Serum metabolomics reveals the effects of accompanying treatment on fatigue in patients with multiple myeloma

Biomarkers of fatigue in oncology: A systematic review

Cancer-related fatigue (CRF) is a distressing side effect of cancer and treatment, affecting both patients during active treatment and survivors, negatively impacting quality of life. While its exact cause remains uncertain, various mechanisms such as immune dysfunction, HPA-axis dysfunction, and treatment toxicity are proposed. Inflammatory biomarkers of CRF have been explored in previous research, but non-inflammatory markers have not been comprehensively studied. This systematic review analysed 33 studies to identify non-inflammatory peripheral blood biomarkers associated with CRF. Promising markers included Hb, blood coagulation factors, BDNF, tryptophan, GAA, mtDNA, platinum, CA125, and cystatin-C. Inconsistent findings were observed for other markers like VEGF, leptin, and stress hormones. Most studies focused on adults. Research in pediatrics is limited. This review showed partial evidence for the inflammaging hypothesis (neurotoxicity due to neuro-inflammation) laying at the basis of CRF. Further research, especially in pediatrics, is needed to confirm this hypothesis and guide future biomarker studies.

Multidimensional Predictors of Cancer-Related Fatigue Based on the Predisposing, Precipitating, and Perpetuating (3P) Model: A Systematic Review

Cancer-related fatigue (CRF) is a widespread symptom with high prevalence in cancer patients, seriously affecting their quality of life. In the context of precision care, constructing machine learning-based prediction models for early screening and assessment of CRF is beneficial to this situation. To further understand the predictors of CRF for model construction, we conducted a comprehensive search in PubMed, Web of Science, Embase, and Scopus databases, combining CRF with predictor-related terms. A total of 27 papers met the inclusion criteria. We evaluated the above studies into three subgroups following the predisposing, precipitating, and perpetuating (3P) factor model. (1) Predisposing factors-baseline fatigue, demographic characteristics, clinical characteristics, psychosocial traits and physical symptoms. (2) Precipitating factors-type and stage of chemotherapy, inflammatory factors, laboratory indicators and metabolic changes. (3) Perpetuating factors-a low level of physical activity and poorer nutritional status. Future research should prioritize large-scale prospective studies with emerging technologies to identify accurate predictors of CRF. The assessment and management of CRF should also focus on the above factors, especially the controllable precipitating factors, to improve the quality of life of cancer survivors.