Novel protein kinase C (nPKC) family member, Protein kinase C epsilon (PKCε) is an AKC kinase superfamily member. It is associated with neurological and metabolic diseases as well as human cancer. No study so far has been conducted to identify genetic variations and their effect on PKCε folding and functioning. The present study aimed to identify mutational hotspots in PKCε and disease-causing non-synonymous variants (nsSNPs) along with the investigation of nsSNPs impact on protein dynamics. Twenty-nine in silico tools were applied to determine nsSNPs’ deleteriousness, their impact on protein dynamics and disease association, along with the prediction of PKCε post-translational modification (PTM) sites. The present study’s outcomes indicated that most nsSNPs were concentrated in the PKCε hinge region and C-terminal tail. Most pathogenic variants mapped on the kinase domain. Regulatory domain variants influenced PKCε interaction with molecular players, whereas kinase domain variants were predicted to impact its phosphorylation pattern and protein–protein interactions. Most PTM sites were mapped on the hinge region. PKCε nsSNPs have an association with oncogenicity and its expression dysregulation is responsible for poor overall survival. Understanding nsSNPs structural impact is a primary step necessary for delineating the relationship of genetic level differences with protein phenotype. The obtained knowledge can eventually help in disease diagnosis and therapy design.
Background The protein kinase C (PKC) family of serine/threonine kinases contains more than ten isozymes that are involved in multiple signaling pathways, including cell cycle regulation and carcinogenesis. The PKCε isozyme is an oncogene known to be upregulated in various signaling pathways involved in hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC). However, there is no known association of missense SNPs in PKCε with this disease, which can be a potential biomarker for early diagnosis and treatment. This research reveals a novel missense SNP in PKCε that is associated with HCV-induced HCC in the Pakistani population. Methods The PKCε SNP with amino acid substitution of E14K was chosen for wet lab analysis. Tetra ARMS-PCR was employed for the identification of high-risk SNP in PKCε of HCV-induced HCC patients. Liver function testing was also performed for comparison between the liver condition of the HCC patient and control group, and the viral load of HCC patient samples was evaluated to determine any alteration in the viral infectivity between different genotypes of the selected high-risk PKCε variant SNP. Results Frequency distribution of the homozygous GG genotype was found to be highest among HCV-induced HCC patients and was also found to be significantly associated with disease development and progression. The p values of comparative data obtained for the other two genotypes, heterozygous AG and homozygous AA, of the SNP also showed the significance of the data for these alleles. Still, their odds ratio and relative risk analysis did not indicate their association with HCV-induced HCC. Conclusion The distribution of a genotype GG of PKCε has been found in HCV- induced HCC patients. Therefore, these PKCε SNP have the potential to be biomarkers for HCV-induced HCC. Further investigation using a larger sample size would provide additional insight into these initial data and open a new avenue for a better prognosis of this disease.
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