TSCCA: A Tensor Sparse CCA Method for Detecting MicroRNA-gene Patterns from Multiple Cancers

Overview

Journal PLoS Comput Biol

Specialty Biology

Date 2021 Jun 1

PMID 34061840

Citations 5

Authors

Wenwen Min

Tsung-Hui Chang

Shihua Zhang

Xiang Wan

Affiliations

Soon will be listed here.

Abstract

Existing studies have demonstrated that dysregulation of microRNAs (miRNAs or miRs) is involved in the initiation and progression of cancer. Many efforts have been devoted to identify microRNAs as potential biomarkers for cancer diagnosis, prognosis and therapeutic targets. With the rapid development of miRNA sequencing technology, a vast amount of miRNA expression data for multiple cancers has been collected. These invaluable data repositories provide new paradigms to explore the relationship between miRNAs and cancer. Thus, there is an urgent need to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data in a pan-cancer paradigm. In this study, we present a tensor sparse canonical correlation analysis (TSCCA) method for identifying cancer-related miRNA-gene modules across multiple cancers. TSCCA is able to overcome the drawbacks of existing solutions and capture both the cancer-shared and specific miRNA-gene co-expressed modules with better biological interpretations. We comprehensively evaluate the performance of TSCCA using a set of simulated data and matched miRNA/gene expression data across 33 cancer types from the TCGA database. We uncover several dysfunctional miRNA-gene modules with important biological functions and statistical significance. These modules can advance our understanding of miRNA regulatory mechanisms of cancer and provide insights into miRNA-based treatments for cancer.

Citing Articles

MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets.

Erdem C, Birtwistle M Front Syst Biol. 2024; 3.

PMID: 38269333 PMC: 10807051. DOI: 10.3389/fsysb.2023.1099413.

A primer on correlation-based dimension reduction methods for multi-omics analysis.

Downing T, Angelopoulos N J R Soc Interface. 2023; 20(207):20230344.

PMID: 37817584 PMC: 10565429. DOI: 10.1098/rsif.2023.0344.

MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms.

Erdem C, Gross S, Heiser L, Birtwistle M Nat Commun. 2023; 14(1):3991.

PMID: 37414767 PMC: 10326020. DOI: 10.1038/s41467-023-39729-2.

An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types.

Tiong K, Sintupisut N, Lin M, Cheng C, Woolston A, Lin C PLOS Digit Health. 2023; 1(12):e0000151.

PMID: 36812605 PMC: 9931374. DOI: 10.1371/journal.pdig.0000151.

MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification.

Moon S, Lee H Bioinformatics. 2022; 38(8):2287-2296.

PMID: 35157023 PMC: 10060719. DOI: 10.1093/bioinformatics/btac080.

References

Ghandi M, Huang F, Jane-Valbuena J, Kryukov G, Lo C, McDonald 3rd E . Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019; 569(7757):503-508. PMC: 6697103. DOI: 10.1038/s41586-019-1186-3. View

Yoon S, Nguyen H, Jo W, Kim J, Chi S, Park J . Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets. Nucleic Acids Res. 2019; 47(9):e53. PMC: 6511842. DOI: 10.1093/nar/gkz139. View

Bryan K, Terrile M, Bray I, Domingo-Fernandez R, Watters K, Koster J . Discovery and visualization of miRNA-mRNA functional modules within integrated data using bicluster analysis. Nucleic Acids Res. 2013; 42(3):e17. PMC: 3919560. DOI: 10.1093/nar/gkt1318. View

Huang S, Xue P, Han X, Zhang C, Yang L, Liu L . Exosomal miR-130b-3p targets SIK1 to inhibit medulloblastoma tumorigenesis. Cell Death Dis. 2020; 11(6):408. PMC: 7264172. DOI: 10.1038/s41419-020-2621-y. View

Boyer A, Walter D, Sorensen C . DNA replication and cancer: From dysfunctional replication origin activities to therapeutic opportunities. Semin Cancer Biol. 2016; 37-38:16-25. DOI: 10.1016/j.semcancer.2016.01.001. View

Parkhomenko E, Tritchler D, Beyene J . Sparse canonical correlation analysis with application to genomic data integration. Stat Appl Genet Mol Biol. 2009; 8:Article 1. DOI: 10.2202/1544-6115.1406. View

Witten D, Tibshirani R . Extensions of sparse canonical correlation analysis with applications to genomic data. Stat Appl Genet Mol Biol. 2009; 8:Article28. PMC: 2861323. DOI: 10.2202/1544-6115.1470. View

Zhang S, Li Q, Liu J, Zhou X . A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics. 2011; 27(13):i401-9. PMC: 3117336. DOI: 10.1093/bioinformatics/btr206. View

Bracken C, Scott H, Goodall G . A network-biology perspective of microRNA function and dysfunction in cancer. Nat Rev Genet. 2016; 17(12):719-732. DOI: 10.1038/nrg.2016.134. View

10.

Cheng F, Liang H, Butte A, Eng C, Nussinov R . Personal Mutanomes Meet Modern Oncology Drug Discovery and Precision Health. Pharmacol Rev. 2018; 71(1):1-19. PMC: 6294046. DOI: 10.1124/pr.118.016253. View

11.

Jansson M, Lund A . MicroRNA and cancer. Mol Oncol. 2012; 6(6):590-610. PMC: 5528350. DOI: 10.1016/j.molonc.2012.09.006. View

12.

Kozomara A, Birgaoanu M, Griffiths-Jones S . miRBase: from microRNA sequences to function. Nucleic Acids Res. 2018; 47(D1):D155-D162. PMC: 6323917. DOI: 10.1093/nar/gky1141. View

13.

Yang D, Sun Y, Hu L, Zheng H, Ji P, Pecot C . Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer. Cancer Cell. 2013; 23(2):186-99. PMC: 3603369. DOI: 10.1016/j.ccr.2012.12.020. View

14.

Liu F, Zhang F, Li X, Liu Q, Liu W, Song P . Prognostic role of miR-17-92 family in human cancers: evaluation of multiple prognostic outcomes. Oncotarget. 2017; 8(40):69125-69138. PMC: 5620325. DOI: 10.18632/oncotarget.19096. View

15.

Huang H, Lin Y, Li J, Huang K, Shrestha S, Hong H . miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res. 2019; 48(D1):D148-D154. PMC: 7145596. DOI: 10.1093/nar/gkz896. View

16.

Chen J, Zhang S . Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics. 2016; 32(11):1724-32. DOI: 10.1093/bioinformatics/btw059. View

17.

Gu J, Fan K, Zhang J, Chen H, Wang S . Suppression of microRNA-130b inhibits glioma cell proliferation and invasion, and induces apoptosis by PTEN/AKT signaling. Int J Mol Med. 2017; 41(1):284-292. PMC: 5746316. DOI: 10.3892/ijmm.2017.3233. View

18.

Chu D, Liao L, Ng M, Zhang X . Sparse canonical correlation analysis: new formulation and algorithm. IEEE Trans Pattern Anal Mach Intell. 2013; 35(12):3050-65. DOI: 10.1109/TPAMI.2013.104. View

19.

Marshall E, Sage A, Ng K, Martinez V, Firmino N, Bennewith K . Small non-coding RNA transcriptome of the NCI-60 cell line panel. Sci Data. 2017; 4:170157. PMC: 5654365. DOI: 10.1038/sdata.2017.157. View

20.

Vallejo D, Caparros E, Dominguez M . Targeting Notch signalling by the conserved miR-8/200 microRNA family in development and cancer cells. EMBO J. 2011; 30(4):756-69. PMC: 3041954. DOI: 10.1038/emboj.2010.358. View