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Exploring the predictive "psycho-biomarkers" for checkpoint immunotherapy in cancer

  • 0Department of Breast, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China.
Frontiers in Immunology +

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August 5, 2025

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August 5, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study identifies depression-related genes as novel biomarkers for predicting patient response to immune checkpoint inhibitors (ICIs) therapy, offering insights into tumor immunity and personalized cancer treatment strategies.

Area Of Science

  • Oncology
  • Immunology
  • Genetics
  • Psychoneuroimmunology

Background

  • Cancer immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized cancer treatment, improving survival rates.
  • However, early mortality risks and variable patient responses necessitate reliable predictive biomarkers.
  • Depression is recognized as a factor influencing tumor immunity, yet its genetic markers' role in ICI response remains unexplored.

Purpose Of The Study

  • To investigate the potential of depression-related genes as predictive biomarkers for patient response to immune checkpoint inhibitor (ICI) therapy.
  • To develop and validate a predictive model for clinical outcomes in cancer patients treated with ICIs, utilizing depression-associated genetic signatures.
  • To explore the correlation between a depression-related gene signature and key clinical parameters like tumor mutational burden and immune cell infiltration.

Main Methods

  • Utilized public datasets (TCGA, GEO) from ICI-treated patients for comprehensive analysis, including bulk mRNA sequencing and co-expression network construction.
  • Employed regression analyses (Cox proportional hazards, Lasso) to identify eight key depression-related genes for a predictive model.
  • Assessed correlations between the developed predictive score and clinical parameters such as tumor mutational burden (TMB) and immune cell infiltration.

Main Results

  • A predictive model based on eight depression-related genes effectively stratified patients into high- and low-responsiveness groups for ICI therapy.
  • The model demonstrated strong predictive accuracy for disease-free survival (DFS) across internal and external validation datasets.
  • The identified response stratification correlated significantly with immune cell abundance and tumor mutational burden (TMB) in cancer patients.

Conclusions

  • Depression-related genetic profiles can serve as potential biomarkers for predicting response to immune checkpoint inhibitor (ICI) therapy.
  • These genetic markers are associated with tumor mutational status and immune system alterations, providing a basis for personalized treatment strategies.
  • The findings support the use of depression-related genetic traits for early intervention and improved prognosis in specific cancer types.

Keywords:

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