Considering smoking status, coexpression network analysis of non-small cell lung cancer at different cancer stages, exhibits important genes and pathways.
Zahra MortezaeiMahmood TavallaeiSayed Mostafa HosseiniPublished in: Journal of cellular biochemistry (2019)
Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer among smokers, nonsmokers, women, and young individuals. Tobacco smoking and different stages of the NSCLC have important roles in cancer evolution and require different treatments. Existence of poorly effective therapeutic options for the NSCLC brings special attention to targeted therapies by considering genetic alterations. In this study, we used RNA-Seq data to compare expression levels of RefSeq genes and to find some genes with similar expression levels. We utilized the "Weighted Gene Co-expression Network Analysis" method for three different datasets to create coexpressed genetic modules having relations with the smoking status and different stages of the NSCLC. Our results indicate seven important genetic modules having important associations with the smoking status and cancer stages. Based on investigated genetic modules and their biological explanation, we then identified 13 newly candidate genes and 7 novel transcription factors in association with the NSCLC, the smoking status, and cancer stages. We then examined those results using other datasets and explained our results biologically to illustrate some important genes in relation with the smoking status and metastatic stage of the NSCLC that can bring some crucial information about cancer evolution. Our genetic findings also can be used as some therapeutic targets for different clinical conditions of the NSCLC.
Keyphrases
- small cell lung cancer
- genome wide
- network analysis
- papillary thyroid
- smoking cessation
- rna seq
- advanced non small cell lung cancer
- squamous cell
- copy number
- poor prognosis
- genome wide identification
- squamous cell carcinoma
- transcription factor
- brain metastases
- magnetic resonance
- machine learning
- healthcare
- metabolic syndrome
- adipose tissue
- young adults
- childhood cancer
- social media
- working memory
- deep learning
- artificial intelligence
- gene expression
- middle aged
- mass spectrometry
- genome wide analysis