Evidence map›Paper›PMID 36601658›Full record

ReviewTechnology in cancer research & treatment

Molecular Classification, Treatment, and Genetic Biomarkers in Triple-Negative Breast Cancer: A Review.

Boya Lu, Elango Natarajan, Hanumantha Rao Balaji Raghavendran, Uma Devi Markandan

Open access · goldAbstract readReview
In one paragraph

Review in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 2 pooled it
14.7field-weighted citation impact, top 1% 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

32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.

  1. The impact of PET imaging on triple negative breast cancer: an updated evidence-based perspective.European journal of nuclear medicine and molecular imaging · 2024
    Pooled it
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  8. Survivin targeting triple-fusion vaccineMedical oncology (Northwood, London, England) · 2025
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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

4 authors at 3 institutions in 2 countries.

Boya LuDepartment of Mechanical Engineering, Faculty of Engineering, Technology and Built Environment, 125743UCSI University, Kuala Lumpur, Malaysia.ORCID 0000-0001-8870-2944
Elango NatarajanDepartment of Mechanical Engineering, Faculty of Engineering, Technology and Built Environment, 125743UCSI University, Kuala Lumpur, Malaysia.
Hanumantha Rao Balaji RaghavendranFaculty of Clinical Research, Central Research Facility, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India.
Uma Devi MarkandanFaculty of Medicine, 37447University of Malaya, Kuala Lumpur, Malaysia.
UCSI University · MYSri Ramachandra Institute of Higher Education and Research · INUniversity of Malaya · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is the most common malignancy and the second most common cause of cancer-related mortality in women. Triple-negative breast cancers do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptor 2 and have a higher recurrence rate, greater metastatic potential, and lower overall survival rate than those of other breast cancers. Treatment of triple-negative breast cancer is challenging; molecular-targeted therapies are largely ineffective and there is no standard treatment. In this review, we evaluate current attempts to classify triple-negative breast cancers based on their molecular features. We also describe promising treatment methods with different advantages and discuss genetic biomarkers and other prediction tools. Accurate molecular classification of triple-negative breast cancers is critical for patient risk categorization, treatment decisions, and surveillance. This review offers new ideas for more effective treatment of triple-negative breast cancer and identifies novel targets for drug development.

Indexed as

Breast NeoplasmsTriple Negative Breast NeoplasmsBiomarkers, TumorErb-b2 Receptor Tyrosine KinasesFemaleHumansTreatment OutcomeBiomarkers, TumorErb-b2 Receptor Tyrosine Kinasesbiomarkersmolecular classificationpredictiontreatmenttriple-negative breast cancer

Identifiers

PMID36601658
PMCPMC9829998
OpenAlexW4313545294

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.