Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential
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 clinica...
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BMC
2025-05-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03590-x |
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| author | Pengfei Bao Taiwei Wang Xiaofan Liu Shaozhen Xing Hanjin Ruan Hongli Ma Yuhuan Tao Qing Zhan Efres Belmonte-Reche Lizheng Qin Zhengxue Han Minghui Mao Mengtao Li Zhi John Lu |
| author_facet | Pengfei Bao Taiwei Wang Xiaofan Liu Shaozhen Xing Hanjin Ruan Hongli Ma Yuhuan Tao Qing Zhan Efres Belmonte-Reche Lizheng Qin Zhengxue Han Minghui Mao Mengtao Li Zhi John Lu |
| author_sort | Pengfei Bao |
| collection | DOAJ |
| description | 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. Graphical Abstract |
| format | Article |
| id | doaj-art-a0fc20d299b34a988f90f36b2a67bd61 |
| institution | DOAJ |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-a0fc20d299b34a988f90f36b2a67bd612025-08-20T03:09:20ZengBMCGenome Biology1474-760X2025-05-0126112510.1186/s13059-025-03590-xPeak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potentialPengfei Bao0Taiwei Wang1Xiaofan Liu2Shaozhen Xing3Hanjin Ruan4Hongli Ma5Yuhuan Tao6Qing Zhan7Efres Belmonte-Reche8Lizheng Qin9Zhengxue Han10Minghui Mao11Mengtao Li12Zhi John Lu13MOE Key Laboratory of Bioinformatics, State Key Lab of Green Biomanufacturing, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua UniversityMOE Key Laboratory of Bioinformatics, State Key Lab of Green Biomanufacturing, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua UniversityMOE Key Laboratory of Bioinformatics, State Key Lab of Green Biomanufacturing, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua UniversityMOE Key Laboratory of Bioinformatics, State Key Lab of Green Biomanufacturing, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua UniversityDepartment of Oral and Maxillofacial & Head and Neck Oncology, Beijing Stomatological Hospital, Capital Medical UniversityMOE Key Laboratory of Bioinformatics, State Key Lab of Green Biomanufacturing, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua UniversityMOE Key Laboratory of Bioinformatics, State Key Lab of Green Biomanufacturing, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua UniversityMOE Key Laboratory of Bioinformatics, State Key Lab of Green Biomanufacturing, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua UniversityCentre for Genomics and Oncological Research (GENYO)Department of Oral and Maxillofacial & Head and Neck Oncology, Beijing Stomatological Hospital, Capital Medical UniversityDepartment of Oral and Maxillofacial & Head and Neck Oncology, Beijing Stomatological Hospital, Capital Medical UniversityDepartment of Oral and Maxillofacial & Head and Neck Oncology, Beijing Stomatological Hospital, Capital Medical UniversityDepartment of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical CollegeMOE Key Laboratory of Bioinformatics, State Key Lab of Green Biomanufacturing, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua UniversityAbstract 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. Graphical Abstracthttps://doi.org/10.1186/s13059-025-03590-xCell-free RNACfRNA fragmentPeak callingSncRNALiquid biopsy biomarkerTissue-of-origin |
| spellingShingle | Pengfei Bao Taiwei Wang Xiaofan Liu Shaozhen Xing Hanjin Ruan Hongli Ma Yuhuan Tao Qing Zhan Efres Belmonte-Reche Lizheng Qin Zhengxue Han Minghui Mao Mengtao Li Zhi John Lu Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential Genome Biology Cell-free RNA CfRNA fragment Peak calling SncRNA Liquid biopsy biomarker Tissue-of-origin |
| title | Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential |
| title_full | Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential |
| title_fullStr | Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential |
| title_full_unstemmed | Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential |
| title_short | Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential |
| title_sort | peak analysis of cell free rna finds recurrently protected narrow regions with clinical potential |
| topic | Cell-free RNA CfRNA fragment Peak calling SncRNA Liquid biopsy biomarker Tissue-of-origin |
| url | https://doi.org/10.1186/s13059-025-03590-x |
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