A Multicenter Study on the Heterogeneity of Symptom Clusters and the Underlying Mechanisms of Gut Microbiota and Metabolites Among Childhood Cancer Survivors
Conditions: Cancer, Glioma, Leukemia (Both ALL and AML)
Sponsor: The Children's Hospital of Zhejiang University School of Medicine

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Research Protocol
Background
With advances in diagnostic and therapeutic technologies, an increasing number of
pediatric cancer patients can achieve long-term survival following standardized
treatment. The 5-year net survival rate for common childhood cancers has reached
80–95% in high-income regions such as Europe, North America, and East Asia. In
China, the long-term survival rate of childhood cancer is slightly lower than that in
developed countries, with an overall 5-year relative survival rate of 71.9%. The
improved survival rate has led to a growing population of childhood cancer
survivors (CCS).
Despite improved long-term survival, the impact of cancer and its treatment on CCS
cannot be overlooked. Treatments such as chemotherapy, radiotherapy, surgery, and
targeted immunotherapy prolong survival but also cause varying degrees of
physiological damage. Studies have shown that CCS often experience 10–16
concurrent physical and psychological symptoms, among which fatigue, pain,
insomnia, depression, and nausea are the most common. These symptoms typically
occur in clusters, where interactions among symptoms may produce synergistic and
amplifying effects, leading to greater negative outcomes. This further exacerbates
symptom burden, significantly affects treatment outcomes and health-related quality
of life, and may even shorten survival.
Symptom management has become a critical component of CCS care. Understanding
the interaction mechanisms among multiple symptoms is of great clinical significance
for healthcare providers.
Symptom cluster identification is a classical dimensionality reduction approach in
clinical practice aimed at simplifying complex symptom interactions. According to
Kim et al., a symptom cluster consists of two or more concurrent and interrelated
symptoms that may share common or distinct etiologies. Symptom clusters are
relatively independent, while symptoms within a cluster may reinforce or accelerate
each other or promote the development of other symptoms. Compared with single
symptoms, the combined effects of symptom interactions lead to more severe negative
outcomes.
Existing studies indicate that although symptom clusters in CCS show heterogeneity
due to differences in cancer types, treatment stages, assessment tools, and analytical
methods, core symptom cluster types demonstrate cross-study stability. For example,
Fang et al. constructed a symptom network including 31 symptoms among children
undergoing chemotherapy and identified five clusters: mucocutaneous symptoms,
physical
symptoms,
self-image
disturbance,
gastrointestinal
symptoms,
and

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psychological symptoms. These findings are consistent with studies by Yeh et al. and
Hockenberry et al.
In recent years, research on childhood cancer symptoms in China has shifted from
cross-sectional to longitudinal designs, aiming to explore dynamic changes in
symptom clusters across disease stages. For instance, longitudinal tracking of children
with acute lymphoblastic leukemia showed that although the number of symptom
clusters varied across treatment stages (3–6 clusters), physical, gastrointestinal, and
psychological symptom clusters consistently existed, indicating temporal stability of
core clusters.
Based on existing evidence, symptom clusters in pediatric cancer can be broadly
categorized into three types:Disease- and treatment-related symptom clusters (e.g.,
malnutrition, cardiopulmonary toxicity, motor dysfunction, cognitive impairment,
mucosal damage); Physical symptom clusters (e.g., pain, fatigue, sleep disturbance,
nausea, vomiting, anorexia); Psychological symptom clusters (e.g., anxiety,
depression, irritability, worry).
These clusters are repeatedly identified across different diseases and treatment stages,
suggesting long-term stability and potential biologically driven mechanisms rather
than short-term treatment effects.
However, current research has several limitations:
Stability verification relies mainly on short-term repeated measurements (≤3 months)
or cross-sectional comparisons;
Heterogeneity analyses are limited to single cancer types or treatment stages;
Definitions of core symptoms lack standardized criteria and quantitative cross-time
stability measures.
Therefore, this study will employ repeated measurements at baseline, 3 months, and 6
months, combined with exploratory factor analysis and latent growth mixture
modeling, to systematically validate symptom cluster stability across cancer types and
treatment stages.
Potential mechanisms underlying symptom cluster stability may involve:
Psychosocial
factors
(e.g.,
personal
traits,
psychological
status,
social
environment);Pathophysiological processes (e.g., chemotherapy-induced mucosal
injury, neuroinflammation).
In addition, growing evidence from epidemiology and microbiome research suggests
that gut microbiota and their metabolites play important roles in mediating disease

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and behavioral outcomes. Gut microbiota influence host metabolism, immune
regulation, and inflammation through pathways such as the gut–brain axis, gut–lung
axis, gut–bone axis, and others. These findings provide new insights into the
biological mechanisms underlying symptom clusters in pediatric cancer.
Objectives
To describe the prevalence and severity of common symptoms in CCS, identify
symptom cluster types, and validate their longitudinal stability using baseline and
follow-up data.
To explore heterogeneity in symptom cluster trajectories; identify demographic,
physiological, psychological, and social factors influencing trajectory patterns;
quantify their relative importance; and examine their interactions and predictive
effects on quality of life.
To compare gut microbiota composition and diversity across different symptom
cluster trajectory subgroups, identify characteristic taxa and metabolites associated
with high symptom burden, and explore underlying microbiome-related mechanisms.
Methods
Phase I: Identification and Validation of Symptom Clusters
A longitudinal design will be used. Field surveys will assess symptom prevalence and
severity. Exploratory factor analysis will identify symptom clusters and core
symptoms based on baseline data. Cross-sample and longitudinal validation will be
conducted to assess stability over time.
Phase II: Trajectories, Influencing Factors, and Quality of Life
Latent growth mixture modeling will be used to identify symptom cluster trajectories.
Logistic regression and machine learning algorithms will determine influencing
factors and their relative importance. Network analysis will examine temporal
interactions. Negative binomial and zero-inflated Poisson models will evaluate the
predictive effects on quality of life.

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Phase III: Gut Microbiota and Metabolomics Mechanisms
A nested case–control study will be conducted. Biological samples will be collected
for:16S rRNA sequencing (microbiome analysis); Liquid chromatography–mass
spectrometry (metabolomics)
Differences in microbiota composition, diversity, and metabolites between trajectory
subgroups will be analyzed. Machine learning methods will identify key microbial
taxa and metabolites.
Sample Size
Sample size is determined based on statistical requirements:
Phase I: ≥200 participants (for stable factor analysis results)
Phase II: Based on latent growth models, regression, and network analysis
requirements
Phase III: Derived from Phases I and II
Considering feasibility and potential attrition, the total sample size is estimated at
approximately 300 participants.
Study Procedures
Phase I
Baseline survey conducted during hospitalization, followed by 3- and 6-month
follow-ups. Data collected include demographics, clinical information, and symptom
assessments.
Phase II
Participants completing all follow-ups will be included. Only non-invasive variables
(e.g., demographics, clinical data, lifestyle factors) will be analyzed.
Phase III

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Biological samples will be collected under fasting conditions. Case and control groups
will be defined based on symptom trajectory patterns. Multi-omics and statistical
analyses will be conducted.
Outcome Measures
All measures are based on standardized instruments.
A self-designed questionnaire will collect demographic and clinical information for
both children and their parents.