Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine BiomarkersLin, Abbas-Aghababazadeh, Su
et alClin Breast Cancer (2025)
Abstract: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare costs. Early identification of patients at risk for severe RD can facilitate preventive management and reduce severe symptoms. This study evaluated the utility of subjective and objective factors, such as patient-reported outcomes (PROs) and serum cytokines, for predicting RD in breast cancer patients. The performance of machine learning (ML) and logistic regression-based models were compared.Data from 147 breast cancer patients who underwent radiotherapy was analyzed to develop prognostic models. ML algorithms, including neural networks, random forest, XGBoost, and logistic regression, were employed to predict clinically significant Grade 2+ RD. Clinical factors, PROs, and cytokine biomarkers were incorporated into the risk models. Model performance was evaluated using nested cross-validation with separate loops for hyperparameter tuning and calculating performance metrics.Feature selection identified 18 predictors of Grade 2+ RD including smoking, radiotherapy boost, reduced motivation, and the cytokines interleukin-4, interleukin-17, interleukin-1RA, interferon-gamma, and stromal cell-derived factor-1a. Incorporating these predictors, the XGBoost model achieved the highest performance with an area under the curve (AUC) of 0.780 (95% CI: 0.701-0.854). This was not significantly improved over the logistic regression model, which demonstrated an AUC of 0.714 (95% CI: 0.629-0.798).Clinical risk factors, PROs, and serum cytokine levels provide complementary prognostic information for predicting severe RD in breast cancer patients undergoing radiotherapy. ML and logistic regression models demonstrated comparable performance for predicting clinically significant RD with AUC>0.70.Copyright © 2025 The Author(s). Published by Elsevier Inc. All rights reserved.
Identification and Expression Analyses of IL-17/IL-17R Gene Family in Snakehead (Channa argus) Following Nocardia seriolae InfectionHan, Su, Che
et alGenes (Basel) (2025) 16 (3)
Abstract: The interleukin 17 (IL-17) family, known for its proinflammatory properties, is important in immune responses against bacterial and fungal infections. To exert its immune function, the IL-17 family typically binds to IL-17 receptor (IL-17R) to facilitate signal transduction.This study identified, cloned and analyzed seven IL-17 and nine IL-17R family members in snakeheads.A duplication event occurred in snakehead IL-17s and IL-17Rs, but bioinformatics analyses indicated that these genes were conserved in both protein domains and evolutionary processes. Tissue distribution analysis revealed that IL-17s/IL-17Rs were widely distributed in the detected tissues, with relatively high expression levels in immune tissues. Upon Nocardia seriolae stimulation, most members were expressed, particularly IL-17C2, IL-17D, IL-17N, IL-17RA1, IL-17RA2, IL-17RC1, and IL-17RE1, which were significantly upregulated in gill and intestine.These results suggested that IL-17s and IL-17Rs played a crucial role in mucosal immunity against bacterial infection, providing insights into immunoprophylactic strategies for bacterial diseases in aquaculture.
The impact of biologics targeting the IL-17 and IL-23 pathways on metabolic indicators in plaque psoriasisJiang, Li, Zheng
Arch Dermatol Res (2025) 317 (1), 643
Abstract: This study aims to compare the efficacy of IL-17 and IL-23 biologics in the treatment of plaque psoriasis (Psoriasis vulgaris) in patients with metabolic syndrome (MetS) and to explore the effects of different biologics on metabolic indicators, particularly regarding the differences in efficacy during long-term treatment. This is a randomized controlled clinical trial involving 120 moderates to severe plaque psoriasis patients, of which 60 have metabolic syndrome and 60 do not. The patients were randomly assigned to three groups: IL-17 biologics group, IL-23 biologics group, and cyclosporine control group. Treatment lasted for three months, with evaluation indicators including psoriatic lesion assessment (PASI score), blood glucose levels, lipid profile (triglycerides, HDL-C), inflammatory markers (CRP, ESR, IL-6), etc. Patients were assessed at baseline, after one month, and after three months of treatment for both clinical efficacy and changes in metabolic indicators. Both IL-17 and IL-23 biologics demonstrated superior efficacy compared to cyclosporine in treating plaque psoriasis. After one month and three months of treatment, PASI scores in the IL-17 and IL-23 groups were significantly lower than in the control group, and the therapeutic effects were more pronounced (P < 0.05). The IL-17 and IL-23 groups also showed better improvements in blood glucose, blood lipids (TG and HDL-C), and inflammatory markers (CRP, ESR, IL-6) compared to the control group. After three months of treatment, fasting blood glucose, fasting insulin, triglycerides, and CRP levels were significantly lower in the IL-17 and IL-23 groups than in the control group (P < 0.05). Metabolic syndrome had some impact on treatment outcomes, with the efficacy of IL-17 and IL-23 biologics being lower in patients with metabolic abnormalities compared to those without metabolic syndrome. However, the IL-23 biologic showed less impact from metabolic syndrome. IL-17 biologics had a rapid effect in the short term, while IL-23 biologics demonstrated superior efficacy in long-term treatment, particularly at the three-month mark, where both efficacy and metabolic improvements were better than the IL-17 group. Both IL-17 and IL-23 biologics are more effective than cyclosporine in treating plaque psoriasis and can improve metabolic indicators in patients. Although metabolic syndrome impacts the efficacy of IL-17 biologics, IL-23 biologics are less affected by metabolic syndrome and demonstrate better long-term efficacy. Therefore, IL-23 biologics are recommended for long-term treatment in plaque psoriasis patients with metabolic syndrome.© 2025. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Lanatoside C Inhibits Proliferation and Induces Apoptosis in Human Prostate Cancer Cells Through the TNF/IL-17 Signaling PathwayHuang, Huang, Jin
et alInt J Mol Sci (2025) 26 (6)
Abstract: Prostate cancer remains a leading cause of cancer-related morbidity and mortality among men globally, with limited therapeutic options for advanced and metastatic disease. The therapeutic potential of natural compounds has attracted increasing attention in cancer treatment. Lanatoside C (Lan C), a cardiac glycoside derived from Digitalis lanata, has demonstrated promising anticancer activity across various cancer types. However, its role and mechanisms in prostate cancer remain underexplored. In this study, evidence shows that Lan C significantly inhibits the proliferation of prostate cancer cells, as demonstrated by reduced cell viability, suppressed colony formation, and G2/M cell cycle arrest. Additionally, Lan C promotes apoptosis and inhibits the migration and invasion of prostate cancer cells. Mechanistically, transcriptomic analysis identified differentially expressed genes, which were further validated at both the mRNA and protein levels. Our findings suggest that Lan C exerts its effects by modulating the TNF/IL-17 signaling pathway, influencing the tumor microenvironment and regulating key processes involved in tumor progression, immune response, and apoptosis.
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