Highly Sensitive Detection of Engrailed‑2 Protein Biomarker in Urine for Using Solution-Gated Graphene Transistor Diagnosis of Prostate Cancer
Engrailed-2 (EN2) protein, a transcription factor in the homologous domain expressed in prostate cancer (PCa) cells and secreted into the urine, is considered a promising biomarker for noninvasive detection of PCa. EN2 protein in urine samples can be obtained by noninvasive means, but the low biomar...
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Published in | ACS sensors Vol. 10; no. 3; pp. 2080 - 2089 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
American Chemical Society
28.03.2025
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Subjects | |
Online Access | Get full text |
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Summary: | Engrailed-2 (EN2) protein, a transcription factor in the homologous domain expressed in prostate cancer (PCa) cells and secreted into the urine, is considered a promising biomarker for noninvasive detection of PCa. EN2 protein in urine samples can be obtained by noninvasive means, but the low biomarker concentration in urine samples poses a great challenge for noninvasive detection of the PCa biomarker. Herein, we develop a solution-gated graphene transistor (SGGT) biosensor to detect the biomarker of the EN2 protein for PCa diagnosis. The aptamer probes are immobilized to the gold gate electrode through Au–S bonds. The effect of aptamer configurations on the biosensor’s responses is also investigated. It can be found that the SGGT biosensor with the long-chain probes with a stem-like loop structure exhibits optimal performance. The limit of detection of biosensors can reach 0.1 fg/mL, and a rapid response time of 19 min is achieved. The SGGT biosensor also exhibits high specificity for the EN2 protein. More importantly, testing of clinical urine samples indicates that our sensor can distinguish PCa patients from non-PCa subjects. Compared to traditional hospital prostate-specific antigen tests, our sensor exhibits better accuracy for the noninvasive diagnosis of PCa. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2379-3694 2379-3694 |
DOI: | 10.1021/acssensors.4c03320 |