Evidence map›Paper›PMID 34819069›Full record

ArticleBMC medical genomics2021

Are we there yet? A machine learning architecture to predict organotropic metastases.

Michael Skaro, Marcus Hill, Yi Zhou, Shannon Quinn, Melissa B Davis, Andrea Sboner, Mandi Murph, Jonathan Arnold

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 88% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Deciphering organotropism reveals therapeutic targets in metastasis.Frontiers in cell and developmental biology · 2025
    Review
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

8 authors at 2 institutions in 3 countries.

Michael Skaro *Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA. Michael.Skaro@uga.edu.ORCID 0000-0002-4960-875X
Marcus HillDepartment of Computer Science, University of Georgia, Athens, GA, 30602, USA.
Yi ZhouInstitute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA.
Shannon QuinnInstitute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA.
Melissa B DavisCaryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY, 10065, USA.
Andrea SbonerCaryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY, 10065, USA.
Mandi MurphDepartment of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, GA, 30602, USA.
Jonathan Arnold *Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA. Arnold@uga.edu.
University of Georgia · USNewYork–Presbyterian Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND &

aimsCancer metastasis into distant organs is an evolutionarily selective process. A better understanding of the driving forces endowing proliferative plasticity of tumor seeds in distant soils is required to develop and adapt better treatment systems for this lethal stage of the disease. To this end, we aimed to utilize transcript expression profiling features to predict the site-specific metastases of primary tumors and second, to identify the determinants of tissue specific progression.

methodsWe used statistical machine learning for transcript feature selection to optimize classification and built tree-based classifiers to predict tissue specific sites of metastatic progression.

resultsWe developed a novel machine learning architecture that analyzes 33 types of RNA transcriptome profiles from The Cancer Genome Atlas (TCGA) database. Our classifier identifies the tumor type, derives synthetic instances of primary tumors metastasizing to distant organs and classifies the site-specific metastases in 16 types of cancers metastasizing to 12 locations.

conclusionsWe have demonstrated that site specific metastatic progression is predictable using transcriptomic profiling data from primary tumors and that the overrepresented biological processes in tumors metastasizing to congruent distant loci are highly overlapping. These results indicate site-specific progression was organotropic and core features of biological signaling pathways are identifiable that may describe proliferative plasticity in distant soils.

Indexed as

Machine LearningNeoplasmsDatabases, FactualGene Expression ProfilingHumansTranscriptomeCancerMachine learningMetastatic organotropismTranscriptomic profiling

Identifiers

PMID34819069
PMCPMC8611885
OpenAlexW3217450298

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.