Objective: This thesis addresses a neglected aspect of bioinformatics research of hemifacial microsomia (HFM). Existing research stops short of prediction based on big data. This study combines multiple databases to explore underlying pathogenesis using bioinformatic approach.
Methods: The research consisted of multiple bioinformatic methods, included pathogenic genes analyses, protein-protein interaction network construction, functional enrichment, and mining target genes related miRNA, for studying pathogenic genes of HFM.
Results: Total of 140 genes were identified as potential genes in the study. The protein-protein interaction networks for pathogenic genes were constructed, which contained 138 nodes and 243 edges with RAF1, MAP2K1, MAP2K2, MAPK3, MAPK1, EGFR, BRAF, LMNA, ESPR1, and SFN as the hub genes. These genes were discovered significantly enriched in MAPK pathway. Besides, the whole of interactions between miRNAs and the top 5 hub genes were revealed.
Conclusions: Our results indicated that occurrence of HFM is attributed to a variety of genes. Furthermore, the interactions of pathogenic genes were further elucidated by using bioinformatics approach. It reveals the MAPK pathway play an essential role in its pathogenesis. It may provide a novel perspective on better understanding the pathogenesis and more accurate early screening of HFM.
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