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Biomedicines
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Emerging Technologies in Liquid Biopsy of Cancers
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Emerging Technologies in Liquid Biopsy of Cancers

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 4189

Special Issue Editor

Dr. Lingdi Yin

E-Mail Website
Guest Editor
Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
Interests: pancreatic cancer; cancer genetics; tumor microenvironment; general surgery; bioinformatics

Special Issue Information

Dear Colleagues,

Liquid biopsy is revolutionizing the diagnosis and monitoring of cancer by enabling the non-invasive detection of tumor-derived biomarkers (e.g., ctDNA, CTCs, exosomes) in blood or other biofluids. This Special Issue explores cutting-edge technological advances in the field, with an emphasis on novel methodologies such as high-sensitivity detection platforms (single-cell analysis, microfluidics, CRISPR-based assays), multi-omics integration (fragmentomics, epigenomics, proteomics), and AI-driven bioinformatics pipelines; emerging biomarkers, with the discovery and validation of novel signatures (cfDNA fragmentation patterns, methylation profiles, metabolite panels) for early cancer detection, the monitoring of minimal residual disease, and the prediction of therapy resistance; and clinical translation, with developments addressing challenges such as low-abundance target isolation, the standardization of pre-analytical protocols, and longitudinal monitoring to accelerate clinical adoption. We welcome original research, reviews, and perspectives on in silico biomarker discovery, point-of-care devices, nanotechnology applications, and multi-modal liquid biopsy frameworks. Submissions should highlight technological novelty, analytical/clinical validation, and translational potential across solid and hematologic malignancies. This Special Issue invites innovators, clinical researchers, and biotech stakeholders to contribute to this Special Issue and shape the next generation of precision oncology tools.

Dr. Lingdi Yin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • liquid biopsy
  • circulating tumor cells (CTCs)
  • circulating tumor DNA (ctDNA)
  • exosomes
  • cancer diagnostics
  • biomarker discovery
  • minimally invasive monitoring
  • emerging technologies
  • clinical translation
  • molecular profiling

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Published Papers (2 papers)

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Research

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13 pages, 2837 KB  
Article
Dynamic Surveillance of Minimal Residual Disease via a Tumor-Informed Circulating Tumor DNA Assay for Outcome Prediction in Small-Cell Lung Cancer: An Exploratory Pilot Study
by Qiuyi Zhang, Die Dai, Yikun Yang, Lihong Guo, Jiesheng Su, Shiqi Lyu, Suni Huang, Meng Zhang and Jianhua Chang
Biomedicines 2026, 14(5), 972; https://doi.org/10.3390/biomedicines14050972 - 23 Apr 2026
Viewed by 556
Abstract
Background: Small-cell lung cancer (SCLC) represents an aggressive malignancy associated with a poor prognosis, underscoring the critical demand for enhanced monitoring methodologies. Circulating tumor DNA (ctDNA) constitutes a promising non-invasive biomarker; however, reports employing highly sensitive tumor-informed assays in SCLC remain scarce. [...] Read more.
Background: Small-cell lung cancer (SCLC) represents an aggressive malignancy associated with a poor prognosis, underscoring the critical demand for enhanced monitoring methodologies. Circulating tumor DNA (ctDNA) constitutes a promising non-invasive biomarker; however, reports employing highly sensitive tumor-informed assays in SCLC remain scarce. This investigation aimed to assess the clinical utility of a personalized ctDNA monitoring strategy for predicting therapeutic outcomes and resistance in SCLC patients. Methods: This prospective observational study enrolled patients diagnosed with unresectable SCLC. Whole exome sequencing was conducted on baseline tumor specimens to design customized 16-plex multiplex polymerase chain reaction (PCR) panels. Serial blood samples were obtained at baseline, at six-week intervals during treatment, and upon disease progression. Detection of ctDNA-based minimal residual disease (MRD) was performed using a tumor-informed assay (Huajianwei® bespoke MRD) with ultra-deep sequencing. Results: Among seven evaluable patients, the baseline ctDNA-MRD positivity rate was 100%. A significant positive correlation was observed between the baseline ctDNA levels and radiographic tumor burden (r = 0.821, 95% confidence interval [CI] 0.179–0.973, p = 0.034). Longitudinal analysis indicated that patients exhibiting an early decline in MRD levels demonstrated a non-significant trend toward superior progression-free survival (PFS) compared to those with an MRD increase. Though this between-group difference did not reach conventional statistical significance, it represented a trend-level finding (p = 0.0665, hazard ratio [HR] = 0.24, 95% CI: 0.02–3.19), with no definitive prognostic association confirmed in this pilot cohort. Notably, an elevation in MRD preceded radiographic progression by as much as 135 days in certain instances. Conclusions: This study shows that dynamic tumor-informed ctDNA-based MRD monitoring reflects tumor burden changes and may correlate with clinical outcomes in SCLC, supporting its potential to guide personalized treatment and facilitate earlier therapeutic interventions compared to conventional imaging techniques. Prospective multicenter validation is needed to confirm its clinical utility. Full article
(This article belongs to the Special Issue Emerging Technologies in Liquid Biopsy of Cancers)
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Review

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34 pages, 5602 KB  
Review
Liquid Biopsy in Early Screening of Cancers: Emerging Technologies and New Prospects
by Hanyu Zhu, Zhenyu Li, Kunxin Xie, Sajjaad Hassan Kassim, Cheng Cao, Keyu Huang, Zipeng Lu, Chenshan Ma, Ying Li, Kuirong Jiang and Lingdi Yin
Biomedicines 2026, 14(1), 158; https://doi.org/10.3390/biomedicines14010158 - 12 Jan 2026
Cited by 1 | Viewed by 3235
Abstract
Liquid biopsy is moving beyond mutation-centric assays to multimodal frameworks that integrate cell-free DNA (cfDNA) signals with additional analytes such as circulating tumor cells (CTCs) and extracellular vesicles (EVs). In this review, we summarize emerging technologies across analytes for early cancer detection, emphasizing [...] Read more.
Liquid biopsy is moving beyond mutation-centric assays to multimodal frameworks that integrate cell-free DNA (cfDNA) signals with additional analytes such as circulating tumor cells (CTCs) and extracellular vesicles (EVs). In this review, we summarize emerging technologies across analytes for early cancer detection, emphasizing sequencing and error-suppression strategies and the growing evidence for multi-cancer early detection (MCED), tissue-of-origin (TOO) inference, diagnostic triage, and longitudinal surveillance. At low tumor fractions, fragmentomic and methylation features preserve tissue and chromatin context; when combined with radiomics using deep learning, they support blood-first, high-specificity risk stratification, increase positive predictive value (PPV), reduce unnecessary procedures, and enhance early prediction of treatment response and relapse. Building on these findings, we propose a pathway-aware workflow: initial blood-based risk scoring, followed by organ-directed imaging, and targeted secondary testing when indicated. We further recommend that model reports include not only discrimination metrics but also calibration, decision-curve analysis, PPV/negative predictive value (NPV) at fixed specificity, and TOO accuracy, alongside multi-site external validation and blinded dataset splits to improve generalizability. Overall, liquid biopsy is transitioning from signal discovery to deployable multimodal decision systems; standardized pre-analytical and analytical workflows, robust error suppression, and prospective real-world evaluations will be pivotal for clinical implementation. Full article
(This article belongs to the Special Issue Emerging Technologies in Liquid Biopsy of Cancers)
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