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

  • Samuel C. LeeEmail author
  • Alistair Quinn
  • Thin Nguyen
  • Svetha Venkatesh
  • Thomas P. Quinn
Original Article

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.

Keywords

Tanscriptomics Machine-learning Cancer MicroRNA 

Notes

Author contributions

SCL and TPQ reviewed the literature, designed the project, performed the analyses, and drafted the manuscript. AQ reviewed the literature and helped draft the manuscript. All authors edited and approved the final manuscript.

Funding

Not applicable.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interests

Supplementary material

11033_2019_5025_MOESM1_ESM.pdf (2 mb)
Electronic supplementary material 1 (PDF 2097 kb)

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin UniversityGeelongAustralia
  2. 2.Centre for Molecular and Medical Research, Deakin UniversityGeelongAustralia
  3. 3.Bioinformatics Core Research GroupDeakin UniversityGeelongAustralia

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