Your browser doesn't support javascript.
loading
A cross-cancer metastasis signature in the microRNA-mRNA axis of paired tissue samples.
Lee, Samuel C; Quinn, Alistair; Nguyen, Thin; Venkatesh, Svetha; Quinn, Thomas P.
Affiliation
  • Lee SC; Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia. samleenz@me.com.
  • Quinn A; Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia.
  • Nguyen T; Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia.
  • Venkatesh S; Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia.
  • Quinn TP; Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia.
Mol Biol Rep ; 46(6): 5919-5930, 2019 Dec.
Article in En | MEDLINE | ID: mdl-31410687
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.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Gene Expression Regulation, Neoplastic / Neoplasm Metastasis / Neoplasms Type of study: Prognostic_studies Limits: Humans Language: En Journal: Mol Biol Rep Year: 2019 Document type: Article Affiliation country: Australia

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Gene Expression Regulation, Neoplastic / Neoplasm Metastasis / Neoplasms Type of study: Prognostic_studies Limits: Humans Language: En Journal: Mol Biol Rep Year: 2019 Document type: Article Affiliation country: Australia
...