Phage-based biosensors for Cancer biomarker detection and diagnosis.

Authors

Qamar Abuhassan, Ghaleb Oriquat, Subbulakshmi Ganesan, Subhashree Ray, V Ramesh Kumar, Vipasha Sharma, Ashish Singh Chauhan, Kholbek Shermatov

Published in

Clinica chimica acta; international journal of clinical chemistry. Pages 121332. Sep 09, 2026. Epub Sep 09, 2026.

Abstract

Early cancer identification is necessary to reduce mortality. Traditional methods for identifying cancers can be problematic. Tissue biopsies are invasive procedures, and immunoassays vary widely in their sensitivity while requiring complex laboratory instrumentation. Therefore, there is a need for portable, rapid, and noninvasive point-of-care stations. Phage-based biosensors have emerged as a means to satisfy the requirements for these types of tools. This article examines phage-based biosensors in three categories: electrochemical, optical, and acoustic. Electrochemical sensors utilize label-free (EIS, SWV) methodologies to achieve highly sensitive detection of circulating tumor cells and cancer biomarkers. Optical biosensors utilizing either quantum dots or liquid crystals can provide up to 20-fold greater sensitivity than ELISA tests. Acoustic biosensors utilizing piezoelectric resonators can deliver results rapidly (< 5 min) and portably. In addition, arrays of phage can be utilized in conjunction with deep learning technologies to facilitate early and noninvasive detection of lung cancer through breath analysis of volatile organic compounds. Additional advantages of using phage-based biosensors include: very high stability, low cost of production, genetically tailorability, and usability across different applications, which would provide viable alternatives to liquid biopsy as well as point-of-care testing techniques for early identification of cancers. While clinical validation and multiplexing are currently recognized as areas that require further refinement/development within this field, phage-based sensors have great potential for use in the early identification of disease.

PMID:
42716240
Bibliographic data and abstract were imported from PubMed on 10 Sep 2026.

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