血清蛋白预测儿童期癌症幸存者治疗相关心肌病

IF 12 1区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Jacc: Cardiooncology Pub Date : 2025-01-01 DOI:10.1016/j.jaccao.2024.10.004

Suresh Poudel PhD , Him Shrestha PhD , Yue Pan PhD , Qian Li PhD , Kendrick Li PhD , Cindy Im PhD , Stephanie B. Dixon MD , Matthew J. Ehrhardt MD , Daniel A. Mulrooney MD , Suiping Zhou PhD , Haiyan Tan PhD , Anthony A. High PhD , Paul W. Burridge PhD , Smita Bhatia MD , John L. Jefferies MD , Kirsten K. Ness PhD , Melissa M. Hudson MD , Leslie L. Robison PhD , Gregory T. Armstrong MD , Junmin Peng PhD , Yadav Sapkota PhD

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引用次数: 0

摘要

背景：蒽环类药物是一种对许多儿科恶性肿瘤非常有效的化疗药物，可引起心肌病，这是成年幸存者的主要晚期效应。蒽环类药物相关性心肌病的早期检测和靶向干预需要生物标志物。目的：本研究的目的是确定无症状儿童癌症幸存者的血清蛋白和/或代谢物是否可以区分症状性心肌病。方法：使用基于非靶向质谱的方法，对来自SJLIFE （St. Jude终身队列研究）的75名无症状亚临床心肌病幸存者和75名单独匹配的无心肌病幸存者的血清样本中867种蛋白质和218种代谢物进行了分析。模型是在最具影响力的差异表达蛋白和代谢物的基础上开发的，使用具有最小绝对收缩和选择算子惩罚的条件逻辑回归。在23名患有严重或症状性心肌病的独立幸存者和23名单独匹配的无心肌病幸存者中评估了表现最佳的模型。结果：一个27蛋白的模型使用条件逻辑回归确定，绝对收缩最小，选择算子惩罚区分症状性或严重心肌病需要心力衰竭药物的独立幸存者；23名单独匹配的有或无心肌病的幸存者中有19人被正确区分，准确率为82.6% （95% CI: 71.4%-93.8%）。途径富集分析显示，27个蛋白在各种生物过程中富集，其中许多与蒽环类药物相关的心肌病有关。结论：建立了基于亚临床心肌病血清蛋白差异表达的风险模型，该模型能够在独立匹配的样本中准确区分严重心肌病的风险。这些蛋白作为心肌病风险生物标志物的进一步评估应在外部更大的队列中进行，并通过前瞻性研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Serum Proteins Predict Treatment-Related Cardiomyopathy Among Survivors of Childhood Cancer

Background

Anthracyclines, a highly effective chemotherapy for many pediatric malignancies, cause cardiomyopathy, a major late effect in adult survivors. Biomarkers are needed for early detection and targeted interventions for anthracycline-associated cardiomyopathy.

Objectives

The aim of this study was to determine if serum proteins and/or metabolites in asymptomatic childhood cancer survivors can discriminate symptomatic cardiomyopathy.

Methods

Using an untargeted mass spectrometry–based approach, 867 proteins and 218 metabolites were profiled in serum samples of 75 asymptomatic survivors with subclinical cardiomyopathy and 75 individually matched survivors without cardiomyopathy from SJLIFE (St. Jude Lifetime Cohort Study). Models were developed on the basis of the most influential differentially expressed proteins and metabolites, using conditional logistic regression with a least absolute shrinkage and selection operator penalty. The best performing model was evaluated in 23 independent survivors with severe or symptomatic cardiomyopathy and 23 individually matched cardiomyopathy-free survivors.

Results

A 27-protein model identified using conditional logistic regression with a least absolute shrinkage and selection operator penalty discriminated symptomatic or severe cardiomyopathy requiring heart failure medications in independent survivors; 19 of 23 individually matched survivors with and without cardiomyopathy were correctly discriminated with 82.6% (95% CI: 71.4%-93.8%) accuracy. Pathway enrichment analysis revealed that the 27 proteins were enriched in various biological processes, many of which have been linked to anthracycline-related cardiomyopathy.

Conclusions

A risk model was developed on the basis of the differential expression of serum proteins in subclinical cardiomyopathy, which accurately discriminated the risk for severe cardiomyopathy in an independent, matched sample. Further assessment of these proteins as biomarkers of cardiomyopathy risk should be conducted in external larger cohorts and through prospective studies.

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来源期刊

Jacc: Cardiooncology Multiple-

CiteScore

12.50

自引率

6.30%

发文量

106

期刊介绍： JACC: CardioOncology is a specialized journal that belongs to the esteemed Journal of the American College of Cardiology (JACC) family. Its purpose is to enhance cardiovascular care for cancer patients by publishing high-quality, innovative scientific research and sharing evidence-based knowledge. The journal aims to revolutionize the field of cardio-oncology and actively involve and educate professionals in both cardiovascular and oncology fields. It covers a wide range of topics including pre-clinical, translational, and clinical research, as well as best practices in cardio-oncology. Key areas of focus include understanding disease mechanisms, utilizing in vitro and in vivo models, exploring novel and traditional therapeutics (across Phase I-IV trials), studying epidemiology, employing precision medicine, and investigating primary and secondary prevention. Amyloidosis, cardiovascular risk factors, heart failure, and vascular disease are some examples of the disease states that are of particular interest to the journal. However, it welcomes research on other relevant conditions as well.