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Background <p>Lung 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.</p> Methods <p>We 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.</p> Results <p>We 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.</p> Conclusion <p>We 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月21日

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  • Qianyun Shen,
  • Yijie Yang,
  • Maoying Guan,
  • Hegen Li

摘要

Background

Lung 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.

Methods

We 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.

Results

We 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.

Conclusion

We 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.