Advances in medical care have significantly improved survival among children with cancer. In China, the 5-year survival rate has reached 71.9%. Despite these improvements, many survivors continue to experience multiple co-occurring symptoms, such as fatigue, pain, sleep disturbance, and depression, which may adversely affect their quality of life.

These symptoms often occur together as symptom clusters and may reflect shared underlying biological mechanisms. This study aims to characterize symptom clusters among childhood cancer survivors and to explore their potential biological basis.

Participants will complete questionnaire assessments at multiple time points to evaluate symptom patterns and changes over time. In addition, stool samples will be collected to analyze gut microbiota composition and metabolite profiles. The study will examine the associations between symptom clusters and gut microbiota-metabolite features.

Findings from this study are expected to improve understanding of symptom burden in childhood cancer survivors and to provide evidence for the development of targeted symptom management strategies.

Background

Advances in diagnostic and therapeutic strategies have markedly improved survival among children with cancer. In high-income regions, the 5-year net survival rate for common childhood cancers has reached 80% to 95%, while in China the overall 5-year relative survival rate is approximately 71.9%. As survival improves, the number of childhood cancer survivors (CCS) continues to increase. However, the long-term impact of cancer and its treatment remains substantial.

Cancer treatments, including chemotherapy, radiotherapy, surgery, and targeted immunotherapy, may lead to a wide range of persistent physical and psychological symptoms. CCS often experience multiple concurrent symptoms, such as fatigue, pain, sleep disturbance, depression, and nausea. These symptoms frequently co-occur and interact, forming symptom clusters that may exacerbate overall symptom burden and negatively affect treatment outcomes, health-related quality of life, and long-term prognosis.

Symptom clusters have been widely reported across different cancer types and treatment stages. Despite observed heterogeneity, core symptom clusters-particularly physical, gastrointestinal, and psychological clusters-demonstrate relative stability across studies and over time. Longitudinal evidence suggests that while the number and composition of symptom clusters may vary, core clusters persist across treatment phases, indicating potential underlying biological mechanisms.

In addition to psychosocial factors, emerging evidence highlights the role of the gut microbiota and its metabolites in influencing host physiology and symptom expression. The gut microbiota interacts with the host through metabolic, immune, and neuroendocrine pathways, and may contribute to symptom heterogeneity via interconnected systems such as the gut-brain axis. Chemotherapy-induced inflammation and intestinal barrier disruption may further mediate these processes, suggesting a potential biological basis for persistent symptom clusters in CCS.

Rationale

Although symptom clusters in CCS have been increasingly studied, several critical gaps remain. First, most existing studies rely on cross-sectional data or short-term follow-up, limiting the ability to assess the long-term stability of symptom clusters. Second, research is often restricted to single cancer types or treatment stages, lacking comprehensive evaluation across diseases and treatment trajectories. Third, the identification of core symptoms and clusters is often inconsistent, with limited use of quantitative indicators to assess stability across time.

Furthermore, while psychosocial factors have been explored, the biological mechanisms underlying symptom clusters remain insufficiently understood. In particular, prospective evidence linking gut microbiota, metabolites, and symptom clusters in pediatric cancer populations is scarce. Existing studies are often limited to animal models or single biological pathways, and do not capture the complex interactions within the microbiota-immune-neuro network.

Addressing these gaps is essential for improving the understanding of symptom cluster heterogeneity and stability, and for identifying potential targets for intervention. A comprehensive approach integrating longitudinal symptom assessment with microbiome and metabolomic data may provide novel insights into the mechanisms underlying symptom clusters in CCS.

Objectives

The primary objective of this study is to characterize the heterogeneity and stability of symptom clusters among childhood cancer survivors and to explore their underlying mechanisms.

The specific objectives are:

1. To identify and classify symptom clusters in CCS across different cancer types and treatment stages
2. To examine the longitudinal stability of core symptom clusters using repeated measurements at baseline, 3 months, and 6 months
3. To analyze the associations between symptom clusters and gut microbiota composition and metabolite profiles
4. To explore the potential biological mechanisms linking gut microbiota-metabolite alterations with symptom cluster development

This study aims to provide evidence for a more comprehensive understanding of symptom burden in CCS and to inform the development of targeted, mechanism-based symptom management strategies.

Inclusion Criteria for Children:

• Aged 8-18 years;
• Clinically and pathologically confirmed diagnosis of cancer (including leukemia, lymphoma, central nervous system tumors, or other common solid tumors);
• Currently in the rehabilitation/maintenance phase or completed treatment ≥6 months prior to enrollment;
• No cognitive impairment; able to understand, communicate, and complete questionnaires independently;
• Written informed consent/assent obtained from participants and their legal guardians.

Exclusion Criteria for Children:

• Presence of severe treatment-related sequelae (e.g., significant organ dysfunction or neurological impairment) that may interfere with symptom assessment;
• Poor compliance or inability to independently complete interviews/questionnaires;
• Concurrent participation in other interventional clinical trials that may influence study outcomes;
• Planned major surgery or initiation of new chemotherapy/radiotherapy during the study period.

Inclusion Criteria for Caregivers:

• Primary caregiver of an eligible CCS participant;
• Aged ≥20 years;
• Able to understand study content and complete questionnaires, with adequate literacy and communication ability;
• Written informed consent provided prior to participation.

Exclusion Criteria for Caregivers:

• Experience of major adverse life events within the past 6 months (e.g., bereavement, divorce) that may affect psychological assessment
• Presence of severe cognitive impairment;
• Presence of severe aphasia, psychiatric disorders, or other conditions preventing independent completion of assessments.

This study will recruit childhood cancer survivors (CCS) aged 8-18 years and their primary caregivers. Eligible CCS have a clinically confirmed diagnosis of cancer (including leukemia, lymphoma, central nervous system tumors, and other common solid tumors) and are in the rehabilitation/maintenance phase or have completed treatment ≥6 months prior to enrollment. Participants must have no cognitive impairment and be able to understand, communicate, and complete assessments.

Primary caregivers aged ≥20 years who provide daily care for eligible CCS will also be included. All participants will provide informed consent/assent.

This cohort captures diverse cancer types and survivorship stages to examine symptom clusters, their longitudinal trajectories, and related psychosocial and biological factors, including gut microbiota and metabolites.

We have not yet determined whether individual participant data (IPD) will be shared because the dataset includes sensitive information from children and their parents, as well as biological data (e.g., microbiome data), which raises privacy and ethical considerations. Decisions regarding data sharing will depend on institutional policies, ethical approvals, and the development of appropriate data protection and de-identification procedures.