Bioinformatics Identification of lncRNA‐Associated Molecular Signatures Potentially Related to Quercetin Response in Breast Cancer

ABSTRACT Breast cancer affects approximately one in every eight women globally, as reported by the World Health Organization. Among all female malignant tumors, breast cancer has the highest fatality rate, surpassed only by lung cancer. Advanced stages of this disease are almost always fatal, and despite the advancements in surgical techniques and carefully designed chemotherapy regimens, relapse remains nearly inevitable. Although there are various chemical therapies for the treatment of breast cancer that can destroy the tumor or inhibit its growth, they often come with a variety of side‐effects. The polyphenolic compound quercetin can be found in numerous food plants. Previous research strongly suggests quercetin's potential as a cancer therapy. Various types of cancer cell lines have demonstrated that quercetin can induce apoptosis and suppress the proliferation of cancer cells. Therefore, we conducted an integrative bioinformatics analysis to identify molecular signatures, signaling pathways, and lncRNA‐associated gene relationships potentially related to quercetin‐associated transcriptional changes in breast cancer. The identified candidates were subsequently explored using protein–protein interaction and public regulatory databases. These findings are intended to generate hypotheses regarding potential lncRNA‐associated mechanisms rather than establish direct causal quercetin–lncRNA interactions. We found several genes involved in different metabolic pathways in relation to breast cancer and quercetin treatment.

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Publication Details

Journal
Computational and Systems Oncology
Published
2026-09-04
DOI
https://doi.org/10.1002/cso2.70026
Primary Topic
Cancer-related molecular mechanisms research
Type
article
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article

Bioinformatics Identification of lncRNA‐Associated Molecular Signatures Potentially Related to Quercetin Response in Breast Cancer

Mahboobeh Mehrabani Natanzi, Naghmeh Zhalehjoo, Zohreh Khodaii
Computational and Systems Oncology
Cancer-related molecular mechanisms research
article

Bioinformatics Identification of lncRNA‐Associated Molecular Signatures Potentially Related to Quercetin Response in Breast Cancer

Mahboobeh Mehrabani Natanzi, Naghmeh Zhalehjoo, Zohreh Khodaii
article en

Abstract

ABSTRACT Breast cancer affects approximately one in every eight women globally, as reported by the World Health Organization. Among all female malignant tumors, breast cancer has the highest fatality rate, surpassed only by lung cancer. Advanced stages of this disease are almost always fatal, and despite the advancements in surgical techniques and carefully designed chemotherapy regimens, relapse remains nearly inevitable. Although there are various chemical therapies for the treatment of breast cancer that can destroy the tumor or inhibit its growth, they often come with a variety of side‐effects. The polyphenolic compound quercetin can be found in numerous food plants. Previous research strongly suggests quercetin's potential as a cancer therapy. Various types of cancer cell lines have demonstrated that quercetin can induce apoptosis and suppress the proliferation of cancer cells. Therefore, we conducted an integrative bioinformatics analysis to identify molecular signatures, signaling pathways, and lncRNA‐associated gene relationships potentially related to quercetin‐associated transcriptional changes in breast cancer. The identified candidates were subsequently explored using protein–protein interaction and public regulatory databases. These findings are intended to generate hypotheses regarding potential lncRNA‐associated mechanisms rather than establish direct causal quercetin–lncRNA interactions. We found several genes involved in different metabolic pathways in relation to breast cancer and quercetin treatment.

Computational and Systems OncologyVol. 6(1)
Jahrom University of Medical Sciences (IR)
Good health and well-being
Openalex Percentile: Top 14%
Cancer-related molecular mechanisms research
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