Multi-scale representation of proteomic data exhibits distinct microRNA regulatory modules in non-smoking female patients with lung adenocarcinoma

Lawrence W. Chan, Fengfeng Wang, Fei Meng, Lili Wang, S. C.Cesar Wong, Joseph S. Au, Sijun Yang, William C. Cho

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Adenocarcinoma in female non-smokers is an under-explored subgroup of non-small cell lung cancer (NSCLC), in which the molecular mechanism and genetic risk factors remain unclear. We analyzed the protein profiles of plasma samples of 45 patients in this subgroup and 60 non-cancer subjects using surface-enhanced laser desorption/ionization time-of- flight mass spectrometry. Among 85 peaks of mass spectra, the differential expression analysis identified 15 markers based on False Discovery Rate control and the Discrete Wavelet Transforms further selected a cluster of 6 markers that were consistently observed at multiple scales of mass-charge ratios. This marker cluster, corresponding to 7 unique proteins, was able to distinguish the female non-smokers with adenocarcinoma from non-cancer subjects with a value of accuracy of 87.6%. We also predicted the role of competing endogenous RNAs (ceRNAs) in 3 out of these 7 proteins. Other studies reported that these ceRNAs and their targeting microRNAs, miR-206 and miR-613, were significantly associated with NSCLC. This study paves a crucial path for further investigating the genetic markers and molecular mechanism of this special NSCLC subgroup.

Original languageEnglish
Pages (from-to)51-56
Number of pages6
JournalComputers in Biology and Medicine
Volume102
Early online date12 Sep 2018
DOIs
Publication statusPublished - 1 Nov 2018

Keywords

  • Lung adenocarcinoma
  • Marker cluster
  • Mass spectrometry
  • microRNA
  • Multi-scale representation
  • Regulatory modules

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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