Prognostic Value and Clinicopathological Features of MicroRNA-206 in Various Cancers: A Meta-Analysis.
Rongqiang LiuShiyang ZhengShengjia PengYajie YuJianwen FangSiwen TanFan YaoZhihua GuoYi ShaoPublished in: BioMed research international (2020)
It has been reported that microRNA-206(miR-206) plays an important role in cancers and could be used as a prognostic biomarker. However, the results are controversial. Therefore, we summarize all available evidence and present a meta-analysis to estimate the prognostic value of miR-206 in various cancers. The relevant studies were collected by searching PubMed, EMBASE, and Web of Science databases until August 21, 2020. Hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were applied to explore the association between miR-206 and survival results and clinicopathologic features. Sources of heterogeneity were investigated by subgroup analysis and sensitivity analysis. Publication bias was evaluated using Egger's test. Twenty articles involving 2095 patients were included in the meta-analysis. The pooled HR showed that low miR-206 expression was significantly associated with unfavourable overall survival (OS) (HR = 2.03, 95 CI%: 1.53-2.70, P < 0.01). In addition, we found that low miR-206 expression predicted significantly negative association with tumor stage (III-IV VS. I-II) (OR = 4.20, 95% CI: 2.17-8.13, P < 0.01), lymph node status (yes VS. no) (OR = 3.58, 95%: 1.51-8.44, P = 0.004), distant metastasis (yes VS. no) (OR = 3.19, 95%: 1.07-9.50, P = 0.038), and invasion depth (T3 + T4 vs. T2 + T1) (OR = 2.43, 95%: 1.70-3.49, P < 0.01). miR-206 can be used as an effective prognostic indicator in various cancers. Further investigations are warranted to validate the present results.
Keyphrases
- cell proliferation
- long non coding rna
- poor prognosis
- long noncoding rna
- lymph node
- systematic review
- end stage renal disease
- public health
- chronic kidney disease
- squamous cell carcinoma
- newly diagnosed
- early stage
- radiation therapy
- single cell
- drinking water
- clinical trial
- meta analyses
- big data
- deep learning
- single molecule
- rectal cancer
- childhood cancer
- high speed
- study protocol