Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer.
Özgür Cem ErkinBetul ComertpayEsra GövPublished in: Bioinformatics and biology insights (2022)
Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein-protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20 , AURKA , CDK1 , EZH2 , and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL , FYN , LRKK2 , and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.
Keyphrases
- brain metastases
- small cell lung cancer
- bioinformatics analysis
- gene expression
- end stage renal disease
- genome wide
- newly diagnosed
- ejection fraction
- chronic kidney disease
- dna methylation
- protein protein
- peritoneal dialysis
- healthcare
- primary care
- poor prognosis
- small molecule
- prognostic factors
- genome wide identification
- risk assessment
- binding protein
- climate change
- cell proliferation
- long non coding rna
- tyrosine kinase
- human health
- advanced non small cell lung cancer
- genome wide analysis
- highly efficient
- rna seq
- epidermal growth factor receptor
- free survival