Recent advances in ovarian cancer diagnosis using 2D nanomaterials-based electrochemical biosensors: a review.

Ovarian cancer (OC) is a deadly disease with a high mortality rate, primarily due to its often asymptomatic nature in early stages and the absence of effective screening methods. Conventional imaging methods have several drawbacks, such as difficulty in detecting small tumors, challenges in differentiating between benign and malignant tumors, dangerous radiation exposure, high cost, low sensitivity, and low specificity in detecting early-stage malignancies. On the other hand, 2D nanomaterials-based electrochemical biosensors present novel opportunities for early detection of ovarian cancer biomarkers, leveraging their unique properties such as high surface-to-volume ratio and excellent electrical conductivity. These materials enhance sensor sensitivity and selectivity, enabling the detection of biomarkers at ultra-low concentrations, thus improving diagnostic accuracy. Their compatibility with miniaturization further facilitates the development of portable, point-of-care devices, revolutionizing early screening efforts in ovarian cancer detection. This review paper overviews recent advancements in OC biomarker detection using 2D nanomaterials-based electrochemical biosensors. A detailed discussion is presented on the electrochemical detection of carbohydrate antigen 125 (CA125), mucin 1 (MUC1), and human epididymis protein 4 (HE4) biomarkers regarding sensing materials, sensing approach, analytical technique, and performance characteristics like limit of detection and linear range. Other OC biomarkers such as SKOV3, breast cancer gene 1 (BRCA1), stress-induced phosphoprotein 1 (STIP1), and flavin adenine dinucleotide are also briefly discussed. Finally, the review discusses the difficulties and future outlooks of diagnosing OC via an electrochemical sensing approach. The present review article emphasizes the application of 2D nanomaterials in the early diagnosis of ovarian cancer through an electrochemical sensing approach. [ABSTRACT FROM AUTHOR]