Abstract
Interaction between genes is one driving force that can influence a biological outcome. In a genetic disease such as cancer, understanding genetic interactions may help us elucidate mechanisms sustaining cancer growth. A computational approach is one way to detect genetic interactions in the context of cancer. In this article, we introduce a tool, GenExSt, and its underlying method to study gene interactions. We applied our method to discover gene-pairs whose expressions demonstrate patterns of correlation. For this demonstration, we selected ten breast cancer gene expression data sets from the Genomic Data Commons Data Portal through National Cancer Institute. We focused on genes that suppress genome instability, or instability suppressing genes (GIS), many of which play an important role in cancer. We applied our method to an inter-comparison across data sets. Here we tested statistical normalization approaches derived from the combined expressions of randomly selected, single, housekeeping (HK) genes, and from the calculated mean of three expressions. In addition, our method derives \(R^{2}\) values from linear models in which the expressions of all possible pairs of GIS genes are placed in a linear model to produce heatmaps to indicate probable correlations. We show that results from our method are suited to normalized data, extracted from multiple genes simultaneously, rather than using single gene expression values. GenExSt may be used to study gene expression data in other settings provided that the concept of gene interactions is appropriate in the context.
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References
Atwan, Z.W., et al.: GAPDH spike RNA as an alternative for housekeeping genes in relative gene expression assay using real-time PCR. Bul. Nat. Res. Centre 44(1), 1–8 (2020)
Burrell, R.A., McGranahan, N., Bartek, J., Swanton, C.: The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501(7467), 338 (2013)
Feng, C., Wang, H., Lu, N., Chen, T., Hua, H.E., Lu, Y., et al.: Log-transformation and its implications for data analysis. Shanghai Arch. Psychiatry 26(2), 105 (2014)
Chen, H., Li, C., Peng, X., Zhou, Z., Weinstein, J.N., Caesar-Johnson, S.J., Demchok, J.A., Felau, I., Kasapi, M., Ferguson, M.L., et al.: A pan-cancer analysis of enhancer expression in nearly 9000 patient samples. Cell 173(2), 386–399 (2018)
Conrad, L.B., Lin, K.Y., Nandu, T., Gibson, B.A., Lea, J.S., Lee Kraus, W.: ADP-ribosylation levels and patterns correlate with gene expression and clinical outcomes in ovarian cancers. Mol. Cancer Therapeut. 19(1), 282–291 (2020)
Cordell, H.J.: Detecting gene-gene interactions that underlie human diseases. Nat. Rev. Genet. 10(6), 392–404 (2009)
de Almeida, B.P., Vieira, A.F., Paredes, J., Bettencourt-Dias, M., Barbosa-Morais, N.L.: Pan-cancer association of a centrosome amplification gene expression signature with genomic alterations and clinical outcome. PLoS Comput. Biol. 15(3), e1006832 (2019)
Dehnen, L., Janz, M., Verma, J.K., Psathaki, O.K., Langemeyer, L., Fröhlich, F., Heinisch, J.J., Meyer, H., Ungermann, C., Paululat, A.: A trimeric metazoan rab7 GEF complex is crucial for endocytosis and scavenger function. J. Cell Sci. (2020)
Eisenberg, E., Levanon, E.Y.: Human housekeeping genes, revisited. Trends Genet. 29(10), 569–574 (2013)
Farmer, H., McCabe, N., Lord, C.J., Tutt, A.N.J., Johnson, D.A., Richardson, T.B., Santarosa, M., Dillon, K.J., Hickson, I., Knights, C., et al.: Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434(7035), 917 (2005)
Fernandez-Pozo, N., Haas, F.B., Meyberg, R., Ullrich, K.K., Hiss, M., Perroud, P.-F., Hanke, S., Kratz, V., Powell, A.F., Vesty, E.F., et al.: Peatmoss (physcomitrella expression atlas tool): a unified gene expression atlas for the model plant physcomitrella patens. Plant J. 102(1), 165–177 (2020)
Gaudelet, T., Malod-Dognin, N., Sánchez-Valle, J., Pancaldi, V., Valencia, A., Pržulj, N.: Unveiling new disease, pathway, and gene associations via multi-scale neural network. PloS one 15(4), e0231059 (2020)
Plotly Technologies Inc. Collaborative data science (2015)
Jerby-Arnon, L., Pfetzer, N., Waldman, Y.Y., McGarry, L., James, D., Shanks, E., Seashore-Ludlow, B., Weinstock, A., Geiger, T., Clemons, P.A., et al.: Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality. Cell 158(5), 1199–1209 (2014)
Liu, L., Dalal, C., Heineike, B., Abate, A.R.: High throughput gene expression profiling of yeast colonies with microgel-culture drop-seq. Lab on a Chip (2019)
Luidy-Imada, E., Matam, T., Collado-Torres, L., Dinalankara, W., Stupnikov, A., Wilks, C., Jaffe, A.E., Langmead, B., Leek, J.T., Favorov, A., et al.: Differential analysis of gene expression across the human genome using recount2 and fantom-cat (2018)
Nijman, S.M.B.: Synthetic lethality: general principles, utility and detection using genetic screens in human cells. FEBS Lett. 585(1), 1–6 (2011)
Putnam, C.D., Srivatsan, A., Nene, R.V., Martinez, S.L., Clotfelter, S.P., Bell, S.N., Somach, S.B., De Souza, J.E., Fonseca, A.F., De Souza, S.J., et al.: A genetic network that suppresses genome rearrangements in saccharomyces cerevisiae and contains defects in cancers. Nat. Commu. 7, 11256 (2016)
Rahit, K.M., Tarailo-Graovac, M.: Genetic modifiers and rare mendelian disease. Genes 11(3), 239 (2020)
Soneson, C., Robinson, M.D.: Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15(4), 255 (2018)
Spainhour, J.C.G., Lim, H.S., Yi, S.V., Qiu, P.: Correlation patterns between DNA methylation and gene expression in the cancer genome atlas. Cancer Inf. 18, 1176935119828776 (2019)
Tang, W., Bertaux, F., Thomas, P., Stefanelli, C., Saint, M., Marguerat, S., Shahrezaei, V.: baynorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data. Bioinformatics 36(4), 1174–1181 (2020)
Tsherniak, A., Vazquez, F., Montgomery, P.G., Weir, B.A., Kryukov, G., Cowley, G.S., Gill, S., Harrington, W.F., Pantel, S., Krill-Burger, J., et al.: Defining a cancer dependency map. Cell 170(3), 564–576 (2017)
Van Leeuwen, J., Pons, C., Mellor, J.C., Yamaguchi, T.N., Friesen, H., Koschwanez, J., Mattiazzi Ušaj, M., Pechlaner, M., Mehmet Takar, Matej Ušaj, et al. Exploring genetic suppression interactions on a global scale. Science, 354(6312), aag0839 (2016)
Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., Speleman, F.: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 3(7), research0034–1 (2002)
Wang, X., Fu, A.Q., McNerney, M.E., White, K.P.: Widespread genetic epistasis among cancer genes. Nat. Commun. 5, 4828 (2014)
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We would like to thank Janyl Jumadinova for her help in proofing this manuscript.
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Bonham-Carter, O., Thu, Y.M. (2021). GenExSt: A Tool to Identify Correlation of Gene Expression After Normalization with Housekeeping Genes. In: Arai, K. (eds) Advances in Information and Communication. FICC 2021. Advances in Intelligent Systems and Computing, vol 1364. Springer, Cham. https://doi.org/10.1007/978-3-030-73103-8_5
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DOI: https://doi.org/10.1007/978-3-030-73103-8_5
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