Establishment of a predictive model for severe acute radiotherapy adverse reactions in nasopharyngeal carcinoma patients based on Olink proteomics
10.3760/cma.j.cn115355-20240124-00051
- VernacularTitle:基于Olink蛋白质组学建立鼻咽癌患者重度急性放疗不良反应预测模型
- Author:
Yaning ZHOU
1
;
Ya LIU
;
Dan ZUO
;
Junlin YI
;
Dan LI
;
Ye ZHANG
Author Information
1. 国家癌症中心 国家肿瘤临床医学研究中心 中国医学科学院北京协和医学院肿瘤医院放疗科，北京 100021
- Keywords:
Nasopharyngeal neoplasms;
Radiotherapy;
Radiation injuries;
Cytokines;
logistic models
- From:
Cancer Research and Clinic
2024;36(5):321-327
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the relationship between the inflammatory cytokines level in the plasma of nasopharyngeal carcinoma patients before radiotherapy and acute radiotherapy adverse reactions, and to establish a preliminary model for predicting the risk of severe acute adverse reactions during radiotherapy.Methods:A cross-sectional study was conducted. A total of 85 nasopharyngeal carcinoma patients who received radical radiotherapy in Cancer Hospital of Chinese Academy of Medical Sciences from May 2016 to March 2019 were retrospectively collected. The highest grade adverse reactions of radiation oral mucositis, radiation dermatitis and xerostomia during radiotherapy were evaluated according to the American Cancer Radiotherapy Collaboration (RTOG) acute radiation injury evaluation criteria, and the above adverse reactions ≥ grade 3 were treated as the severity. Olink proteomics technology was used to detect the level of 92 inflammatory cytokines (the standardized protein expression values) in the plasma of patients before radiotherapy for the first time. Single factor analysis of variance and independent sample t-test were used to analyze the relationship between inflammatory cytokines and clinical factors, as well as acute adverse reactions during radiotherapy. Based on inflammatory cytokines and/or the clinical factors, binary logistic regression was used to construct a predictive model for the risk of severe acute radiotherapy adverse reactions. Whether the most severe adverse reactions assessed by the American RTOG acute radiation injury evaluation criteria during radiotherapy were severe or not were taken as the gold standard. Receiver operating characteristic (ROC) curve was used to analyze the effectiveness of the established models for judging the severe acute adverse reactions. Results:Among the 85 patients, 68 were males and 17 were females, with the median age [ M ( Q1, Q3)] of 49 years (43 years, 60 years). All patients received radical radiotherapy, of which 64 cases were treated with combination chemotherapy or targeted therapy. A total of 19 cases (22.1%) experienced severe acute radiotherapy adverse reactions. There were statistically significant differences in the levels of interleukin (IL)-22 receptor A1 (IL-22RA1), IL-18 receptor 1(IL-18R1), eotaxin-1 (CCL11), tumor necrosis factor ligand superfamily member 14 (TNFSF14), FMS-like tyrosine kinase 3 ligand (Flt3L), and monocyte chemotactic protein 2 (MCP-2) in the plasma of patients with grade 1, 2, 3 acute radiation oral mucositis before radiotherapy; there were statistically significant differences in the levels of CD244 (all P < 0.05); there were statistically significant differences in the levels of CD244, CC chemokines ligand 20 (CCL20), leukemia inhibitory factor ligand (LIF-R) and IL-4 in the plasma of patients with grade 1, 2, 3 acute radiation dermatitis before radiotherapy (all P < 0.05); there were statistically significant differences in the levels of IL-12B, CXC chemokines ligand 11 (CCL11), LIF-R and IL-33 in the plasma between patients with grade 1 and grade 2 xerostomia before radiotherapy (all P<0.05). The result of single factor analysis of variance showed that the clinical factors were not associated with severe acute radiation adverse reactions (all P > 0.05). Binary logistic regression model M1 was established by selecting 6 clinical factors including age, T staging, N staging, clinical staging, whether to receive chemotherapy or not and whether to suffer from diabetes or not in the literatures. Based on cytokine function and previous literatures, the binary logistic regression model M2 was established by selecting IL-22RA1, IL-18R1, MCP-2, CCL11, CD244, CCL20 and IL-33 from the differential cytokines. A binary logistic regression model M3 was established by combining the above clinical factors with cytokines. The ROC curve analysis showed that the area under the curve of the M1, M2, M3 predictive models for judging the severe acute radiation adverse reactions was 0.781, 0.841, 0.868, respectively. Conclusions:There were differences in the expression levels of various inflammatory cytokines in plasma before radiotherapy among patients with different grades of acute radiotherapy adverse reactions. Building the models based on plasma inflammatory cytokine levels combined with clinical factors before the first radiotherapy could effectively predict the risk of severe acute radiotherapy adverse reactions in patients with nasopharyngeal carcinoma.
