Metabolomic profiling of dried blood spots reveals gender-specific discriminant models for the diagnosis of small cell lung cancer

Li Yu, Kefeng Li, Xiangmin Li, Chao Guan, Tingting Sun, Xiaoye Zhang

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The accurate diagnosis of small cell lung cancer (SCLC) at initial presentation is essential to ensure appropriate treatment. No validated blood biomarkers that could distinguish SCLC from non-small cell lung cancer (NSCLC) has yet been developed. Dried blood spot (DBS) microsampling has gained increasing interest in biomarkers discovery. In this study, we first performed metabolomic profiling of DBS samples from 37 SCLC, 40 NSCLC, and 37 controls. Two gender-specific multianalyte discriminant models were established for males and females, respectively to distinguish SCLC from NSCLC and controls. The receiver operator characteristic (ROC) curve analysis showed the diagnostic accuracy of 95% (95% CI: 83%-100%) in males SCLC using five metabolites in DBS and 94% (95% CI: 74%-100%) for females using another set of five metabolites. The robustness of the models was confirmed by the random permutation tests (P < 0.01 for both). The performance of the discriminant models was further evaluated using a validation cohort with 78 subjects. The developed discriminant models yielded an accuracy of 91% and 81% for males and females, respectively, in the validation cohort. Our results highlighted the potential clinical utility of the metabolomic profiling of DBS as a convenient and effective approach for the diagnosis of SCLC.

Original languageEnglish
Pages (from-to)978-995
Number of pages18
JournalAging
Volume12
Issue number1
DOIs
Publication statusPublished - 15 Jan 2020
Externally publishedYes

Keywords

  • Differential diagnosis
  • Dried blood spot
  • Gender differences
  • Metabolomics
  • Small cell lung cancer

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