Abnormal sub-pathways competitively regulated by lncRNAs contribute to postmenopausal osteoporosis

Guan,Bing-Gang; Cai,Xiao-Xi

doi:10.3892/etm.2019.7326

Abnormal sub-pathways competitively regulated by lncRNAs contribute to postmenopausal osteoporosis

Authors:
- Bing-Gang Guan
- Xiao-Xi Cai
View Affiliations

Affiliations: Spine Surgery, Tianjin Hospital, Tianjin 300211, P.R. China, Department of Orthopedics, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, P.R. China
Published online on: February 27, 2019 https://doi.org/10.3892/etm.2019.7326
Pages: 2894-2900
Copyright: © Guan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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

Abnormal sub-pathways competitively regulated by long non-coding RNAs (lncRNAs) for postmenopausal osteoporosis (PO) based on integration of lncRNA-mRNA expression data and pathway network topologies were inve-stigated. Interesting lncRNA-mRNA pairs were selected by Pearson's correlation coefficient (PCC) algorithm on the basis of lncRNA-miRNA and miRNA-mRNA interactions and gene expression profiles. Then, lncRNAs in interesting pairs were embedded into pathway graphs as signature nodes by linking to their regulated-mRNAs, and lncRNA competitively regulated pathways (LCRPs) were gained for PO patients. Moreover, sub-pathways were detected dependent on the shortest distance similarity and the pathway topology. The abnormal sub-pathways were determined utilizing the Wallenius approximation methods through evaluating the statistical significance of sub-pathways. In total 75 interesting lncRNA-mRNA pairs (representing 17 lncRNAs and 74 mRNAs) were identified. Subsequently, 42 LCRPs were extracted from pathway graphs by signature lncRNA regulated mRNAs. Moreover, 14 abnormal sub-pathways with P<0.05 were obtained between PO patients and controls, such as sub-pathways of PI3K-Akt signaling pathway and long-term potentiation. This finding may facilitate understanding the molecular mechanism of PO, and point a new direction to identify potential biomarkers for treatment and prevention of the disease.

View Figures

View References

1	Watts NB, Bilezikian JP, Camacho PM, Greenspan SL, Harris ST, Hodgson SF, Kleerekoper M, Luckey MM, McClung MR, Pollack RP, et al: American Association of Clinical Endocrinologists Medical Guidelines for Clinical Practice for the diagnosis and treatment of postmenopausal osteoporosis. Endocr Pract. 3:1–37. 2010. View Article : Google Scholar
2	Unni S, Yao Y, Milne N, Gunning K, Curtis JR and LaFleur J: An evaluation of clinical risk factors for estimating fracture risk in postmenopausal osteoporosis using an electronic medical record database. Osteoporos Int. 26:581–587. 2015. View Article : Google Scholar : PubMed/NCBI
3	Reid IR, Bolland MJ and Grey A: Effects of vitamin D supplements on bone mineral density: A systematic review and meta-analysis. Lancet. 383:146–155. 2014. View Article : Google Scholar : PubMed/NCBI
4	Neer RM, Arnaud CD, Zanchetta JR, Prince R, Gaich GA, Reginster JY, Hodsman AB, Eriksen EF, Ish-Shalom S, Genant HK, et al: Effect of parathyroid hormone (1–34) on fractures and bone mineral density in postmenopausal women with osteoporosis. N Engl J Med. 344:1434–1441. 2001. View Article : Google Scholar : PubMed/NCBI
5	Lips P: Epidemiology and predictors of fractures associated with osteoporosis. Am J Med. 103:3S–8S; discussion 8S-11S. 1997. View Article : Google Scholar : PubMed/NCBI
6	McClung MR, Grauer A, Boonen S, Bolognese MA, Brown JP, Diez-Perez A, Langdahl BL, Reginster JY, Zanchetta JR, Wasserman SM, et al: Romosozumab in postmenopausal women with low bone mineral density. N Engl J Med. 370:412–420. 2014. View Article : Google Scholar : PubMed/NCBI
7	Ebert MS and Sharp PA: Emerging roles for natural microRNA sponges. Curr Biol. 20:R858–R861. 2010. View Article : Google Scholar : PubMed/NCBI
8	Tay Y, Rinn J and Pandolfi PP: The multilayered complexity of ceRNA crosstalk and competition. Nature. 505:344–352. 2014. View Article : Google Scholar : PubMed/NCBI
9	Koyutürk M, Grama A and Szpankowski W: An efficient algorithm for detecting frequent subgraphs in biological networks. Bioinformatics. 20 (Suppl 1):i200–i207. 2004. View Article : Google Scholar : PubMed/NCBI
10	Li C, Han J, Yao Q, Zou C, Xu Y, Zhang C, Shang D, Zhou L, Zou C, Sun Z, et al: Subpathway-GM: Identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways. Nucleic Acids Res. 41:e1012013. View Article : Google Scholar : PubMed/NCBI
11	Li C, Li X, Miao Y, Wang Q, Jiang W, Xu C, Li J, Han J, Zhang F, Gong B, et al: SubpathwayMiner: A software package for flexible identification of pathways. Nucleic Acids Res. 37:e1312009. View Article : Google Scholar : PubMed/NCBI
12	Shi X, Xu Y, Zhang C, Feng L, Sun Z, Han J, Su F, Zhang Y, Li C and Li X: Subpathway-LNCE: Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies. Oncotarget. 7:69857–69870. 2016. View Article : Google Scholar : PubMed/NCBI
13	Yang JH, Li JH, Shao P, Zhou H, Chen YQ and Qu LH: starBase: A database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res. 39 (Suppl_1):D202–D209. 2011. View Article : Google Scholar : PubMed/NCBI
14	Xiao F, Zuo Z, Cai G, Kang S, Gao X and Li T: miRecords: An integrated resource for microRNA-target interactions. Nucleic Acids Res 37 (Database). D105–D110. 2009. View Article : Google Scholar
15	Skala M: Hypergeometric tail inequalities: Ending the insanity. Statistics, eprint arXiv:1311.5939, 2013. https://arxiv.org/pdf/1311.5939.pdf
16	Niwattanakul S, Singthongchai J, Naenudorn E and Wanapu S: Using of Jaccard Coefficient for Keywords Similarity. Lecture Notes in Engineering & Computer Science, 2013. International MultiConference of Engineers and Computer Scientists 2013. I. Hong Kong: pp. pp380–384. 2013
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:e152003. 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	Nahler G: Pearson correlation coefficient. Dictionary of Pharmaceutical Medicine. Springer; Vienna: pp. pp1322009, View Article : Google Scholar
20	Best DJ and Roberts DE: Algorithm AS 89: The Upper Tail Probabilities of Spearman's Rho. J R Stat Soc [Ser A]. 24:377–379. 1975.
21	Whitlock MC: Combining probability from independent tests: The weighted Z-method is superior to Fisher's approach. J Evol Biol. 18:1368–1373. 2005. View Article : Google Scholar : PubMed/NCBI
22	Routledge R: Fisher's Exact Test. In: Encyclopedia of Biostatistics. Armitage P and Colton T (eds). John Wiley & Sons; Ltd., New York, NY: 2005
23	Benjamini Y, Drai D, Elmer G, Kafkafi N and Golani I: Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 125:279–284. 2001. View Article : Google Scholar : PubMed/NCBI
24	Fog A: Calculation methods for Wallenius' noncentral hypergeometric distribution. Commun Stat Simul Comput. 37:258–273. 2008. View Article : Google Scholar
25	Epstein MP, Duncan R, Jiang Y, Conneely KN, Allen AS and Satten GA: A permutation procedure to correct for confounders in case-control studies, including tests of rare variation. Am J Hum Genet. 91:215–223. 2012. View Article : Google Scholar : PubMed/NCBI
26	Stuss M, Rieske P, Cegłowska A, Stêpień-Kłos W, Liberski PP, Brzeziańska E and Sewerynek E: Assessment of OPG/RANK/RANKL gene expression levels in peripheral blood mononuclear cells (PBMC) after treatment with strontium ranelate and ibandronate in patients with postmenopausal osteoporosis. J Clin Endocrinol Metab. 98:E1007–E1011. 2013. View Article : Google Scholar : PubMed/NCBI
27	Martini M, De Santis MC, Braccini L, Gulluni F and Hirsch E: PI3K/AKT signaling pathway and cancer: An updated review. Ann Med. 46:372–383. 2014. View Article : Google Scholar : PubMed/NCBI
28	De Luca A, Maiello MR, D'Alessio A, Pergameno M and Normanno N: The RAS/RAF/MEK/ERK and the PI3K/AKT signalling pathways: Role in cancer pathogenesis and implications for therapeutic approaches. Expert Opin Ther Targets. 16 (Suppl 2):S17–S27. 2012. View Article : Google Scholar : PubMed/NCBI
29	Sabine VS, Crozier C, Brookes CL, Drake C, Piper T, van de Velde CJ, Hasenburg A, Kieback DG, Markopoulos C, Dirix L, et al: Mutational analysis of PI3K/AKT signaling pathway in tamoxifen exemestane adjuvant multinational pathology study. J Clin Oncol. 32:2951–2958. 2014. View Article : Google Scholar : PubMed/NCBI