Crosstalk analysis of dysregulated pathways in preeclampsia

Wang,Tao; Shi,Xing-Zhen; Wu,Wen-Hua

doi:10.3892/etm.2019.7178

Crosstalk analysis of dysregulated pathways in preeclampsia

Authors:
- Tao Wang
- Xing-Zhen Shi
- Wen-Hua Wu
View Affiliations

Affiliations: Department of Obstetrics, Chengdu Women and Children Center Hospital, Chengdu, Sichuan 610000, P.R. China, Department of Gynaecology and Obstetrics, First People's Hospital of Jinan, Jinan, Shandong 250011, P.R. China, Department of Orthopedics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
Published online on: January 16, 2019 https://doi.org/10.3892/etm.2019.7178
Pages: 2298-2304

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

A crosstalk between multiple biological pathways has been proposed in biological processes. However, the existence and degree of this phenomenon in patients with preeclampsia (PE) have not been strictly investigated. Thus, this study explored an dysregulated pathway set (DPS) for PE based on pathway crosstalk network (PCN) related analysis. In the present study, four steps were performed in the inference of DPS: acquiring data of gene expression, pathway and protein-protein interaction (PPI) construction; building a PCN through integrating the information in these datasets and Pearson's correlation coefficient (PCC). A principal component analysis (PCA) approach was used to compute the activity of every pathway for selecting seed pathway of PCN. DPS was evaluated by measuring of an area under the receiver operating characteristics curve (AUC) and seed pathway from PCN. Consequently, a total of 420 pathways and 6,032 crosstalks were mapped to the PCN, in which RIG-I/MDA5-mediated induction of IFN-α/β pathways was identified as the seed pathway that had the greatest changes in activity scores across PE patients and normal controls. DPS was composed of 15 dysregulated pathways and 46 crosstalks, in which CLEC7A (Dectin-1) signaling possessed the highest degree of 12, which indicated it exerted an important role in the DPS. Our results revealed crosstalk between pathways and the DPS crucial for PE pathogenesis, which aid in excavating potential biomarkers of PE therapy and unveil the underlying pathological mechanism of this disease.

View Figures

View References

1	Villar J, Say L, Gülmezoglu A, Merialdi M, Lindheimer MD, Betran AP and Piaggio G: Eclampsia and Pre-Eclampsia: A worldwide health problem for 2000 years. Pre-Eclampsia. Critchley H, Maclean A, Poston L and Walker J: RCOG Press; London: pp. 189–207. 2003
2	Al-Jameil N, Aziz Khan F, Fareed Khan M and Tabassum H: A brief overview of preeclampsia. J Clin Med Res. 6:1–7. 2014.PubMed/NCBI
3	Kenny LC, Black MA, Poston L, Taylor R, Myers JE, Baker PN, McCowan LM, Simpson NAB, Dekker GA, Roberts CT, et al: Early pregnancy prediction of preeclampsia in nulliparous women, combining clinical risk and biomarkers: The Screening for Pregnancy Endpoints (SCOPE) international cohort study. Hypertension. 64:644–652. 2014. View Article : Google Scholar : PubMed/NCBI
4	Myatt L, Redman CW, Staff AC, Hansson S, Wilson ML, Laivuori H, Poston L, Roberts JM and Colaboratory GP; Global Pregnancy CoLaboratory, : Strategy for standardization of preeclampsia research study design. Hypertension. 63:1293–1301. 2014. View Article : Google Scholar : PubMed/NCBI
5	Redman CWG and Sargent IL: Immunology of pre-eclampsia. Am J Reprod Immunol. 63:534–543. 2010. View Article : Google Scholar : PubMed/NCBI
6	Eiland E, Nzerue C and Faulkner M: Preeclampsia 2012. J Pregnancy. 2012:5865782012. View Article : Google Scholar : PubMed/NCBI
7	Yong HE, Melton PE, Johnson MP, Freed KA, Kalionis B, Murthi P, Brennecke SP, Keogh RJ and Moses EK: Genome-wide transcriptome directed pathway analysis of maternal preeclampsia susceptibility genes. PLoS One. 10:e01282302015. View Article : Google Scholar : PubMed/NCBI
8	Long W, Shi Z, Fan S, Liu L, Lu Y, Guo X, Rong C, Cui X and Ding H: Association of maternal KIR and fetal HLA-C genes with the risk of preeclampsia in the Chinese Han population. Placenta. 36:433–437. 2015. View Article : Google Scholar : PubMed/NCBI
9	Grill S, Rusterholz C, Zanetti-Dällenbach R, Tercanli S, Holzgreve W, Hahn S and Lapaire O: Potential markers of preeclampsia - A review. Reprod Biol Endocrinol. 7:702009. View Article : Google Scholar : PubMed/NCBI
10	Bi D, Ning H, Liu S, Que X and Ding K: Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer. Comput Biol Chem. 56:71–83. 2015. View Article : Google Scholar : PubMed/NCBI
11	Li Y, Agarwal P and Rajagopalan D: A global pathway crosstalk network. Bioinformatics. 24:1442–1447. 2008. View Article : Google Scholar : PubMed/NCBI
12	Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC and Conklin BR: MAPPFinder: Using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol. 4:R72003. View Article : Google Scholar : PubMed/NCBI
13	Dawson JA and Kendziorski C: An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments. Biometrics. 68:455–465. 2012. View Article : Google Scholar : PubMed/NCBI
14	Herse F, Dechend R, Harsem NK, Wallukat G, Janke J, Qadri F, Hering L, Muller DN, Luft FC and Staff AC: Dysregulation of the circulating and tissue-based renin-angiotensin system in preeclampsia. Hypertension. 49:604–611. 2007. View Article : Google Scholar : PubMed/NCBI
15	Textoris J, Ivorra D, Ben Amara A, Sabatier F, Ménard JP, Heckenroth H, Bretelle F and Mege JL: Evaluation of current and new biomarkers in severe preeclampsia: A microarray approach reveals the VSIG4 gene as a potential blood biomarker. PLoS One. 8:e82638. 2013. View Article : Google Scholar : PubMed/NCBI
16	Taminau J, Meganck S and Lazar C: Using the inSilicoMerging package. https://bioc.ism.ac.jp/packages/2.11/bioc/vignettes/inSilicoMerging/inst/doc/inSilicoMerging.pdf
17	Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B and Speed TP: Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31:e15. 2003. View Article : Google Scholar : PubMed/NCBI
18	Bolstad BM, Irizarry RA, Astrand M and Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 19:185–193. 2003. View Article : Google Scholar : PubMed/NCBI
19	Bolstad B: Affy: Built-in processing methods. 2003.https://users.soe.ucsc.edu/~leslie/MOUSE/chucktest/src/affy/doc/builtinMethods.pdf
20	Allen JD, Wang S, Chen M, Girard L, Minna JD, Xie Y and Xiao G: Probe mapping across multiple microarray platforms. Brief Bioinform. 13:547–54. 2012. View Article : Google Scholar : PubMed/NCBI
21	Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, Gillespie M, Kamdar MR, et al: The Reactome pathway knowledgebase. Nucleic Acids Res. 42:D472–D477. 2014. View Article : Google Scholar : PubMed/NCBI
22	Ahn T, Lee E, Huh N and Park T: Personalized identification of altered pathways in cancer using accumulated normal tissue data. Bioinformatics. 30:i422–i429. 2014. View Article : Google Scholar : PubMed/NCBI
23	Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A and Tsafou KP: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43:D447–D452. 2015. View Article : Google Scholar : PubMed/NCBI
24	Liu KQ, Liu ZP, Hao JK, Chen L and Zhao XM: Identifying dysregulated pathways in cancers from pathway interaction networks. BMC Bioinformatics. 13:1262012. View Article : Google Scholar : PubMed/NCBI
25	Haynes W: Student's t-test. Encyclopedia of Systems Biology. Dubitzky W, Wolkenhauer O, Cho KH and Yokota H: Springer; New York, NY: 2013, View Article : Google Scholar
26	Nahler G: Pearson correlation coefficient. Dictionary of Pharmaceutical Medicine. Springer; Vienna: pp. 1322009, View Article : Google Scholar
27	Bro R and Smilde AK: Principal component analysis. Anal Methods. 6:2812–2831. 2014. View Article : Google Scholar
28	Chang C-C and Lin C-J: Libsvm: A library for support vector machines. ACM Trans Intell Syst Technol. 2:272011. View Article : Google Scholar
29	Huang J and Ling CX: Using auc and accuracy in evaluating learning algorithms, knowledge and data engineering. IEEE T Knowl Data En. 17:299–310. 2005. View Article : Google Scholar
30	Rojatkar DV, Chinchkhede KD and Sarate GG: Handwritten Devnagari consonants recognition using MLPNN with five fold cross validation. International Conference on Circuits, Power and Computing Technologies. IEEE; Nagercoil, India: pp. 1222–1226. 2013
31	Glazko GV and Emmert-Streib F: Unite and conquer: Univariate and multivariate approaches for finding differentially expressed gene sets. Bioinformatics. 25:2348–2354. 2009. View Article : Google Scholar : PubMed/NCBI
32	Khatri P, Sirota M and Butte AJ: Ten years of pathway analysis: Current approaches and outstanding challenges. PLOS Comput Biol. 8:e10023752012. View Article : Google Scholar : PubMed/NCBI
33	Donato M, Xu Z, Tomoiaga A, Granneman JG, Mackenzie RG, Bao R, Than NG, Westfall PH, Romero R and Draghici S: Analysis and correction of crosstalk effects in pathway analysis. Genome Res. 23:1885–1893. 2013. View Article : Google Scholar : PubMed/NCBI
34	Bradley EW, Ruan MM, Vrable A and Oursler MJ: Pathway crosstalk between Ras/Raf and PI3K in promotion of M-CSF-induced MEK/ERK-mediated osteoclast survival. J Cell Biochem. 104:1439–1451. 2008. View Article : Google Scholar : PubMed/NCBI
35	Liu Z-P, Wang Y, Zhang X-S and Chen L: Network-based analysis of complex diseases. IET Systems Biology. 6:22–33. 2012. View Article : Google Scholar : PubMed/NCBI
36	Barabási AL and Oltvai ZN: Network biology: Understanding the cell's functional organization. Nat Rev Genet. 5:101–113. 2004. View Article : Google Scholar : PubMed/NCBI
37	Tong AHY, Lesage G, Bader GD, Ding H, Xu H, Xin X, Young J, Berriz GF, Brost RL, Chang M, et al: Global mapping of the yeast genetic interaction network. Science. 303:808–813. 2004. View Article : Google Scholar : PubMed/NCBI
38	Kelley R and Ideker T: Systematic interpretation of genetic interactions using protein networks. Nat Biotechnol. 23:561–566. 2005. View Article : Google Scholar : PubMed/NCBI
39	Nasirudeen AM, Wong HH, Thien P, Xu S, Lam KP and Liu DX: RIG-I, MDA5 and TLR3 synergistically play an important role in restriction of dengue virus infection. PLoS Negl Trop Dis. 5:e9262011. View Article : Google Scholar : PubMed/NCBI
40	Broquet AH, Hirata Y, McAllister CS and Kagnoff MF: RIG-I/MDA5/MAVS are required to signal a protective IFN response in rotavirus-infected intestinal epithelium. J Immunol. 186:1618–1626. 2011. View Article : Google Scholar : PubMed/NCBI
41	Chatterjee P, Weaver LE, Chiasson VL, Young KJ and Mitchell BM: Do double-stranded RNA receptors play a role in preeclampsia? Placenta. 32:201–205. 2011. View Article : Google Scholar : PubMed/NCBI
42	Wang D, Xia D and Dubois RN: The crosstalk of PTGS2 and EGF signaling pathways in colorectal cancer. Cancers (Basel). 3:3894–3908. 2011. View Article : Google Scholar : PubMed/NCBI