Google Scholar
  9. 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. View at: Publisher Site | Google Scholar
  10. 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. View at: Google Scholar
  11. 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. View at: Publisher Site | Google Scholar
  12. 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. View at: Publisher Site | Google Scholar
  13. 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. View at: Publisher Site | Google Scholar
  14. 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. View at: Google Scholar
  15. 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. View at: Google Scholar
  16. 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. View at: Publisher Site | Google Scholar
  17. 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. View at: Publisher Site | Google Scholar
  18. 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. View at: Publisher Site | Google Scholar
  19. 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. View at: Publisher Site | Google Scholar
  20. 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. View at: Publisher Site | Google Scholar
  21. 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. View at: Publisher Site | Google Scholar
  22. 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. View at: Publisher Site | Google Scholar
  23. 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. View at: Google Scholar
  24. 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. View at: Publisher Site | Google Scholar
  25. 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. View at: Publisher Site | Google Scholar
  26. 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. View at: Publisher Site | Google Scholar
  27. 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. View at: Publisher Site | Google Scholar
  28. 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. View at: Publisher Site | Google Scholar
  29. 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. View at: Google Scholar
  30. 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. View at: Publisher Site | Google Scholar
  31. 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. View at: Publisher Site | Google Scholar
  32. 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. View at: Publisher Site | Google Scholar
  33. 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. View at: Publisher Site | Google Scholar
  34. 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. View at: Publisher Site | Google Scholar
  35. 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. View at: Publisher Site | Google Scholar
  36. 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. View at: Publisher Site | Google Scholar
  37. 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. View at: Publisher Site | Google Scholar
  38. 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. View at: Publisher Site | Google Scholar
  39. 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. View at: Publisher Site | Google Scholar
  40. 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. View at: Publisher Site | Google Scholar
  41. 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. View at: Publisher Site | Google Scholar
  42. 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. View at: Publisher Site | Google Scholar
  43. 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. View at: Publisher Site | Google Scholar
  44. 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. View at: Publisher Site | Google Scholar
  45. 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. View at: Publisher Site | Google Scholar
  46. 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. View at: Publisher Site | Google Scholar

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.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views111
Downloads30
Citations

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.