4.6 Article

Prognostic and clinicopathological significance of SNHG6 in human cancers: a meta-analysis

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BMC CANCER
卷 20, 期 1, 页码 -

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BMC
DOI: 10.1186/s12885-020-6530-3

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Long non-coding RNA; SNHG6; Cancer; Prognosis; Clinical parameters

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资金

  1. Project of new clinical diagnosis and treatment technology of Jiangsu Province [BE2016799]

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BackgroundRecently, accumulating evidence has suggested that the aberrant expression of SNHG6 exists in a variety of tumors and has a correlation with poor clinical outcomes across cancer patients. Considering the inconsistent data among published studies, we aim to assess the prognostic effect of SNHG6 on malignancies.MethodsWe retrieved relevant publications in Web of Science, Embase, MEDLINE, PubMed and Cochrane Library based on predefined selection criteria, up to October 1, 2019. Pooled hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were utilized to evaluate the correlation between SNHG6 and overall survival (OS), recurrence-free survival (RFS) and progression-free survival (PFS) as well as clinicopathology.ResultsIn total, 999 patients from 14 articles were enrolled in our meta-analysis. The results revealed that augmented SNHG6 expression was significantly correlated with poor OS (HR=2.20, 95% CI=1.76-2.75, P<0.001) and RFS (HR=3.10, 95% CI=1.90-5.07, P<0.001), but not with PFS (HR=2.11, 95% CI=0.82-5.39, P=0.120). In addition to lung cancer and ovarian cancer, subgroup analysis showed that the prognostic value of SNHG6 across multiple tumors was constant as the tumor type, sample size, and methods of data extraction changed. Moreover, cancer patients with enhanced SNHG6 expression were prone to advanced TNM stage (OR=3.31, 95% CI=2.46-4.45, P<0.001), distant metastasis (OR=4.67, 95% CI=2.98-7.31, P<0.001), lymph node metastasis (OR=2.59, 95% CI=1.41-4.77, P=0.002) and deep tumor invasion (OR=3.75, 95% CI=2.10-6.69, P<0.001), but not associated with gender, histological grade and tumor size.ConclusionsSNHG6 may serve as a promising indicator in the prediction of prognosis and clinicopathological features in patients with different kinds of tumors.

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