Generic placeholder image

Current Proteomics

Editor-in-Chief

ISSN (Print): 1570-1646
ISSN (Online): 1875-6247

Research Article

Impact of Liver Cancer Somatic Mutations on Protein Structures and Functions

Author(s): Amna Amin Sethi and Nisar Ahmed Shar*

Volume 18, Issue 2, 2021

Published on: 15 April, 2020

Page: [204 - 211] Pages: 8

DOI: 10.2174/1570164617666200415155637

Price: $65

conference banner
Abstract

Background: Cancers result due to the dysregulation of gene expression. They can be identified on the basis of driver mutations and genetic signatures. Proteins are macromolecules that regulate the structure and function of body organs. Missense somatic mutations play a critical role in the development of cancer by altering the underlying properties of corresponding proteins. The extent to which the chemical properties and composition of amino acid are changed in cancer is still under investigation.

Objective: The main objective of this study is to identify amino acid changes that might be responsible for causing liver cancer. It also aims to identify frequently mutated genes associated with liver cancer.

Methods: The mutation data of Hepatocellular Carcinoma (HCC) in coding variants was retrieved from COSMIC (Catalogue of Somatic Mutations in Cancer) databases. Different bioinformatics tools were used to study genetic alterations at the protein level. The identified amino acid replacements were compared with Grantham’s distance to determine similarity/ dissimilarity between substituted amino acids.

Results: The results show that TP53, CTNNB1, MUC16, PCLO, and TTN genes were frequently mutated in liver cancer. This study also reveals that the non-synonymous mutations, in analyzed dataset, cause loss of Alanine.

Conclusion: The amino acid replacements, identified in this study, may act as signatures for early diagnosis of liver cancer. They may also be helpful in understanding the development of liver cancer.

Keywords: Amino acids, mutations, liver cancer, Grantham's distance, replacement, Hepatocellular Carcinoma (HCC).

Graphical Abstract

[1]
Tsuber, V.; Kadamov, Y.; Brautigam, L.; Berglund, U.W.; Helleday, T. Mutations in cancer cause gain of cysteine, histidine, and tryptophan at the expense of a net loss of arginine on the proteome level. Biomolecules, 2017, 7(3), 1-17.
[http://dx.doi.org/10.3390/biom7030049] [PMID: 28671612]
[2]
Pon, J.R.; Marra, M.A. Driver and passenger mutations in cancer. Annu. Rev. Pathol., 2015, 10, 25-50.
[http://dx.doi.org/10.1146/annurev-pathol-012414-040312] [PMID: 25340638]
[3]
Bozic, I.; Antal, T.; Ohtsuki, H.; Carter, H.; Kim, D.; Chen, S.; Karchin, R.; Kinzler, K.W.; Vogelstein, B.; Nowak, M.A. Accumulation of driver and passenger mutations during tumor progression. Proc. Natl. Acad. Sci. USA, 2010, 107(43), 18545-18550.
[http://dx.doi.org/10.1073/pnas.1010978107] [PMID: 20876136]
[4]
Kaminker, J.S.; Zhang, Y.; Watanabe, C.; Zhang, Z. CanPredict: a computational tool for predicting cancer-associated missense mutations. Nucleic Acids Res., 2007, 35(Web Server issue), W595-598.
[http://dx.doi.org/10.1093/nar/gkm405] [PMID: 17537827]
[5]
Li, B.; Krishnan, V.G.; Mort, M.E.; Xin, F.; Kamati, K.K.; Cooper, D.N.; Mooney, S.D.; Radivojac, P. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics, 2009, 25(21), 2744-2750.
[http://dx.doi.org/10.1093/bioinformatics/btp528] [PMID: 19734154]
[6]
Vazquez, A.; Kamphorst, J.J.; Markert, E.K.; Schug, Z.T.; Tardito, S.; Gottlieb, E. Cancer metabolism at a glance. J. Cell Sci., 2016, 129(18), 3367-3373.
[http://dx.doi.org/10.1242/jcs.181016] [PMID: 27635066]
[7]
Błażej, P.; Mackiewicz, D.; Wnętrzak, M.; Mackiewicz, P. The impact of selection at the amino acid level on the usage of synonymous codons. G3 (Bethesda), 2017, 7(3), 967-981.
[http://dx.doi.org/10.1534/g3.116.03812] [PMID: 28122952]
[8]
Inigo, M.; Keiran, M.R.; Moritz, G.; Kevin, J.D.; Kerstin, H.; Peter, V.L.; Helen, D.; Michael, R.; Stratton, P.J.C. Universal patterns of selection in cancer and somatic tissues article universal patterns of selection in cancer and somatic tissues. Cell, 2017, 171, 1029-1041.
[9]
Ananieva, E. Targeting amino acid metabolism in cancer growth and anti-tumor immune response. World J. Biol. Chem., 2015, 6(4), 281-289.
[http://dx.doi.org/10.4331/wjbc.v6.i4.281] [PMID: 26629311]
[10]
Lawrence, M.S.; Stojanov, P.; Polak, P.; Kryukov, G.V.; Cibulskis, K.; Sivachenko, A.; Carter, S.L.; Stewart, C.; Mermel, C.H.; Roberts, S.A.; Kiezun, A.; Hammerman, P.S.; McKenna, A.; Drier, Y.; Zou, L.; Ramos, A.H.; Pugh, T.J.; Stransky, N.; Helman, E.; Kim, J.; Sougnez, C.; Ambrogio, L.; Nickerson, E.; Shefler, E.; Cortés, M.L.; Auclair, D.; Saksena, G.; Voet, D.; Noble, M.; DiCara, D.; Lin, P.; Lichtenstein, L.; Heiman, D.I.; Fennell, T.; Imielinski, M.; Hernandez, B.; Hodis, E.; Baca, S.; Dulak, A.M.; Lohr, J.; Landau, D-A.; Wu, C.J.; Melendez-Zajgla, J.; Hidalgo, M.A.; Koren, A.; McCarroll, S.A.; Mora, J.; Crompton, B.; Onofrio, R.; Parkin, M.; Winckler, W.; Ardlie, K.; Gabriel, S.B.; Roberts, C.W.M.; Biegel, J.A.; Stegmaier, K.; Bass, A.J.; Garraway, L.A.; Meyerson, M.; Golub, T.R.; Gordenin, D.A.; Sunyaev, S.; Lander, E.S.; Getz, G. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature, 2013, 499(7457), 214-218.
[http://dx.doi.org/10.1038/nature12213] [PMID: 23770567]
[11]
Ludmil, B.A.; Serena, N.Z.D.C.W.; Samuel, A.J.R.; Aparicio, Sam, B.A.V.B.; Graham, R. Signatures of mutational processes in human cancer. Nature, 2013, 500, 415-421.
[12]
Tan, H.; Bao, J.; Zhou, X. Genome-wide mutational spectra analysis reveals significant cancer-specific heterogeneity. Sci. Rep., 2015, 5, 12566.
[http://dx.doi.org/10.1038/srep12566] [PMID: 26212640]
[13]
Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2018, 68(6), 394-424.
[http://dx.doi.org/10.3322/caac.21492] [PMID: 30207593]
[14]
Llovet, J.M.; Zucman-Rossi, J.; Pikarsky, E.; Sangro, B.; Schwartz, M.; Sherman, M.; Gores, G. Hepatocellular carcinoma. Nat. Rev. Dis. Primers, 2016, 2, 16018.
[http://dx.doi.org/10.1038/nrdp.2016.18] [PMID: 27158749]
[15]
New global cancer data. New global cancer data: GLOBOCAN 2018 |UICC. 2018.https://www.uicc.org/new-global-cancer-data-globocan-2018 Available from:
[16]
Olsen, S.K.; Brown, R.S.; Siegel, A.B. Hepatocellular carcinoma: review of current treatment with a focus on targeted molecular therapies. Therap. Adv. Gastroenterol., 2010, 3(1), 55-66.
[http://dx.doi.org/10.1177/1756283X09346669] [PMID: 21180590]
[17]
Lawrence, M.S.; Stojanov, P.; Mermel, C.H.; Robinson, J.T.; Garraway, L.A.; Golub, T.R.; Meyerson, M.; Gabriel, S.B.; Lander, E.S.; Getz, G. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature, 2014, 505(7484), 495-501.
[http://dx.doi.org/10.1038/nature12912] [PMID: 24390350]
[18]
The 20 Amino Acids: hydrophobic, hydrophilic, polar and charged amino acids. Available from: https://proteinstructures.com/Structure/Structure/ami no-acids.html
[19]
Grantham, R. Amino acid difference formula to help explain protein evolution. Science, 1974, 185(4154), 862-864.
[http://dx.doi.org/10.1126/science.185.4154.862] [PMID: 4843792]
[20]
Podoly, E.; Hanin, G.; Soreq, H. Alanine-to-threonine substitutions and amyloid diseases: butyrylcholinesterase as a case study. Chem. Biol. Interact., 2010, 187(1-3), 64-71.
[http://dx.doi.org/10.1016/j.cbi.2010.01.003] [PMID: 20060816]
[21]
Zheng, Y.; Cui, Q. Microscopic mechanisms that govern the titration response and pKa values of buried residues in staphylococcal nuclease mutants. Proteins, 2017, 85(2), 268-281.
[http://dx.doi.org/10.1002/prot.25213] [PMID: 27862310]
[22]
Chimenti, M.S.; Khangulov, V.S.; Robinson, A.C.; Heroux, A.; Majumdar, A.; Schlessman, J.L.; García-Moreno, B. Structural reorganization triggered by charging of Lys residues in the hydrophobic interior of a protein. Structure, 2012, 20(6), 1071-1085.
[http://dx.doi.org/10.1016/j.str.2012.03.023] [PMID: 22632835]
[23]
Mathe, E.; Olivier, M.; Kato, S.; Ishioka, C.; Hainaut, P.; Tavtigian, S.V. Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods. Nucleic Acids Res., 2006, 34(5), 1317-1325.
[http://dx.doi.org/10.1093/nar/gkj518] [PMID: 16522644]
[24]
Jordan, I.K.; Kondrashov, F.A.; Adzhubei, I.A.; Wolf, Y.I.; Koonin, E.V.; Kondrashov, A.S.; Sunyaev, S. Erratum: auniversal trend of amino acid gain and loss in protein evolution. Nature, 2005, 435, 528.
[http://dx.doi.org/10.1038/nature03656]
[25]
Felder, M.; Kapur, A.; Gonzalez-Bosquet, J.; Horibata, S.; Heintz, J.; Albrecht, R.; Fass, L.; Kaur, J.; Hu, K.; Shojaei, H.; Whelan, R.J.; Patankar, M.S. MUC16 (CA125): tumor biomarker to cancer therapy, a work in progress. Mol. Cancer, 2014, 13, 129.
[http://dx.doi.org/10.1186/1476-4598-13-129] [PMID: 24886523]
[26]
Kim, H.; Kim, Y.M. Pan-cancer analysis of somatic mutations and transcriptomes reveals common functional gene clusters shared by multiple cancer types. Sci. Rep., 2018, 8(1), 6041.
[http://dx.doi.org/10.1038/s41598-018-24379-y] [PMID: 29662161]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy