Immune indicators as predictors of cancer-related fatigue: A risk prediction model in pan-cancer patients

Guixin He¹Ting Ge¹Baohui Wang^2*Jianchun Yu^1*Wentao Li^1*

• ¹First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
• ²First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Chinese Medical University, Hangzhou, China

Cancer-related fatigue (CRF) is a common and multifactorial symptom that significantly impairs the quality of life in patients with cancer. This study aimed to identify immune and clinical factors associated with CRF in pan-cancer patients and to develop a risk prediction model to support personalized clinical treatment. A retrospective analysis was conducted using clinical data from 146 cancer patients hospitalized in the Oncology Department of the First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine. Collected variables included demographic characteristics, disease-related information, immunological indicators, and Brief Fatigue Inventory (BFI) scores. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors, and a predictive model was constructed and evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Multivariate analysis identified increasing age, increased absolute counts (AC) of CD4⁺CD38⁻T cells, and decreased AC of CD4⁺CD28⁻T cells as independent risk factors for CRF (P < 0.05). The model demonstrated moderate predictive performance, with an area under the ROC curve (AUC) of 0.725 in the training set and 0.581 in the validation set. These findings suggest that chronic inflammation related to immunosenescence and immune remodeling may contribute to CRF. Further research is warranted to validate the model in diverse populations and to develop targeted strategies aimed at alleviating fatigue and improving quality of life in cancer patients.