Sharma Madhu, Verma Rohit Kumar, Kumar Sunil, Kumar Vibhor
Department for Computational Biology, Indraprastha Institute of Information Technology, Delhi 110020, India.
Department of Surgical oncology, All India Institute of Medical sciences, New Delhi 110029, India.
Comput Struct Biotechnol J. 2021 Dec 7;20:26-39. doi: 10.1016/j.csbj.2021.12.001. eCollection 2022.
Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation.
游离DNA（cfDNA）甲基化谱分析被认为在液体活检中很有前景且可能可靠，可用于研究疾病进展并开发可靠且一致的诊断和预后生物标志物。血浆中cfDNA的释放有几种不同机制，因此它能提供有关人体动态变化的信息。由于cfDNA具有片段化性质、浓度低且背景噪声高，其分析在癌症诊断中的常规应用面临若干挑战。由于异质性、生物标志物敏感性、平台偏差和批次效应，cfDNA甲基化谱分析中的此类挑战进一步加剧。本综述阐述了cfDNA甲基化的起源、其谱分析以及诊断分析中相关的计算问题。在此，我们还思考了处理癌症异质性情况的多标志物方法，并探索基于5-羟甲基胞嘧啶（5hmC）的cfDNA甲基化模式的标志物的效用。此外，我们对cfDNA甲基化分析的反卷积和机器学习方法进行了批判性概述。我们对当前方法的综述揭示了利用cfDNA甲基化检测早期癌症的分析策略有进一步改进的潜力。
