Identification and validation of core genes as promising diagnostic signature in hepatocellular carcinoma based on integrated bioinformatics approach.
Pradeep KumarAmit Kumar SinghKavindra Nath TiwariSunil Kumar MishraVishnu D RajputTatiana MinkinaSimona CavaluOvidiu PopPublished in: Scientific reports (2022)
The primary objective of this investigation was to determine the hub genes of hepatocellular carcinoma (HCC) through an in silico approach. In the current context of the increased incidence of liver cancers, this approach could be a useful prognostic biomarker and HCC prevention target. This study aimed to examine hub genes for immune cell infiltration and their good prognostic characteristics for HCC research. Human genes selected from databases (Gene Cards and DisGeNET) were used to identify the HCC markers. Further, classification of the hub genes from communicating genes was performed using data derived from the targets' protein-protein interaction (PPI) platform. The expression as well as survival studies of all these selected genes were validated by utilizing databases such as GEPIA2, HPA, and immune cell infiltration. Based on the studies, five hub genes (TP53, ESR1, AKT1, CASP3, and JUN) were identified, which have been linked to HCC. They may be an important prognostic biomarker and preventative target of HCC. In silico analysis revealed that out of five hub genes, the TP53 and ESR1 hub genes potentially act as key targets for HCC prevention and treatment.
Keyphrases
- bioinformatics analysis
- genome wide
- genome wide identification
- protein protein
- genome wide analysis
- dna methylation
- network analysis
- small molecule
- machine learning
- gene expression
- poor prognosis
- endothelial cells
- cell proliferation
- deep learning
- big data
- artificial intelligence
- copy number
- high throughput
- binding protein
- electronic health record
- transcription factor
- free survival
- single cell
- replacement therapy
- young adults
- estrogen receptor
- combination therapy