ReviewBiotech (Basel (Switzerland))2025
Innovative Approaches to EMT-Related Biomarker Identification in Breast Cancer: Multi-Omics and Machine Learning Methods.
Review in Biotech (Basel (Switzerland)), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Review
- The central role of EMT in tumor progression: mechanistic drivers, biomarker discovery, and therapeutic horizons.Frontiers in pharmacology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most prevalent cancer among women and is challenging to diagnose and treat due to its diverse subtypes and stages. Precision medicine aims to improve early detection, prognosis, and treatment planning by identifying new clinical biomarkers. The review emphasizes the importance of using cutting-edge technology and artificial intelligence (AI) to identify new biomarkers associated with epithelial-mesenchymal transition (EMT). During EMT, epithelial cells transform into a mesenchymal state, a process driven by genetic and epigenetic alterations that facilitate cancer progression. The review discusses how statistical analysis and machine learning methods applied to multi-omics data facilitate the discovery of novel EMT-related biomarkers, thereby advancing therapeutic strategies. This conclusion is supported by numerous clinical and preclinical studies on breast cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.