PPWD1 is associated with the occurrence of postmenopausal osteoporosis as determined by weighted gene co‑expression network analysis

Qian,Guo‑Feng; Yuan,Lu‑Shun; Chen,Min; Ye,Dan; Chen,Guo‑Ping; Zhang,Zhe; Li,Cheng‑Jiang; Vijayan,Vijith; Xiao,Yu

doi:10.3892/mmr.2019.10570

PPWD1 is associated with the occurrence of postmenopausal osteoporosis as determined by weighted gene co‑expression network analysis

Authors:
- Guo‑Feng Qian
- Lu‑Shun Yuan
- Min Chen
- Dan Ye
- Guo‑Ping Chen
- Zhe Zhang
- Cheng‑Jiang Li
- Vijith Vijayan
- Yu Xiao
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Affiliations: Department of Endocrinology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, P.R. China, Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, P.R. China, Institute for Transfusion Medicine, Hannover Medical School, D‑30625 Hannover, Germany
Published online on: August 7, 2019 https://doi.org/10.3892/mmr.2019.10570
Pages: 3202-3214
Copyright: © Qian et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Postmenopausal osteoporosis (PMO) is the most common type of primary osteoporosis (OP), a systemic skeletal disease. Although many factors have been revealed to contribute to the occurrence of PMO, specific biomarkers for the early diagnosis and therapy of PMO are not available. In the present study, a weighted gene co‑expression network analysis (WGCNA) was performed to screen gene modules associated with menopausal status. The turquoise module was verified as the clinically significant module, and 12 genes (NUP133, PSMD12, PPWD1, RBM8A, CRNKL1, PPP2R5C, RBM22, PIK3CB, SKIV2L2, PAPOLA, SRSF1 and COPS2) were identified as ‘real’ hub genes in both the protein‑protein interaction (PPI) network and co‑expression network. Furthermore, gene expression analysis by microarray in blood monocytes from pre‑ and post‑menopausal women revealed an increase in the expression of these hub genes in postmenopausal women. However, only the expression of peptidylprolyl isomerase domain and WD repeat containing 1 (PPWD1) was correlated with bone mineral density (BMD) in postmenopausal women. In the validation set, a similar expression pattern of PPWD1 was revealed. Functional enrichment analysis revealed that the fatty acid metabolism pathway was significantly abundant in the samples that exhibited a higher expression of PPWD1. Collectively, PPWD1 is indicated as a potential diagnostic biomarker for the occurrence of PMO.

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1	Kanis JA, Melton LJ III, Christiansen C, Johnston CC and Khaltaev N: The diagnosis of osteoporosis. J Bone Miner Res. 9:1137–1141. 1994. View Article : Google Scholar : PubMed/NCBI
2	Si L, Winzenberg TM, Jiang Q, Chen M and Palmer AJ: Projection of osteoporosis-related fractures and costs in China: 2010–2050. Osteoporos Int. 26:1929–1937. 2015. View Article : Google Scholar : PubMed/NCBI
3	Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A and Tosteson A: Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. J Bone Miner Res. 22:4652007. View Article : Google Scholar : PubMed/NCBI
4	Eastell R, O'Neill TW, Hofbauer LC, Langdahl B, Reid IR, Gold DT and Cummings SR: Postmenopausal osteoporosis. Nat Rev Dis Primers. 2:160692016. View Article : Google Scholar : PubMed/NCBI
5	Almeida M, Martin-Millan M, Ambrogini E, Bradsher R III, Han L, Chen XD, Roberson PK, Weinstein RS, O'Brien CA, Jilka RL and Manolagas SC: Estrogens attenuate oxidative stress and the differentiation and apoptosis of osteoblasts by DNA-binding-independent actions of the ERα. J Bone Miner Res. 25:769–781. 2010.PubMed/NCBI
6	Goettsch C, Babelova A, Trummer O, Erben RG, Rauner M, Rammelt S, Weissmann N, Weinberger V, Benkhoff S, Kampschulte M, et al: NADPH oxidase 4 limits bone mass by promoting osteoclastogenesis. J Clin Inves. 123:4731–4738. 2013. View Article : Google Scholar
7	Langfelder P and Horvath S: WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics. 9:5592008. View Article : Google Scholar : PubMed/NCBI
8	Yuan L, Zeng G, Chen L, Wang G and Wang X, Cao X, Lu M, Liu X, Qian G, Xiao Y and Wang X: Identification of key genes and pathways in human clear cell renal cell carcinoma (ccRCC) by co-expression analysis. Int J Biol Sci. 14:266–279. 2018. View Article : Google Scholar : PubMed/NCBI
9	Chen L, Yuan L, Wang Y, Wang G, Zhu Y, Cao R, Qian G, Xie C, Liu X, Xiao Y and Wang X: Co-expression network analysis identified FCER1G in association with progression and prognosis in human clear cell renal cell carcinoma. Int J Biol Sci. 13:1361–1372. 2017. View Article : Google Scholar : PubMed/NCBI
10	Yuan L, Chen L, Qian K, Wang G, Lu M, Qian G, Cao X, Jiang W, Xiao Y and Wang X: A novel correlation betweenATP5A1gene expression and progression of human clear cell renal cell carcinoma identified by co-expression analysis. Oncol Rep. 39:5252018.PubMed/NCBI
11	Yuan L, Chen L, Qian K, Qian G, Wu CL, Wang X and Xiao Y: Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC). Genom Data. 14:132–140. 2017. View Article : Google Scholar : PubMed/NCBI
12	Chen L, Yuan L, Wang G, Cao R, Peng J, Shu B, Qian G, Wang X and Xiao Y: Identification and bioinformatics analysis of miRNAs associated with human muscle invasive bladder cancer. Mol Med Rep. 16:8709–8720. 2017. View Article : Google Scholar : PubMed/NCBI
13	Yuan L, Shu B, Chen L, Qian K, Wang Y, Qian G, Zhu Y, Cao X, Xie C, Xiao Y and Wang X: Overexpression of COL3A1 confers a poor prognosis in human bladder cancer identified by co-expression analysis. Oncotarget. 8:70508–70520. 2017. View Article : Google Scholar : PubMed/NCBI
14	Shi H, Zhang L, Qu Y, Hou L, Wang L and Zheng M: Prognostic genes of breast cancer revealed by gene co-expression network analysis. Oncol Lett. 14:4535–4542. 2017. View Article : Google Scholar : PubMed/NCBI
15	Zhang Y, Wang J, Ji LJ, Li L, Wei M, Zhen S and Wen CC: Identification of key gene modules of neuropathic pain by co-expression analysis. J Cell Biochem. 118:44362017. View Article : Google Scholar : PubMed/NCBI
16	Horvath S and Dong J: Geometric interpretation of gene co-expression network analysis. PLoS Comput Biol. 4:e10001172008. View Article : Google Scholar : PubMed/NCBI
17	Farber CR: Identification of a gene module associated with BMD through the integration of network analysis and genome-wide association data. J Bone Miner Res. 25:2359–2367. 2010. View Article : Google Scholar : PubMed/NCBI
18	Zhang L, Liu YZ, Zeng Y, Zhu W, Zhao YC, Zhang JG, Zhu JQ, He H, Shen H, Tian Q, et al: Network-based proteomic analysis for postmenopausal osteoporosis in Caucasian females. Proteomics. 16:12–28. 2016. View Article : Google Scholar : PubMed/NCBI
19	Chen YC, Guo YF, He H, He H, Lin X, Wang XF, Zhou R, Li WT, Pan DY, Shen J and Deng HW: Integrative analysis of genomics and transcriptome data to identify potential functional genes of BMDs in females. J Bone Miner Res. 31:1041–1049. 2016. View Article : Google Scholar : PubMed/NCBI
20	Nomura N, Nagase T, Miyajima N, Sazuka T, Tanaka A, Sato S, Seki N, Kawarabayasi Y, Ishikawa K and Tabata S: Prediction of the coding sequences of unidentified human genes. II. The coding sequences of 40 new genes (KIAA0041-KIAA0080) deduced by analysis of cDNA clones from human cell line KG-1 (supplement). DNA Res. 1:251–162. 1994. View Article : Google Scholar : PubMed/NCBI
21	Jurica MS and Moore MJ: Pre-mRNA splicing: Awash in a sea of proteins. Mol Cell. 12:5–14. 2003. View Article : Google Scholar : PubMed/NCBI
22	Davis TL, Walker JR, Ouyang H, MacKenzie F, Butler-Cole C, Newman EM, Eisenmesser EZ and Dhe-Paganon S: The crystal structure of human WD40 repeat-containing peptidylprolyl isomerase (PPWD1). FEBS J. 275:2283–2295. 2008. View Article : Google Scholar : PubMed/NCBI
23	Lee WC, Guntur AR, Long F and Rosen CJ: Energy metabolism of the osteoblast: Implications for osteoporosis. Endocr Rev. 38:255–266. 2017. View Article : Google Scholar : PubMed/NCBI
24	Kruger MC, Coetzee M, Haag M and Weiler H: Long-chain polyunsaturated fatty acids: Selected mechanisms of action on bone. Prog Lipid Res. 49:438–449. 2010. View Article : Google Scholar : PubMed/NCBI
25	Frey JL, Li Z, Ellis JM, Zhang Q, Farber CR, Aja S, Wolfgang MJ, Clemens TL and Riddle RC: Wnt-Lrp5 signaling regulates fatty acid metabolism in the osteoblast. Mol Cell Biol. 35:1979–1991. 2015. View Article : Google Scholar : PubMed/NCBI
26	Kushwaha P, Wolfgang MJ and Riddle RC: Fatty acid metabolism by the osteoblast. Bone. 115:8–14. 2018. View Article : Google Scholar : PubMed/NCBI
27	Lin G, Wang H, Dai J, Li X, Guan M, Gao S, Ding Q, Wang H and Fang H: Conjugated linoleic acid prevents age-induced bone loss in mice by regulating both osteoblastogenesis and adipogenesis. Biochem Biophys Res Commun. 490:813–823. 2017. View Article : Google Scholar : PubMed/NCBI
28	Yeh LCC, Ford JJ, Lee JC and Adamo ML: Palmitate attenuates osteoblast differentiation of fetal rat calvarial cells. Biochem Biophys Res Commun. 450:777–781. 2014. View Article : Google Scholar : PubMed/NCBI
29	Kim HJ, Yoon HJ, Kim SY and Yoon YR: A medium-chain fatty acid, capric acid, inhibits RANKL-induced osteoclast differentiation via the suppression of NF-κB signaling and blocks cytoskeletal organization and survival in mature osteoclasts. Mol Cells. 37:598–604. 2014. View Article : Google Scholar : PubMed/NCBI
30	Lavadogarcía J, Ronceromartin R, Moran JM, Pedrera-Canal M, Aliaga I, Leal-Hernandez O, Rico-Martin S and Canal- Macias ML: Long-chain omega-3 polyunsaturated fatty acid dietary intake is positively associated with bone mineral density in normal and osteopenic Spanish women. PLoS One. 13:e01905392018. View Article : Google Scholar : PubMed/NCBI