Evidence map›Paper›PMID 40399547›Full record

ArticleDiscover oncology2025

Arginine methylation patterns in LUAD: defining prognostic subtypes and relevance to immunotherapy.

Qianyun Shen, Yijie Yang, Maoying Guan, Hegen Li

Abstract read
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited 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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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Qianyun Shen *Department of Oncology, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Yijie Yang *Department of Oncology, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Maoying GuanDepartment of Oncology, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Hegen LiDepartment of Oncology, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China. SPEDLA1@163.com.

Funding

National Natural Science Foundation of China 82374532
6 · The paper itself

Abstract

backgroundLung cancer remains the leading cause of cancer-related death worldwide, with lung adenocarcinoma (LUAD) being the most common subtype. Arginine methylation, driven by protein arginine methyltransferases (PRMTs) has been connected to cancer biology, particularly in modulating cancer immunity. Thus, developing a PRMTs-related prognostic model might help create more personalized treatment plans for LUAD patients.

methodsWe conducted an integrative analysis using multi-omics data from LUAD samples within the TCGA and GEO database, focusing on the expression profiles of nine PRMTs. Employing machine learning, we developed a PRMTs-related prognostic model, to evaluate the clinical and immunological features of LUAD patients.

resultsWe stratified 440 LUAD patients into two distinct clusters (PRMTCluster A and B), which exhibited significant differences in prognosis and immune infiltration. The PRMTs-related prognostic model, incorporating genes CLIC6, CLDN2, and BPIFB1, was significantly associated with patient outcomes and immune signature. RT-qPCR showed that the expression level of PRMT1, PRMT3, PRMT4, PRMT5, and PRMT7 was significantly upregulated in H1975 and A549 cells than in BEAS 2B cells.

conclusionWe developed a PRMTs-related prognostic model for assessing prognosis and immunotherapy responses in LUAD. This model was vital for developing more personalized and effective treatment plans for LUAD patients.

Indexed as

ImmuneLUADLung cancerPRMTsPrognosis

Identifiers

PMID40399547
PMCPMC12095734

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