A Latent profile and network intervention analysis of psychosomatic symptoms in lung cancer immunotherapy patients
4 affiliations
- 1Phase I Clinical Ward, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- 2Department of Thoracic Medicine, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- 3Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- 4Department of Thoracic Radiotherapyl, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
Abstract
Background: Immune checkpoint inhibitors improve non-small cell lung cancer prognosis but induce complex, heterogeneous psychosomatic symptoms. Traditional symptom-focused approaches fail to capture underlying patient heterogeneity and interaction mechanisms. Methods: In this cross-sectional study of 971 lung cancer patients undergoing immunotherapy, symptom phenotypes were identified using Latent Profile Analysis. Influencing factors were analyzed via multivariate logistic regression. Multivariate logistic regression was used to analyze influencing factors, and symptom networks and optimal intervention targets were investigated using network analysis and computer-simulated interventions. Results: The study identified three phenotypes: “High Symptom Burden with Comorbid Distress”(C1, 25.1%), “Emotional Distress Dominant”(C2, 29.2%), and “Mild Adaptive”(C3, 45.6%). Logistic regression (FDR‑adjusted q < 0.05) revealed that smoking history (OR=28.1, 95% CI: 5.2–151.8), clinical stage IV, and poor ECOG performance status (OR range: 5.9–8.4) were strong risk factors for the high‑burden phenotype (C1), while combination therapy (OR=0.22, 95% CI: 0.08–0.56), living with family (OR=0.15, 95% CI: 0.05–0.44), and absence of tumor metastasis were protective. Female sex (OR=3.3) was a risk factor for C2. Network analysis revealed core symptoms and bridging symptoms specific to each phenotype: Fatigue was the core symptom for C1, with interference in general activities serving as the bridging symptom; C2’s core symptom was sleep disturbance, with loss of interest serving as the bridging symptom; in C3, diminished enjoyment of life functioned as both core and bridging symptom. Based on cross‑sectional data, computer simulations further suggested potential intervention targets for each phenotype: fatigue for C1, enjoyment of life for C3, and interpersonal networks for C2. Network density was significantly higher in C1 and C2 than in C3 (both p < 0.001). Conclusions: This study highlights the complex interplay between somatic symptoms and anxiety‑depression in lung cancer immunotherapy patients, offering hypothesis‑generating evidence for precise intervention pathways tailored to distinct symptom phenotypes. It provides preliminary insights toward advancing symptom management toward stratified, personalized intervention models.
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BibTeX
@article{zheng2026latent,
title = {{A Latent profile and network intervention analysis of psychosomatic symptoms in lung cancer immunotherapy patients}},
author = {Danwen Zheng and Hang Gao and Yanfei Zou and Qiuyue Shao and Xinyan Yu and Lanying Qiu and Xiaoli Sun},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357460},
url = {https://doi.org/10.1371/journal.pone.0357460},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A Latent profile and network intervention analysis of psychosomatic symptoms in lung cancer immunotherapy patients
AU - Danwen Zheng
AU - Hang Gao
AU - Yanfei Zou
AU - Qiuyue Shao
AU - Xinyan Yu
AU - Lanying Qiu
AU - Xiaoli Sun
PY - 2026
DA - 2026/09/09
JO - PLOS One
DO - 10.1371/journal.pone.0357460
UR - https://doi.org/10.1371/journal.pone.0357460
AB - Background: Immune checkpoint inhibitors improve non-small cell lung cancer prognosis but induce complex, heterogeneous psychosomatic symptoms. Traditional symptom-focused approaches fail to capture underlying patient heterogeneity and interaction mechanisms. Methods: In this cross-sectional study of 971 lung cancer patients undergoing immunotherapy, symptom phenotypes were identified using Latent Profile Analysis. Influencing factors were analyzed via multivariate logistic regression. Multivariate logistic regression was used to analyze influencing factors, and symptom networks and optimal intervention targets were investigated using network analysis and computer-simulated interventions. Results: The study identified three phenotypes: “High Symptom Burden with Comorbid Distress”(C1, 25.1%), “Emotional Distress Dominant”(C2, 29.2%), and “Mild Adaptive”(C3, 45.6%). Logistic regression (FDR‑adjusted q < 0.05) revealed that smoking history (OR=28.1, 95% CI: 5.2–151.8), clinical stage IV, and poor ECOG performance status (OR range: 5.9–8.4) were strong risk factors for the high‑burden phenotype (C1), while combination therapy (OR=0.22, 95% CI: 0.08–0.56), living with family (OR=0.15, 95% CI: 0.05–0.44), and absence of tumor metastasis were protective. Female sex (OR=3.3) was a risk factor for C2. Network analysis revealed core symptoms and bridging symptoms specific to each phenotype: Fatigue was the core symptom for C1, with interference in general activities serving as the bridging symptom; C2’s core symptom was sleep disturbance, with loss of interest serving as the bridging symptom; in C3, diminished enjoyment of life functioned as both core and bridging symptom. Based on cross‑sectional data, computer simulations further suggested potential intervention targets for each phenotype: fatigue for C1, enjoyment of life for C3, and interpersonal networks for C2. Network density was significantly higher in C1 and C2 than in C3 (both p < 0.001). Conclusions: This study highlights the complex interplay between somatic symptoms and anxiety‑depression in lung cancer immunotherapy patients, offering hypothesis‑generating evidence for precise intervention pathways tailored to distinct symptom phenotypes. It provides preliminary insights toward advancing symptom management toward stratified, personalized intervention models.
SN - 1932-6203
ER -