Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis:...

Area of Science:

• Oncology
• Metabolic Biology
• Computational Biology

Background:

• Metabolic reprogramming is linked to tumor progression, but its role in lung adenocarcinoma (LUAD) heterogeneity and prognosis is not fully understood.
• Understanding inter-patient variability in LUAD is crucial for developing effective precision medicine strategies.

Purpose of the Study:

• To investigate the impact of metabolic reprogramming on LUAD heterogeneity and prognosis.
• To develop a robust prognostic model for LUAD using an ensemble learning approach.
• To create a user-friendly tool for clinical application in LUAD risk stratification and treatment.

Main Methods:

• A cellular hierarchy framework, malignant & metabolism reprogramming (MMR), was developed using a malignant and metabolic gene set.
• A three-stage ensemble learning pipeline, incorporating a genetic algorithm (GA), was designed for survival prediction.
• The pipeline utilized double training sets to prevent overfitting and a gene-pairing method to mitigate batch effects.

Main Results:

• A novel three-stage-MMR (3S-MMR) score was developed, reflecting diverse LUAD biological aspects.
• The 3S-MMR score demonstrated generalizability as a predictor of prognosis and potential immunotherapy response across multiple LUAD cohorts.
• An accessible web tool was created to facilitate clinical adoption for risk scoring and therapy stratification in LUAD patients.

Conclusions:

• The proposed 3S-MMR score provides valuable insights into LUAD biology and precision medicine.
• The ensemble learning pipeline offers a generalizable approach for developing prognostic models in various diseases.
• This work facilitates improved treatment strategies and prognostic assessment for lung adenocarcinoma patients.