Identification of molecular subtypes and a prognostic signature based on m6A/m5C/m1A-related genes in lung adenocarcinoma

Yu Zhang ¹, Qiuye Jia ¹, Fangfang Li ¹

⁰Department of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650302, Yunnan, China.

Scientific Reports +

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March 30, 2024

View abstract on PubMed

Summary

Researchers developed a six-gene prognostic signature for lung adenocarcinoma (LUAD) using RNA modification data. This model aids in predicting patient survival and personalizing LUAD treatments, improving outcomes for this common cancer.

Area Of Science

Oncology
Molecular Biology
Bioinformatics

Background

Lung adenocarcinoma (LUAD) is a leading cause of cancer death globally.
RNA modifications (m6A, m5C, m1A) are implicated in LUAD progression, drug resistance, and immunotherapy response.
A predictive model for LUAD prognosis based on these RNA modification genes is lacking.

Purpose Of The Study

To identify genes associated with m6A/m5C/m1A modifications in LUAD.
To develop a prognostic signature for LUAD patients.
To explore the relationship between the signature, immune microenvironment, and treatment response.

Main Methods

Utilized The Cancer Genome Atlas (TCGA) data and GeneCards database for gene identification.
Employed ConsensusClusterPlus for molecular subtyping.
Constructed a prognostic signature using differential expression and univariate Cox analyses.
Assessed immune cell infiltration and tumor stemness using ESTIMATE, CIBERSORT, TIMER, ssGSEA, and TIDE.

Main Results

Identified a six-gene prognostic signature (SNHG12, PABPC1, IGF2BP1, FOXM1, CBFA2T3, CASC8).
Higher risk scores correlated with poorer survival and increased immune checkpoint expression.
Significant associations found between RNA modification genes, risk model, immune infiltration, and tumor stemness.
The signature showed potential in predicting chemotherapeutic and small-molecule drug responses.

Conclusions

Developed a robust six-gene prognostic signature for LUAD.
The signature aids in predicting prognosis and guiding personalized treatment strategies for LUAD patients.
This model offers potential for improved personalized management in LUAD care.

Keywords:

Lung adenocarcinoma Molecule subtypes Signature m1A m5C m6A