biostudies-literatureWang JNational Natural Science Foundation of China (National Science Foundation of China)156https://www.ebi.ac.uk/biostudies/studies/S-EPMC10761679biostudies-literatureUnknown15(1)Cell-free RNAs (cfRNAs) offer an opportunity to detect diseases from a transcriptomic perspective, however, existing techniques have fallen short in generating a comprehensive cell-free transcriptome profile. We develop a sensitive library preparation method that is robust down to 100 µl input plasma to analyze cfRNAs independent of their 5'-end modifications. We show that it outperforms adapter ligation-based method in detecting a greater number of cfRNA species. We perform transcriptome-wide characterizations in 165 lung cancer, 30 breast cancer, 37 colorectal cancer, 55 gastric cancer, 15 liver cancer, and 133 cancer-free participants and demonstrate its ability to identify transcriptomic changes occurring in early-stage tumors. We also leverage machine learning analyses on the differenNature communicationsTerminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification.PMC1076167981970053892025868224102282170070Tian JYang HHu YHuang JYang MJi LZhang SWang JGuo QGou DNiu YLi XHe YTang PChen XWang YXu YKang KfalseTerminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification.Cell-free RNAs (cfRNAs) offer an opportunity to detect diseases from a transcriptomic perspective, however, existing techniques have fallen short in generating a comprehensive cell-free transcriptome profile. We develop a sensitive library preparation method that is robust down to 100 µl input plasma to analyze cfRNAs independent of their 5'-end modifications. We show that it outperforms adapter ligation-based method in detecting a greater number of cfRNA species. We perform transcriptome-wide characterizations in 165 lung cancer, 30 breast cancer, 37 colorectal cancer, 55 gastric cancer, 15 liver cancer, and 133 cancer-free participants and demonstrate its ability to identify transcriptomic changes occurring in early-stage tumors. We also leverage machine learning analyses on the differen2024-01-01T00:00:00Z2024 Jan2025-04-05T12:46:17.947Z2025-04-05T12:46:17.947ZS-EPMC107616793816805410.1038/s41467-023-44461-y