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一种基于新型炎症标志物的晚期肺淋巴上皮瘤样癌预后模型。

A Novel Inflammation-Marker-Based Prognostic Model for Advanced Pulmonary Lymphoepithelioma-Like Carcinoma.

作者信息

Chen Xueyuan, Liu Tingting, Mo Silang, Yang Yuwen, Chen Xiang, Hong Shaodong, Zhou Ting, Chen Gang, Zhang Yaxiong, Ma Yuxiang, Ma Yuanzheng, Zhang Li, Zhao Yuanyuan

机构信息

Medical Oncology Department, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People's Republic of China.

Department of Clinical Research, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.

出版信息

J Inflamm Res. 2025 Feb 20;18:2433-2445. doi: 10.2147/JIR.S502286. eCollection 2025.

Abstract

PURPOSE

This study aimed to investigate the prognostic value of inflammation markers for advanced pulmonary lymphoepithelioma-like carcinoma (PLELC) and develop an effective prognostic model based on inflammation markers to predict the overall survival (OS) of this population.

METHODS

Cox regression analysis was performed on 18 clinical and inflammation features, and a nomogram was created to predict overall survival (OS). The nomogram was evaluated by the concordance index (C-index), the time-dependent area under the receiver operating (ROC) curves (AUCs), calibration curves, and Decision Curve Analysis (DCA).

RESULTS

This study included a training cohort (n = 177) and a validation cohort (n = 77). The following variables were found to be independent prognostic factors for OS and used in the nomogram: Hepatitis B virus surface antigen status, gender, neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein-to-albumin ratio (CAR). The C-indexes of the nomogram in the training and validation cohort were 0.740 (95% CI: 0.706-0.747) and 0.733 (95% CI: 0.678-0.788), respectively. Furthermore, time-dependent AUCs and well-fitted calibration curves showed good discriminative ability in both cohorts. Additionally, among the subset of EBV DNA data (n = 111), both ROC curve and DCA curve analysis demonstrated that the nomogram plus EBV DNA provided superior predictive performance compared to EBV DNA or the nomogram alone. Patients who received chemoimmunotherapy as the first-line treatment had better OS (not reached vs 44.4 months, P = 0.015) than those with chemotherapy alone and those who received immunotherapy at any line had better OS than those who never received it (not reached vs 31.0 months, P < 0.001).

CONCLUSION

This study established and validated a prognostic nomogram model for patients with advanced PLELC. Combining the nomogram with EBV DNA more effectively predicted the prognosis of patients than the nomogram alone. Immunotherapy was found to be a critical treatment option for PLELC.

摘要

目的

本研究旨在探讨炎症标志物对晚期肺淋巴上皮瘤样癌(PLELC)的预后价值,并基于炎症标志物建立有效的预后模型以预测该人群的总生存期(OS)。

方法

对18项临床和炎症特征进行Cox回归分析,并创建列线图以预测总生存期(OS)。通过一致性指数(C指数)、受试者工作特征(ROC)曲线下的时间依赖性面积(AUC)、校准曲线和决策曲线分析(DCA)对列线图进行评估。

结果

本研究包括一个训练队列(n = 177)和一个验证队列(n = 77)。发现以下变量是OS的独立预后因素并用于列线图:乙肝病毒表面抗原状态、性别、中性粒细胞与淋巴细胞比值(NLR)和C反应蛋白与白蛋白比值(CAR)。训练队列和验证队列中列线图的C指数分别为0.740(95%CI:0.706 - 0.747)和0.733(95%CI:0.678 - 0.788)。此外,时间依赖性AUC和拟合良好的校准曲线在两个队列中均显示出良好的鉴别能力。另外,在EBV DNA数据子集(n = 111)中,ROC曲线和DCA曲线分析均表明,与单独的EBV DNA或列线图相比,列线图加EBV DNA具有更好的预测性能。接受化疗免疫疗法作为一线治疗的患者的OS优于单纯化疗患者(未达到 vs 44.4个月,P = 0.015),并且在任何线接受免疫疗法的患者的OS优于从未接受过免疫疗法的患者(未达到 vs 31.0个月,P < 0.001)。

结论

本研究为晚期PLELC患者建立并验证了一个预后列线图模型。将列线图与EBV DNA相结合比单独使用列线图更有效地预测了患者的预后。发现免疫疗法是PLELC的关键治疗选择。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae32/11859127/e45c9edfdf40/JIR-18-2433-g0001.jpg

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