| Literature DB >> 33550881 |
Rongqiang Liu1,2, Weihao Kong3, Shiyang Zheng4, Chenyu Yu1, Yajie Yu1, Yuling Xu1, Linsen Ye5, Yi Shao1.
Abstract
The prognostic significance of miR-24 in tumors has not been determined. Therefore, we conducted a meta-analysis to systematically assess the correlation between miR-24 and its prognostic value in cancers PubMed, EMBASE, and Web of Science databases were used to search relevant articles (up to 1 October 2020). Studies that evaluated the prognostic value of miR-24 in tumors were included. The hazard ratio (HR) and odds ratio (OR) with 95% confidence intervals (CI) were used to evaluate survival outcomes and clinical characteristics. All data analyses were implemented using STATA 12.0 software. A total of 17 studies from 15 articles involving 1705 patients were collected for the meta-analysis. The pooled analysis revealed that elevated miR-24 expression was obviously associated with poor overall survival (OS) (HR = 1.66, 95% CI: 1.20-2.31). Furthermore, we also found that elevated miR-24 expression was positively correlated with tumor size (large or small) and tumor stage (III-IV vs I-II). Elevated miR-24 expression indicates poor prognosis and may be a promising prognostic marker in different cancers. Our findings needed to be verified through further investigations. [Figure: see text].Entities:
Keywords: cancer; meta-analysis; mir-24; prognosis
Year: 2021 PMID: 33550881 PMCID: PMC8291878 DOI: 10.1080/21655979.2021.1875662
Source DB: PubMed Journal: Bioengineered ISSN: 2165-5979 Impact factor: 3.269
Figure 1.Flow chart of the literature search
The basic information of included studies
| Study | Country | Tumor type | No. of patients | microRNA type | Detected sample | Detected method | Analysis type | Survival analysis | Source of HR | NOS score |
|---|---|---|---|---|---|---|---|---|---|---|
| Dong 2018 | China | GC | 247 | miR‑24 | Tissue specimens | RT-qPCR | Multivariate | OS | Reported | 7 |
| Gao 2015 | China | CRC | 175 | miR‑24-3p | Tissue specimens | RT-qPCR | Multivariate | OS | Reported | 7 |
| Organista-Nava 2015A | Mexico | ALL | 111 | miR-24 | Tissue specimens | RT-qPCR | Univaritae | OS | K-M curves | 7 |
| Organista-Nava 2015B | Mexico | AML | 36 | miR-24 | Tissue specimens | RT-qPCR | Univaritae | OS | K-M curves | 7 |
| Kerimis 2017 | Greece | CRC | 182 | miR‑24-3p | Tissue specimens | RT-qPCR | Multivariate | OS, DFS | Reported | 7 |
| Le 2012 | China | LC | 82 | miR‑24 | Serum | RT-qPCR | Multivariate | OS | Reported | 7 |
| Liu 2014 | China | HCC | 207 | miR‑24 | Tissue specimens | RT-qPCR | Univaritae | OS, RFS | Reported | 7 |
| Liu 2018 | China | Osteosarcoma | 84 | miR‑24 | Tissue specimens | RT-qPCR | Univaritae | OS | K-M curves | 7 |
| Meng 2014 | China | HCC | 72 | miR‑24-3p | Tissue specimens | RT-qPCR | Multivariate | OS, DFS | Reported | 7 |
| Su 2018 | China | NPC | 23 | miR‑24 | Tissue specimens | RT-qPCR | Univaritae | PFS | K-M curves | 6 |
| Wu 2017 | China | GC | 28 | miR‑24 | Tissue specimens | RT-qPCR | Univaritae | OS | K-M curves | 7 |
| Yin 2015 | China | AML | 84 | miR‑24 | Tissue specimens | RT-qPCR | Univaritae | OS, RFS | K-M curves | 7 |
| Zhao 2015A | China | LC | 53 | miR‑24 | Tissue specimens | RT-qPCR | Univaritae | RFS | K-M curves | 6 |
| Zhao 2015B | China | LC | 67 | miR‑24 | Serum | RT-qPCR | Univaritae | RFS | K-M curves | 6 |
| Zhao 2016 | China | TSCC | 84 | miR‑24 | Tissue specimens | RT-qPCR | Univaritae | OS | K-M curves | 6 |
| Zhao 2017 | China | TSCC | 90 | miR‑24 | Tissue specimens | RT-qPCR | Multivariate | OS | K-M curves | 7 |
| Zhou 2018 | China | LC | 50 | miR‑24 | Tissue specimens | RT-qPCR | Univaritae | OS | Reported | 7 |
Abbreviation: GC: gastric cancer; LC: lung cancer; CRC: colorectal cancer; ALL: acute lymphoblastic leukemia; AML: acute myeloid leukemia; HCC: hepatocellular carcinoma; NPC: nasopharyngeal carcinoma; TSCC: tongue squamous cell carcinoma.
Figure 2.Forest plot of the association between elevated miR-24 expression and OS
Subgroup analysis for OS
| Stratifed analysis | No. of studies | Pooled HR (95%CI) | Heterogeneity | |||
|---|---|---|---|---|---|---|
| I2 (%) | Model | |||||
| Cancer type | ||||||
| GC | 2 | 2.96 (0.65–13.49) | 0.161 | 88.5 | 0.003 | Random |
| CRC | 2 | 1.02 (0.19–5.56) | 0.981 | 82.5 | 0.017 | Fixed |
| leukemia | 3 | 1.40 (1.17–1.69) | 0 | 48.8 | 0.142 | Fixed |
| TSCC | 2 | 1.84 (0.77–4.38) | 0.167 | 72.6 | 0.056 | Random |
| HCC | 2 | 3.03 (2.14–4.30) | 0 | 45.1 | 0.177 | Fixed |
| LC | 2 | 2.28 (1.21–4.29) | 0.011 | 0 | 0.843 | Fixed |
| Osteosarcoma | 1 | 0.38 (0.20–0.70) | ||||
| Analysis type | ||||||
| Univariate | 8 | 1.62 (1.10–2.39) | 0.015 | 84.3 | 0 | Random |
| Multivariate | 6 | 1.79 (0.87–3.65) | 0.112 | 76.7 | 0.001 | Random |
| Race | ||||||
| Caucasian | 3 | 1.55 (1.25–1.92) | 0 | 16.3 | 0.303 | Fixed |
| Asian | 11 | 1.61 (1.04–2.49) | 0.034 | 84.3 | 0 | Random |
| Source of HR | ||||||
| Reported | 7 | 2.01 (1.07–3.78) | 0.03 | 79.9 | 0 | Random |
| SC | 7 | 1.43 (0.99–2.05) | 0.056 | 78.5 | 0 | Fixed |
| Sample size | ||||||
| ≥100 | 5 | 2.04 (0.98–4.22) | 0.055 | 88 | 0 | Random |
| <100 | 9 | 1.50 (1.03–2.19) | 0.034 | 73.7 | 0 | Fixed |
| detected sample | ||||||
| Tissue | 13 | 1.64 (1.17–2.31) | 0.004 | 82.3 | 0 | Random |
| Serum | 1 | 2.095 (0.741–5.923) | ||||
| miRNA type | ||||||
| miR-24 | 11 | 1.76 (1.23–2.50) | 0.002 | 81.6 | 0 | Random |
| miR-24-3p | 3 | 1.32 (0.43–4.05) | 0.629 | 81.8 | 0 | Random |
Figure 3.Forest plot of the association between elevated miR-24 expression and DFS/PFS/RFS
Relationship between elevated miR-24 expression and clinicopathological features
| Clinicopathologic features | No. of studies | Estimate OR (95%CI) | p-value | Heterogeneity | ||
|---|---|---|---|---|---|---|
| I2(%) | Model | |||||
| Gender (Male vs Female) | 7 | 1.10 (0.73–1.65) | 0.658 | 0 | 0.603 | Fixed |
| Age (Old vs Young) | 7 | 0.84 (0.59–1.22) | 0.366 | 0 | 0.902 | Fixed |
| Tumor diamter (Big vs Small) | 5 | 1.25 (1.04–2.15) | 0 | 0 | 0.216 | Fixed |
| Tumor stage ((III–IV vs I–II) | 4 | 2.41 (1.53–2.90) | 0.002 | 30.8 | 0.014 | Fixed |
| Lymph node status (Yes vs No) | 2 | 0.89 (0.42–1.66) | 0.595 | 0 | 0.765 | Fixed |
Figure 4.Sensitivity analysis for OS
Figure 5.Sensitivity analysis for DFS/PFS/RFS
Figure 6.Funnel plots for publication bias for OS
Figure 7.Funnel plots for publication bias for DFS/PFS/RFS. (a) Begg’s test to evaluate DFS/PFS/RFS data. (b) Trim and fill to evaluate DFS/PFS/RFS data