Evidence map›Paper›PMID 40131566›Full record

ArticleDiscover oncology2025

Prognosis model of patients with breast cancer based on metabolism-related LncRNAs.

Dan Zhang, Shiwei Ma, Liling Yang, Hongyuan Liu, Han Jiang, Yan Wang

Abstract read
PubMed Full text at PMC Publisher
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 1 paper.

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0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

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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

6 authors.

Dan ZhangIntensive Care Unit, Mianyang Central Hospital, University of Electronic Science and Technology of China, No.12 Changjia Alley, Jingzhong Street, Fucheng District, Mianyang, 621000, Sichuan, China. 15281660541@163.com.
Shiwei MaHealth Management Center, Mianyang Central Hospital, University of Electronic Science and Technology of China, Mianyang, China.
Liling YangDepartment of Nephrology, Mianyang Central Hospital, University of Electronic Science and Technology of China, Mianyang, China.
Hongyuan LiuDepartment of Neurosurgery, Mianyang Central Hospital, University of Electronic Science and Technology of China, Mianyang, China.
Han JiangIntensive Care Unit, Mianyang Central Hospital, University of Electronic Science and Technology of China, No.12 Changjia Alley, Jingzhong Street, Fucheng District, Mianyang, 621000, Sichuan, China.
Yan WangIntensive Care Unit, Mianyang Central Hospital, University of Electronic Science and Technology of China, No.12 Changjia Alley, Jingzhong Street, Fucheng District, Mianyang, 621000, Sichuan, China.

Funding

research foundation of Mianyang Central Hospital 2020FH08
6 · The paper itself

Abstract

objectiveMetabolism-related lncRNAs may play a significant role in the occurrence and development of breast cancer. This study aims to identify metabolism-related lncRNAs with high predictive value for prognosis and to construct a model that can predict the prognosis of breast cancer individually.

methodsTranscriptome data and clinical data of patients with breast cancer were retrieved from the TCGA database, and metabolism-related genes were sourced from the GSEA database. Metabolism-related lncRNAs in breast cancer were obtained through differential expression analysis and Pearson correlation analysis. Prognostic-related lncRNAs were further screened using Univariate Cox regression and LASSO regression. Kaplan-Meier survival analysis was performed and the survival curve of the two groups was drawn. Univariate and Multivariate Cox regression analyses were conducted to identify the independent prognostic factors, which were subsequently integrated into a nomogram for individualized prognostic prediction.

resultsThrough differential analysis, 2135 differential lncRNAs were obtained, of which 231 were metabolism-related lncRNAs. Using Univariate Cox regression and LASSO regression, a risk prediction model incorporating 19 metabolism-related lncRNAs was constructed. The survival curve suggested that patients with high-risk scores had a poor prognosis compared to those with low-risk scores (P < 0.05). Cox regression analysis further identified that age, stage classification, distant metastasis and risk score as independent prognostic factors to construct a nomogram. KEGG pathway enrichment analysis revealed that differential lncRNAs may be related to JAK-STAT signaling pathway, MAPK signaling pathway and mTOR signaling pathway. Finally, based on the analysis of the CIBERSORT algorithm, lncRNAs used in the construction of the model had a strong correlation with CD8

conclusionBioinformatics methods were utilized to identify metabolism-related lncRNAs associated with breast cancer prognosis, and a prognostic risk model was constructed, laying a solid foundation for the study of metabolism-related lncRNAs in breast cancer.

Indexed as

Breast cancerCIBERSORTLASSOLncRNAMetabolismPrognostic model

Identifiers

PMID40131566
PMCPMC11937465

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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.