In a recent study in 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).
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
Effective biosensors are vital in advancing medical diagnostics, particularly for cancer detection, where early diagnosis can significantly impact treatment outcomes. Saliva is emerging as a promising diagnostic medium due to its numerous advantages: it is non-invasive, easy to collect, and contains a wide array of biomarkers that can indicate disease states, including cancer. The presence of these biomarkers in saliva makes it an attractive alternative to traditional diagnostic methods that often require more invasive procedures.
Recent research has highlighted the potential of surface-enhanced Raman scattering (SERS) as a powerful technique for detecting low-abundance molecules in complex biological samples. SERS enhances the Raman signal of target molecules through interaction with metal nanostructures, allowing for the sensitive detection of specific cancer biomarkers even in low concentrations. By exploiting these unique properties, biosensors that utilize SERS can provide rapid and accurate diagnostic results.
The 3D-PHP sensor designed by the researchers leverages gold nanostructures' unique properties to maximize sensitivity and specificity in detecting cancer biomarkers within saliva samples. This innovative approach aims to improve the early detection of lung cancer, ultimately enhancing patient care and treatment options.
The Current Study
The 3D plasmonic hexaplex (3D-PHP) sensor was fabricated using a one-step direct gold reduction method. In this process, gold chloride hydrate (HAuCl4) was mixed with ascorbic acid (AA) and sodium citrate (SC) under controlled conditions to produce multispike hexaplex-shaped gold nanostructures on a cellulose acetate (CA) paper substrate. The reaction conditions were carefully optimized to achieve a uniform distribution of nanostructures, which significantly enhanced the sensor's surface area and surface-enhanced Raman scattering (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, as well as benign controls. Each saliva sample was directly applied to the sensor without any pretreatment.
Raman spectra were acquired using a Raman spectrometer equipped with a 785 nm laser. The spectral data was then analyzed with a logistic regression-based machine learning model, which classified the samples into malignant and benign categories. Key Raman peaks associated with specific metabolites were identified and correlated with the presence of lung cancer.
The model's performance was evaluated using metrics such as sensitivity, specificity, and accuracy, providing valuable insights into the sensor's diagnostic capabilities.
Results and Discussion
The results indicated that the 3D-PHP sensor exhibited exceptional SERS sensitivity, successfully detecting specific Raman peaks associated with lung cancer biomarkers in saliva. The average size of the hexaplex particles was approximately 377 nm, with protrusions measuring around 69 nm, contributing to an enhanced surface area that increased interaction with saliva.
The study reported a sensitivity of 91.2 %, a specificity of 80.2 %, and an overall accuracy of 87.5 % in distinguishing malignant samples from benign ones. These findings underscore the sensor's potential as a reliable diagnostic tool for lung cancer detection.
The integration of the 3D-PHP sensor with a conventional saliva collection tube was also 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
This study represents a significant advancement in non-invasive cancer diagnostics through the development of the 3D plasmonic hexaplex sensor. By leveraging the unique properties of gold nanostructures and integrating them with a saliva collection system, the sensor achieves high sensitivity and specificity in detecting lung cancer. The effective use of machine learning to analyze Raman spectral data further amplifies the technology's potential for clinical application.
The findings indicate that the 3D-PHP sensor could play a vital role in early-stage lung cancer diagnosis, providing a practical and efficient solution for on-site screening. Future research should focus on optimizing the sensor for other cancer types and assessing its applicability across various diagnostic settings, ultimately contributing to enhanced patient care and improved health 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