Revolutionizing Cancer Detection: Harnessing Quantum Dots and Graphene-Based Nanobiosensors for Lung and Breast Cancer Diagnosis

There is an ongoing public health crisis connected to lung and breast cancers worldwide, which requires advances in early detection technologies to improve patient outcomes. There is growing interest in nanotechnology as an alternative method for cancer diagnosis, especially quantum dots (QDs) and graphene-based nanobiosensors, which could prove to be very efficient tools. There are many recent advancements in the use of QDs and graphene-based nanobiosensors for the early detection of lung and breast cancers, and this review summarizes the development of these technologies. Due to their unique optical and electronic properties, QDs can be used to detect cancer with incredible sensitivity and selectivity. As a result of the tunable emission spectrum of these devices, it is possible to detect multiple biomarkers associated with lung and breast cancers simultaneously, enhancing the accuracy of diagnostic processes. Furthermore, due to QDs’ biocompatibility and stability, they are highly suitable for monitoring tumor progression, enabling real-time monitoring of tumor progression during treatment. Graphene is a two-dimensional carbon allotrope with outstanding mechanical, electrical, and biocompatible properties, so it is an ideal candidate for nanobiosensors. Graphene-based nanobiosensors can detect high levels of biomarkers in cancer patients’ tissues thanks to their large surface areas and enhanced electron transfer rates. As a result, they have been demonstrated to be highly sensitive and specific. Additionally, graphene’s compatibility with various biological samples enables its integration into nanobiosensors for rapid and cost-effective cancer screening. Considering the convergence of nanotechnology and biomedicine, it has been possible to make remarkable progress in cancer diagnosis due to this convergence. With QDs and graphene-based nanobiosensors, early lung and breast cancer stages can be detected more effectively and efficiently, improving patient outcomes through timely interventions and personalized treatment strategies.