A Review of Lung Cancer Biomarkers Detectable Via Sers: Challenges, Advances, and Future Directions
DOI:
https://doi.org/10.29070/nb14sv55Keywords:
Lung cancer biomarkers, SERS, nanotechnology, early detection, diagnostic spectroscopyAbstract
Lung cancer is one of the most common and deadly forms of cancer in the world, accounting for a significant proportion of all cancer-related deaths each year. Despite advancements in imaging and biopsy techniques, early detection of lung cancer remains a significant challenge due to the limitations of existing diagnostic tools in terms of sensitivity, invasiveness, and cost. This study addresses the growing need for non-invasive, sensitive, and rapid detection platforms by exploring the application of Surface-Enhanced Raman Spectroscopy (SERS) in identifying lung cancer biomarkers.
The methodology that has been utilized for the research is a conceptual review approach that relies on secondary data. A synthesis of information pertaining to the surface-enhanced Raman spectroscopy compatibility of biomarkers for lung cancer was produced using a thematic analysis of papers from the World Health Organization and the International Agency for Research on Cancer, as well as recent research gathered from PubMed and Scopus. The study centered around proteins (EGFR, CYFRA 21-1), DNA methylation markers (SHOX2, RASSF1A), microRNAs (miR-21, miR-126), and volatile organic compounds (VOCs) detectable in body fluids like serum, sputum, plasma, and exhaled breath.
The most important discoveries that were made included a comparison of the performance metrics of a variety of SERS nanostructures (e.g., Au, Ag, Au@Ag core-shell) in the detection of these biomarkers, as well as a comprehensive analysis of the obstacles that are presented by the reproducibility of the signals and biological interference. The necessity of simulation models in the optimization of substrate design was emphasized by the study, which also urged for the establishment of standardized synthesis methods and the use of spectrum interpretation driven by artificial intelligence in order to ensure clinical dependability.
The paper concluded with a proposed theoretical roadmap that emphasized multi-biomarker detection platforms, portable SERS devices, machine learning integration, and policy alignment for future clinical translation. Combining nanotechnology, AI, and biological research, the results provide a thorough approach for building scalable, non-invasive diagnostic tools for early identification of lung cancer.
Downloads
References
1. A plasma 9 microRNA signature for lung cancer early detection: a multicenter analysis. (2025). Biomarker Research, 13, Article 74. https://doi.org/10.1186/s40364 025 00787 x
2. Awiaz, G., Habib, D., Qasim, U., Liu, C., Martinez, J., & Li, P. (2023). Recent advances of Au@Ag core shell SERS based biosensors: Effect of shape, size, and morphology on enhancement. Exploration, 3(2), Article 20220072. https://doi.org/10.1002/EXP.20220072
3. Bao, X., Wu, L., & Zhang, Y. (2023). Highly sensitive detection of CYFRA21 1 with a SERS sensor in serum for early lung cancer diagnosis. Frontiers in Bioengineering and Biotechnology, 11, Article 1251595. https://doi.org/10.3389/fbioe.2023.1251595
4. Dama, E., Colangelo, T., Fina, E., Cremonesi, M., Kallikourdis, M., Veronesi, G., & Bianchi, F. (2021). Biomarkers and lung cancer early detection: state of the art. Cancers, 13(15), Article 3919. https://doi.org/10.3390/cancers13153919
5. Li, J., Zhang, H., & Li, Q. (2025). Dual mode SERS and colorimetric sensor for lung cancer detection via hexanal in exhaled breath. Sensors and Actuators B: Chemical, 398, 134947. https://doi.org/10.1016/j.snb.2025.134947
6. Lin, Y., Chen, H., & Wang, S. (2025). Multi cancer early detection based on serum surface enhanced Raman spectroscopy with deep learning. BMC Medicine, 23, Article 67. https://doi.org/10.1186/s12916 025 03887 5
Free PDF: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11846373/
7. Lu, D., Zhao, Y., & Chen, H. (2025). Recent progress in SERS technology applications in lung cancer detection: from biomarkers to samples. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 320, 122655. https://doi.org/10.1016/j.saa.2025.122655
8. Meng, X., Song, X., Wang, Y., Liu, Y., Xie, Y., Xu, L., Yu, J., Qiu, L., Lin, J., & Wang, X. (2025). Application and development of SERS technology in detection of VOC gases. Materials Chemistry Frontiers, 9, 349 366. https://doi.org/10.1039/D4QM00812J
9. Seijo, L. M., Robinson, L. A., & Garcia, M. G. (2019). Biomarkers in lung cancer screening: achievements and challenges. Translational Lung Cancer Research, 8(4), 480 494. https://doi.org/10.21037/tlcr.2019.08.04
10. Sloan Dennison, S., Patel, S., Kumar, R., & Chen, J. (2024). Advancing SERS as a quantitative technique: challenges in reproducibility and substrate consistency. Nano Convergence, 11(1), Article 12. https://doi.org/10.1186/s40580 024 00443 4
11. Tahir, M. A., Neagoe, M., Baciu, C., & Dina, N. (2021). Surface enhanced Raman spectroscopy for bioanalysis and early cancer diagnosis: advantages, challenges, and prospects. Nanoscale, 13(25), 10429 10443. https://doi.org/10.1039/D1NR00708D
12. Trends in lung cancer incidence and mortality (1990 2019): national and state level trends. (2023). JCO Global Oncology, 9, GO.23.00255. Retrieved from https://ascopubs.org/doi/10.1200/GO.23.00255
13. Yuan, K., Zhou, X., Liu, Y., Zhu, Z., & Li, N. (2022). Nanomaterials meet surface enhanced Raman scattering: recent advances and reproducibility challenges. Journal of Nanobiotechnology, 20, Article 4017111. https://doi.org/10.1186/s12951 022 01711 3
This work is licensed under a 