Downregulated miR-646 in clear cell renal carcinoma correlated with tumour metastasis by targeting the nin one binding protein (NOB1)
Background: Nin one binding protein (NOB1) was identified as a potential oncogene in human glioma and miR-646 plays an important role in human growth and development. However, the underlying molecular mechanisms of NOB1 in tumorigenicity and its correlation with miR-646 in renal cell carcinoma (RCC)...
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Veröffentlicht in: | British journal of cancer 2014-09, Vol.111 (6), p.1188-1200 |
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Sprache: | eng |
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Zusammenfassung: | Background:
Nin one binding protein (NOB1) was identified as a potential oncogene in human glioma and miR-646 plays an important role in human growth and development. However, the underlying molecular mechanisms of NOB1 in tumorigenicity and its correlation with miR-646 in renal cell carcinoma (RCC) have not been investigated.
Methods:
We performed bioinformatic analysis to explore miRNA targeting NOB1. The expression of NOB1 and miR-646 from 100 cases of clear cell RCC (ccRCC) and 30 cases of adjacent non-tumour tissues were detected by quantitative real-time PCR. The expression of miR-646 was correlated with NOB1 expression, tumour features and patient metastasis-free survival. The effect of overexpression of mir-646 on renal cancer cell proliferation was detected by colony formation in soft agar. Using a xenograft tumour model, we observed the
in vivo
tumorigenesis effect of miR-646 and NOB1.
Results:
miR-646 negatively regulated NOB1 and inhibited the proliferation and migration of renal cancer cells. There was a significant upregulation of NOB1 in ccRCC and it was further increased in metastatic cases, while miR-646 was downregulated in tumour tissues and further decreased in metastatic ccRCC. Additionally, expression of miR-646 was inversely correlated with the expression of NOB1. The downregulation of miR-646 also indicated a higher probability of developing metastasis. Most importantly, miR-646 expression was an independent predictor of ccRCC metastasis by the univariate analysis and binary logistic regression model (both
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ISSN: | 0007-0920 1532-1827 |
DOI: | 10.1038/bjc.2014.382 |