Evidence map›Paper›PMID 42624926›Full record

ArticleNaunyn-Schmiedeberg's archives of pharmacology2026

Integrative systems biology identifies PSMB2 as a core oxidative stress-associated target in triple-negative breast cancer.

Fahad M Alshabrmi

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Article in Naunyn-Schmiedeberg's archives of pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

1 author.

Fahad M AlshabrmiDepartment of Medical Laboratories, College of Applied Medical Sciences, Qassim University, 51452, Buraydah, Saudi Arabia. fshbrmy@qu.edu.sa.ORCID https://orcid.org/0000-0001-5827-6040

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Triple-negative breast cancer (TNBC) represents one of the most aggressive and therapeutically challenging subtypes of breast cancer, characterized by the absence of estrogen receptor, progesterone receptor, and HER2 expression. Due to the lack of specific molecular targets, treatment options for TNBC remain limited, resulting in higher recurrence rates, increased metastatic potential, and poorer overall survival compared with other breast cancer subtypes. Therefore, identifying novel therapeutic targets and potential bioactive compounds is essential for improving TNBC management. In this study, an integrative systems biology and computational pharmacology approach was employed to explore potential molecular targets underlying oxidative stress-mediated mechanisms in TNBC and to identify plant-derived compounds with therapeutic relevance. Transcriptomic analysis was performed to identify differentially expressed genes between TNBC and normal ductal tissues, followed by weighted gene coexpression network analysis to detect disease-associated gene modules. Intersection analysis integrating phytochemical targets, oxidative stress-related genes, and network-derived candidate genes identified seven potential key genes. Functional enrichment analysis revealed that these genes were mainly involved in oxidative stress response, DNA repair mechanisms, proteasome activity, and cell cycle regulation pathways. Immune infiltration analysis further demonstrated significant remodeling of the tumor immune microenvironment in TNBC. Machine learning algorithms identified PSMB2 as the most robust core gene associated with TNBC. Finally, molecular docking analysis demonstrated favorable binding interactions between the predicted target protein and several phytochemical compounds, particularly 6-gingerol, curcumin, and demethoxycurcumin. These findings highlight the potential of integrating computational approaches with natural product screening to identify novel therapeutic strategies for TNBC.

Indexed as

Machine learning biomarkersOxidative stressPSMB2Triple-negative breast cancer

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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.