Evidence map›Paper›PMID 39441216›Full record

ArticleJournal of cancer research and clinical oncology2024

Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients.

Xiliang Zhang, Hanjie Guo, Xiaolong Li, Wei Tao, Xiaoqing Ma, Yuxing Zhang, Weidong Xiao

RetractedAbstract readRetracted Publication
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cancer research and clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. 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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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

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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
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Xiliang Zhang *Department of General Surgery, Xinqiao Hospital, Army Medical University, No. 83 Xinqiao Main Street, Shapingba District, Chongqing, 400037, China.
Hanjie Guo *Department of General Surgery, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China.
Xiaolong LiDepartment of General Surgery, Xinqiao Hospital, Army Medical University, No. 83 Xinqiao Main Street, Shapingba District, Chongqing, 400037, China.
Wei TaoDepartment of General Surgery, Xinqiao Hospital, Army Medical University, No. 83 Xinqiao Main Street, Shapingba District, Chongqing, 400037, China.
Xiaoqing MaDepartment of General Surgery, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China.
Yuxing ZhangDepartment of General Surgery, The Sixth Medical Center of PLA General Hospital, 6 Fucheng Road, Haidian, Beijing, 100048, P.R. China. doctor_zyx1984@163.com.
Weidong XiaoDepartment of General Surgery, Xinqiao Hospital, Army Medical University, No. 83 Xinqiao Main Street, Shapingba District, Chongqing, 400037, China. xiaoweidong@tmmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis.

methodsWe used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level.

resultsOur single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets.

conclusionsThis study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management.

Indexed as

Breast NeoplasmsMachine LearningPolyaminesSingle-Cell AnalysisBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticHumansPrognosisTumor MicroenvironmentBiomarkers, TumorPolyaminesBreast cancerMachine learningPolyamine metabolismRisk scoring model

Identifiers

PMID39441216
PMCPMC11499360

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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.