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Integrating Multi-omics And Machine Learning Survival Frameworks To Build A Prognostic Model Based On Immune Function And Cell Death Patterns In A Lung Adenocarcinoma Cohort.

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Integrating Multi-omics And Machine Learning Survival Frameworks To Build A Prognostic Model Based On Immune Function And Cell Death Patterns In A Lung Adenocarcinoma Cohort.

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Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function

Yiluo Xie^1,2, Huili Chen³, Mei Tian¹

¹Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease, MolecularDiagnosis Center, Joint Research Center for Regional Diseases of Institute of Health and Medicine (IHM), First Affiliated Hospital of Bengbu Medical University, Bengbu, China.

Frontiers in Immunology

|September 30, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Keywords:

immunotherapy efficacy lung adenocarcinoma machine learning precision medicine programmed cell death

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This study identifies three lung adenocarcinoma subtypes and a novel prognostic model (PIGRS) based on immune and cell death genes. PSME3 shows potential as a new prognostic factor for lung adenocarcinoma.

Area of Science:

Oncology
Immunology
Genetics

Background:

Programmed cell death (PCD) and immune-related genes are critical in lung adenocarcinoma (LUAD) development and prognosis.
The prognostic impact of the interplay between immune genes and cell death in LUAD requires further investigation.

Purpose of the Study:

To investigate the prognostic significance of immune-related genes and cell death patterns in LUAD.
To develop a robust computational framework for analyzing multi-omics data in LUAD.
To identify novel prognostic biomarkers for LUAD.

Main Methods:

Applied 10 clustering algorithms to multi-omics data (cell death genes, immune genes, methylation, somatic mutations) for LUAD molecular typing.
Developed an immune-associated programmed cell death model (PIGRS) using a machine learning framework with 101 algorithm combinations.

Conducted in vitro experiments to explore the role of PSME3 in LUAD.

Main Results:

Categorized TCGA-LUAD patients into three subtypes (CS1, CS2, CS3) with distinct prognoses; CS3 showed the best outcome.
The PIGRS model, comprising 15 high-impact genes, demonstrated strong prognostic performance for LUAD patients.
Identified PSME3 as a potential novel prognostic factor in lung adenocarcinoma, with limited prior study.

Conclusions:

Bioinformatic analysis successfully identified three LUAD subtypes with clinical significance.
The PIGRS model offers a powerful tool for prognostic assessment in LUAD.
PSME3 may influence apoptosis in LUAD cells via the PI3K/AKT/Bcl-2 pathway, suggesting its potential as a therapeutic target.