Evidence map›Paper›PMID 37274274›Full record

ArticleFrontiers in oncology2023

Heterogeneity in clinical prognosis, immune infiltration and molecular characteristics of three glycolytic subtypes in lower-grade gliomas.

Shenglian Wu, Lulu Sheng, Shucai Fan, Xi Guo, Biao Zhu, Cheng Wu, Bing Lei

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 36% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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

7 authors at 2 institutions in 1 country.

Shenglian WuDepartment of Neurosurgery, Cancer Center, Zhejiang Provincial People's Hospital Affiliated to Hangzhou Medical College, Hangzhou, ZheJiang, China.
Lulu ShengDepartment of Neurosurgery, Cancer Center, Zhejiang Provincial People's Hospital Affiliated to Hangzhou Medical College, Hangzhou, ZheJiang, China.
Shucai FanDepartment of Neurosurgery, Cancer Center, Zhejiang Provincial People's Hospital Affiliated to Hangzhou Medical College, Hangzhou, ZheJiang, China.
Xi GuoDepartment of Neurosurgery, Cancer Center, Zhejiang Provincial People's Hospital Affiliated to Hangzhou Medical College, Hangzhou, ZheJiang, China.
Biao ZhuDepartment of Neurosurgery, Cancer Center, Zhejiang Provincial People's Hospital Affiliated to Hangzhou Medical College, Hangzhou, ZheJiang, China.
Cheng WuDepartment of Neurosurgery, Cancer Center, Zhejiang Provincial People's Hospital Affiliated to Hangzhou Medical College, Hangzhou, ZheJiang, China.
Bing LeiDepartment of Neurosurgery, Cancer Center, Zhejiang Provincial People's Hospital Affiliated to Hangzhou Medical College, Hangzhou, ZheJiang, China.
Hangzhou Medical College · CNZhejiang Provincial People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and purpose: Lower-grade gliomas (LGG) exhibit a wide range of metabolic pathway changes, and metabolic reprogramming can be largely seen as a result of oncogenic driving events. Glycolysis, an important pathway of tumor energy source, has been poorly studied in gliomas. The aim of this article is to analyze the relationship between glycolysis and lower-grade glioma development and prognosis in order to explore the heterogeneous relevance of glycolysis in lower-grade gliomas. Methods and results: Our study searched the TCGA database and identified three glycolytic subtypes with significant prognostic differences by unsupervised clustering analysis of core glycolytic genes, named C1, C2, and C3. By analysis of clinical prognosis, somatic cell variation, and immune infiltration, we found that C3 had the best prognosis with molecular features of IDHmut-codel, followed by C1 with major molecular features of IDHmut-non-codel, G -CIMP high subtype, while C2 had the worst prognosis, mainly exhibiting IDHwt, G-CIMP low and mesenchymal-like subtypes with seven important CNV features, including CDKN2A/B deletion, chr7 gain and chr10 deletion, chr19/20 co-gain, EGFR amplification and PDGFRA/B deletion phenotypes were significantly increased, with the highest level of stemness and significant T-cell depletion features. Finally, to quantify the level of abnormal glycolysis and its impact on prognosis, we developed GlySig to reflect the glycolytic activity of LGG and integrated molecular features to construct nomogram that can be independently assessed to predict prognosis. Conclusions: Our study analyzed the tumor characteristics of different glycolytic states, and our findings explain and describe the heterogeneity of glycolytic metabolism within diffuse LGGs.

Indexed as

glycolysisimmunitylower-grade gliomametabolismstemness

Identifiers

PMID37274274
PMCPMC10233122
OpenAlexW4377022950

What OpenQuestion holds

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LicenceCC BY
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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.