Identification of Multidimensional Regulatory Modules Through Multi-Graph Matching With Network Constraints

Objective: The accumulation of large amounts of multidimensional genomic data provides new opportunities to study multilevel biological regulatory associations. Identifying multidimensional regulatory modules (md-modules) from omics data is crucial to provide a comprehensive understanding of the regulatory mechanisms of biological systems. Methods: We develop a multi-graph matching with multiple network constraints (MGMMNC) model to identify the md-modules. The MGMMNC model aims to accurately capture highly relevant md-modules by considering the relationships intra- and inter-multidimensional omics data, including interactions within a network and cycle consistency information. The proposed technique adopts a novel graph-smoothing similarity measurement for the highly contaminated genetic data. Results: The superiority and effectiveness of MGMMNC have been demonstrated by comparative experiments with three state-of-the-art techniques using simulated and cervical cancer data. Conclusion: MGMMNC can accurately and efficiently identify the md-modules that are significantly enriched in gene ontology biological processes and in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Many different level molecules in the same md-module collaboratively regulate the same pathway. Moreover, the md-modules are capable of stratifying patients into subtypes with significant survival differences. Significance: The problem of identifying multidimensional regulatory modules from omics data is formulated as a multi-graph matching problem, and multiple network constraints and cycle consistency information are seamlessly integrated into the matching model.

[1]  Shibing Deng,et al.  Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer , 2014, Nature Genetics.

[2]  Chi-Ying F. Huang,et al.  miRTarBase: a database curates experimentally validated microRNA–target interactions , 2010, Nucleic Acids Res..

[3]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[4]  Emmanuel J. Candès,et al.  A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..

[5]  Harold L. Moses,et al.  Abstract 4083: Deletion of the BMP receptor BMPR1a results in EMT and impairs mammary gland tumor formation and metastasis , 2015 .

[6]  P. Laird,et al.  Discovery of multi-dimensional modules by integrative analysis of cancer genomic data , 2012, Nucleic acids research.

[7]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[8]  Xiaowei Zhou,et al.  Multi-image Matching via Fast Alternating Minimization , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  Michael Ittmann,et al.  Sprouty4, a suppressor of tumor cell motility, is downregulated by DNA methylation in human prostate cancer , 2006, The Prostate.

[10]  S. Formenti,et al.  Translational control in cancer , 2010, Nature Reviews Cancer.

[11]  A. Dejean,et al.  SUMO and the robustness of cancer , 2017, Nature Reviews Cancer.

[12]  Wenwen Min,et al.  A Two-Stage Method to Identify Joint Modules From Matched MicroRNA and mRNA Expression Data , 2016, IEEE Transactions on NanoBioscience.

[13]  Joshua M. Korn,et al.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2008, Nature.

[14]  Shannon L. Risacher,et al.  Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method , 2016, Bioinform..

[15]  Wei Shi,et al.  Lower Expression of SPRY4 Predicts a Poor Prognosis and Regulates Cell Proliferation in Colorectal Cancer , 2016, Cellular Physiology and Biochemistry.

[16]  José Portugal,et al.  Sp1 transcription factor: A long-standing target in cancer chemotherapy. , 2015, Pharmacology & therapeutics.

[17]  Stephen Safe,et al.  Cooperative Coactivation of Estrogen Receptor α in ZR-75 Human Breast Cancer Cells by SNURF and TATA-binding Protein* , 2002, The Journal of Biological Chemistry.

[18]  Andrew M. Gross,et al.  Network-based stratification of tumor mutations , 2013, Nature Methods.

[19]  Xia Lin,et al.  Direct Interaction of c-Myc with Smad2 and Smad3 to Inhibit TGF-β-Mediated Induction of the CDK Inhibitor p15(Ink4B). , 2016, Molecular cell.

[20]  C. Sander,et al.  Mutual exclusivity analysis identifies oncogenic network modules. , 2012, Genome research.

[21]  Jian Peng,et al.  A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information , 2017, RECOMB 2017.

[22]  Bin Tean Teh,et al.  Exome sequencing of liver fluke–associated cholangiocarcinoma , 2012, Nature Genetics.

[23]  Vikas Singh,et al.  Solving the multi-way matching problem by permutation synchronization , 2013, NIPS.

[24]  Guoqiang Han,et al.  Finding Correlated Patterns via High-Order Matching for Multiple Sourced Biological Data , 2019, IEEE Transactions on Biomedical Engineering.

[25]  M. Ritchie,et al.  Methods of integrating data to uncover genotype–phenotype interactions , 2015, Nature Reviews Genetics.

[26]  Shi-Hua Zhang,et al.  Identifying multi-layer gene regulatory modules from multi-dimensional genomic data , 2012, Bioinform..

[27]  Raphael A Nemenoff,et al.  Sprouty-4 Inhibits Transformed Cell Growth, Migration and Invasion, and Epithelial-Mesenchymal Transition, and Is Regulated by Wnt7A through PPARγ in Non–Small Cell Lung Cancer , 2010, Molecular Cancer Research.

[28]  L. V. Domnina,et al.  Actin isoforms and reorganization of adhesion junctions in epithelial-to-mesenchymal transition of cervical carcinoma cells , 2012, Biochemistry (Moscow).

[29]  Jiali Han,et al.  Systematic analyses of a novel lncRNA‐associated signature as the prognostic biomarker for Hepatocellular Carcinoma , 2018, Cancer medicine.

[30]  Shi-tao Zhang,et al.  Identification of key genes associated with the effect of estrogen on ovarian cancer using microarray analysis , 2016, Archives of Gynecology and Obstetrics.

[31]  Wei Wang,et al.  ZNF280B promotes the growth of gastric cancer in vitro and in vivo. , 2018, Oncology letters.

[32]  Margaret A. Goodell,et al.  DNMT3A in haematological malignancies , 2015, Nature Reviews Cancer.

[33]  J. Azizkhan-Clifford,et al.  Sp1 and the ‘hallmarks of cancer’ , 2015, The FEBS journal.

[34]  Jinyu Chen,et al.  Discovery of two-level modular organization from matched genomic data via joint matrix tri-factorization , 2018, Nucleic acids research.

[35]  Jing Yin,et al.  Activin Type II Receptor Restoration in ACVR2-Deficient Colon Cancer Cells Induces Transforming Growth Factor-β Response Pathway Genes , 2004, Cancer Research.

[36]  Gary D. Bader,et al.  Pathway Commons, a web resource for biological pathway data , 2010, Nucleic Acids Res..

[37]  Devarajan Karunagaran,et al.  Curcumin and Emodin Down-Regulate TGF-β Signaling Pathway in Human Cervical Cancer Cells , 2015, PloS one.

[38]  Eun-Sun Choi,et al.  Modulation of specificity protein 1 by mithramycin A as a novel therapeutic strategy for cervical cancer , 2014, Scientific Reports.

[39]  Melanie A. Huntley,et al.  Recurrent R-spondin fusions in colon cancer , 2012, Nature.

[40]  Steven J. M. Jones,et al.  Integrated genomic characterization of endometrial carcinoma , 2013, Nature.

[41]  Guoqiang Han,et al.  WaveDec: A Wavelet Approach to Identify Both Shared and Individual Patterns of Copy-Number Variations , 2018, IEEE Transactions on Biomedical Engineering.

[42]  Vanita Vanas,et al.  Sprouty4 interferes with cell proliferation and migration of breast cancer-derived cell lines , 2014, Tumor Biology.

[43]  Richard D Cummings,et al.  Protein glycosylation in cancer. , 2015, Annual review of pathology.

[44]  Tomlinson,et al.  The importance of p53 pathway genetics in inherited and somatic cancer genomes , 2016, Nature Reviews Cancer.

[45]  C. Hill,et al.  Alterations in components of the TGF-beta superfamily signaling pathways in human cancer. , 2006, Cytokine & growth factor reviews.

[46]  Leonidas J. Guibas,et al.  Consistent Shape Maps via Semidefinite Programming , 2013, SGP '13.

[47]  Guoqiang Han,et al.  HOGMMNC: a higher order graph matching with multiple network constraints model for gene‐drug regulatory modules identification , 2018, Bioinform..

[48]  Shi-Hua Zhang,et al.  Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data , 2016, Bioinform..

[49]  L. Mishra,et al.  Mutations of Chromatin Structure Regulating Genes in Human Malignancies , 2016, Current protein & peptide science.

[50]  Juan Liu,et al.  A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules , 2011, Bioinform..