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Integrating machine learning and multi-omics analysis to develop an asparagine metabolism immunity index for improving clinical outcome and drug sensitivity in lung adenocarcinoma

  • 0Central Laboratory, The Second Affiliated Hospital of Guilin Medical University, Guilin , 541199, Guangxi, China. chunhongli@glmc.edu.cn.
Immunologic Research +

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September 25, 2024

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September 25, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new asparagine metabolism immunity index (AMII) aids in predicting lung adenocarcinoma (LUAD) patient survival. Lower AMII scores correlate with better prognosis and chemotherapy sensitivity, offering insights for personalized LUAD treatment.

Area Of Science

  • Oncology
  • Metabolic pathways
  • Bioinformatics

Background

  • Lung adenocarcinoma (LUAD) often presents with advanced disease and poor prognosis due to insidious symptoms.
  • Abnormal asparagine metabolism (AM) is a potential therapeutic target, but its clinical significance in LUAD remains unclear.
  • Developing reliable prognostic markers is crucial for improving clinical decision-making in LUAD management.

Purpose Of The Study

  • To investigate the clinical significance of abnormal asparagine metabolism reprogramming in LUAD patients.
  • To develop and validate an asparagine metabolism immunity index (AMII) for prognostic prediction in LUAD.
  • To assess the potential of AMII in guiding clinical classification and personalized treatment strategies for LUAD.

Main Methods

  • Collected data on 864 asparagine metabolism-related genes (AMGs) from LUAD patients.
  • Utilized a machine-learning framework to construct the asparagine metabolism immunity index (AMII).
  • Validated AMII performance in predicting survival across multiple cohorts (TCGA-LUAD, GSE72094, GSE37745, GSE30219) using survival analysis, univariate/multivariate analyses, and nomogram construction.

Main Results

  • The developed AMII effectively predicted survival prognosis in LUAD patients across multiple independent cohorts.
  • Patients in the high-AMII group exhibited poorer prognosis, while those in the low-AMII group showed better survival.
  • AMII was identified as an independent risk factor for LUAD, and AMII-based nomograms demonstrated robust prognostic accuracy and clinical utility.
  • LUAD patients with lower AMII scores showed increased sensitivity to common chemotherapeutic drugs.

Conclusions

  • The asparagine metabolism immunity index (AMII) is a promising novel tool for prognostic prediction and clinical classification in LUAD.
  • AMII provides valuable insights for clinical decision-making and the development of personalized management strategies for LUAD patients.
  • The findings suggest AMII could help identify LUAD patients who may benefit more from specific chemotherapeutic interventions.

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