Label-free surface-enhanced Raman spectroscopy for diagnosis and analysis of serum samples with different types lung cancer

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 261; p. 120021
• Main Authors: Lei, Jia, Yang, Dafu, Li, Rui, Dai, ZhaoXia, Zhang, Chenlei, Yu, Zhanwu, Wu, Shifa, Pang, Lu, Liang, Shanshan, Zhang, Yi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.11.2021
• Subjects: PCA; PLS-DA; Serum; Silver nanoparticles; Surface-enhanced Raman spectroscopy; Serum; PLS-DA; Silver nanoparticles; Surface-enhanced Raman spectroscopy; PCA
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2021.120021

[Display omitted] •Surface-enhanced Raman spectroscopy is useful for early detection of lung cancer.•PLS-DA was superior to PCA in the classification of lung cancer and normal subjects.•PLS-DA model could not completely separate the three types of lung cancer. Screening and detection of early lung cancer is important for diagnosis and prognosis. Intervention in early stage of lung cancer can significantly improve the cure and survival of patients. Surface-enhanced Raman spectroscopy (SERS) is an increasingly popular method of diagnosing cancer. We used silver nanoparticles (AgNPs) as the Raman-enhanced substrate to increase Raman signals, which contributes to the subsequent classification of lung cancer and normal serum. SERS acquired from the serum indicated the difference in biochemical components between cancerous (n = 51) lung serum and normal (n = 18) serum. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were utilized to establish the identification model, and the various indicators of PLS-DA were all superior to those of the PLS model. Our study offers a new proposal for the universal applicability of analysis and identification with SERS of serum samples in clinical diagnosis.