Evidence map›Paper›PMID 38439848›Full record

ArticleHeliyon2024

Machine learning determines stemness associated with simple and basal-like canine mammary carcinomas.

Pedro L P Xavier, Maycon Marção, Renan L S Simões, Maria Eduarda G Job, Ricardo de Francisco Strefezzi, Heidge Fukumasu, Tathiane M Malta

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 31% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 2 institutions in 1 country.

Pedro L P XavierLaboratory of Comparative and Translational Oncology (LOCT), Department of Veterinary Medicine, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo, Pirassununga, São Paulo, Brazil.
Maycon MarçãoCancer Epigenomics Laboratory, Department of Clinical Analysis, Toxicology and Food Sciences, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil.
Renan L S SimõesCancer Epigenomics Laboratory, Department of Clinical Analysis, Toxicology and Food Sciences, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil.
Maria Eduarda G JobLaboratory of Comparative and Translational Oncology (LOCT), Department of Veterinary Medicine, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo, Pirassununga, São Paulo, Brazil.
Ricardo de Francisco StrefezziLaboratory of Comparative and Translational Oncology (LOCT), Department of Veterinary Medicine, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo, Pirassununga, São Paulo, Brazil.
Heidge FukumasuLaboratory of Comparative and Translational Oncology (LOCT), Department of Veterinary Medicine, Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo, Pirassununga, São Paulo, Brazil.
Tathiane M MaltaCancer Epigenomics Laboratory, Department of Clinical Analysis, Toxicology and Food Sciences, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, Brazil.
Universidade de São Paulo · BRUniversidade de Ribeirão Preto · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Simple and complex carcinomas are the most common type of malignant Canine Mammary Tumors (CMTs), with simple carcinomas exhibiting aggressive behavior and poorer prognostic. Stemness is an ability associated with cancer initiation, malignancy, and therapeutic resistance, but is still few elucidated in canine mammary tumor subtypes. Here, we first validated, using CMT samples, a previously published canine one-class logistic regression machine learning algorithm (OCLR) to predict stemness (mRNAsi) in canine cancer cells. Then, using the canine mRNAsi, we observed that simple carcinomas exhibit higher stemness than complex carcinomas and other histological subtypes. Also, we confirmed that stemness is higher and associated with basal-like CMTs and with NMF2 metagene signature, a tumor-specific DNA-repair metagene signature. Using correlation analysis, we selected the top 50 genes correlated with higher stemness, and the top 50 genes correlated with lower stemness and further performed a gene set enrichment analysis to observe the biological processes enriched for these genes. Finally, we suggested two promise stemness-associated targets in CMTs,

Indexed as

Basal-like tumorsCanine mammary tumorsMachine learningSimple carcinomasStemness

Identifiers

PMID38439848
PMCPMC10909659
OpenAlexW4392133211

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.