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Systematic Pathway Screening via Integrated Machine Learning Identifies FOXO-Mediated Transcription Signature for

Shuqi Wu1, Chenxi Deng1,2, Chaofan Fan3

  • 1Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China, fimmu.com.

Human Mutation
|January 21, 2026
PubMed
Abstract

Insights

A new machine learning model, the FOXO-related signature (FRS), effectively predicts non-small cell lung cancer (NSCLC) patient response to immune checkpoint inhibitors (ICIs). This biomarker stratifies patients, identifying those likely to benefit most from immunotherapy for improved survival outcomes.

Area of Science:

  • Oncology
  • Immunotherapy
  • Bioinformatics

Background:

  • Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality worldwide.
  • Immune checkpoint inhibitors (ICIs) improve outcomes for some NSCLC patients, but biomarkers are needed for personalized treatment selection.

Purpose of the Study:

  • To identify robust biomarkers for predicting immunotherapy response in NSCLC patients.
  • To develop a predictive model integrating transcriptomic data and machine learning algorithms.

Main Methods:

  • Transcriptomic data from 584 NSCLC patients treated with ICIs were analyzed.
  • 101 machine learning algorithm combinations were applied to 12,025 pathways to identify prognostic signatures.
  • The FOXO-mediated transcription pathway combined with Lasso-RSF algorithms was selected as the optimal predictor.

Main Results:

  • The FOXO-related signature (FRS) stratified patients into high-risk and low-risk groups, significantly impacting progression-free survival (PFS) and overall survival (OS).
  • FRS demonstrated superior predictive accuracy compared to clinical variables and existing models.
  • Immunohistochemistry (IHC) and immune profiling supported FRS's biological relevance and association with immunotherapy response.

Conclusions:

  • The machine learning-derived FRS is a potent predictor of immunotherapy response and survival in NSCLC.
  • FRS offers a clinically translatable tool for precision oncology by integrating FOXO-mediated immune regulation insights.
Keywords:
FOXO-mediated transcription pathwayimmune checkpointsimmune infiltrationimmunotherapynon–small cell lung cancer

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