Integrative Analysis of Methylation and Gene Expression in Lung Adenocarcinoma and Squamous Cell Lung Carcinoma

Hao Zhang, Zhou Jin, Ling Cheng, Bin Zhang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Lung cancer is a highly prevalent type of cancer with a poor 5-year survival rate of about 4–17%. Eighty percent lung cancer belongs to non-small-cell lung cancer (NSCLC). For a long time, the treatment of NSCLC has been mostly guided by tumor stage, and there has been no significant difference between the therapy strategy of lung adenocarcinoma (LUAD) and squamous cell lung carcinoma (SCLC), the two major subtypes of NSCLC. In recent years, important molecular differences between LUAD and SCLC are increasingly identified, indicating that targeted therapy will be more and more histologically specific in the future. To investigate the LUAD and SCLC difference on multi-omics scale, we analyzed the methylation and gene expression data together. With the Boruta method to remove irrelevant features and the MCFS (Monte Carlo Feature Selection) method to identify the significantly important features, we identified 113 key methylation features and 23 key gene expression features. HNF1B and TP63 were found to be dysfunctional on both methylation and gene expression levels. The experimentally determined interaction network suggested that TP63 may play an important role in connecting methylation genes and expression genes. Many of the discovered signature genes have been supported by literature. Our results may provide directions of precision diagnosis and therapy of LUAD and SCLC.

Original languageEnglish
Article number3
JournalFrontiers in Bioengineering and Biotechnology
Volume8
DOIs
StatePublished - 7 Feb 2020
Externally publishedYes

Keywords

  • Boruta
  • Monte Carlo Feature Selection
  • gene expression
  • lung adenocarcinoma
  • methylation
  • squamous cell lung carcinoma

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