Transcriptome profiles associated with selenium-deficiency-dependent oxidative stress identify potential diagnostic and therapeutic targets in liver cancer cells.
Damla GözenDeniz Cansen KahramanKübra NarciHuma ShehwanaOzlen KonuRengul Cetin AtalayPublished in: Turkish journal of biology = Turk biyoloji dergisi (2021)
Hepatocellular carcinoma (HCC) is one of the most common cancer types with high mortality rates and displays increased resistance to various stress conditions such as oxidative stress. Conventional therapies have low efficacies due to resistance and off-target effects in HCC. Here we aimed to analyze oxidative stress-related gene expression profiles of HCC cells and identify genes that could be crucial for novel diagnostic and therapeutic strategies. To identify important genes that cause resistance to reactive oxygen species (ROS), a model of oxidative stress upon selenium (Se) deficiency was utilized. The results of transcriptome-wide gene expression data were analyzed in which the differentially expressed genes (DEGs) were identified between HCC cell lines that are either resistant or sensitive to Se-deficiency-dependent oxidative stress. These DEGs were further investigated for their importance in oxidative stress resistance by network analysis methods, and 27 genes were defined to have key roles; 16 of which were previously shown to have impact on liver cancer patient survival. These genes might have Se-deficiency-dependent roles in hepatocarcinogenesis and could be further exploited for their potentials as novel targets for diagnostic and therapeutic approaches.
Keyphrases
- oxidative stress
- induced apoptosis
- genome wide
- gene expression
- dna damage
- genome wide identification
- diabetic rats
- ischemia reperfusion injury
- reactive oxygen species
- dna methylation
- endoplasmic reticulum stress
- network analysis
- bioinformatics analysis
- cell cycle arrest
- genome wide analysis
- signaling pathway
- single cell
- cardiovascular disease
- copy number
- heat shock
- squamous cell carcinoma
- cell death
- young adults
- type diabetes
- replacement therapy
- big data
- data analysis
- risk assessment
- squamous cell
- transcription factor
- deep learning
- mass spectrometry
- high resolution