Evidence map›Paper›PMID 41208958›Full record

ArticleFrontiers in immunology2025

ENO1-related gene signature predicts prognosis and therapeutic response in diffuse large B-cell lymphoma.

Wenli Yan, Xiaoxi Liu, Beibei Gao, Shanshan Zhang, Jinhong Ren, Yang Lu, Limei Ai, Jinsong Yan, Haina Wang

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

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

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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. Review
  2. Review
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

9 authors.

Wenli Yan *Department of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, The Second Hospital of Dalian Medical University, Dalian, China.
Xiaoxi Liu *Department of Hematology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China.
Beibei Gao *Department of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, The Second Hospital of Dalian Medical University, Dalian, China.
Shanshan ZhangLiaoning Medical Center for Hematopoietic Stem Cell Transplantation, the Second Hospital of Dalian Medical University, Dalian, China.
Jinhong RenShanxi Key Laboratory of Innovative Drug for the Treatment of Serious Diseases Basing on the Chronic Inflammation, College of Traditional Chinese Medicine and Food Engineering, Shanxi University of Chinese Medicine, Taiyuan, China.
Yang LuDepartment of Hematology, Chaoyang Central Hospital, Chaoyang, China.
Limei AiDepartment of Hematology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China.
Jinsong YanDepartment of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, The Second Hospital of Dalian Medical University, Dalian, China.
Haina WangDepartment of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, The Second Hospital of Dalian Medical University, Dalian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Alpha-enolase (ENO1), the enzyme catalyzing 2-phosphoglycerate conversion to phosphoenolpyruvate, is highly expressed in diffuse large B-cell lymphoma (DLBCL) and correlates with adverse clinical outcomes. Thus, understanding the relationship between ENO1-related gene (ERG) network and DLBCL is imperative. Here, we integrated multi-omics profiling (RIP-seq, RNA-seq, and protein interactome analysis) to identify ERGs and established a prognostic model by machine learning algorithms. Methods: We identified eleven hub genes (CHERP, SYNE2, INTS1, FAP, MMP9, LRP5, RBM8A, PRMT5, SLC25A6, PABPC4, PSTPIP2) using RNA sequencing, RNA immunoprecipitation sequencing, and protein interaction profiling. A prognostic model was constructed using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression in the GSE10846 dataset and validated in two independent cohorts. DLBCL patients were stratified into high- and low-risk groups based on the model, and clinical characteristics were compared. The tumor immune microenvironment (TIME) was analyzed using CIBERSORT and xCell algorithms to explore correlations with the ERG score. Drug sensitivity assays in DLBCL cell lines were performed to validate the model's predictive capacity for chemotherapy response. Furthermore, the functional role of PABPC4, a key gene in the scoring system, was investigated through Results: A prognostic model including 11 hub genes was established. Patients in the high-risk group exhibited worse clinical outcomes and an immunosuppressive TIME, characterized by altered expression of immune checkpoint-related proteins. This group demonstrated increased sensitivity to vincristine, etoposide, and oxaliplatin. Knockdown of PABPC4 significantly inhibited cell proliferation, reduced colony formation, and delayed tumor growth Conclusions: The ERG scoring system offers a robust and precise tool for predicting survival and guiding personalized treatment in DLBCL patients.

Indexed as

Biomarkers, TumorDNA-Binding ProteinsLymphoma, Large B-Cell, DiffusePhosphopyruvate HydrataseTranscriptomeTumor Suppressor ProteinsAnimalsCell Line, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMicePrognosisTumor MicroenvironmentBiomarkers, TumorDNA-Binding ProteinsENO1 protein, humanPhosphopyruvate HydrataseTumor Suppressor ProteinsDLBCLENO1PABPC4prognosistherapeutic response

Identifiers

PMID41208958
PMCPMC12588913

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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.