Prognostic value of a newly identified MALAT1 alternatively spliced transcript in breast cancer

新发现的 MALAT1 剪接转录本在乳腺癌中的预后价值

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作者:Didier Meseure, Sophie Vacher, François Lallemand, Kinan Drak Alsibai, Rana Hatem, Walid Chemlali, Andre Nicolas, Leanne De Koning, Eric Pasmant, Celine Callens, Rosette Lidereau, Antonin Morillon, Ivan Bieche

Background

Epigenetic deregulation is considered as a new hallmark of cancer. The long non-coding RNA MALAT1 has been implicated in several cancers; however, its role in breast cancer is still little known.

Conclusions

Our results reveal a complex expression pattern of various MALAT1 transcript variants in breast tumours, and suggest that this pattern of expressions should be taken into account to evaluate MALAT1 as predictive biomarker and therapeutic target.

Methods

We used RT-PCR, in situ hybridisation, and RPPA methods to quantify (i) the full-length (FL) and an alternatively spliced variant (Δsv) of MALAT1, and (ii) a panel of transcripts and proteins involved in MALAT1 pathways, in a large series of breast tumours from patients with known clinical/pathological status and long-term outcome.

Results

MALAT1 was overexpressed in 14% (63/446) of the breast tumours. MALAT1-overexpressed tumour epithelial cells showed marked diffuse nuclear signals and numerous huge nuclear speckles. Screening of the dbEST database led to the identification of Δsv-MALAT1, a major alternatively spliced MALAT1 transcript, with a very different expression pattern compared with FL-MALAT1. This alternative Δsv-MALAT1 transcript was mainly underexpressed (18.8%) in our breast tumour series. Multivariate analysis showed that alternative Δsv-MALAT1 transcript is an independent prognostic factor. Δsv-MALAT1 expression was associated with alterations of the pre-mRNAs alternative splicing machinery, and of the Drosha-DGCR8 complex required for non-coding RNA biogenesis. Alternative Δsv-MALAT1 transcript expression was associated to YAP protein status and with an activation of the PI3K-AKT pathway. Conclusions: Our results reveal a complex expression pattern of various MALAT1 transcript variants in breast tumours, and suggest that this pattern of expressions should be taken into account to evaluate MALAT1 as predictive biomarker and therapeutic target.

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