Evidence map›Paper›PMID 36622242›Full record

ArticleGenetics and molecular biology2023

Systematically analyzed molecular characteristics of lung adenocarcinoma using metabolism-related genes classification.

Xiaoming Huang, Feng Zhang, Junqi Lin, Shaoming Lin, Guanle Shen, Xiaozhu Chen, Wenbiao Chen

Abstract read
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Article in Genetics and molecular biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xiaoming HuangThe Affiliated Hospital of Southern Medical University, People's Hospital of Longhua, Department of respiratory medicine, Shenzhen, China.
Feng ZhangThe First Affiliated Hospital of Jinan University, Department of Intensive Care Unit, Guangzhou, China.
Junqi LinThe Affiliated Hospital of Southern Medical University, People's Hospital of Longhua, Department of respiratory medicine, Shenzhen, China.
Shaoming LinThe Affiliated Hospital of Southern Medical University, People's Hospital of Longhua, Department of respiratory medicine, Shenzhen, China.
Guanle ShenThe Affiliated Hospital of Southern Medical University, People's Hospital of Longhua, Department of respiratory medicine, Shenzhen, China.
Xiaozhu ChenThe Affiliated Hospital of Southern Medical University, People's Hospital of Longhua, Department of Medical Ultrasound Department, Shenzhen, China.
Wenbiao ChenThe Affiliated Hospital of Southern Medical University, People's Hospital of Longhua, Department of respiratory medicine, Shenzhen, China.ORCID http://orcid.org/0000-0002-1028-6319

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High heterogeneity of lung adenocarcinoma (LUAD) is a major clinical challenge. This study aims to characterize the molecular features of LUAD through classification based on metabolism-related genes. A total of 500 LUAD samples from The Cancer Genome Atlas (TCGA) and 612 from Gene Expression Omnibus (GEO) were integrated with 2,753 metabolism-related genes to determine the molecular classification. Systematic bioinformatics analysis was used to conduct correlation analysis between metabolism-related classification and molecular characteristics of LUAD. LUAD patients were divided into three molecular clusters (C1-C3). Survival analysis revealed that C1 and C2 showed good and poor prognoses, respectively. Associational analysis of classification and molecular characteristics revealed that C1 was associated with low pathological stage, metabolic pathways, high metabolic process, active immune process and checkpoint, sensitive drug response, as well as a low genetic mutation. Nevertheless, C2 was associated with high pathological stage, carcinogenic pathways, low metabolic process, inactive immune signatures, resistant drug response, and frequent genetic mutation. Eventually, a classifier with 60 metabolic genes was constructed, confirming the robustness of molecular classification on LUAD. Our findings promote the understanding of LUAD molecular characteristics, and the research data may be used for providing information be helpful for clinical diagnosis and treatment.

Identifiers

PMID36622242
PMCPMC9830935

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