Cervical cancer biomarker screening based on Raman spectroscopy and multivariate statistical analysis

作者全名：Fan, Qiwen; Ding, Hongli; Mo, Huixia; Tang, Yishu; Wu, Guohua; Yin, Longfei

作者地址：[Fan, Qiwen; Mo, Huixia] Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China; [Ding, Hongli; Tang, Yishu] Chongqing Med Univ, Dept Lab Med, Affiliated Hosp 1, Chongqing 400016, Peoples R China; [Wu, Guohua; Yin, Longfei] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China

通信作者：Mo, HX (通讯作者)，Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China.; Tang, YS (通讯作者)，Chongqing Med Univ, Dept Lab Med, Affiliated Hosp 1, Chongqing 400016, Peoples R China.

来源：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

ESI学科分类：CHEMISTRY

WOS号：WOS:001240389200001

JCR分区：Q1

影响因子：4.3

年份：2024

卷号：317

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Cervical cancer; Surface-enhanced Raman spectroscopy; Multivariate statistical analysis; Tumor markers; Serum

摘要：Cervical cancer (CC) stands as one of the most prevalent malignancies among females, and the examination of serum tumor markers(TMs) assumes paramount significance in both its diagnosis and treatment. This research delves into the potential of combining Surface -Enhanced Raman Spectroscopy (SERS) with Multivariate Statistical Analysis (MSA) to diagnose cervical cancer, coupled with the identification of prospective serum biomarkers. Serum samples were collected from 95 CC patients and 81 healthy subjects, with subsequent MSA employed to analyze the spectral data. The outcomes underscore the superior efficacy of Partial Least Squares Discriminant Analysis (PLS-DA) within the MSA framework, achieving predictive accuracy of 97.73 %, and exhibiting sensitivities and specificities of 100 % and 95.83 % respectively. Additionally, the PLS-DA model yields a Variable Importance in Projection (VIP) list, which, when coupled with the biochemical information of characteristic peaks, can be utilized for the screening of biomarkers. Here, the Random Forest (RF) model is introduced to aid in biomarker screening. The two findings demonstrate that the principal contributing features distinguishing cervical cancer Raman spectra from those of healthy individuals are located at 482, 623, 722, 956, 1093, and 1656 cm - 1 , primarily linked to serum components such as DNA, tyrosine, adenine, valine, D-mannose, and amide I. Predictive models are constructed for individual biomolecules, generating ROC curves. Remarkably, D-mannose of V (C -N) exhibited the highest performance, boasting an AUC value of 0.979. This suggests its potential as a serum biomarker for distinguishing cervical cancer from healthy subjects.

基金机构：National Natural Science Foundation of China (NSFC) [62,071,059, 11971072]; Beijing Key Laboratory of Work Safety Intelligent Monitoring

基金资助正文：<B>Acknowledgments</B> This study is supported by National Natural Science Foundation of China (NSFC) (Grants 62,071,059 and 11971072) and Beijing Key Laboratory of Work Safety Intelligent Monitoring.