Bao Pengfei, Wang Taiwei, Liu Xiaofan, Xing Shaozhen, Ruan Hanjin, Ma Hongli, Tao Yuhuan, Zhan Qing, Belmonte-Reche Efres, Qin Lizheng, Han Zhengxue, Mao Minghui, Li Mengtao, Lu Zhi John
MOE Key Laboratory of Bioinformatics, State Key Lab of Green Biomanufacturing, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
Institute for Precision Medicine, Tsinghua University, Beijing, 100084, China.
Genome Biol. 2025 May 8;26(1):119. doi: 10.1186/s13059-025-03590-x.
Cell-free RNAs (cfRNAs) can be detected in biofluids and have emerged as valuable disease biomarkers. Accurate identification of the fragmented cfRNA signals, especially those originating from pathological cells, is crucial for understanding their biological functions and clinical value. However, many challenges still need to be addressed for their application, including developing specific analysis methods and translating cfRNA fragments with biological support into clinical applications.
We present cfPeak, a novel method combining statistics and machine learning models to detect the fragmented cfRNA signals effectively. When test in real and artificial cfRNA sequencing (cfRNA-seq) data, cfPeak shows an improved performance compared with other applicable methods. We reveal that narrow cfRNA peaks preferentially overlap with protein binding sites, vesicle-sorting sites, structural sites, and novel small non-coding RNAs (sncRNAs). When applied in clinical cohorts, cfPeak identified cfRNA peaks in patients' plasma that enable cancer detection and are informative of cancer types and metastasis.
Our study fills the gap in the current small cfRNA-seq analysis at fragment-scale and builds a bridge to the scientific discovery in cfRNA fragmentomics. We demonstrate the significance of finding low abundant tissue-derived signals in small cfRNA and prove the feasibility for application in liquid biopsy.
游离RNA（cfRNA）可在生物流体中检测到，并已成为有价值的疾病生物标志物。准确识别片段化的cfRNA信号，尤其是那些源自病理细胞的信号，对于理解其生物学功能和临床价值至关重要。然而，其应用仍需解决许多挑战，包括开发特定的分析方法以及将具有生物学支持的cfRNA片段转化为临床应用。
我们提出了cfPeak，这是一种结合统计和机器学习模型来有效检测片段化cfRNA信号的新方法。在真实和人工cfRNA测序（cfRNA-seq）数据中进行测试时，cfPeak与其他适用方法相比表现出更好的性能。我们发现狭窄的cfRNA峰优先与蛋白质结合位点、囊泡分选位点、结构位点和新型小非编码RNA（sncRNA）重叠。当应用于临床队列时，cfPeak在患者血浆中识别出能够进行癌症检测且能提供癌症类型和转移信息的cfRNA峰。
我们的研究填补了当前小cfRNA-seq在片段水平分析方面的空白，并为cfRNA片段组学的科学发现搭建了一座桥梁。我们证明了在小cfRNA中发现低丰度组织来源信号的重要性，并证明了其在液体活检中应用的可行性。
