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Integrative machine learning approach for forecasting lung cancer chemosensitivity: From algorithm to cell line

Jinghong Chen1, Yonglin Yi2, Chunqian Yang2

  • 1Department of Oncology, Zhujiang Hospital, The Second School of Clinical Medicine, Southern Medical University; Donghai County People's Hospital (Affiliated Kangda College of Nanjing Medical University), Lianyungang 222000, China.

Computational and Structural Biotechnology Journal
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a machine learning model to predict lung cancer chemotherapy response. TMED4 and DYNLRB1 gene expression predicts resistance, enabling personalized treatment selection for better patient outcomes.

Keywords:
Cell Line ValidationChemosensitivityLung CancerMachine LearningPrediction

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Lung cancer chemotherapy response varies significantly between patients.
  • Predicting individual responses is crucial for optimizing treatment and prognosis.

Purpose of the Study:

  • Develop a predictive model for chemotherapy response in lung cancer.
  • Integrate multi-omics and clinical data using machine learning.

Main Methods:

  • Utilized data from Genomics of Drug Sensitivity in Cancer and Gene Expression Omnibus databases.
  • Employed 45 machine learning algorithms, focusing on random forest and support vector machine.
  • Assessed the impact of key genes on chemotherapy response in cell lines.

Main Results:

  • A combined random forest and support vector machine model showed superior predictive performance.
  • Chemotherapy-sensitive patients had significantly longer overall survival.
  • TMED4 and DYNLRB1 gene expression correlated with chemotherapy resistance.
  • Gene knockdown enhanced lung cancer cell line chemosensitivity.

Conclusions:

  • A high-performance machine learning model for predicting lung cancer chemotherapy response was developed.
  • TMED4 and DYNLRB1 are key genes associated with chemotherapy resistance.
  • A web server is available for clinical application, facilitating personalized chemotherapy selection.