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.