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Missense Mutations in Exons 18-24 of EGFR in Hepatocellular Carcinoma Tissues.

ABSTRACT: Epidermal growth factor receptor (EGFR), a transmembrane tyrosine kinase receptor, plays important roles in various cancers. In nonsmall cell lung cancer (NSCLC), EGFR mutations cluster around the ATP-binding pocket (exons 18-21) and some of these mutations activate the kinase and induce an increased sensitivity to EGFR-tyrosine kinase inhibitors. Nevertheless, data of EGFR mutations in HCC are limited. In this study, we investigated EGFR expression by immunohistochemistry and EGFR mutations (exons 18-24) by PCR cloning and sequencing. EGFR overexpression in HCC and matched nontumor tissues were detected in 13/40 (32.5%) and 10/35 (28.6%), respectively. Moreover, missense and silent mutations were detected in 13/33 (39.4%) and 11/33 (33.3%) of HCC tissues, respectively. The thirteen different missense mutations were p.L730P, p.V742I, p.K757E, p.I780T, p.N808S, p.R831C, p.V851A, p.V897A, p.S912P, p.P937L, p.T940A, p.M947V, and p.M947T. We also found already known SNP, p.Q787Q (CAG>CAA), in 13/33 (39.4%) of HCC tissues. However, no significant association was detected between EGFR mutations and EGFR overexpression, tissue, age, sex, tumor size, AFP, HBsAg, TP53, and Ki-67. Further investigation is warranted to validate the frequency and activity of these missense mutations, as well as their roles in HCC tumorigenesis and in EGFR-targeted therapy.

SUBMITTER: Panvichian R

PROVIDER: S-EPMC4575985 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Missense mutations in the first five exons of F9, which encodes factor FIX, represent 40% of all mutations that cause hemophilia B. To address the ongoing debate regarding in silico identification of disease-causing mutations at these exons, we analyzed 215 missense mutations from www.factorix.org using six in silico prediction tools, which are the most common used programs for analysis prediction of impact of mutations on the protein structure and function, with further advantage of using similar approaches. We developed different algorithms to integrate multiple predictions from such tools. In order to approach a structural analysis on FIX we performed a modeling of five selected pathogenic mutations. RESULTS:SIFT, PolyPhen-2 HumDiv, SNAP2, and MutationAssessor were the most successful in identifying true non-causative and causative mutations. A proposed function integrating these algorithms (wgP4) was the most sensitive (90.1%), specific (22.6%), and accurate (87%) than similar functions, and identified 187 variants as deleterious. Clinical phenotype was significantly associated with predicted causative mutations at all five exons. However, PolyPhen-2 HumDiv was more successful in linking clinical severity to specific exons, while functions that integrate 4-6 predictions were more successful in linking phenotype to genotypes at the light chain (exons 3-5). The most important value of integrating multiple predictions is the inclusion of scores derived from different approaches. Modeling of protein structure showed the effects of pathogenic nsSNPs on structure and function of FIX. CONCLUSIONS:A simple function that integrates information from different in silico programs yields the best prediction of mutated phenotypes. However, the specificity, sensitivity, and accuracy of genotype-phenotype predictions depend on specific characteristics of the protein domain and the disease of interest as we validated by the structural analysis of selected pathogenic F9 mutations. The proposed function integrating algorithm (wgP4) might be useful for the analysis of nsSNPs impact on other genes.

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Missense Mutations in Exons 18-24 of EGFR in Hepatocellular Carcinoma Tissues.

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