Clinical Significance of UCA1 to Predict Metastasis and Poor Prognosis of Digestive System Malignancies: A Meta-Analysis.
Xiao-Dong SunChen HuanWei QiuDa-Wei SunXiao-Ju ShiChuan-Lei WangChao JiangGuang-Yi WangGuo-Yue LvPublished in: Gastroenterology research and practice (2016)
Purpose. Urothelial carcinoma-associated 1 (UCA1) has been reported to be overexpressed and correlated with progression in various cancers. However, the association between UCA1 expression and some clinicopathological features of digestive system malignancies, such as metastasis and survival, remains inconclusive. Therefore, a meta-analysis was performed to investigate the clinical significance of UCA1 in digestive system malignancies. Methods. Relevant literatures were searched in PubMed, Web of Science, Cochrane Library, and Embase databases updated to May 2016. Results. A total of 1089 patients from 10 studies were included in this meta-analysis. Meta-analysis results showed that digestive system malignancy patients with UCA1 overexpression were significantly more susceptible to developing lymph node metastasis (LNM) (OR = 1.85, 95% CI: 1.28-2.67) and distant metastasis (DM) (OR = 3.14, 95% CI: 1.77-5.58) and suffer from poor overall survival (OS) (HR = 2.31, 95% CI: 1.89-2.82, univariate analysis; HR = 2.24, 95% CI: 1.69-2.98, multivariate analysis) and poor disease-free survival (DFS) (HR = 2.65, 95% CI: 1.59-4.43, univariate analysis; HR = 2.50, 95% CI: 1.62-3.86, multivariate analysis). Conclusion. UCA1 overexpression was correlated with LNM, DM, poor OS, and poor DFS. UCA1 may serve as an indicator for metastasis and poor prognosis in digestive system malignancies.
Keyphrases
- poor prognosis
- systematic review
- long non coding rna
- free survival
- lymph node metastasis
- squamous cell carcinoma
- cell proliferation
- public health
- type diabetes
- end stage renal disease
- randomized controlled trial
- prognostic factors
- newly diagnosed
- peritoneal dialysis
- transcription factor
- lymph node
- ejection fraction
- metabolic syndrome
- artificial intelligence
- meta analyses
- young adults
- deep learning