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Physical fatigue and depression are prioritized intervention targets for breaking the vicious cycle of psychoneurological symptom cluster interactions in breast cancer patients

In a cross-sectional study of 304 breast cancer patients, network analysis identified physical fatigue (EI=1.883) and depression (EI=0.794) as core symptoms, while computer-simulated interventions showed that targeting depression produced the largest reduction in symptom sum scores (from 5.98 to 4.67). The findings suggest that physical fatigue and depression should be prioritized as intervention targets to disrupt symptom interactions within the psychoneurological symptom cluster.

Last updated: Jul 17, 2026•0 RCTs•📖 Read as article →

Evidence Score

Evidence Score32/100
Human RCT☆☆☆☆☆
Meta-analysis☆☆☆☆☆
Mechanism★★★★★
Safety★★★★☆
Confidencelow

Study Evidence

Study 1. Navigating specific targets of psychoneurological symptom cluster in breast cancer: a computer-simulated network analysis.

observational

Cai J, Liu Z, Liu X, Wan C, Duan X · Frontiers in oncology (2026)

Participants: N/A
Duration: N/A
Intervention: Computer-simulated interventions targeting individual symptoms within the psychoneurological symptom cluster
Outcome: Symptom sum scores, expected influence (EI), bridge expected influence (bEI), network structure
Effect Size: N/A
Population: 304 breast cancer patients who received treatment in the Breast Surgery Department from June 2025 to January 2026

Result:

PubMed: 42434734DOI: 10.3389/fonc.2026.1869133

Mechanism Graph

Physical fatigue and depression identified as core symptoms with high expected influence in the symptom network
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Computer-simulated interventions modeled the effect of reducing each symptom on the overall symptom cluster
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Depression reduction produced the largest decrease in total sum score (from 5.98 to 4.67)
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Targeting physical fatigue and depression may break the vicious cycle of symptom interactions

Limitations

  • ⚠Cross-sectional design limits causal inference despite Bayesian network analysis
  • ⚠Self-report measures may introduce bias and do not capture objective sleep or fatigue parameters
  • ⚠Computer-simulated interventions are not actual clinical interventions and require real-world validation

Frequently Asked Questions

What is the psychoneurological symptom cluster (PNSC) in breast cancer?▼

PNSC includes sleep disturbance, cancer-related fatigue, emotional distress (anxiety and depression), and pain, which commonly occur together during and after anti-cancer treatment in breast cancer patients.

Which symptoms were identified as the most important intervention targets?▼

Physical fatigue and depression were identified as core symptoms with the highest expected influence, and computer simulations showed that targeting depression produced the largest reduction in overall symptom burden.

How was the network analysis conducted in this study?▼

The study used Gaussian network models for static symptom interrelationships, Bayesian network analysis for directional associations, and computer-simulated interventions to explore dynamic symptom changes.

What were the main limitations of this study?▼

The study was cross-sectional, relied on self-report data, and used computer simulations rather than actual clinical interventions, so real-world validation is needed.

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References

  1. 1.Cai J, Liu Z, Liu X, Wan C, Duan X. "Navigating specific targets of psychoneurological symptom cluster in breast cancer: a computer-simulated network analysis.." Frontiers in oncology, 2026. PMID: 42434734 DOI: 10.3389/fonc.2026.1869133
Disclaimer: This content is for educational purposes only and is not medical advice. Evidence scores reflect the quality and quantity of available research, not clinical recommendations. Always consult a healthcare professional before starting any supplement or intervention.