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Investigation Into the Association Between Neurotransmitters, Immune Features, and Lung Adenocarcinoma: Identifying

Jiangtao You1, Tianren Wang1, Qingshi Wang2

  • 1Department of Thoracic Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China, xjtu.edu.cn.

Stem Cells International
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning model using GABA-related gene features (GABARFs) to predict lung adenocarcinoma (LUAD) prognosis and treatment response. GABARFs show potential for early detection and personalized therapy in LUAD patients.

Keywords:
gamma-aminobutyric acidimmunotherapylung adenocarcinomamachine learningneurotransmitters

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Area of Science:

  • Oncology
  • Genomics
  • Machine Learning

Background:

  • Lung adenocarcinoma (LUAD) has a high mortality rate with limited diagnostic biomarkers.
  • Gamma-aminobutyric acid (GABA) plays a role in tumor progression beyond the central nervous system, but its role in LUAD is under-researched.

Purpose of the Study:

  • To develop a machine learning framework for identifying GABA-related gene features (GABARFs) for LUAD prognosis.
  • To establish GABARFs as a prognostic tool and assess their correlation with clinical outcomes, immune infiltration, and therapeutic response.

Main Methods:

  • Developed a machine learning framework screening 124 GABA-related genes (GABARgenes) using 10 algorithms.
  • Constructed GABA-related features (GABARFs) and validated them using training and external datasets.
  • Performed multiomics analyses and evaluated GABARF risk subgroups' response to immunotherapy and chemotherapy.

Main Results:

  • Identified 38 GABARgenes significantly correlated with overall survival (OS) in LUAD patients.
  • GABARF demonstrated strong prognostic performance, serving as an independent predictor of OS.
  • GABARF risk subgroups showed distinct biological functions, mutation statuses, immune infiltration, and varying responses to immunotherapy and chemotherapy.

Conclusions:

  • The novel machine learning-based GABARF model shows promise for prognostic prediction, prevention, and personalized treatment in LUAD.
  • GABARF may reflect tumor stemness, offering insights into neural-immune-stemness interactions for tailored therapies.
  • Further investigation into GABARF mechanisms at molecular, cellular, and tumor immune microenvironment levels is warranted.