A selected reaction monitoring mass spectrometric assessment of biomarker candidates diagnosing large-cell neuroendocrine lung carcinoma by the scaling method using endogenous references

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Selected reaction monitoring mass spectrometry (SRM-MS) -based semi-quantitation was performed to assess the validity of 46 selected candidate proteins for specifically diagnosing large-cell neuroendocrine lung carcinoma (LCNEC) and differentiating it from other lung cancer subtypes. The scaling method was applied in this study using specific SRM peak areas (AUCs) derived from the endogenous reference protein that normalizes all SRM AUCs obtained for the candidate proteins. In a screening verification study, we found that seven out of the 46 candidate proteins were statistically significant for the LCNEC phenotype, including 4F2hc cell surface antigen heavy chain (4F2hc/CD98) (p-ANOVA ≤ 0.0012), retinal dehydrogenase 1 (p-ANOVA ≤ 0.0029), apolipoprotein A-I (p-ANOVA ≤ 0.0004), β-enolase (p-ANOVA ≤ 0.0043), creatine kinase B-type (p-ANOVA ≤ 0.0070), and galectin- 3-binding protein (p-ANOVA = 0.0080), and phosphatidylethanolamine-binding protein 1 (p-ANOVA ≤ 0.0012). In addition, we also identified candidate proteins specific to the smallcell lung carcinoma (SCLC) subtype. These candidates include brain acid soluble protein 1 (p-ANOVA < 0.0001) and γ-enolase (p-ANOVA ≤ 0.0013). This new relative quantitationbased approach utilizing the scaling method can be applied to assess hundreds of protein candidates obtained from discovery proteomic studies as a first step of the verification phase in biomarker development processes.


  • Tetsuya Fukuda
  • Masaharu Nomura
  • Yasufumi Kato
  • Hiromasa Tojo
  • Kiyonaga Fujii
  • Toshitaka Nagao
  • Yasuhiko Bando
  • Thomas E. Fehniger
  • György Marko-Varga
  • Haruhiko Nakamura
  • Harubumi Kato
  • Toshihide Nishimura
External organisations
  • Tokyo Medical University
  • Niizashiki Central General Hospital
  • Biosys Technologies, Inc
  • Osaka University
  • St. Marianna University School of Medicine
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Medical Biotechnology
Original languageEnglish
Article numbere0176219
JournalPLoS ONE
Issue number4
Publication statusPublished - 2017 Apr 1
Publication categoryResearch