REVIEW article
Front. Chem.
Sec. Analytical Chemistry
This article is part of the Research TopicAdvancements in Analytical ChemistryView all 6 articles
Surface-Enhanced Raman Spectroscopy for Label-Free Cancer Liquid Biopsy: From Fundamentals to Clinical Analysis of Biofluid
Provisionally accepted
- 1Lanzhou University First Hospital, Lanzhou, China
- 2Chinese Academy of Sciences Shenzhen Institute of Advanced Technology, Shenzhen, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Cancer remains one of the major public health problems due to its high morbidity and mortality globally. Because metastasis is the major cause of cancer death, developing new approaches for early diagnosis is of paramount importance in this context. Surface-enhanced Raman scattering (SERS) has emerged as a cutting-edge analytical technique. SERS features an exceptional sensitivity and specificity, enabling rapid non-destructive detection of trace-level samples. Therefore, SERS technology is widely used across medical disciplines, particularly in cancer diagnosis for early-stage and non-invasive diagnostic evaluation. Using liquid biopsy with rich metabolic information, SERS has facilitated the identification, analysis, and progression monitoring of various cancers. In this review, we systematically summarize recent advances in label-free SERS-based cancer diagnostics. We first outline the fundamental principles of SERS, key substrate fabrication methodologies, and essential spectral analysis techniques. We then highlight the applications of label-free SERS in liquid biopsy using various biofluids, including blood, urine, saliva, and sweat. Finally, we discuss current challenges and future directions in this rapidly evolving field.
Keywords: Cancer, Clinical biofluids, deep learning, Label-free detection, liquid biopsy, SERS
Received: 07 Sep 2025; Accepted: 15 Dec 2025.
Copyright: © 2025 Chen, Zhao, Cai, Zhang, Peng, Li and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhibin Wang
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.