Structural Understanding of the Functional Consequences of Missense Mutation

The NRAS gene is a well-known oncogene that acts as a major player in carcinogenesis. Mutations in the NRAS gene have been linked to multiple types of human tumors. Therefore, the identification of the most deleterious single nucleotide polymorphisms (SNPs) in the NRAS gene is necessary to understand the key factors of tumor pathogenesis and therapy. We aimed to retrieve NRAS missense SNPs and analyze them comprehensively using sequence and structure approaches to determine the most deleterious SNPs that could increase the risk of carcinogenesis. We also adopted structural biology methods and docking tools to investigate the behavior of the filtered SNPs. After retrieving missense SNPs and analyzing them using six in silico tools, 17 mutations were found to be the most deleterious mutations in NRAS. All SNPs except S145L were found to decrease NRAS stability, and all SNPs were found on highly conserved residues and important functional domains, except R164C. In addition, all mutations except G60E and S145L showed a higher binding affinity to GTP, implicating an increase in malignancy tendency. As a consequence, all other 14 mutations were expected to increase the risk of carcinogenesis, with 5 mutations (G13R, G13C, G13V, P34R, and V152F) expected to have the highest risk. Thermodynamic stability was ensured for these SNP models through molecular dynamics simulation based on trajectory analysis. Free binding affinity toward the natural substrate, GTP, was higher for these models as compared to the native NRAS protein. The Gly13 SNP proteins depict a differential conformational state that could favor nucleotide exchange and catalytic potentiality. A further application of experimental methods with all these 14 mutations could reveal new insights into the pathogenesis and management of different types of tumors.

Background: TP53 mutations are the most frequent mutations in hepatocellular carcinoma (HCC) and affect the occurrence and development of this cancer type. Therefore, it is essential to clarify the function and mechanism of TP53 mutations in HCC.

Methods: We performed a sequence of bioinformatic analyses to elucidate the characteristics of TP53 mutations in HCC. We downloaded the data of hepatocellular carcinoma from The Cancer Genome Atlas database and used different R packages for serial analyses, including gene mutation analysis, copy number variation analysis, analysis of the tumor mutational burden and microsatellite instability, differential gene expression analysis, and functional enrichment analysis of TP53 mutations, and performed gene set enrichment analysis. We established a protein-protein interaction network using the STRING online database and used the Cytoscape software for network visualization, and hub gene screening. In addition, we performed anticancer drug sensitivity analysis using data from the Genomics of Drug Sensitivity in Cancer. Immune infiltration and prognosis analyses were also performed.

Results: Missense mutations accounted for a great proportion of HCC mutations, the frequency of single nucleotide polymorphisms was high, and C > T was the most common form of single nucleotide variations. TP53 had a mutation rate of 30% and was the most commonly mutated gene in HCC. In the TP53 mutant group, the tumor mutational burden (p < 0.001), drug sensitivity (p < 0.05), ESTIMATE score (p = 0.038), and stromal score (p < 0.001) dramatically decreased. The Cytoscape software screened ten hub genes, including CT45A1, XAGE1B, CT55, GAGE2A, PASD1, MAGEA4, CTAG2, MAGEA10, MAGEC1, and SAGE1. The prognostic model showed a poor prognosis in the TP53 mutation group compared with that in the wild-type group (overall survival, p = 0.023). Univariate and multivariate cox regression analyses revealed that TP53 mutation was an independent risk factor for the prognosis of HCC patients (p <0.05). The constructed prognostic model had a favorable forecast value for the prognosis of HCC patients at 1 and 3 years (1-year AUC = 0.752, 3-years AUC = 0.702).

Conclusion: This study further deepened our understanding of TP53-mutated HCC, provided new insights into a precise individualized therapy for HCC, and has particular significance for prognosis prediction.