Bao, Pengfei (P);Wang, Taiwei (T);Liu, Xiaofan (X);Xing, Shaozhen (S);Ruan, Hanjin (H);Ma, Hongli (H);Tao, Yuhuan (Y);Zhan, Qing (Q);Belmonte-Reche, Efres (E);Qin, Lizheng (L);Han, Zhengxue (Z);Mao, Minghui (M);Li, Mengtao (M);Lu, Zhi John (ZJ);

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Genome Biol.2025 May 08;26(1):119.doi:10.1186/s13059-025-03590-x

Abstract

BACKGROUND: 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.

RESULTS: 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.

CONCLUSIONS: 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.