Evidence map›Paper›PMID 42446626›Full record

ArticleMolecular biology reports2026

Serum TP53 autoantibodies as a non-invasive diagnostic biomarker for canine mammary tumours: comparative insights from human and experimental rat model.

Pradeep Kumar, Sonal Saxena, Sameer Shrivastava, Owais Khan, Rajeshwar Khandare, Partha Pratim Borah, Sabapathi Nagappan, R V Purushotham, M Saminathan, Abhinav Kumar

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Molecular biology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Pradeep KumarVeterinary Immunology Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, Uttar Pradesh, India.
Sonal SaxenaVeterinary Immunology Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, Uttar Pradesh, India. sonalvet@gmail.com.
Sameer ShrivastavaVeterinary Immunology Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, Uttar Pradesh, India. sameer_vet@rediffmail.com.
Owais KhanVeterinary Immunology Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, Uttar Pradesh, India.
Rajeshwar KhandareDivision of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, Uttar Pradesh, India.
Partha Pratim BorahDivision of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, Uttar Pradesh, India.
Sabapathi NagappanVeterinary Immunology Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, Uttar Pradesh, India.
R V PurushothamVeterinary Immunology Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, Uttar Pradesh, India.
M SaminathanDivision of Virology, ICAR-Indian Veterinary Research Institute, Mukteswar campus, Nainital- 263138, Mukteswar, Uttarakhand, India.
Abhinav KumarSchool of Artificial Intelligence and Data Science, Indian Institute of Technology, Rupnagar-140001, Ropar, Punjab, India.

Funding

Indian Council of Agricultural Research ICAR LAL Bahadur Shastry Outstanding Young Scientist Award ProjectF.no 5-4/2022-A.C(e.21930) and Advanced Research Project on Canines.
6 · The paper itself

Abstract

backgroundCanine mammary tumours (CMTs) represent a valuable spontaneous model for human breast cancer, as both share comparable pathological features and frequent alterations in the TP53 pathway. Although TP53 involvement in tumour development is well established, the diagnostic relevance of circulating TP53 autoantibodies in dogs remains insufficiently characterized. This study evaluated the diagnostic utility of TP53 autoantibodies in CMTs using a recombinant TP53-based immunoassay, supported by comparative analyses in human and experimental animal models. METHODS AND

resultsRecombinant canine TP53 protein was expressed and employed in indirect ELISA and dot blot assays to detect serum TP53 autoantibodies in 141 canine serum samples, including sera from dogs with mammary tumours and healthy controls. Comparative evaluation included serum samples from 100 human subjects (50 breast cancer patients and 50 healthy individuals) as well as a longitudinal N-nitroso-N-methylurea (NMU)-induced rat mammary tumour model to examine the temporal appearance of autoantibodies before and during tumour development.TP53 autoantibodies were detected in 60.0% of dogs with mammary tumours and were absent in healthy controls (p < 0.0001), demonstrating high diagnostic accuracy (AUC 0.9587; sensitivity 60%; specificity 100%). Comparable trends were observed in human breast cancer sera (AUC 0.8956; sensitivity 52%; specificity 98%). Notably, longitudinal analysis of the NMU-induced rat model revealed the presence of TP53 autoantibodies before overt tumour formation, highlighting their potential role in early cancer detection.

conclusionsThese findings support recombinant TP53-based ELISA as a sensitive, specific, & non-invasive diagnostic approach and identify circulating TP53 autoantibodies as promising early biomarkers in comparative and translational oncology.

Indexed as

AutoantibodiesBiomarkers, TumorMammary Neoplasms, AnimalTumor Suppressor Protein p53AnimalsBreast NeoplasmsDisease Models, AnimalDogsEnzyme-Linked Immunosorbent AssayFemaleHumansRatsAutoantibodiesBiomarkers, TumorTumor Suppressor Protein p53AutoantibodyBiomarkerBreast cancerCanine mammary tumourComparative oncologyELISATP53

Identifiers

PMID42446626

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.