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介绍MCC-PS:一种用于默克尔细胞癌的新型预后评分。

Introducing MCC-PS: a novel prognostic score for Merkel cell carcinoma.

作者信息

Abu Rached Nessr, Becker Jürgen C, Lonsdorf Anke S, Keller Aric, Zeglis Ioannis A, Gambichler Thilo

机构信息

Skin Cancer Center, Department of Dermatology, Venereology and Allergology, Ruhr-University Bochum, Bochum, Germany.

International Centre for Hidradenitis Suppurativa/Acne Inversa (ICH), Department of Dermatology, Venereology and Allergology, Ruhr-University, Bochum, Germany.

出版信息

Front Oncol. 2024 Jul 22;14:1427740. doi: 10.3389/fonc.2024.1427740. eCollection 2024.

DOI:10.3389/fonc.2024.1427740
PMID:39104722
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11298473/
Abstract

INTRODUCTION

Merkel cell carcinoma (MCC) is an aggressive skin cancer with a poor prognosis, which only improved with the introduction of immunotherapies. An MCC prediction model with high diagnostic accuracy is lacking. The aim was to develop an MCC prognostic score (MCC-PS) based on combinations of previously proposed risk factors.

METHODS

A multicentric, retrospective study was conducted to develop MCC-PS, which included age, neuron-specific enolase (NSE), C-reactive protein (CRP), creatinine, bilirubin, and international normalized ratio (INR). Creatinine, bilirubin, and INR were used to calculate the model of end-stage liver disease (MELD) score. A total of 98 patients were included in the study, including 36.7% with stage I according to American Joint Committee on Cancer 2018 ( = 36), 30.6% with stage II ( = 30), 25.5% with stage III ( = 25), and 7.1% with stage IV ( = 7). Survival data of MCC patients were correlated with selected laboratory parameters and risk factors. Primary endpoint was MCC-specific survival (MSS) and the secondary endpoint was progression-free survival. Several statistical methods were used to develop the prognostic score, including correlation analysis, Kaplan-Meier curves, Cox regression, and time-dependent receiver operating characteristic analysis.

RESULTS

The MCC-PS is based on the sum of the following baseline variables: elevated CRP (≥5.5 mg/l), elevated NSE (≥22.8 µg/l), MELD score ≥ 11, and age ≥ 75 years. An MELD score ≥ 11 was scored as 4 points, elevated NSE level as 3 points, elevated CRP level as 2 points, and age ≥ 75 years as 1 point. A high-risk group according to the MCC-PS was characterized by a score of 4 or more points. The high-risk group was associated with a worse prognosis than the low-risk group (1-year MSS 62%, 2-year 43.1%, 5-year 17.6% as compared to 1-year MSS 79.5%, 3-year 75%, 5-year 72%). Notably, the developed MCC-PS predicts MCC outcome measures with high accuracy (3-year MSS: area under the curve (AUC) 0.934, sensitivity 87.5% and specificity 82.2%; 5-year MSS: AUC 0.93, sensitivity 89% and specificity 82%).

CONCLUSION

MCC-PS is the first prognostic score predicting MCC outcome with a high accuracy based on five easily available laboratory parameters and patient's age. An MCC-PS of 4 or more indicates a high-risk patient associated with a poor prognosis.

摘要

引言

默克尔细胞癌(MCC)是一种侵袭性皮肤癌,预后较差,免疫疗法的引入才使其有所改善。目前缺乏具有高诊断准确性的MCC预测模型。本研究旨在基于先前提出的风险因素组合开发一种MCC预后评分(MCC - PS)。

方法

进行了一项多中心回顾性研究以开发MCC - PS,该研究纳入了年龄、神经元特异性烯醇化酶(NSE)、C反应蛋白(CRP)、肌酐、胆红素和国际标准化比值(INR)。肌酐、胆红素和INR用于计算终末期肝病模型(MELD)评分。共有98例患者纳入研究,根据美国癌症联合委员会2018年标准,I期患者占36.7%(n = 36),II期患者占30.6%(n = 30),III期患者占25.5%(n = 25),IV期患者占7.1%(n = 7)。MCC患者的生存数据与选定的实验室参数和风险因素相关。主要终点是MCC特异性生存(MSS),次要终点是无进展生存。使用了多种统计方法来开发预后评分,包括相关性分析、Kaplan - Meier曲线、Cox回归和时间依赖性受试者工作特征分析。

结果

MCC - PS基于以下基线变量之和:CRP升高(≥5.5 mg/l)、NSE升高(≥22.8 μg/l)、MELD评分≥11以及年龄≥75岁。MELD评分≥11计4分,NSE水平升高计3分,CRP水平升高计2分,年龄≥75岁计1分。根据MCC - PS,评分4分及以上为高危组。高危组的预后比低危组差(1年MSS:62%,2年43.1%,5年17.6%,而低危组1年MSS为79.5%,3年75%,5年72%)。值得注意的是,所开发的MCC - PS能高度准确地预测MCC的预后指标(3年MSS:曲线下面积(AUC)为0.934,敏感性87.5%,特异性82.2%;5年MSS:AUC为0.93,敏感性89%,特异性82%)。

结论

MCC - PS是首个基于五个易于获取的实验室参数和患者年龄,能高度准确预测MCC预后的评分系统。MCC - PS为4分及以上表明患者为高危,预后较差。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cc/11298473/644f7eda14b6/fonc-14-1427740-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cc/11298473/d36651228c07/fonc-14-1427740-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cc/11298473/339fc6f01688/fonc-14-1427740-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cc/11298473/d00eb30a05a5/fonc-14-1427740-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cc/11298473/644f7eda14b6/fonc-14-1427740-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cc/11298473/d36651228c07/fonc-14-1427740-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cc/11298473/339fc6f01688/fonc-14-1427740-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cc/11298473/d00eb30a05a5/fonc-14-1427740-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cc/11298473/644f7eda14b6/fonc-14-1427740-g004.jpg

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