In a recent article published in the journal Biosensors and Bioelectronics, researchers introduced a novel three-dimensional plasmonic hexaplex (3D-PHP) sensor integrated with a saliva collection tube, aimed at enhancing the detection of lung cancer through surface-enhanced Raman scattering (SERS). The sensor's design leverages the unique properties of gold nanostructures to improve sensitivity and specificity in identifying cancer biomarkers in saliva samples.
Study: 3D plasmonic hexaplex paper sensor for label-free human saliva sensing and machine learning-assisted early-stage lung cancer screening. Image Credit: Katy Pack/Shutterstock.com
Background
The development of effective biosensors has been a focal point in medical diagnostics, particularly for cancer detection. Saliva, as a diagnostic medium, offers several advantages, including ease of collection and the presence of various biomarkers that can indicate disease states. Previous studies have demonstrated the potential of SERS in amplifying signals from these biomarkers, making it a powerful tool for detecting low-abundance molecules.
The 3D-PHP sensor is constructed using a simple one-step method that involves the reduction of gold precursors in the presence of ascorbic acid and sodium citrate, resulting in multispike hexaplex-shaped gold nanostructures. These structures are designed to enhance the interaction with saliva, thereby improving the sampling efficiency and SERS signal intensity.
The Current Study
The 3D plasmonic hexaplex (3D-PHP) sensor was fabricated using a one-step direct gold reduction method. Gold chloride hydrate (HAuCl₄) was mixed with ascorbic acid (AA) and sodium citrate (SC) in a controlled environment to form multispike hexaplex-shaped gold nanostructures on a cellulose acetate (CA) paper substrate. The reaction conditions were optimized to achieve a uniform distribution of nanostructures, enhancing the sensor's surface area and SERS activity.
The morphology of the gold nanostructures was characterized using scanning electron microscopy (SEM) and transmission electron microscopy (TEM), confirming the formation of distinct multispike features with an average size of approximately 377 nm. The SERS performance of the 3D-PHP sensor was evaluated using saliva samples collected from patients diagnosed with lung cancer and benign controls. Each saliva sample was directly applied onto the sensor without any pretreatment.
Raman spectra were acquired using a Raman spectrometer equipped with a 785 nm laser. The spectral data were analyzed using a logistic regression-based machine learning model to classify the samples into malignant and benign categories. Key Raman peaks associated with specific metabolites were identified and correlated with lung cancer presence.
The model's performance was assessed through metrics such as sensitivity, specificity, and accuracy, providing insights into the sensor's diagnostic capabilities. This integration of advanced nanostructures with machine learning techniques demonstrates the potential for rapid, on-site lung cancer screening using non-invasive saliva analysis.
Results and Discussion
The results demonstrated that the 3D-PHP sensor exhibited remarkable SERS sensitivity, with the ability to detect specific Raman peaks associated with lung cancer biomarkers in saliva. The average sizes of the hexaplex particles were measured to be approximately 377 nm, with protrusions around 69 nm, which contributed to the enhanced surface area and increased interaction with the saliva.
The study reported a sensitivity of 91.2%, specificity of 80.2%, and an overall accuracy of 87.5% in distinguishing between malignant and benign samples. These findings underscore the sensor's potential as a reliable diagnostic tool for lung cancer.
The integration of the 3D-PHP sensor with a conventional saliva collection tube was highlighted as a significant advancement for on-site disease screening. The unique 3D structure of the sensor not only facilitated effective sampling but also enhanced the SERS signal due to the increased number of hotspots created by the multispike morphology. The study also explored the influence of varying concentrations of sodium citrate on particle size and SERS activity, identifying an optimal concentration that maximized signal intensity. The correlation between the experimental results and numerical simulations further validated the sensor's design and functionality.
Moreover, the investigation into the most informative Raman peak positions provided insights into the metabolic changes associated with lung cancer, suggesting that the sensor could aid in early-stage diagnosis. The ability to classify samples accurately using machine learning techniques indicates a promising direction for future research, potentially leading to the development of more sophisticated diagnostic platforms that can analyze various human biofluids.
Conclusion
In conclusion, the study presents a significant advancement in the field of non-invasive cancer diagnostics through the development of a 3D plasmonic hexaplex sensor. By harnessing the unique properties of gold nanostructures and integrating them with a saliva collection system, the sensor demonstrates high sensitivity and specificity for lung cancer detection. The successful application of machine learning to analyze Raman spectral data further enhances the potential of this technology for clinical use.
The findings suggest that the 3D-PHP sensor could play a crucial role in early-stage lung cancer diagnosis, offering a practical and efficient solution for on-site screening. Future research may focus on optimizing the sensor for other types of cancers and exploring its applicability in various diagnostic settings, ultimately contributing to improved patient care and outcomes.
Journal Reference
Linh V. T. N., Kim H., et al. (2024). 3D plasmonic hexaplex paper sensor for label-free human saliva sensing and machine learning-assisted early-stage lung cancer screening. Biosensors and Bioelectronics 244, 115779. DOI: 10.1016/j.bios.2023.115779, https://www.sciencedirect.com/science/article/pii/S0956566323007212