ABSTRACT
A successful method to address disease-specific module discovery is the integration of the gene expression data with the protein-protein interaction (PPI) network. Although many algorithms have been developed for this purpose, they focus only on the network genes (mostly on the well-connected ones); totally neglecting the genes whose interactions are partially or totally not known. In addition, they only make use of the gene expression data which does not give the complete picture about the actual protein expression levels. The cell uses different mechanisms, such as microRNAs, to post-transcriptionally regulate the proteins without affecting the corresponding genes' expressions. Due to this complexity, using a single data type is definitely not the correct way to find the correct module(s). Today, the unprecedented amount of publicly available disease-related heterogeneous data encourages the development of new methodologies to better understand complex diseases.
In this work, we propose a novel workflow Mica, which, to the best of our knowledge, is the first study integrating miRNA, mRNA, and PPI information to identify disease-specific gene modules. The novelty of the Mica lies in many directions, such as the early modification of mRNA expression with microRNA to better highlight the indirect dependencies between the genes. We applied Mica on microRNA-Seq and mRNA-Seq data sets of 699 invasive ductal carcinoma samples and 150 invasive lobular carcinoma samples from the Cancer Genome Atlas Project (TCGA). The Mica modules are shown to unravel new and interesting dependencies between the genes. Additionally, the modules accurately differentiate between the case and control samples while being highly enriched with disease-specific pathways and genes.
- D. Baek, J. Villén, C. Shin, et al. The impact of micrornas on protein output. Nature, 455(7209):64--71, 2008.Google ScholarCross Ref
- A.-L. Barabási, N. Gulbahce, and J. Loscalzo. Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12(1):56--68, 2011.Google ScholarCross Ref
- A. J. Bell and T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural computation, 7(6):1129--1159, 1995. Google ScholarDigital Library
- C. Blenkiron, L. D. Goldstein, N. P. Thorne, et al. Microrna expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol, 8(10):R214, 2007.Google ScholarCross Ref
- K. Broberg, E. Huynh, K. S. Engström, et al. Association between polymorphisms in rmi1, top3a, and blm and risk of cancer, a case-control study. BMC cancer, 9(1):140, 2009.Google ScholarCross Ref
- J.-F. Cardoso and A. Souloumiac. Blind beamforming for non-gaussian signals. In IEE Proc. F (Radar and Signal Processing), volume 140, pages 362--370, 1993.Google ScholarCross Ref
- K.-L. Chan, P. S. North, and I. D. Hickson. Blm is required for faithful chromosome segregation and its localization defines a class of ultrafine anaphase bridges. The EMBO journal, 26(14):3397--3409, 2007.Google ScholarCross Ref
- X. Chang, T. Xu, Y. Li, and K. Wang. Dynamic modular architecture of protein-protein interaction networks beyond the dichotomy of date and party hubs. Scientific reports, 3, 2013.Google Scholar
- S. Chavali, S. Bruhn, K. Tiemann, et al. MicroRNAs act complementarily to regulate disease-related mRNA modules in human diseases. RNA, 19(11):1552--1562, 2013.Google ScholarCross Ref
- P. Chiappetta, M.-C. Roubaud, and B. Torrésani. Blind source separation and the analysis of microarray data. Journal of Comp Biol, 11(6):1090--1109, 2004.Google ScholarCross Ref
- A. Cretu, X. Sha, J. Tront, et al. Stress sensor gadd45 genes as therapeutic targets in cancer. Cancer therapy, 7(A):268, 2009.Google Scholar
- Y. Cun and H. Fröhlich. Network and data integration for biomarker signature discovery via network smoothed t-statistics. PloS one, 8(9):e73074, 2013.Google ScholarCross Ref
- C.-X. Deng. Brca1: cell cycle checkpoint, genetic instability, dna damage response and cancer evolution. Nucleic acids research, 34(5):1416--1426, 2006.Google Scholar
- S.-l. Ding, J.-C. Yu, S.-T. Chen, et al. Genetic variants of blm interact with rad51 to increase breast cancer susceptibility. Carcinogenesis, 30(1):43--49, 2009.Google ScholarCross Ref
- M. T. Dittrich, G. W. Klau, A. Rosenwald, et al. Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinf., 24(13):i223--i231, 2008. Google ScholarDigital Library
- M. R. Fabian, N. Sonenberg, and W. Filipowicz. Regulation of mRNA translation and stability by microRNAs. Ann. review of bioch., 79:351--379, 2010.Google Scholar
- A. R. Grosso, S. Martins, and M. Carmo-Fonseca. The emerging role of splicing factors in cancer. EMBO reports, 9(11):1087--1093, 2008.Google ScholarCross Ref
- J. Himberg, A. Hyvärinen, and F. Esposito. Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage, 22(3):1214--1222, 2004.Google ScholarCross Ref
- A. Hollestelle, J. H. Nagel, M. Smid, et al. Distinct gene mutation profiles among luminal-type and basal-type breast cancer cell lines. Br. Can. Res. and Treat., 121(1):53--64, 2010.Google ScholarCross Ref
- J. L. Horn. A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2):179--185, 1965.Google ScholarCross Ref
- M.-C. Hsu, K.-T. Lee, W.-C. Hsiao, et al. The dyslipidemia-associated snp on the apoa1/c3/a5 gene cluster predicts post-surgery poor outcome in taiwanese breast cancer patients: a 10-year follow-up study. BMC cancer, 13(1):330, 2013.Google ScholarCross Ref
- S.-D. Hsu, F.-M. Lin, W.-Y. Wu, et al. miRTarBase: a database curates experimentally validated microRNA--target interactions. Nucl. Acids Res., 39(suppl 1):D163--D169, 2011.Google ScholarCross Ref
- G. T. Huang, C. Athanassiou, and P. V. Benos. mirConnX: condition-specific mRNA-microRNA network integrator. Nucl. Acids Res., 39(suppl 2):W416--W423, 2011.Google ScholarCross Ref
- A. Hyvärinen. Fast and robust fixed-point algorithms for independent component analysis. Neural Networks, IEEE Transactions on, 10(3):626--634, 1999. Google ScholarDigital Library
- A. Hyvärinen. Independent component analysis: recent advances. Philos. Trans. of the Royal Soc. A: Math., Phys. and Eng. Sci., 371(1984), 2013.Google Scholar
- T. Ideker, O. Ozier, B. Schwikowski, and A. F. Siegel. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinf., 18(Suppl 1):S233--S240, 2002.Google ScholarCross Ref
- T. Ideker and R. Sharan. Protein networks in disease. Genome Res., 18(4):644--652, 2008.Google ScholarCross Ref
- M. V. Iorio and C. M. Croce. microRNA involvement in human cancer. Carcinogenesis, 33(6):1126--1133, 2012.Google ScholarCross Ref
- M. Koyutürk. Algorithmic and analytical methods in network biology. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2(3):277--292, 2010.Google ScholarCross Ref
- I. Lal, K. Dittus, and C. E. Holmes. Platelets, coagulation and fibrinolysis in breast cancer progression. Breast Cancer Research, 15(4):1--11, 2013.Google ScholarCross Ref
- H.-S. Le and Z. Bar-Joseph. Integrating sequence, expression and interaction data to determine condition-specific mirna regulation. Bioinformatics, 29(13):i89--i97, 2013.Google ScholarCross Ref
- M. Lee, M. Daniels, M. Garnett, and A. Venkitaraman. A mitotic function for the high-mobility group protein HMG20b regulated by its interaction with the brc repeats of the brca2 tumor suppressor. Oncogene, 30(30):3360--3369, 2011.Google ScholarCross Ref
- F. Lerebours, S. Vacher, C. Andrieu, et al. Nf-kappa b genes have a major role in inflammatory breast cancer. BMC cancer, 8(1):41, 2008.Google ScholarCross Ref
- E. Levy-Lahad. Fanconi anemia and breast cancer susceptibility meet again. Nature genetics, 42(5), 2010.Google Scholar
- L. Li, H.-Z. Chen, F.-F. Chen, et al. Global microrna expression profiling reveals differential expression of target genes in 6-hydroxydopamine injured mn9d cells. Neuromolecular medicine, 15(3):593--604, 2013.Google ScholarCross Ref
- P. Li, Y. Lin, Y. Zhang, et al. SSX2IP promotes metastasis and chemotherapeutic resistance of hepatocellular carcinoma. Jr. of Trans. Med., 2013.Google ScholarCross Ref
- Y.-O. Li, T. Adalı, and V. D. Calhoun. Estimating the number of independent components for functional magnetic resonance imaging data. Human brain mapping, 28(11):1251--1266, 2007.Google Scholar
- W. Liebermeister. Linear modes of gene expression determined by independent component analysis. Bioinformatics, 18(1):51--60, 2002.Google ScholarCross Ref
- X.-J. Ma, R. Salunga, J. T. Tuggle, et al. Gene expression profiles of human breast cancer progression. Proc. of the Nat. Acad. of Sci., 100(10):5974--5979, 2003.Google ScholarCross Ref
- K. Mitra, A.-R. Carvunis, S. K. Ramesh, and T. Ideker. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Gen., 14(10):719--732, 2013.Google ScholarCross Ref
- J. D. Osborne, J. Flatow, M. Holko, et al. Annotating the human genome with disease ontology. BMC genomics, 10(Suppl 1):S6, 2009.Google ScholarCross Ref
- I. Pucci-Minafra, P. Cancemi, M. R. Marabeti, et al. Proteomic profiling of 13 paired ductal infiltrating breast carcinomas and non-tumoral adjacent counterparts. PROT.-Clin. App., 1(1):118--129, 2007.Google ScholarCross Ref
- F. Revillion, J. Bonneterre, and J. Peyrat. ERBB2 oncogene in human breast cancer and its clinical significance. Euro. Jr. of Cancer, 34(6):791--808, 1998.Google ScholarCross Ref
- M. Rotival, T. Zeller, P. S. Wild, et al. Integrating genome-wide genetic variations and monocyte expression data reveals trans-regulated gene modules in humans. PLoS gen., 7(12):e1002367, 2011.Google Scholar
- R. Schachtner, D. Lutter, P. Knollmüller, et al. Knowledge-based gene expression classification via matrix factorization. Bioinf., 24(15):1688--1697, 2008. Google ScholarDigital Library
- M. Scholz, S. Gatzek, A. Sterling, et al. Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinf., 20(15):2447--2454, 2004. Google ScholarDigital Library
- A. D. Singhi, A. Cimino-Mathews, R. B. Jenkins, et al. Myc gene amplification is often acquired in lethal distant breast cancer metastases of unamplified primary tumors. Modern Path., 25(3):378--387, 2011.Google ScholarCross Ref
- R. J. Slebos, X. Wang, X. Wang, et al. Proteomic analysis of colon and rectal carcinoma using standard and customized databases. Scientific data, 2, 2015.Google Scholar
- M.-N. Song, P.-G. Moon, J.-E. Lee, et al. Proteomic analysis of breast cancer tissues to identify biomarker candidates by gel-assisted digestion and label-free quantification methods using LC-MS/MS. Arch. of Pharm. Res., 35(10):1839--1847, 2012.Google ScholarCross Ref
- A. E. Teschendorff, M. Journée, P. A. Absil, et al. Elucidating the altered transcriptional programs in breast cancer using independent component analysis. PLoS Comp. Biol., 3(8):e161, 2007.Google ScholarCross Ref
- Y. C. Tsai and A. M. Weissman. The unfolded protein response, degradation from the endoplasmic reticulum, and cancer. Genes & cancer, 1(7):764--778, 2010.Google ScholarCross Ref
- J. S. Tsang, M. S. Ebert, and A. van Oudenaarden. Genome-wide dissection of microrna functions and cotargeting networks using gene set signatures. Molecular cell, 38(1):140--153, 2010.Google ScholarCross Ref
- G. Turashvili, J. Bouchal, K. Baumforth, et al. Novel markers for differentiation of lobular and ductal invasive breast carcinomas by laser microdissection and microarray analysis. BMC Cancer, 7(1):55, 2007.Google ScholarCross Ref
- I. Ulitsky, A. Krishnamurthy, R. M. Karp, and R. Shamir. DEGAS: de novo discovery of dysregulated pathways in human diseases. PLoS One, 5(10):e13367, 2010.Google ScholarCross Ref
- N. Wasif, M. A. Maggard, C. Y. Ko, et al. Invasive lobular vs. ductal breast cancer: a stage-matched comparison of outcomes. Ann. of Surg. Oncol., 17(7):1862--1869, 2010.Google ScholarCross Ref
- G. Yu. ReactomePA: Reactome Pathway Analysis, 2014. R package version 1.4.0.Google Scholar
- G. Yu, L. Wang, Y. Han, and Q. He. clusterprofiler: an r package for comparing biological themes among gene clusters. OMICS: A Jr. of Int. Biol., 16(5):284--287, 2012.Google ScholarCross Ref
- S. Zhang, Q. Li, J. Liu, and X. J. Zhou. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microrna-gene regulatory modules. Bioinformatics, 27(13):i401--i409, 2011. Google ScholarDigital Library
- S. Zhang, C.-C. Liu, W. Li, et al. Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucl. Acids Res., 40(19):9379--9391, 2012.Google ScholarCross Ref
- H. Zhao, A. Langerød, Y. Ji, et al. Different gene expression patterns in invasive lobular and ductal carcinomas of the breast. Mol. Biol. of the Cell, 15(6):2523--2536, 2004.Google ScholarCross Ref
Index Terms
- MICA: MicroRNA integration for active module discovery
Recommendations
Identification and analysis of the regulatory network of Myc and microRNAs from high-throughput experimental data
As a transcription factor, c-Myc exerts significant influence in cancer development by regulating transcription of a large number of target genes including microRNAs. However, details of regulatory networks composed of Myc, microRNAs, and microRNA ...
Investigating Gene and MicroRNA Expression in Glioblastoma
IJCBS '09: Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent ComputingGlioblastoma is the most common primary brain tumor in adults. Here we present an integrated analysis of microRNA expression and gene expression in 237 tumor tissues and 10 normal tissues. We indentified 1,236 genes, and 131 pathways significantly ...
DNA methylation-regulated microRNA pathways in ovarian serous cystadenocarcinoma
Perform an integrate analysis of DNA methylation, microRNA expression and mRNA expression data.Meta-analysis was performed to reduce the effects of biological heterogeneity among different batches of data.Proposed a systematic strategy to construct ...
Comments