IDENTIFICATION OF SIGNIFICANT RNA-BINDING PROTEINS IN THE PROCESS OF CD44 SPLICING USING THE BOOSTED BETA REGRESSION ALGORITHM

V. O. Novosad; Новосад В. О.

doi:10.31857/S2686738922601023

IDENTIFICATION OF SIGNIFICANT RNA-BINDING PROTEINS IN THE PROCESS OF CD44 SPLICING USING THE BOOSTED BETA REGRESSION ALGORITHM

Авторлар: Novosad V.O.¹^,2
Мекемелер:
1. Faculty of Biology and Biotechnology, National Research University Higher School of Economics,
2. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences
Шығарылым: Том 510, № 1 (2023)
Беттер: 316-321
Бөлім: Articles
URL: https://vestnik.nvsu.ru/2686-7389/article/view/651117
DOI: https://doi.org/10.31857/S2686738922601023
EDN: https://elibrary.ru/QFUIKM
ID: 651117

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат
Рұқсат жабық

Рұқсат берілді
Рұқсат жабық

Тек жазылушылар үшін

Аннотация

The expression of RNA-binding proteins and their interaction with the spliced pre-mRNA are the key factors in determining the final isoform profile. Transmembrane protein CD44 is involved in differentiation, invasion, motility, growth and survival of tumor cells, and is also a commonly accepted marker of cancer stem cells and epithelial-mesenchymal transition. However, the functions of the isoforms of this protein differ significantly. In this paper, we developed a method based on the boosted beta regression algorithm for identification of the significant RNA-binding proteins in the splicing process by modeling the isoform ratio. The application of this method to the analysis of CD44 splicing in colorectal cancer cells revealed 20 significant RNA-binding proteins. Many of them were previously shown as EMT regulators, but for the first time presented as potential CD44 splicing factors.

Негізгі сөздер

alternative splicing, CD44, CRC, boosted beta regression, RNA-binding proteins, RNA-seq, TCGA

Авторлар туралы

V. Novosad

Faculty of Biology and Biotechnology, National Research University Higher School of Economics, ; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences

Хат алмасуға жауапты Автор.
Email: vnovosad@hse.ru
Russian Federation, Moscow; Russian Federation, Moscow

Әдебиет тізімі

Pan Q., Shai O., Lee L.J., Frey B.J., Blencowe B.J., Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing // Nat. Genet. 2008. V. 40. P. 1413–1415.
Wang Z., Burge C.B., Splicing regulation: From a parts list of regulatory elements to an integrated splicing code, // RNA. 2008. V. 14. P. 802–813.
Wang Z., Xiao X., Van Nostrand E., Burge C.B., General and Specific Functions of Exonic Splicing Silencers in Splicing Control // Mol. Cell. 2006. V. 23. P. 61–70.
Xiong H.Y., Barash Y., Frey B.J., Bayesian prediction of tissue-regulated splicing using RNA sequence and cellular context // Bioinformatics. 2011. V. 27. P. 2554–2562.
Hartmann B., Valcárcel J., Decrypting the genome’s alternative messages // Curr. Opin. Cell Biol. 2009. V. 21. P. 377–386.
Xiong H.Y., Alipanahi B., Lee L.J., Bretschneider H., Merico D., Yuen R.K.C., Hua Y., Gueroussov S., Najafabadi H.S., Hughes T.R., Morris Q., Barash Y., Krainer A.R., Jojic N., Scherer S.W., Blencowe B.J., Frey B.J., The human splicing code reveals new insights into the genetic determinants of disease // Science. 2015. № 80. P. 347.
Barash Y., Calarco J.A., Gao W., Pan Q., Wang X., Shai O., Blencowe B.J., Frey B.J., Deciphering the splicing code // Nature. 2010. V. 465. P. 53–59.
Cereda M., Pozzoli U., Rot G., Juvan P., Schweitzer A., Clark T., Ule J., RNAmotifs: prediction of multivalent RNA motifs that control alternative splicing, // Genome Biol. 2014. V. 15. P. R20.
Leung M.K.K., Xiong H.Y., Lee L.J., Frey B.J., Deep learning of the tissue-regulated splicing code, // Bioinformatics. 2014. V. 3. P. i121–i129.
Xu H., Niu M., Yuan X., Wu K., Liu A., CD44 as a tumor biomarker and therapeutic target., // Exp. Hematol. Oncol. 2020. V. 9. P. 36.
Robinson M.D., McCarthy D.J., Smyth G.K., edgeR: a Bioconductor package for differential expression analysis of digital gene expression data., // Bioinformatics. 2010. V. 26. P. 139–40.
Giudice G., Sánchez-Cabo F., Torroja C., Lara-Pezzi E., ATtRACT–a database of RNA-binding proteins and associated motifs, Database. 2016 (2016) baw035.
Giulietti M., Piva F., D’Antonio M., D’Onorio De Meo P., Paoletti D., Castrignanò T., D’Erchia A.M., Picardi E., Zambelli F., Principato G., Pavesi G., Pesole G., SpliceAid-F: a database of human splicing factors and their RNA-binding sites, // Nucleic Acids Res. 2013. V. 41. P. D125–D131.
Novosad V.O., Polikanova I.S., Tonevitsky E.A., Mal’tseva D.V., Expression of CD44 isoforms in human colorectal cancer patient samples and cell lines, // Cell Technol. Biol. Med. 2022. V. 1. P. 49–54.
Bühlmann P., Hothorn T., Boosting Algorithms: Regularization, Prediction and Model Fitting, Stat. Sci. 2007. V. 22.
Hofner B., Mayr A., Schmid M., gamboostLSS : An R Package for Model Building and Variable Selection in the GAMLSS Framework, J. Stat. Softw. 2016. V. 74.
Kim E.J., Kim J.S., Lee S., Lee H., Yoon J., Hong J.H., Chun S.H., Sun D.S., Won H.S., Hong S.A., Kang K., Jo J.Y., Choi M., Shin D.H., Ahn Y., Ko Y.H., QKI, a miR-200 target gene, suppresses epithelial-to-mesenchymal transition and tumor growth, // Int. J. Cancer. 2019. V. 145. P. 1585–1595.
Liang R., Zhang J., Liu Z., Liu Z., Li Q., Luo X., Li Y., Ye J., Lin Y., Mechanism and Molecular Network of RBM8A-Mediated Regulation of Oxaliplatin Resistance in Hepatocellular Carcinoma, // Front. Oncol. 2021. V. 10.
Harvey S.E., Xu Y., Lin X., Gao X.D., Qiu Y., Ahn J., Xiao X., Cheng C., Coregulation of alternative splicing by hnRNPM and ESRP1 during EMT, // RNA. 2018. V. 24. P. 1326–1338.
Xie C., Zhou M., Lin J., Wu Z., Ding S., Luo J., Zhan Z., Cai Y., Xue S., Song Y., EEF1D Promotes Glioma Proliferation, Migration, and Invasion through EMT and PI3K/Akt Pathway, // Biomed Res. Int. 2020 (2020) 1–12.