ArticleThe journal of liquid biopsy2024
Expanding the clinical utility of liquid biopsy by using liquid transcriptome and artificial intelligence.
Article in The journal of liquid biopsy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-enabled liquid biopsy in cancer: a systematic review and meta- analysis of diagnostic performance and biological implications.Frontiers in oncology · 2026Pooled it
- Using cell-free RNA to identify B- and T-cell clonality for diagnosis and monitoring of B- and T-cell neoplasms.FEBS open bio · 2026Article
- Multifeature sequencing-based liquid biopsy for cancer diagnosis and monitoring.Genome medicine · 2026Review
- Review
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Liquid Biopsy: Current advancements in clinical practice for bladder cancer.The journal of liquid biopsy · 2025Review
- Recent advances in liquid biopsy for precision oncology: emerging biomarkers and clinical applications in lung cancer.Future oncology (London, England) · 2025Review
- The growing field of liquid biopsy and its Snowball effect on reshaping cancer management.The journal of liquid biopsy · 2025Review
- The impact of liquid biopsy in breast cancer: Redefining the landscape of non-invasive precision oncology.The journal of liquid biopsy · 2025Review
- Liquid Biopsy Combined with Multi-Omics Approaches in Diagnosis, Management, and Progression of Diabetic Retinopathy.Biomedicines · 2025Review
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Most of the current utilization of liquid biopsy (LBx) is based on analyzing cell-free DNA(cfDNA). There is limited data on using cell-free RNA (cfRNA) levels (liquid transcriptome) in LBx. The major hurdles for using liquid transcriptome is its low level in circulation and the dilutional effects of various tissues that may pour their RNA into circulation. We explored the potential of using artificial intelligence (AI) to normalize the cancer-specific cfRNA and to enable liquid transcriptome to predict diagnosis. cfRNA transcriptomic data from 1009 peripheral blood samples was generated by hybrid capture next generation sequencing (NGS). Using two-thirds of samples for training and one third for testing, we demonstrate that AI is able to distinguish between normal control (N = 368) and patients with solid tumors (N = 404) with AUC = 0.820 (95 % CI: 0.760-0.879), patients with myeloid neoplasms (N = 99) with AUC = 0.858 (95 % CI: 0.793-0.924) and patients with lymphoid neoplasms (N = 128) with AUC = 0.788 (95 % CI: 0.687-0.888). Specific diagnosis was also possible when patients with lung, breast, colorectal, and myelodysplastic subgroups were tested. This data suggests that liquid transcriptomics when used with AI has the potential of transforming "liquid biopsy" to "true" biopsy, replacing the need for tissue biopsy.
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