Evidence map›Paper›PMID 36782138›Full record

ArticleBMC cancer2023

Identification of four metabolic subtypes and key prognostic markers in lung adenocarcinoma based on glycolytic and glutaminolytic pathways.

Jinjin Zhang, Xiaopeng Wang, Congkuan Song, Qi Li

Abstract read
In one paragraph

Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jinjin Zhang *Department of Respiratory and Critical Care Medicine, Puren Hospital Affiliated to Wuhan Uiversity of Science and Technology, Wuhan, 430081, China.
Xiaopeng Wang *Department of Respiratory and Critical Care Medicine, Puren Hospital Affiliated to Wuhan Uiversity of Science and Technology, Wuhan, 430081, China.
Congkuan Song *Department of Thoracic Surgery, Renmin Hospital of Wuhan University, No.238 Jiefang Road, Wuchang District, Wuhan, 430060, China. sck2018@whu.edu.cn.
Qi LiDepartment of Respiratory and Critical Care Medicine, Puren Hospital Affiliated to Wuhan Uiversity of Science and Technology, Wuhan, 430081, China. optimum1212@163.com.

Funding

Medical Research Project of Wuhan Health Commission WX21D08
6 · The paper itself

Abstract

backgroundGlucose and glutamine are the main energy sources for tumor cells. Whether glycolysis and glutaminolysis play a critical role in driving the molecular subtypes of lung adenocarcinoma (LUAD) is unknown. This study attempts to identify LUAD metabolic subtypes with different characteristics and key genes based on gene transcription profiling data related to glycolysis and glutaminolysis, and to construct prognostic models to facilitate patient outcome prediction.

methodsLUAD related data were obtained from the Cancer Genome Atlas and Gene Expression Omnibus, including TCGA-LUAD, GSE42127, GSE68465, GSE72094, GSE29013, GSE31210, GSE30219, GSE37745, GSE50081. Unsupervised consensus clustering was used for the identification of LUAD subtypes. Differential expression analysis, weighted gene co-expression network analysis (WGCNA) and CytoNCA App in Cytoscape 3.9.0 were used for the screening of key genes. The Cox proportional hazards model was used for the construction of the prognostic risk model. Finally, qPCR analysis, immunohistochemistry and immunofluorescence colocalization were used to validate the core genes of the model.

resultThis study identified four distinct characterized LUAD metabolic subtypes, glycolytic, glutaminolytic, mixed and quiescent types. The glycolytic type had a worse prognosis than the glutaminolytic type. Nine genes (CXCL8, CNR1, AGER, ALB, S100A7, SLC2A1, TH, SPP1, LEP) were identified as hub genes driving the glycolytic/glutaminolytic LUAD. In addition, the risk assessment model constructed based on three genes (SPP1, SLC2A1 and AGER) had good predictive performance and could be validated in multiple independent external LUAD cohorts. These three genes were differentially expressed in LUAD and lung normal tissues, and might be potential prognostic markers for LUAD.

conclusionLUAD can be classified into four different characteristic metabolic subtypes based on the glycolysis- and glutaminolysis-related genes. Nine genes (CXCL8, CNR1, AGER, ALB, S100A7, SLC2A1, TH, SPP1, LEP) may play an important role in the subtype-intrinsic drive. This metabolic subtype classification, provides new biological insights into the previously established LUAD subtypes.

Indexed as

Adenocarcinoma of LungLung NeoplasmsCell DivisionGlycolysisHumansPrognosisGlutaminolysisGlycolysisLung adenocarcinoma (LUAD)Metabolic subtype

Identifiers

PMID36782138
PMCPMC9926575

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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.