Identification of Stage-Specific microRNAs that Govern the Early Stages of Sequential Oral Oncogenesis by Strategically Bridging Human Genetics with Epigenetics and Utilizing an Animal Model.
Iphigenia GintoniStavros VassiliouGeorge P ChrousosChristos YapijakisPublished in: International journal of molecular sciences (2024)
Oral squamous cell carcinoma (OSCC) is a highly prevalent and aggressive malignancy, with mortality rates reaching 60%, mainly due to its excessive diagnostic delay. MiRNAs, a class of crucial epigenetic gene-expression regulators, have emerged as potential diagnostic biomarkers, with >200 molecules exhibiting expressional dysregulation in OSCC. We had previously established an in silico methodology for the identification of the most disease-specific molecules by bridging genetics and epigenetics. Here, we identified the stage-specific miRNAs that govern the asymptomatic early stages of oral tumorigenesis by exploiting seed-matching and the reverse interplay between miRNA levels and their target genes' expression. Incorporating gene-expression data from our group's experimental hamster model of sequential oral oncogenesis, we bioinformatically detected the miRNAs that simultaneously target/regulate >75% of the genes that are characteristically upregulated or downregulated in the consecutive stages of hyperplasia, dysplasia, and early invasion, while exhibiting the opposite expressional dysregulation in OSCC-derived tissue and/or saliva specimens. We found that all stages share the downregulation of miR-34a-5p, miR124-3p, and miR-125b-5p, while miR-1-3p is under-expressed in dysplasia and early invasion. The malignant early-invasion stage is distinguished by the downregulation of miR-147a and the overexpression of miR-155-5p, miR-423-3p, and miR-34a-5p. The identification of stage-specific miRNAs may facilitate their utilization as biomarkers for presymptomatic OSCC diagnosis.
Keyphrases
- gene expression
- cell proliferation
- dna methylation
- bioinformatics analysis
- signaling pathway
- endothelial cells
- type diabetes
- cell migration
- transcription factor
- body mass index
- cardiovascular disease
- molecular docking
- long non coding rna
- machine learning
- poor prognosis
- risk assessment
- electronic health record
- risk factors
- artificial intelligence
- weight loss
- deep learning
- genome wide identification
- molecular dynamics simulations