{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang J"],"funding":["National Natural Science Foundation of China (National Science Foundation of China)"],"pagination":["156"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10761679"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["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 differen"],"journal":["Nature communications"],"pubmed_title":["Terminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification."],"pmcid":["PMC10761679"],"funding_grant_id":["81970053","89202586","82241022","82170070"],"pubmed_authors":["Tian J","Yang H","Hu Y","Huang J","Yang M","Ji L","Zhang S","Wang J","Guo Q","Gou D","Niu Y","Li X","He Y","Tang P","Chen X","Wang Y","Xu Y","Kang K"],"additional_accession":[]},"is_claimable":false,"name":"Terminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification.","description":"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 differen","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jan","modification":"2025-04-05T12:46:17.947Z","creation":"2025-04-05T12:46:17.947Z"},"accession":"S-EPMC10761679","cross_references":{"pubmed":["38168054"],"doi":["10.1038/s41467-023-44461-y"]}}