A Pan-Cancer Network Analysis with Integration of miRNA-Gene Targeting for Multiomics Datasets

Linder, Henry; Zhang, Yuping

doi:10.6339/21-JDS1019

Journal of Data Science

A Pan-Cancer Network Analysis with Integration of miRNA-Gene Targeting for Multiomics Datasets

Volume 19, Issue 4 (2021), pp. 555–568

Henry Linder Yuping Zhang

https://doi.org/10.6339/21-JDS1019

Pub. online: 16 August 2021 Type: Statistical Data Science

Received
12 March 2021

Accepted
17 July 2021

Published
16 August 2021

Abstract

Large-scale genomics studies provide researchers with access to extensive datasets with extensive detail and unprecedented scope that encompasses not only genes, but also more experimental functional units, including non-coding microRNAs (miRNAs). In order to analyze these high-fidelity data while remaining faithful to the underlying biology, statistical methods are necessary that can reflect the full range of understanding in contemporary molecular biology, while remaining flexible enough to analyze a wide range of data and complex phenomena. Leveraging multiple omics datasets, miRNA-gene targets as well as signaling pathway topology, we present an integrative linear model to analyze signaling pathways. Specifically, we use a mixed linear model to characterize tumor and healthy tissue, and execute statistical significance testing to identify pathway disturbances. In this paper, pan-cancer analysis is performed for a wide range of signaling pathways. We discuss specific findings from this analysis, as well as an interactive data visualization available for public consumption that contains the full range of our analytic findings.

Supplementary material

Supplementary Material

Supplementary Materials include descriptions for data and software.

References

Agarwal V, Bell GW, Nam JW, Bartel DP (2015). Predicting effective microRNA target sites in mammalian mRNAs. eLife, 4: e05005.

Backes C, Kehl T, Stöckel D, Fehlmann T, Schneider L, Meese E, et al. (2016). miRPathDB: A new dictionary on microRNAs and target pathways. Nucleic Acids Research, 45(D1): D90–D96. gkw926.

Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, Methodological, 57(1): 289–300.

Cai T, Cai TT, Zhang A (2016). Structured matrix completion with applications to genomic data integration. Journal of the American Statistical Association, 111(514): 621–633.

Dhawan A, Scott JG, Harris AL, Buffa FM (2018). Pan-cancer characterisation of microrna across cancer hallmarks reveals microrna-mediated downregulation of tumour suppressors. Nature Communications, 9(1): 5228.

Falzone L, Scola L, Zanghì A, Biondi A, Di Cataldo A, Libra M, et al. (2018). Integrated analysis of colorectal cancer microRNA datasets: Identification of microRNAs associated with tumor development. Aging (Albany NY), 10(5): 1000.

Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA, et al. (2016). Toward a shared vision for cancer genomic data. The New England Journal of Medicine, 375(12): 1109–1112.

Gurbuz N, Ozpolat B (2019). MicroRNA-based targeted therapeutics in pancreatic cancer. Anticancer Research, 39(2): 529–532.

Hamilton MP, Rajapakshe K, Hartig SM, Reva B, McLellan MD, Kandoth C, et al. (2013). Identification of a pan-cancer oncogenic microRNA superfamily anchored by a central core seed motif. Nature Communications, 4: 2730.

Kim S (2015). ppcor: An R package for a fast calculation to semi-partial correlation coefficients. Communications for Statistical Applications and Methods, 22(6): 665.

Kim YH, Goh TS, Lee CS, Oh SO, Kim JI, Jeung SH, et al. (2017). Prognostic value of microRNAs in osteosarcoma: A meta-analysis. Oncotarget, 8(5): 8726.

Krämer N, Schäfer J, Boulesteix AL (2009). Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics, 10(1): 384.

Kwon H, Song K, Han C, Zhang J, Lu L, Chen W, et al. (2017). Epigenetic silencing of miRNA-34a in human cholangiocarcinoma via EZH2 and DNA methylation: Impact on regulation of Notch pathway. The American Journal of Pathology, 187(10): 2288–2299.

Lewis BP, Burge CB, Bartel DP (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell, 120(1): 15–20.

Linder H, Zhang Y (2019). Iterative integrated imputation for missing data and pathway models with applications to breast cancer subtypes. Communications for Statistical Applications and Methods, 26(4): 411–430.

Loh PL, Wainwright MJ (2012). Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. In: Advances in Neural Information Processing Systems, 2087–2095. Curran Associates, Inc.

Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, et al. (2008). PID: The pathway interaction database. Nucleic Acids Research, 37(suppl_1): D674–D679.

Shojaie A, Michailidis G (2009). Analysis of gene sets based on the underlying regulatory network. Journal of Computational Biology, 16(3): 407–426.

Shojaie A, Michailidis G (2010). Network enrichment analysis in complex experiments. Statistical Applications in Genetics and Molecular Biology, 9(1): Article 22, 34 pages.

Stokowy T, Eszlinger M, Świerniak M, Fujarewicz K, Jarząb B, Paschke R, et al. (2014). Analysis options for high-throughput sequencing in miRNA expression profiling. BMC Research Notes, 7(1): 144.

Sun M, Song H, Wang S, Zhang C, Zheng L, Chen F, et al. (2017). Integrated analysis identifies microRNA-195 as a suppressor of Hippo-YAP pathway in colorectal cancer. Journal of Hematology & Oncology, 10(1): 79.

Tokar T, Pastrello C, Rossos AE, Abovsky M, Hauschild AC, Tsay M, et al. (2017). mirDIP 4.1-integrative database of human microRNA target predictions. Nucleic Acids Research, 46(D1): D360–D370.

Tomczak K, Czerwińska P, Wiznerowicz M (2015). The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Contemporary Oncology, 19(1A): A68.

Wei L, Jin Z, Yang S, Xu Y, Zhu Y, Ji Y (2017). TCGA-assembler 2: Software pipeline for retrieval and processing of TCGA/CPTAC data. Bioinformatics, 34(9): 1615–1617.

Wong NW, Chen Y, Chen S, Wang X (2017). OncomiR: An online resource for exploring pan-cancer microRNA dysregulation. Bioinformatics, 34(4): 713–715.

Zhang Y, Linder MH, Shojaie A, Ouyang Z, Shen R, Baggerly KA, et al. (2018). Dissecting pathway disturbances using network topology and multi-platform genomics data. Statistics in Biosciences, 10: 86–106.

Zhu Y, Qiu P, Ji Y (2014). TCGA-assembler: Open-source software for retrieving and processing TCGA data. Nature Methods, 11(6): 599.

This is a free to read article.

Keywords

hypothesis testing integrative statistical learning large-scale inference multi-view data integration

Metrics

since February 2021

828 Article info views	0 Full article views
394 PDF downloads	268 XML downloads

RSS

Authors

Abstract

Supplementary material

References

Export citation

Copy and paste formatted citation

Download citation in file