ArticleTranslational oncology2025
PMAIP1-mediated glucose metabolism and its impact on the tumor microenvironment in breast cancer: Integration of multi-omics analysis and experimental validation.
Article in Translational oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Integrated single-cell and machine learning analysis identifies PMAIP1 as a novel biomarker for predicting prognosis and immunotherapy response in colorectal cancer.Scientific reports · 2025Article
- A new insight into the impact of copy number variations on cell cycle deregulation of luminal-type breast cancer.Oncology reviews · 2025Review
- The Epithelial Cell-Associated Gene PMAIP1 Serves as a Prognostic Biomarker for Lung Adenocarcinoma and Can Regulate the Stemness of Lung Cancer.Stem cells international · 2025Article
- Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.Frontiers in immunology · 2025Article
- Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlucose metabolism in breast cancer has a potential effect on tumor progression and is related to the immune microenvironment. Thus, this study aimed to develop a glucose metabolism-tumor microenvironment score to provide new perspectives on breast cancer treatment.
methodData were acquired from the Gene Expression Omnibus and UCSC Xena databases, and glucose-metabolism-related genes were acquired from the Gene Set Enrichment Analysis database. Genes with significant prognostic value were identified, and immune infiltration analysis was conducted, and a prognostic model was constructed based on the results of these analyses. The results were validated by in vitro experiments with MCF-7 and MCF-10A cell lines, including expression validation, functional experiments, and bulk sequencing. Single-cell analysis was also conducted to explore the role of specific cell clusters in breast cancer, and Bayes deconvolution was used to further investigate the associations between cell clusters and tumor phenotypes of breast cancer.
resultsFour significant prognostic genes (PMAIP1, PGK1, SIRT7, and SORBS1) were identified, and, through immune infiltration analysis, a combined prognostic model based on glucose metabolism and immune infiltration was established. The model was used to classify clinical subtypes of breast cancer, and PMAIP1 was identified as a potential critical gene related to glucose metabolism in breast cancer. Single-cell analysis and Bayes deconvolution jointly confirmed the protective role of the PMAIP1+ luminal cell cluster.
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Registered trials
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 Socratic graph.