Evidence map›Paper›PMID 39118022›Full record

ArticleBMC cancer2024

Glycolysis-related genes predict prognosis and indicate immune microenvironment features in gastric cancer.

Lu Xu, Jin Liu, Yuanqing An, Lei Zhou, Hui Sun, Zhen Xu, Deqiang Wang, Zhanwen Liang, Caihua Xu, Bingyi Wang and 1 more

Abstract read
In one paragraph

Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

11 authors.

Lu Xu *Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Jin Liu *Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Yuanqing An *Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Lei ZhouDepartment of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Hui SunDepartment of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Zhen XuDepartment of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Deqiang WangDepartment of Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, China.
Zhanwen LiangDepartment of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Caihua XuDepartment of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China. caihuaxu@suda.edu.cn.
Bingyi WangDepartment of Oncology, Changshu No.1 People's Hospital, Suzhou, 215500, China. wby0113@suda.edu.cn.
Wei LiDepartment of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China. liwei10@suda.edu.cn.

Funding

National Natural Science Foundation of China 81873587
6 · The paper itself

Abstract

backgroundGastric cancer (GC) is a major contributor to cancer-related mortality. Glycolysis plays a pivotal role in tumor microenvironment (TME) reprogramming. In this research, the functions of glycolysis-associated genes (GRGs) were evaluated to predict the outcome and reveal the characteristics of the immune microenvironment in individuals with stomach cancer.

methodsThe Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort provided gene expression and clinical data for gastric cancer (GC) patients, which were further authenticated using datasets sourced from the Gene Expression Omnibus (GEO). By referencing the Molecular Signatures Database (MSigDB), a total of 326 GRGs were pinpointed. The various subtypes of GC were outlined through consensus clustering, derived from the expression patterns of these GRGs. Utilizing multivariate Cox regression analysis, a multigene risk score model was formulated. Both the CIBERSORT and ESTIMATE algorithms played a pivotal role in assessing the immune microenvironment. To delve into the biological functions of the key genes, wound healing, transwell invasion, and MTT assays were conducted.

resultsBased on the expression patterns of GRGs, patients were categorized into two distinct groups: the metabolic subtype, designated as cluster A, and the immune subtype, labeled as cluster B. Patients belonging to cluster B exhibited a poorer prognosis. A prognostic risk score model, formulated upon the expression levels of six key GRGs - ME1, PLOD2, NUP50, CXCR4, SLC35A3, and SRD35A3 - emerged as a viable tool for predicting patient outcomes. The downregulation of CXCR4 notably diminished the glycolytic capacity of gastric cancer (GC) cells, alongside their migratory, invasive, and proliferative capabilities. Intriguingly, despite the adverse prognostic implications associated with both the immune subtype (cluster B) and the high-risk cohort, these groups exhibited a favorable immune microenvironment coupled with elevated expression of immune checkpoint genes. Our investigations revealed a positive correlation between high CXCR4 expression and low ME1 expression with the infiltration of CD8

conclusionsIn this study, we discovered that the expression profiles of GRGs hold the potential to forecast the prognosis of gastric cancer (GC) patients, thereby possibly aiding in clinical treatment decision-making.

Indexed as

GlycolysisStomach NeoplasmsTumor MicroenvironmentAdenocarcinomaBiomarkers, TumorCell Line, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedPrognosisBiomarkers, TumorGastric cancerGlycolysisImmune cell infiltrationTumor microenvironment

Identifiers

PMID39118022
PMCPMC11313097

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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.