| Literature DB >> 30083911 |
Congmin Liu1, Jing Jin1, Di Liang1, Zhaoyu Gao1, Yachen Zhang1, Tiantian Guo1, Yutong He2.
Abstract
The present meta-analysis aimed to systematically evaluates the metastasis, clinical stage, and prognostic value regarding the expression levels of PVT1 in various cancers. Relevant literatures were searched in PubMed、Cochrane Library、Wed of science、Embase databases、Chinese National Knowledge Infrastructure and Wanfang from inception up to 22 August 2017. After data were extracted, a meta-analysis was performed using Review Manager 5.3 and Stata 12.0 software. The meta-analysis showed that high expression of PVT1 could predict more lymph node metastasis (LNM) (Odds ratio, OR = 2.83, 95% confidence interval, CI: 1.76-4.54, P < 0.0001), distant metastasis (DM) (OR = 3.60, 95% CI: 1.08-12.03, P = 0.04), advanced clinical stage (OR = 4.37, 95% CI: 3.45-5.54, P < 0.00001) and poor overall survival (Hazard ratio, HR = 2.08, 95% CI: 1.82-2.37, P < 0.00001)in cancer. Subgroup analysis in different systems also showed the same results, including respiratory system、digestive system、urinary system and other systems, especially in respiratory system (LNM, OR = 4.57, 95% CI: 2.41-8.68, P < 0.00001; clinical stage, OR = 5.59, 95% CI: 3.59-8.71, P < 0.00001; OS, HR = 2.43, 95% CI: 1.98-2.99, P < 0.00001). These results suggest that PVT1 could serve as a novel biomarker for metastasis, clinical stage and poor prognosis in various tumors.Entities:
Keywords: Clinical stage; Long non-coding RNA; Meta-analysis; Metastasis; Plasmacytoma variant translocation1; Prognosis
Mesh:
Substances:
Year: 2018 PMID: 30083911 PMCID: PMC6614374 DOI: 10.1007/s12253-018-0451-3
Source DB: PubMed Journal: Pathol Oncol Res ISSN: 1219-4956 Impact factor: 3.201
Fig 1Flowchart of selecting studies for inclusion in this meta-analysis
Metastasis and clinical stage of the eligible studies in this meta-analysis
| Author | Year | Tumor type | Country | Sample size | Detection method | cut-off value | PVT1 expression | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| High expression | High with LNM | High with DM | High with advanced clinical stage | Low expression | Low with LNM | Low with DM | Low with early clinical stage | |||||||
| Cui | 2015 | NSCLC | China | 108 | qRT-PCR | median value | 53 | 29 | 2 | 28 | 55 | 19 | 1 | 13 |
| HuangCS | 2016 | SCLC | China | 120 | qRT-PCR | median value | 60 | 45 | 27 | 46 | 60 | 15 | 4 | 17 |
| Wan | 2016 | NSCLC | China | 105 | qRT-PCR | median value | 56 | 36 | NA | 48 | 49 | 19 | NA | 27 |
| Wu | 2017 | NSCLC | China | 31 | qRT-PCR | median expression | 15 | 9 | NA | 12 | 16 | 3 | NA | 6 |
| Yang | 2014 | NSCLC | China | 82 | qRT-PCR | median value | 65 | 37 | NA | 39 | 17 | 2 | NA | 3 |
| DingJ | 2014 | GC | China | 31 | qRT-PCR | cancer/noncancerous tissue > 1.0 | 19 | 15 | 3 | 14 | 12 | 4 | 0 | 4 |
| Gou | 2017 | HCC | China | 92 | qRT-PCR | NA | 48 | NA | NA | 23 | 44 | NA | NA | 10 |
| HuangC | 2015 | PDAC | China | 85 | qRT-PCR | mean value | 67 | 28 | 11 | 46 | 18 | 13 | 4 | 7 |
| Huang T | 2017 | GC | China | 68 | qRT-PCR | fold-change ≥/≤ mean ratio | 30 | NA | NA | 21 | 38 | NA | NA | 16 |
| Kong | 2015 | GC | China | 80 | qRT-PCR | median value | 40 | 24 | NA | 26 | 40 | 18 | NA | 13 |
| Lan | 2017 | HCC | China | 48 | qRT-PCR | median value | 24 | NA | NA | 19 | 24 | NA | NA | 11 |
| Ren | 2016 | GC | China | 28 | qRT-PCR | NA | 13 | 11 | 1 | 10 | 15 | 6 | 0 | 9 |
| Takahashi | 2014 | CRC | Japan | 164 | qRT-PCR | expression higher or lower than the 20 percentile value | 131 | 69 | 3 | 74 | 33 | 9 | 1 | 9 |
| Wang | 2014 | HCC | China | 89 | qRT-PCR | median value | 45 | 18 | NA | 19 | 44 | 15 | NA | 9 |
| Yuan | 2016 | GC | China | 111 | qRT-PCR | median value | 55 | 30 | 2 | 29 | 56 | 19 | 1 | 14 |
| Zhao | 2017 | PC | China | 34 | qRT-PCR | NA | 18 | NA | 17 | 16 | 16 | NA | 3 | 5 |
| Zheng | 2016 | EC | China | 77 | qRT-PCR | median value | 39 | 23 | NA | 27 | 38 | 7 | NA | 15 |
| HuangY | 2015 | RC | China | 54 | qRT-PCR | median value | 39 | 16 | NA | 24 | 15 | 3 | NA | 4 |
| Zhuang | 2015 | BC | China | 32 | qRT-PCR | NA | 20 | 1 | NA | 19 | 12 | 2 | NA | 5 |
| Song | 2017 | OC | China | 46 | qRT-PCR | mean value | 24 | NA | NA | 16 | 22 | NA | NA | 3 |
| Total | 1485 | 861 | 391 | 66 | 556 | 624 | 154 | 14 | 200 | |||||
NSCLC: non-small cell lung cancer, SCLC: small cell lung cancer, GC: gastric cancer, PDAC: pancreatic ductal adenocarcinoma, CRC: colorectal cancer, HCC: hepatocellular carcinoma, EC: esophageal cancer, RC: renal carcinoma, BC: bladder cancer, PC: pancreatic carcinoma, OC: osteosarcoma, qRT-PCR: quantitative real-time PCR, NA: not available
Overall survival of the eligible studies in this meta-analysis
| Study | Year | Disease | Country | Number | Detection method | Survival analysis | Multivariate analysis | HR statistic | Hazard ratios (95% CI) | Follow up (months) |
|---|---|---|---|---|---|---|---|---|---|---|
| Cui | 2015 | NSCLC | China | 108 | qRT-PCR | OS DFS | Yes | Data in paper | 1.72 (1.14–3.25) | 40 |
| HuangCS | 2016 | SCLC | China | 120 | qRT-PCR | OS | Yes | Data in paper | 1.782 (1.078–2.945) | 96 |
| Wan | 2016 | NSCLC | China | 105 | qRT-PCR | OS PFS | Yes | Data in paper | 2.464 (1.214–4.999) | 40 |
| Wu | 2017 | NSCLC | China | 31 | qRT-PCR | OS | NO | Survival curve | 3.19 (1.16–8.77) | 80 |
| Yang | 2014 | NSCLC | China | 82 | qRT-PCR | OS | Yes | Data in paper | 3.273 (2.184–6.937) | 60 |
| DingC | 2015 | HCC | China | 214 | qRT-PCR | OS RFS | Yes | Data in paper | 0.91 (0.59–1.41) | 120 |
| HuangC | 2015 | PDAC | China | 85 | qRT-PCR | OS | Yes | Data in paper | 3.3013 (1.574–6.673) | 60 |
| Kong | 2015 | GC | China | 80 | qRT-PCR | OS DFS | Yes | Data in paper | 2.092 (1.068–4.096) | 40 |
| Lan | 2017 | HCC | China | 48 | qRT-PCR | OS | NO | Survival curve | 2.65 (0.89–7.92) | 48 |
| Takahashi | 2014 | CRC | Japan | 164 | qRT-PCR | OS | Yes | Data in paper | 2.532 (1.152–10.747) | >120 |
| Wang | 2014 | HCC | China | 89 | qRT-PCR | OS RFS | NO | Survival curve | 1.77(0.69–4.50) | 50 |
| Yuan | 2016 | GC | China | 111 | qRT-PCR | OS PFS | Yes | Data in paper | 2.280 (1.054–4.930) | 40 |
| Zhao | 2017 | PC | China | 34 | qRT-PCR | OS | NO | Survival curve | 1.66 (0.57–4.84) | >14 |
| Marissa | 2015 | Cervical | USA | 121 | qRT-PCR | OS | NO | Survival curve | 1.84(0.88–3.84) | 60 |
| Paolo | 2017 | EOC | Italy | 202 | qRT-PCR | OS PFS | Yes | Data in paper | 2.1 (1.4–3.3) | 200 |
| Song | 2017 | OC | China | 46 | qRT-PCR | OS | NO | Survival curve | 1.79 (0.51–6.32) | 72 |
| Total | 1640 |
NSCLC: non-small cell lung cancer, SCLC: small cell lung cancer, GC: gastric cancer, PDAC: pancreatic ductal adenocarcinoma, CRC: colorectal cancer, HCC: hepatocellular carcinoma, PC: pancreatic carcinoma, OC: osteosarcoma, EOC: epithelial ovarian cancer, OS: overall survival, DFS: disease-free survival, PFS: progression free survival, RFS: recurrence free survival
Fig. 2Forest plot for the association between PVT1 expression levels with LNM
Fig. 3Forest plot of the correlation between PVT1 expression levels and DM in different cancer patients
Fig. 4Forest plot for the association between PVT1 expression levels and clinical stage in different cancer patients
Fig. 5Forest plot for the association between PVT1 expression levels and OS for the included studies
Results of this meta-analysis about the association between PVT1 expression and DFS, PFS or RFS
| Survival period | Eligible studies | Sample size | Heterogeneity (fixed) | Pool HR (95% CI) | Meta regression ( | |
|---|---|---|---|---|---|---|
| I2 (%) | p value | |||||
| Disease-free survival (DFS) | 3 | 378 | 0 | 0.82 | 1.87 (1.40–2.49) | P < 0.0001 |
| Progression free survival (PFS) | 3 | 418 | 0 | 0.45 | 1.85 (1.47–2.32) | P < 0.00001 |
| Recurrence free survival (RFS) | 2 | 303 | 0 | 0.61 | 1.76 (1.19–2.61) | |
Fig. 6Funnel plot analysis of potential publication bias in LNM group
Fig. 7Sensitivity analyses of studies concerning PVT1 and overall survival