Evidence map›Paper›PMID 40259116›Full record

ArticleCell biology and toxicology2025

Machine learning-based identification of cuproptosis-related lncRNA biomarkers in diffuse large B-cell lymphoma.

Wenhao Ouyang, Zijia Lai, Hong Huang, Li Ling

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Article in Cell biology and toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

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

4 authors.

Wenhao Ouyang *Department of Neurology, Shenzhen Hospital, Southern Medical University, No.1333 Xinhu Road, Shenzhen, 518000, Guangdong, China.
Zijia Lai *Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, Guangdong, China.
Hong HuangSchool of Medicine, Guilin Medical University, Guilin, 541000, Guangxi, China.
Li LingDepartment of Neurology, Shenzhen Hospital, Southern Medical University, No.1333 Xinhu Road, Shenzhen, 518000, Guangdong, China. linglirabbit@163.com.

Funding

This study was supported by the Science and Technology Project of Shenzhen JCYJ20210324131614038
6 · The paper itself

Abstract

Multiple machine learning techniques were employed to identify key long non-coding RNA (lncRNA) biomarkers associated with cuproptosis in Diffuse Large B-Cell Lymphoma (DLBCL). Data from the TCGA and GEO databases facilitated the identification of 126 significant cuproptosis-related lncRNAs. Various feature selection methods, such as Univariate Filtering, Lasso, Boruta, and Random Forest, were integrated with a Transformer-based model to develop a robust prognostic tool. This model, validated through fivefold cross-validation, demonstrated high accuracy and robustness in predicting risk scores. MALAT1 was pinpointed using permutation feature importance from machine learning methods and was further validated in DLBCL cell lines, confirming its substantial role in cell proliferation. Knockdown experiments on MALAT1 led to reduced cell proliferation, underscoring its potential as a therapeutic target. This integrated approach not only enhances the precision of biomarker identification but also provides a robust prognostic model for DLBCL, demonstrating the utility of these lncRNAs in personalized treatment strategies. This study highlights the critical role of combining diverse machine learning methods to advance DLBCL research and develop targeted cancer therapies.

Indexed as

Biomarkers, TumorLymphoma, Large B-Cell, DiffuseMachine LearningRNA, Long NoncodingCell Line, TumorCell ProliferationGene Expression Regulation, NeoplasticHumansPrognosisBiomarkers, TumorMALAT1 long non-coding RNA, humanRNA, Long NoncodingCuproptosisDiffuse large B-cell lymphomaLncRNAMachine learningMALAT1

Identifiers

PMID40259116
PMCPMC12011908

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Registered trials

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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.