Evidence map›Paper›PMID 42467335›Full record

ReviewMolecular biology reports2026

Metabolic signatures of triple-negative breast cancer.

Gandhar Pusalkar, Rakshitha Madamakki, Parna Chakraborty, Alessandro Parodi, Manu Lopus

Abstract readReview
PubMed Publisher
In one paragraph

Review 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

5 authors.

Gandhar PusalkarCellular Nanomedicine and Chemical Biology, School of Biological Sciences, UM-DAE Centre for Excellence in Basic Sciences, University of Mumbai, Vidyanagari, 400098, Mumbai, India.
Rakshitha MadamakkiCellular Nanomedicine and Chemical Biology, School of Biological Sciences, UM-DAE Centre for Excellence in Basic Sciences, University of Mumbai, Vidyanagari, 400098, Mumbai, India.
Parna ChakrabortyCellular Nanomedicine and Chemical Biology, School of Biological Sciences, UM-DAE Centre for Excellence in Basic Sciences, University of Mumbai, Vidyanagari, 400098, Mumbai, India.
Alessandro ParodiFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119234, Russia.
Manu LopusCellular Nanomedicine and Chemical Biology, School of Biological Sciences, UM-DAE Centre for Excellence in Basic Sciences, University of Mumbai, Vidyanagari, 400098, Mumbai, India. manu.lopus@cbs.ac.in.

Funding

Scientific and Technological Development of the "Sirius" Federal Territory No. 22-03 dated 27 September 2024
6 · The paper itself

Abstract

Triple-negative breast cancer (TNBC), being one of the most aggressive subtypes of breast malignancies, is characterized by poor prognosis and limited treatment options. As a leading cause of cancer-related mortality among women, TNBC poses unique clinical challenges due to the absence of effective targeted therapies. Conventional treatment strategies, including chemotherapy, often suffer from significant drawbacks such as drug resistance and intolerable side effects, underscoring an urgent need for innovative approaches to improve therapeutic outcomes. A defining hallmark of TNBC is its markedly altered cellular metabolism, which not only supports tumor growth and survival but also contributes to therapy resistance. Elucidating these metabolic alterations could provide critical insights into potential vulnerabilities that may be exploited for therapeutic intervention. This review provides a comprehensive overview of the major metabolic pathways that are dysregulated in TNBC and their relevance to disease progression and therapeutic intervention. Finally, we explore recent advances in metabolomics-driven precision medicine, highlighting the integration of artificial intelligence and machine learning approaches, covering aspects such as data processing, feature selection, and model construction that can accelerate the advancement of personalized treatment strategies for TNBC.

Indexed as

Triple Negative Breast NeoplasmsArtificial IntelligenceFemaleHumansMachine LearningMetabolic Networks and PathwaysMetabolic ReprogrammingMetabolomeMetabolomicsPrecision MedicineArtificial intelligenceMetabolismMetabolomicsTriple-negative breast cancer

Identifiers

What OpenQuestion holds

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Registered trials

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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.