Evidence map›Paper›PMID 34128320›Full record

ArticleJournal of cellular and molecular medicine2021

Identification of a prognostic metabolic gene signature in diffuse large B-cell lymphoma.

Huizhong Wang, Ruonan Shao, Wenjian Liu, Hailin Tang, Yue Lu

Abstract read
In one paragraph

Article in Journal of cellular and molecular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Metabolic Reprogramming and Potential Therapeutic Targets in Lymphoma.International journal of molecular sciences · 2023
    Review
  9. Article
  10. Article
  11. 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

5 authors.

Huizhong WangSun Yat-sen University Cancer Center, Guangzhou, China.ORCID 0000-0002-1469-714X
Ruonan ShaoSun Yat-sen University Cancer Center, Guangzhou, China.
Wenjian LiuSun Yat-sen University Cancer Center, Guangzhou, China.
Hailin TangSun Yat-sen University Cancer Center, Guangzhou, China.
Yue LuSun Yat-sen University Cancer Center, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diffuse large B-cell lymphoma (DLBCL) is a clinically diverse disease. Given the numerous genetic mutations and variations associated with it, a prognostic gene signature that can be related to the overall survival (OS) is a clinical implication. We used the mRNA expression profiles and clinicopathological data of patients with DLBCL from the Gene Expression Omnibus (GEO) database to identify a metabolism-related gene signature. Using LASSO regression analysis, a novel 13-metabolic gene signature was identified to evaluate prognosis. The information gathered was used to construct the nomogram model to improve risk stratification and quantify risk factors for individual patients. We performed gene set enrichment analysis to identify the enriched signalling axes to further understand the underlying biological pathways. The receiver operating characteristic (ROC) curve revealed a satisfactory performance in the training cohorts. The model also showed clinical benefit when compared to the standard prognostic factors (P < .05) in validation cohorts. This study aimed to combine metabolic dysregulation with clinical features of patients with DLBCL to generate a prognostic model that might not only indicate the value of the metabolic microenvironment for prognostic stratification but also improve the decision-making during individual therapy.

Indexed as

Metabolic Networks and PathwaysTranscriptomeBiomarkers, TumorComputational BiologyFemaleGene Regulatory NetworksHumansLymphoma, Large B-Cell, DiffuseMaleMiddle AgedBiomarkers, Tumorclinical prognostic modeldiffuse large B-cell lymphomagene signaturemetabolism

Identifiers

PMID34128320
PMCPMC8278125

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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.