Abstract
Accounting for nine out of ten kidney cancers, kidney renal cell carcinoma (KIRC) is by far the most common type of kidney cancer. In view of limited and ineffective available therapies, understanding the genetic basis of disease becomes important for better diagnosis and treatment. The present studies are based on a single type of genomic data. These studies do not consider interactions between genomic data types and their underlying biological relationships in the disease. However, the current availability of multiple genomic data and the possibility of combining it have facilitated a better understanding of the cancer’s characterization. But high dimensionality and the existence of complex interactions (within and between genomic data types) are the two main challenges of integrative methods to analyze cancer effectively. In this paper, we propose a method to build an integrative model based on Bayesian model averaging procedure for improved prediction of clinical outcome in cancer survival. The proposed method initially uses dimensionality reduction techniques to generate low-dimensional latent features for the predictive models and then incorporates interactions between them. It defines the latent features using principal components and their sparse version. It compares the predictive performance of models based on these two latent features on real data. These models also validate several ccRCC-specific cancer biomarkers previously reported in the literature. Applied on kidney renal cell carcinoma (KIRC) dataset of The Cancer Genome Atlas (TCGA), the method achieves better prediction with sparse principal components model by including latent feature interactions as compared to without including them.
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References
Cairns P (2011) Renal cell carcinoma. Cancer Biomark 9:461–473. https://doi.org/10.3233/cbm-2011-0176
Vera-Badillo FE, Templeton AJ, Duran I, Ocana A, De Gouveia P, Aneja P, Knox JJ, Tannock IF, Escudier B, Amir E (2015) Systemic therapy for non-clear cell renal cell carcinomas: a systematic review and meta-analysis. Eur Urol 67:740–749. https://doi.org/10.1016/j.eururo.2014.05.010
Kashyap D, Tuli HS, Sak K, Garg VK, Goel N, Punia S, Chaudhary A (2019) Role of reactive oxygen species in cancer progression. Curr Pharmacol Rep 5:79–86. https://doi.org/10.1007/s40495-019-00171-y
López JI (2013) Renal tumors with clear cells. A review. Pathol Res Pract 209:137–146. https://doi.org/10.1016/j.prp.2013.01.007
Dondeti VR, Wubbenhorst B, Lal P, Gordan JD, Andrea DK, Attiyeh EF, Simon MC, Nathanson KL (2012) Integrative genomic analyses of sporadic clear cell renal cell carcinoma define disease subtypes and potential new therapeutic targets. Cancer Res 72:112–121. https://doi.org/10.1158/0008-5472.can-11-1698
Sato Y, Yoshizato T, Shiraishi Y, Maekawa S, Okuno Y, Kamura T, Shimamura T, Sato-Otsubo A, Nagae G, Suzuki H, Nagata Y, Yoshida K, Kon A, Suzuki Y, Chiba K, Tanaka H, Niida A, Fujimoto A, Tsunoda T, Morikawa T, Maeda D, Kume H, Sugano S, Fukayama M, Aburatani H, Sanada M, Miyano S, Homma Y, Ogawa S (2013) Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet 45:860–867. https://doi.org/10.1038/ng.2699
Chen J, Zhang D, Zhang W, Tang Y, Yan W, Guo L, Shen B (2013) Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis. J Transl Med 11:169. https://doi.org/10.1186/1479-5876-11-169
Chekouo T, Stingo FC, Doecke JD, Do K-A (2015) miRNA-target gene regulatory networks: a bayesian integrative approach to biomarker selection with application to kidney cancer. Biometrics 71:428–438. https://doi.org/10.1111/biom.12266
Butz H, Szabó PM, Nofech-Mozes R, Rotondo F, Kovacs K, Mirham L, Girgis H, Boles D, Patocs A, Yousef GM (2014) Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma. Clin Chem 60:1314–1326. https://doi.org/10.1373/clinchem.2014.225854
Bluyssen HAR, Wesoły J, Rydzanicz M, Wrzesin T (2013) Genomics and epigenomics of clear cell renal cell carcinoma: recent developments and potential applications. Cancer Lett 341:111–126. https://doi.org/10.1016/j.canlet.2013.08.006
Gregory KB, Momin AA, Coombes KR, Baladandayuthapani V (2014) Latent feature decompositions for integrative analysis of multi-platform genomic data. IEEE/ACM Trans Comput Biol Bioinform 11:984–994. https://doi.org/10.1109/TCBB.2014.2325035
Ma S, Dai Y (2011) Principal component analysis based methods in bioinformatics studies. Brief Bioinform 12:714–722. https://doi.org/10.1093/bib/bbq090
Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, Nordgren H, Farmer P, Praz V, Haibe-kains B, Desmedt C, Larsimont D, Cardoso F, Peterse H, Nuyten D, Buyse M, De Van Vijver MJ, Bergh J, Piccart M, Delorenzi M (2006) Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 98:262–272. https://doi.org/10.1093/jnci/djj052
Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M, Stephens RM, Okamoto A, Yokota J, Tanaka T, Calin GA, Liu C, Croce CM, Harris CC (2006) Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancel Cell 9:189–198. https://doi.org/10.1016/j.ccr.2006.01.025
Engler DA, Gupta S, Growdon WB, Drapkin RI, Nitta M, Petra A, Allred SF, Gross J, Deavers MT, Kuo W, Karlan BY, Bo R, Orsulic S, Gershenson DM, Birrer MJ, Gray JW, Mohapatra G (2012) Genome wide DNA copy number analysis of serous type ovarian carcinomas identifies genetic markers predictive of clinical outcome. PLoS One 7(2):e30996. https://doi.org/10.1371/journal.pone.0030996
Gligorijević V, Pržulj N (2015) Methods for biological data integration: perspectives and challenges. J R Soc Interface. https://doi.org/10.1098/rsif.2015.0571
Wang W, Baladandayuthapani V, Morris JS, Broom BM, Manyam G, Do K-A (2012) iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data. Bioinformatics 29:149–159. https://doi.org/10.1093/bioinformatics/bts655
Network CGAR (2013) Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499:43–49. https://doi.org/10.1038/nature12222.COMPREHENSIVE
Yuan Y, Van Allen EM, Omberg L, Wagle N, Amin-Mansour A, Sokolov A, Byers LA, Xu Y, Hess KR, Diao L, Han L, Huang X, Lawrence MS, Weinstein JN, Stuart JM, Mills GB, Garraway LA, Margolin AA, Getz G, Liang H (2014) Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Nat Biotechnol 32:644–652. https://doi.org/10.1038/nbt.2940
Kashyap D, Tuli HS, Garg VK, Goel N, Bishayee A (2018) Oncogenic and tumor-suppressive roles of microRNAs with special reference to apoptosis: molecular mechanisms and therapeutic potential. Mol Diagn Ther 22:179–201. https://doi.org/10.1007/s40291-018-0316-1
Abualigah LM, Khader AT, Hanandeh ES (2018) A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125. https://doi.org/10.1016/j.engappai.2018.05.003
Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48:4047–4071. https://doi.org/10.1007/s10489-018-1190-6
Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73:4773–4795. https://doi.org/10.1007/s11227-017-2046-2
Qasim Abualigah LM, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5:19–28. https://doi.org/10.5121/ijcsea.2015.5102
Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer International Publishing, Switzerland. https://doi.org/10.1007/978-3-030-10674-4
Jolliffe IT (1986) Choosing a subset of principal components or variables. Principal component analysis: Springer Series in Statistics, 2nd edn. Springer, New York, pp 111–149. https://doi.org/10.1007/978-1-4757-1904-8
Hsu Y-L, Huang P-Y, Chen D-T (2014) Sparse principal component analysis in cancer research. Transl Cancer Res 3:182–190. https://doi.org/10.3978/j.issn.2218-676X.2014.05.06
Raftery AE, Madigan D, Hoeting JA (1997) Bayesian model averaging for linear regression models. J Am Stat Assoc 92:179–191. https://doi.org/10.1080/01621459.1997.10473615
Goel N, Karir P, Garg VK (2017) Role of DNA methylation in human age prediction. Mech Ageing Dev 166:33–41. https://doi.org/10.1016/J.MAD.2017.08.012
Witten DM, Tibshirani R, Hastie T (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10:515–534. https://doi.org/10.1093/biostatistics/kxp008
Raftery AE, Painter IS, Volinsky CT (2005) BMA: an R Package for Bayesian model averaging. R News 5:2–8
Nyhan MJ, Sullivan GCO, Mckenna SL (2008) Role of the VHL (von Hippel-Lindau) gene in renal cancer: a multifunctional tumour suppressor. Biochem Soc Trans 36:472–478. https://doi.org/10.1042/BST0360472
Guinney J, Wang T, Laajala TD, Winner KK, Bare JC, Neto EC, Khan SA, Peddinti G, Airola A, Pahikkala T, Mirtti T, Yu T, Bot BM, Shen L, Abdallah K, Norman T, Friend S, Stolovitzky G, Soule H, Sweeney CJ, Ryan CJ, Scher HI, Sartor O, Xie Y, Aittokallio T, Zhou FL, Costello JC (2017) Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncol 18(1):132–142. https://doi.org/10.1016/s1470-2045(16)30560-5
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Singh, A., Goel, N. & Yogita Integrative Analysis of Multi-Genomic Data for Kidney Renal Cell Carcinoma. Interdiscip Sci Comput Life Sci 12, 12–23 (2020). https://doi.org/10.1007/s12539-019-00345-8
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DOI: https://doi.org/10.1007/s12539-019-00345-8