Therapeutic prognosis of prostate cancer using surface-enhanced Raman scattering of patient urine and multivariate statistical analysis

Yiwei Ma, Jingmao Chi, Zhaoyu Zheng, Athula Attygalle, Isaac Yi Kim, Henry Du

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Surface-enhanced Raman scattering (SERS) is highly sensitive and label-free analytical technique based on Raman spectroscopy aided by field-multiplying plasmonic nanostructures. We report the use of SERS measurements of patient urine in conjunction with biostatistical algorithms to assess the treatment response of prostate cancer (PCa) in 12 recurrent (Re) and 63 nonrecurrent (NRe) patient cohorts. Multiple Raman spectra are collected from each urine sample using monodisperse silver nanoparticles (AgNPs) for Raman signal enhancement. Genetic algorithms-partial least squares-linear discriminant analysis (GA-PLS-LDA) was employed to analyze the Raman spectra. Comprehensive GA-PLS-LDA analyses of these Raman spectral features (p = 3.50 × 10−16) yield an accuracy of 86.6%, sensitivity of 86.0%, and specificity 87.1% in differentiating the Re and NRe cohorts. Our study suggests that SERS combined with multivariate GA-PLS-LDA algorithm can potentially be used to detect and monitor the risk of PCa relapse and to aid with decision-making for optimal intermediate secondary therapy to recurred patients.

Original languageEnglish (US)
Article numbere202000275
JournalJournal of Biophotonics
Volume14
Issue number1
DOIs
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Materials Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Engineering(all)
  • Physics and Astronomy(all)

Keywords

  • genetic algorithm
  • multivariate data analysis
  • non-recurrence
  • prostate cancer
  • recurrence
  • surface-enhanced Raman scattering

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