Evidence map›Paper›PMID 31410687›Full record

ArticleMolecular biology reports2019

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

Samuel C Lee, Alistair Quinn, Thin Nguyen, Svetha Venkatesh, Thomas P Quinn

Abstract read
PubMed Publisher
In one paragraph

Article in Molecular biology reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 43% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Computational and structural biotechnology journal · 2021
    Article
  3. Review
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Samuel C Lee *Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia. samleenz@me.com.ORCID http://orcid.org/0000-0002-6805-4051
Alistair QuinnCentre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia.
Thin NguyenCentre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia.
Svetha VenkateshCentre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia.
Thomas P Quinn *Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, Geelong, 3220, Australia.
Deakin University · AU

Funding

Australian Research Council LP140100240
6 · The paper itself

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.

Indexed as

Biomarkers, TumorDatabases, GeneticForecastingGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMicroRNAsNeoplasm MetastasisNeoplasmsOligonucleotide Array Sequence AnalysisRNA, MessengerBiomarkers, TumorMicroRNAsRNA, MessengerCancerMachine-learningMicroRNATanscriptomics

Identifiers

PMID31410687
OpenAlexW2967982419

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.