Literature DB >> 31410687

A cross-cancer metastasis signature in the microRNA-mRNA axis of paired tissue samples.

Samuel C Lee1, Thomas P Quinn2,3,4, Alistair Quinn2, Thin Nguyen2, Svetha Venkatesh2.   

Abstract

In the progression of cancer, cells acquire genetic mutations that cause uncontrolled growth. Over time, the primary tumour may undergo additional mutations that allow for the cancerous cells to spread throughout the body as metastases. Since metastatic development typically results in markedly worse patient outcomes, research into the identity and function of metastasis-associated biomarkers could eventually translate into clinical diagnostics or novel therapeutics. Although the general processes underpinning metastatic progression are understood, no clear cross-cancer biomarker profile has emerged. However, the literature suggests that some microRNAs (miRNAs) may play an important role in the metastatic progression of several cancer types. Using a subset of The Cancer Genome Atlas (TCGA) data, we performed an integrated analysis of mRNA and miRNA expression with paired metastatic and primary tumour samples to interrogate how the miRNA-mRNA regulatory axis influences metastatic progression. From this, we successfully built mRNA- and miRNA-specific classifiers that can discriminate pairs of metastatic and primary samples across 11 cancer types. In addition, we identified a number of miRNAs whose metastasis-associated dysregulation could predict mRNA metastasis-associated dysregulation. Among the most predictive miRNAs, we found several previously implicated in cancer progression, including miR-301b, miR-1296, and miR-423. Taken together, our results suggest that metastatic samples have a common cross-cancer signature when compared with their primary tumour pair, and that these miRNA biomarkers can be used to predict metastatic status as well as mRNA expression.

Entities:  

Keywords:  Cancer; Machine-learning; MicroRNA; Tanscriptomics

Mesh:

Substances:

Year:  2019        PMID: 31410687     DOI: 10.1007/s11033-019-05025-w

Source DB:  PubMed          Journal:  Mol Biol Rep        ISSN: 0301-4851            Impact factor:   2.316


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10.  Cancer as a Tissue Anomaly: Classifying Tumor Transcriptomes Based Only on Healthy Data.

Authors:  Thomas P Quinn; Thin Nguyen; Samuel C Lee; Svetha Venkatesh
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Review 2.  Functional mechanism and clinical implications of MicroRNA-423 in human cancers.

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