Evidence map›Paper›PMID 40082583›Full record

ArticleScientific reports2025

Machine learning reveals glycolytic key gene in gastric cancer prognosis.

Nan Li, Yuzhe Zhang, Qianyue Zhang, Hao Jin, Mengfei Han, Junhan Guo, Ye Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

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

2 citing papers in PubMed.

  1. Article
  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

7 authors.

Nan Li *China Academy of Electronics and Information Technology, National Engineering Research Center for Public Safety Risk Perception and Control by Big Data (RPP), Beijing, China.
Yuzhe Zhang *The First Laboratory of Cancer Institute, The First Hospital of China Medical University, Shenyang, China.
Qianyue Zhang *China Academy of Electronics and Information Technology, National Engineering Research Center for Public Safety Risk Perception and Control by Big Data (RPP), Beijing, China.
Hao JinChina Academy of Electronics and Information Technology, National Engineering Research Center for Public Safety Risk Perception and Control by Big Data (RPP), Beijing, China.
Mengfei HanChina Academy of Electronics and Information Technology, National Engineering Research Center for Public Safety Risk Perception and Control by Big Data (RPP), Beijing, China.
Junhan GuoCenter for Reproductive Medicine, Henan Key Laboratory of Reproduction and Genetics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Ye ZhangThe First Laboratory of Cancer Institute, The First Hospital of China Medical University, Shenyang, China. zhangyecmu@163.com.

Funding

Natural Science Foundation of China U21B2026
6 · The paper itself

Abstract

Glycolysis is recognized as a central metabolic pathway in the neoplastic evolution of gastric cancer, exerting profound effects on the tumor microenvironment and the neoplastic growth trajectory. However, the identification of key glycolytic genes that significantly affect gastric cancer prognosis remains underexplored. In this work, five machine-learning algorithms were used to elucidate the intimate association between the glycolysis-associated gene phosphofructokinase fructose-bisphosphate 3 (PFKFB3) and the prognosis of gastric cancer patients. Validation across multiple independent datasets confirmed the prognostic significance of PFKFB3. Further, we delved into the functional implications of PFKFB3 in modulating immune responses and biological processes within gastric cancer patients, as well as its broader relevance across multiple cancer types. Results underscore the potential of PFKFB3 as a prognostic biomarker and therapeutic target in gastric cancer. Our project can be found at https://github.com/PiPiNam/ML-GCP .

Indexed as

GlycolysisMachine LearningPhosphofructokinase-2Stomach NeoplasmsBiomarkers, TumorGene Expression Regulation, NeoplasticHumansPrognosisTumor MicroenvironmentBiomarkers, TumorPFKFB3 protein, humanPhosphofructokinase-2Gastric cancerGene identificationMachine learningPFKFB3Prognostic

Identifiers

PMID40082583
PMCPMC11906761

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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.