Evidence map›Paper›PMID 40762889›Full record

ArticleDiscover oncology2025

Exploring the interplay between LDHA and ABCC1 in breast cancer using computational approach.

Bader Alshehri

Abstract read
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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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

2 citing papers in PubMed.

  1. Article
  2. Journal of translational medicine · 2026
    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

1 author.

Bader AlshehriDepartment of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, 11952, Almajmaah, Kingdom of Saudi Arabia. b.alshehri@mu.edu.sa.ORCID http://orcid.org/0000-0002-2847-0106

Funding

Deanship of Scientific Research Majmaah University R-2024-IF-145.
6 · The paper itself

Abstract

backgroundDrug resistance is the major cause of the high death rates in Breast cancer (BC), which continues to be the most frequent disease among women. Chemoresistance is significantly mediated by drug efflux transporters, including ATP-binding cassette transporter ABCC1, and glycolytic enzymes, especially lactate dehydrogenase A (LDHA). Improving treatment results requires an understanding of the expression patterns, genetic changes, and prognostic importance of ABCC1 and LDHA.

objectiveThis study aims to elucidate the role of LDHA and ABCC1 in BC prognosis, tumor progression, and treatment resistance using integrated bioinformatics and in-silico approaches. METHODOLOGY: To investigate the expression and correlation between LDHA and ABCC1, in-silico analysis was done using a range of bioinformatics tools, such as UALCAN, TIMER 2.0, Bc GeneExminer, DISCO, and others, were used. GeneMANIA and STRING databases were used to explore gene–gene and protein–protein interaction networks, while KM Plotter evaluated survival correlations. Functional enrichment and pathway analyses were conducted using Enrichr for Gene Ontology (GO) and KEGG pathways. For therapeutic targeting, structure-based molecular docking was performed using AutoDock Vina, where selected anticancer compounds were docked against LDHA and ABCC1 to identify potential inhibitors.

resultsOur research indicates that the expression levels of LDHA and ABCC1 are elevated in several malignancies including Breast Cancer. The elevated expression levels of LDHA and ABCC1 significantly correlate with worse overall survival. Expression analysis of ENO1 (Enolase 1) and ESR1 (Estrogen Receptor 1) genes in relation to LDHA and ABCC1 mutations in Breast Cancer samples revealed that higher ENO1 expression is observed in mutant samples, while ESR1 expression is significantly reduced, suggesting an association with altered metabolic and hormonal pathways. Furthermore, NHI-2 and Sulfinpyrazone were found as the potential chemical that targets LDHA and ABCC1 through in-silico studies.

conclusionThis study highlights the oncogenic significance of LDHA and ABCC1 in Breast Cancer progression and therapy resistance. The in-silico identification of NHI-2 and sulfinpyrazone as potential inhibitors supports a novel dual-targeting strategy to simultaneously disrupt metabolic and drug efflux pathways, which could enhance therapeutic efficacy and overcome resistance in Breast Cancer. Further experimental validation is warranted to confirm these findings and facilitate clinical translation.

Indexed as

ABCC1Breast cancerDrug resistanceGlycolysisLDHAMetabolism

Identifiers

PMID40762889
PMCPMC12325138

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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.