Predisposing deleterious variants in the cancer-associated human kinases in the global populations.
Salman Ahmed KhanMisbah AnwarAtia GoharMoom R RoosanDaniel C HoessliAmbrina KhatoonMuhammad ShakeelPublished in: PloS one (2024)
Human kinases play essential and diverse roles in the cellular activities of maintaining homeostasis and growth. Genetic mutations in the genes encoding the kinases (or phosphotransferases) have been linked with various types of cancers. In this study, we cataloged mutations in 500 kinases genes in >65,000 individuals of global populations from the Human Genetic Diversity Project (HGDP) and ExAC databases, and assessed their potentially deleterious impact by using the in silico tools SIFT, Polyphen2, and CADD. The analysis highlighted 35 deleterious non-synonymous SNVs in the ExAC and 5 SNVs in the HGDP project. Notably, a higher number of deleterious mutations was observed in the Non-Finnish Europeans (26 SNVs), followed by the Africans (14 SNVs), East Asians (13 SNVs), and South Asians (12 SNVs). The gene set enrichment analysis highlighted NTRK1 and FGFR3 being most significantly enriched among the kinases. The gene expression analysis revealed over-expression of NTRK1 in liver cancer, whereas, FGFR3 was found over-expressed in lung, breast, and liver cancers compared to their expression in the respective normal tissues. Also, 13 potential drugs were identified that target the NTRK1 protein, whereas 6 potential drugs for the FGFR3 target were identified. Taken together, the study provides a framework for exploring the predisposing germline mutations in kinases to suggest the underlying pathogenic mechanisms in cancers. The potential drugs are also suggested for personalized cancer management.
Keyphrases
- endothelial cells
- genome wide
- genetic diversity
- genome wide identification
- copy number
- poor prognosis
- induced pluripotent stem cells
- quality improvement
- pluripotent stem cells
- gene expression
- binding protein
- dna methylation
- human health
- young adults
- risk assessment
- molecular docking
- climate change
- bioinformatics analysis
- long non coding rna
- deep learning
- big data
- amino acid
- squamous cell
- protein protein
- lymph node metastasis