Evidence map›Paper›PMID 38746025›Full record

ReviewMedicine and pharmacy reports2024

Exploring the contrasts: in-depth analysis of human and canine mammary tumors - discoveries at the frontier.

Luciana Madalina Gherman, Diana Tomuleasa, Andrei Cismaru, Andreea Nutu, Ioana Berindan-Neagoe

Abstract readReview
In one paragraph

Review in Medicine and pharmacy reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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.

Luciana Madalina GhermanExperimental Centre of Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.
Diana TomuleasaMEDFUTURE - The Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.
Andrei CismaruResearch Center for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.
Andreea NutuResearch Center for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.
Ioana Berindan-NeagoeResearch Center for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We have examined genomic and transcriptomic abnormalities in human and canine samples to evaluate the canine model's validity for breast cancer research, emphasizing similarities and differences. Both species commonly utilize serum tumor markers and noncoding microRNAs. Immunohistochemistry and immunocytochemistry were employed to illustrate and compare results based on histological diagnoses. In addition to these factors, similarities exist in spontaneous tumor occurrence, age of onset, hormonal influences, and disease progression, including tumor size, clinical stage, and lymph node involvement. Molecular traits such as hormone receptor status, Epidermal Growth Factor Receptor (EGFR), and proliferation markers (Ki67) further endorse the canine model's utility in breast cancer studies. The advancement of technologies facilitates the identification of new cancer-associated molecules, both coding and non-coding genes, underscoring their potential as prognostic/diagnostic biomarkers and therapeutic targets.

Indexed as

canine breast cancer (CBC)comparative oncologyhuman breast cancer (HBC)immune systemimmunotherapytranslational research

Identifiers

PMID38746025
PMCPMC11090284

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.