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

## TL;DR

This study identifies arginine methylation patterns in lung adenocarcinoma to create a prognostic model that could improve personalized treatment and immunotherapy outcomes.

## Contribution

A novel PRMTs-related prognostic model for LUAD prognosis and immunotherapy response is developed using multi-omics data and machine learning.

## Key findings

- Two distinct PRMT clusters (A and B) showed significant differences in prognosis and immune infiltration.
- The model includes CLIC6, CLDN2, and BPIFB1 genes, which are associated with patient outcomes and immune signatures.
- PRMT1, PRMT3, PRMT4, PRMT5, and PRMT7 were upregulated in cancer cell lines compared to normal cells.

## Abstract

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.

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.

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.

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.

The online version contains supplementary material available at 10.1007/s12672-025-02549-5.