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AIMS Mathematics

2023, Volume 8, Issue 6: 14449-14474. doi: 10.3934/math.2023738
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Research article Special Issues

A study of quadratic Diophantine fuzzy sets with structural properties and their application in face mask detection during COVID-19

  • 1.
    Department of Basic Sciences and Humanities, National University of Sciences and Technology, Islamabad, Pakistan
  • 2.
    Department of Mathematics, Faculty of Science and Arts, Mahayl Assir, King Khalid University, Abha, Saudi Arabia
  • 3.
    Department of Mathematics and Computer, Faculty of Science, Ibb University, Ibb, Yemen
  • 4.
    Department of Mathematics and Statistics, The University of Haripur, Haripur, Pakistan
  • 5.
    Department of Mathematics, Higher Institute of Engineering and Technology, King Marriott, Egypt, P.O. Box 3135, Egypt

  • During the COVID-19 pandemic, identifying face masks with artificial intelligence was a crucial challenge for decision support systems. To address this challenge, we propose a quadratic Diophantine fuzzy decision-making model to rank artificial intelligence techniques for detecting masks, aiming to prevent the global spread of the disease. Our paper introduces the innovative concept of quadratic Diophantine fuzzy sets (QDFSs), which are advanced tools for modeling the uncertainty inherent in a given phenomenon. We investigate the structural properties of QDFSs and demonstrate that they generalize various fuzzy sets. In addition, we introduce essential algebraic operations, set-theoretical operations, and aggregation operators. Finally, we present a numerical case study that applies our proposed algorithms to select a unique face mask detection method and evaluate the effectiveness of our techniques. Our findings demonstrate the viability of our mask identification methodology during the COVID-19 outbreak.

    Citation: Muhammad Danish Zia, Esmail Hassan Abdullatif Al-Sabri, Faisal Yousafzai, Murad-ul-Islam Khan, Rashad Ismail, Mohammed M. Khalaf. A study of quadratic Diophantine fuzzy sets with structural properties and their application in face mask detection during COVID-19[J]. AIMS Mathematics, 2023, 8(6): 14449-14474. doi: 10.3934/math.2023738

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  • During the COVID-19 pandemic, identifying face masks with artificial intelligence was a crucial challenge for decision support systems. To address this challenge, we propose a quadratic Diophantine fuzzy decision-making model to rank artificial intelligence techniques for detecting masks, aiming to prevent the global spread of the disease. Our paper introduces the innovative concept of quadratic Diophantine fuzzy sets (QDFSs), which are advanced tools for modeling the uncertainty inherent in a given phenomenon. We investigate the structural properties of QDFSs and demonstrate that they generalize various fuzzy sets. In addition, we introduce essential algebraic operations, set-theoretical operations, and aggregation operators. Finally, we present a numerical case study that applies our proposed algorithms to select a unique face mask detection method and evaluate the effectiveness of our techniques. Our findings demonstrate the viability of our mask identification methodology during the COVID-19 outbreak.


    1.   Introduction

    Osteoporosis (OP) is the most common bone disease characterized by a loss of bone mass and quality that results in fragility fractures. Osteoporosis is responsible for over 8.9 million fractures each year worldwide, with most cases occurring in postmenopausal women [1]. The most frequent osteoporotic fractures are fractures of the hip, wrist and spine, although most fractures in the elderly are probably at least partly related to osteoporosis. To prevent the osteoporosis, clinical risk factors [2,3,4,5,6,7], including age, female gender, rheumatoid arthritis, previous fragility fracture, genetic predisposition, smoking, > 14 units of alcohol per week, early-onset menopause (<45 years of age) and low BMI (<19 kg per m2), should be evaluated. To diagnose the osteoporosis, bone mineral density (BMD) measurement is widely used in clinical medicine as an indirect indicator of osteoporosis and fracture risk [8]. Specifically, the BMD measurement can category the subjects into four statuses, including normal, osteopenia, osteoporosis and severe osteoporosis.

    Bone remodeling is accomplished by two specialized cells: bone-resorbing osteoclasts and bone-forming osteoblasts [9]. Osteoclasts can break down bone, while osteoblasts create new bone. Osteoclasts and osteoblasts can coordinate well for most of the human life [10]. In a pathological state of osteoporosis, this coordination can break down, and the osteoclasts begin to remove more bone than the osteoblasts can create [11]. To understand the molecular mechanism of osteoporosis, several studies introduced molecules, including SNP [12,13,14,15], DNA methylation [16], RNA [17] and microRNA [18], to investigate the genetic, epigenetic and transcriptomic regulators involved in osteoclastogenesis or osteoblastogenesis.

    Osteoclastogenesis has been reported to cause osteoporosis by previous studies [19,20,21], however, the critical functional modules and signaling pathways still need to be explicitly characterized, which is useful for the discovery of the therapeutic targets in osteoclasts for the treatment of skeletal diseases. In this study, we collected gene expression datasets of peripheral blood monocytes (PBMs) from low or high BMD subjects. Notably, PBMs, which are osteoclast progenitor cells and produce cytokines for osteoclastogenesis and bone resorption, could be used for osteoporosis study. Furthermore, network based analysis and overrepresentation enrichment analysis highlighted critical functional modules and signaling pathways, which improved our understanding of the molecular mechanism, and provided the potential drug targets for osteoporosis.

    2.   Materials and method

    2.1.   Gene expression data pre-processing

    Gene expression datasets were obtained from the NCBI Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo) with accession numbers, GSE56814 [22]. Prior to downstream analysis, we firstly mapped the array probes to the GENCODE gene annotation v19. We calculated the average expression values of genes matching multiple probes.

    2.2.   Differential expression analysis

    The differential expression analysis was conducted in R programming language (https://www.r-project.org) with student-t test. The differentially expressed genes were identified at the threshold P-value < 0.05. The up-or down-regulation status was determined based on the fold change for each gene.

    2.3.   Overrepresentation enrichment analysis (OEA)

    Overrepresentation enrichment analysis was also implemented at WEB-based Gene Set Analysis Toolkit (WebGestalt) [23]. The Gene Ontology biological processes and KEGG pathways were selected as the functional database.

    2.4.   Protein-protein interaction network mapping

    The Search Tool for the Retrieval of Interacting Genes/ Proteins (STRING) [24] online software (https://string-db.org) was used to assess the interactions. The interactions of the proteins encoded by the differently expressed genes were searched using STRING online software, and the combined score of > 0.4 was used as the cut-off criterion. The PPI network was visualized using Cytoscape software (http://www.cytoscape.org). The Cytoscape MCODE plug-in (version 3.4.0) was applied to search for clustered sub-networks of highly connected nodes from the PPI network. The resulting network was subjected to module analyses with the Plugin MCODE with the following default parameters: Degree cut-off ≥3.

    2.5.   Construction of co-expression network between differentially expressed mRNAs and lncRNAs

    The Pearson correlation coefficient of DEG-lncRNA pairs was calculated according to their expression values. The co-expressed DEG-lncRNA pairs with an absolute value of the Pearson correlation coefficient of ≥0.8 were selected and the co-expression network was visualized by using Cytoscape software.

    3.   Results

    3.1.   Identification of long non-coding RNAs by microarray probe reannotation

    To identify the probes on the array corresponding to long non-coding RNA (lncRNA) sequences, we re-annotated all probes using GENCODE gene annotation with version 19. Finally, we identified a total of 830 long non-coding RNAs, with gene types such as processed transcript, antisense, long intergenic noncoding RNA (lincRNA) and pseudogene. We found that pseudogene accounted for the most of the identified lncRNAs (45%), followed by the long intergenic noncoding RNAs (29%, Figure 1A). In addition, the reannotation of the probes also mapped 17,051 probes to 17,743 protein-coding genes. We then compared the expression levels of lncRNAs with those of protein-coding genes, and found that expression levels were lower in lncRNAs than protein-coding genes (Figure 1B).

    Figure 1.  Identification of lncRNAs by reannotation of microarray probes. (A) The lncRNA categories, including pseudogene, processed transcript, antisense and lincRNA, counts and percentages are presented in pie chart with colors of purple, green, red and blue. (B) The expression distributions of lncRNAs and protein-coding mRNAs are represented by the red and purple lines.
    DownLoad: Full-Size Img PowerPoint

    3.2.   Differentially expressed mRNAs and lncRNAs

    To uncover genes that may be responsible for osteoporosis, we compared the expression profiles of low BMD subjects with that of high BMD subjects. We identified a total of 496 differentially expressed genes (DE-genes), including 120 up-regulated and 376 down-regulated genes in low BMD subjects (t-test, P-value < 0.05, Supplementary Table 1). We observed that four zinc finger genes, ZNF528, ZNF79, ZNF223 and ZNF765, were included in the top-ten up-regulated genes, indicating that zinc finger genes may play key roles in osteoporosis. Notably, 24 lncRNAs were differentially expressed (DE) between samples with low and high BMD, which were presented in Supplementary Table 1. Visualization of the expression levels of DE-genes revealed that a fraction of normal controls (n = 15) exhibited similar expression patterns with the cases with low BMD (Figure 2). Furthermore, a higher proportion of postmenopausal normal controls were observed in the 15 samples than other normal controls (proportion test, P < 0.05, 66.7% vs. 22.2%), suggesting that postmenopausal normal controls had a higher risk of osteoporosis.

    Table 1.  A total of 496 differentially expressed genes (DE-genes), including 120 up-regulated and 376 down-regulated genes in low BMD subjects (t-test, P-value < 0.05).
    ID gene_type gene_symbol status p-value
    ENSG00000001630.11 protein_coding CYP51A1 Up 0.011965453
    ENSG00000005075.11 protein_coding POLR2J Down 0.018005066
    ENSG00000006282.15 protein_coding SPATA20 Down 0.030683456
    ENSG00000007376.3 protein_coding RPUSD1 Down 0.046007757
    ENSG00000007944.10 protein_coding MYLIP Up 0.012046727
    ENSG00000008952.12 protein_coding SEC62 Up 0.04688829
    ENSG00000014164.6 protein_coding ZC3H3 Down 0.035615575
    ENSG00000020256.15 protein_coding ZFP64 Up 0.035853374
    ENSG00000025770.14 protein_coding NCAPH2 Down 0.035381718
    ENSG00000028277.16 protein_coding POU2F2 Down 0.025284331
    ENSG00000029153.10 protein_coding ARNTL2 Down 0.010597798
    ENSG00000032444.11 protein_coding PNPLA6 Down 0.015278704
    ENSG00000034533.7 protein_coding ASTE1 Up 0.033013833
    ENSG00000039123.11 protein_coding SKIV2L2 Down 0.048932372
    ENSG00000047056.10 protein_coding WDR37 Down 0.034858116
    ENSG00000050344.8 protein_coding NFE2L3 Down 0.033023849
    ENSG00000051523.6 protein_coding CYBA Down 0.007486695
    ENSG00000052723.7 protein_coding SIKE1 Up 0.045307581
    ENSG00000052749.9 protein_coding RRP12 Down 0.026396768
    ENSG00000054803.3 protein_coding CBLN4 Down 0.045582496
    ENSG00000054967.8 protein_coding RELT Down 0.02019792
    ENSG00000062598.13 protein_coding ELMO2 Down 0.025904437
    ENSG00000064932.11 protein_coding SBNO2 Down 0.033420705
    ENSG00000068024.12 protein_coding HDAC4 Down 0.048895859
    ENSG00000068120.10 protein_coding COASY Down 0.009649617
    ENSG00000069764.5 protein_coding PLA2G10 Down 0.003199821
    ENSG00000070404.5 protein_coding FSTL3 Down 0.038524776
    ENSG00000072778.15 protein_coding ACADVL Down 0.028472324
    ENSG00000074842.3 protein_coding C19orf10 Down 0.00105098
    ENSG00000077150.13 protein_coding NFKB2 Down 0.003922325
    ENSG00000078804.8 protein_coding TP53INP2 Down 0.005193897
    ENSG00000079393.16 protein_coding DUSP13 Down 0.024246462
    ENSG00000079432.3 protein_coding CIC Down 0.019836428
    ENSG00000081189.9 protein_coding MEF2C Up 0.047860138
    ENSG00000083312.13 protein_coding TNPO1 Up 0.047418317
    ENSG00000083844.6 protein_coding ZNF264 Up 0.010459153
    ENSG00000087157.14 protein_coding PGS1 Down 0.042327569
    ENSG00000087237.6 protein_coding CETP Down 0.032983061
    ENSG00000087263.12 protein_coding OGFOD1 Up 0.043418236
    ENSG00000087266.11 protein_coding SH3BP2 Down 0.030356676
    ENSG00000087903.8 protein_coding RFX2 Down 0.020836355
    ENSG00000088888.13 protein_coding MAVS Down 0.04764586
    ENSG00000089123.11 protein_coding TASP1 Up 0.028910399
    ENSG00000089248.6 protein_coding ERP29 Down 0.049446948
    ENSG00000089351.10 protein_coding GRAMD1A Down 0.025055632
    ENSG00000089639.6 protein_coding GMIP Down 0.014587545
    ENSG00000090924.10 protein_coding PLEKHG2 Down 0.028301873
    ENSG00000092929.7 protein_coding UNC13D Down 0.013293567
    ENSG00000096088.12 protein_coding PGC Down 0.042062111
    ENSG00000099139.9 protein_coding PCSK5 Up 0.027380288
    ENSG00000099203.2 protein_coding TMED1 Down 0.028235039
    ENSG00000099795.2 protein_coding NDUFB7 Down 0.040697141
    ENSG00000099800.3 protein_coding TIMM13 Down 0.007324296
    ENSG00000099821.9 protein_coding POLRMT Down 0.045124992
    ENSG00000099958.10 protein_coding DERL3 Down 0.04060876
    ENSG00000099991.12 protein_coding CABIN1 Down 0.034876772
    ENSG00000100028.7 protein_coding SNRPD3 Down 0.044318218
    ENSG00000100055.16 protein_coding CYTH4 Down 0.018939808
    ENSG00000100092.16 protein_coding SH3BP1 Down 0.04634582
    ENSG00000100242.11 protein_coding SUN2 Down 0.010030177
    ENSG00000100290.2 protein_coding BIK Down 0.007141866
    ENSG00000100403.10 protein_coding ZC3H7B Down 0.0410179
    ENSG00000100429.13 protein_coding HDAC10 Down 0.033877634
    ENSG00000100532.7 protein_coding CGRRF1 Up 0.013688977
    ENSG00000100802.10 protein_coding C14orf93 Down 0.033127459
    ENSG00000100906.6 protein_coding NFKBIA Down 0.005259074
    ENSG00000101220.13 protein_coding C20orf27 Down 0.034454114
    ENSG00000101473.12 protein_coding ACOT8 Down 0.016141884
    ENSG00000101624.6 protein_coding CEP76 Up 0.013187998
    ENSG00000101665.4 protein_coding SMAD7 Down 0.017676506
    ENSG00000101997.8 protein_coding CCDC22 Down 0.039248809
    ENSG00000102078.11 protein_coding SLC25A14 Down 0.039031376
    ENSG00000102786.10 protein_coding INTS6 Up 0.044913283
    ENSG00000102981.5 protein_coding PARD6A Down 0.041910914
    ENSG00000103024.3 protein_coding NME3 Down 0.027773943
    ENSG00000103047.3 protein_coding TANGO6 Down 0.049704948
    ENSG00000103227.14 protein_coding LMF1 Down 0.022119939
    ENSG00000103426.8 protein_coding CORO7-PAM16 Down 0.014976017
    ENSG00000103449.7 protein_coding SALL1 Down 0.041904944
    ENSG00000103653.12 protein_coding CSK Down 0.010534323
    ENSG00000104856.9 protein_coding RELB Down 0.015505238
    ENSG00000104973.10 protein_coding MED25 Down 0.041089276
    ENSG00000105063.14 protein_coding PPP6R1 Down 0.022586612
    ENSG00000105135.11 protein_coding ILVBL Down 0.034386207
    ENSG00000105497.3 protein_coding ZNF175 Up 0.013075356
    ENSG00000105643.5 protein_coding ARRDC2 Down 0.039560218
    ENSG00000105656.8 protein_coding ELL Down 0.035373441
    ENSG00000106268.11 protein_coding NUDT1 Down 0.03304039
    ENSG00000107362.9 protein_coding ABHD17B Up 0.02410498
    ENSG00000107443.11 protein_coding CCNJ Down 0.029817026
    ENSG00000107984.5 protein_coding DKK1 Down 0.049671011
    ENSG00000108179.9 protein_coding PPIF Down 0.044960461
    ENSG00000108518.7 protein_coding PFN1 Down 0.045797043
    ENSG00000108840.11 protein_coding HDAC5 Down 0.01595402
    ENSG00000108950.7 protein_coding FAM20A Down 0.012800605
    ENSG00000109103.7 protein_coding UNC119 Down 0.03809287
    ENSG00000109736.10 protein_coding MFSD10 Down 0.043758832
    ENSG00000109917.6 protein_coding ZNF259 Down 0.042419417
    ENSG00000109944.6 protein_coding C11orf63 Down 0.036421384
    ENSG00000110063.4 protein_coding DCPS Down 0.044521345
    ENSG00000110080.14 protein_coding ST3GAL4 Down 0.009630116
    ENSG00000110090.8 protein_coding CPT1A Down 0.040715917
    ENSG00000110619.12 protein_coding CARS Down 0.040080075
    ENSG00000110931.14 protein_coding CAMKK2 Down 0.041840274
    ENSG00000112149.5 protein_coding CD83 Down 0.021210986
    ENSG00000112276.9 protein_coding BVES Up 0.013370759
    ENSG00000112312.5 protein_coding GMNN Down 0.047237299
    ENSG00000114650.14 protein_coding SCAP Down 0.032838904
    ENSG00000115073.6 protein_coding ACTR1B Down 0.003540043
    ENSG00000116017.6 protein_coding ARID3A Down 0.038762022
    ENSG00000116670.10 protein_coding MAD2L2 Down 0.043537769
    ENSG00000116852.10 protein_coding KIF21B Down 0.049989022
    ENSG00000116871.11 protein_coding MAP7D1 Down 0.03998345
    ENSG00000116985.6 protein_coding BMP8B Down 0.028382633
    ENSG00000117600.8 protein_coding LPPR4 Down 0.046876942
    ENSG00000118432.11 protein_coding CNR1 Down 0.017637126
    ENSG00000118600.7 protein_coding TMEM5 Down 0.014944131
    ENSG00000119013.4 protein_coding NDUFB3 Down 0.040614225
    ENSG00000119121.17 protein_coding TRPM6 Down 0.048825853
    ENSG00000119669.3 protein_coding IRF2BPL Down 0.005359191
    ENSG00000120217.9 protein_coding CD274 Down 0.043686299
    ENSG00000120699.8 protein_coding EXOSC8 Up 0.032500661
    ENSG00000122203.10 protein_coding KIAA1191 Up 0.048031425
    ENSG00000122490.14 protein_coding PQLC1 Down 0.015129367
    ENSG00000122824.6 protein_coding NUDT10 Down 0.033200739
    ENSG00000123094.11 protein_coding RASSF8 Down 0.029465179
    ENSG00000123358.15 protein_coding NR4A1 Down 0.01086372
    ENSG00000123576.5 protein_coding ESX1 Down 0.030485823
    ENSG00000123689.5 protein_coding G0S2 Down 0.009066817
    ENSG00000123989.9 protein_coding CHPF Down 0.040386914
    ENSG00000125657.3 protein_coding TNFSF9 Down 0.049043875
    ENSG00000125755.14 protein_coding SYMPK Down 0.049805392
    ENSG00000125817.7 protein_coding CENPB Down 0.042137733
    ENSG00000125945.10 protein_coding ZNF436 Up 0.047416168
    ENSG00000125968.7 protein_coding ID1 Down 0.04512393
    ENSG00000127054.14 protein_coding CPSF3L Down 0.04296422
    ENSG00000127124.9 protein_coding HIVEP3 Down 0.000294073
    ENSG00000127483.13 protein_coding HP1BP3 Up 0.047813073
    ENSG00000127540.7 protein_coding UQCR11 Down 0.030739395
    ENSG00000127588.4 protein_coding GNG13 Down 0.039828869
    ENSG00000128059.4 protein_coding PPAT Up 0.006604019
    ENSG00000128228.4 protein_coding SDF2L1 Down 0.003273018
    ENSG00000128271.15 protein_coding ADORA2A Down 0.041530393
    ENSG00000128309.12 protein_coding MPST Down 0.037538756
    ENSG00000128694.7 protein_coding OSGEPL1 Up 0.036634744
    ENSG00000129562.6 protein_coding DAD1 Up 0.039222956
    ENSG00000129667.8 protein_coding RHBDF2 Down 0.030796176
    ENSG00000129744.2 protein_coding ART1 Down 0.001540155
    ENSG00000129968.11 protein_coding ABHD17A Down 0.043478487
    ENSG00000129993.10 protein_coding CBFA2T3 Down 0.031277467
    ENSG00000130522.4 protein_coding JUND Down 0.022542768
    ENSG00000130766.4 protein_coding SESN2 Down 0.013640369
    ENSG00000130935.5 protein_coding NOL11 Up 0.044393023
    ENSG00000131142.9 protein_coding CCL25 Down 0.038185878
    ENSG00000131171.8 protein_coding SH3BGRL Up 0.022819598
    ENSG00000131653.8 protein_coding TRAF7 Down 0.036084131
    ENSG00000131849.10 protein_coding ZNF132 Down 0.028132404
    ENSG00000132275.6 protein_coding RRP8 Down 0.010570164
    ENSG00000132507.13 protein_coding EIF5A Down 0.024112694
    ENSG00000133606.6 protein_coding MKRN1 Up 0.041317442
    ENSG00000133805.11 protein_coding AMPD3 Down 0.046431783
    ENSG00000134107.4 protein_coding BHLHE40 Down 0.038744934
    ENSG00000135047.10 protein_coding CTSL Down 0.0255056
    ENSG00000135390.13 protein_coding ATP5G2 Down 0.02929579
    ENSG00000136048.9 protein_coding DRAM1 Down 0.02091681
    ENSG00000136122.11 protein_coding BORA Up 0.036657962
    ENSG00000136738.10 protein_coding STAM Up 0.0389495
    ENSG00000136840.14 protein_coding ST6GALNAC4 Down 0.016088565
    ENSG00000136877.10 protein_coding FPGS Down 0.023271049
    ENSG00000137124.6 protein_coding ALDH1B1 Up 0.03438067
    ENSG00000137193.9 protein_coding PIM1 Down 0.009876567
    ENSG00000137218.6 protein_coding FRS3 Down 0.042976435
    ENSG00000137309.15 protein_coding HMGA1 Down 0.021493168
    ENSG00000137312.10 protein_coding FLOT1 Down 0.039597455
    ENSG00000137409.14 protein_coding MTCH1 Down 0.040766375
    ENSG00000138382.9 protein_coding METTL5 Up 0.038252044
    ENSG00000139053.2 protein_coding PDE6H Down 0.0207692
    ENSG00000139610.1 protein_coding CELA1 Up 0.038317749
    ENSG00000139668.7 protein_coding WDFY2 Down 0.019753557
    ENSG00000140044.8 protein_coding JDP2 Down 0.036877537
    ENSG00000140553.12 protein_coding UNC45A Down 0.042323486
    ENSG00000140612.9 protein_coding SEC11A Down 0.017070625
    ENSG00000140848.12 protein_coding CPNE2 Down 0.035194471
    ENSG00000140941.8 protein_coding MAP1LC3B Up 0.010046471
    ENSG00000141012.8 protein_coding GALNS Down 0.019933327
    ENSG00000141034.5 protein_coding GID4 Up 0.036023887
    ENSG00000141198.9 protein_coding TOM1L1 Down 0.043796522
    ENSG00000141252.15 protein_coding VPS53 Down 0.020821593
    ENSG00000141505.7 protein_coding ASGR1 Down 0.049034425
    ENSG00000141646.9 protein_coding SMAD4 Up 0.041545876
    ENSG00000141867.13 protein_coding BRD4 Down 0.040247622
    ENSG00000142408.2 protein_coding CACNG8 Down 0.035337949
    ENSG00000142538.1 protein_coding PTH2 Down 0.028959056
    ENSG00000143549.15 protein_coding TPM3 Down 0.005503969
    ENSG00000144120.8 protein_coding TMEM177 Up 0.022405325
    ENSG00000144182.12 protein_coding LIPT1 Up 0.008805655
    ENSG00000144381.12 protein_coding HSPD1 Down 0.048940793
    ENSG00000144468.12 protein_coding RHBDD1 Up 0.048354944
    ENSG00000144655.10 protein_coding CSRNP1 Down 0.03240296
    ENSG00000144791.5 protein_coding LIMD1 Down 0.04142983
    ENSG00000144802.7 protein_coding NFKBIZ Down 0.015266617
    ENSG00000145901.10 protein_coding TNIP1 Down 0.031453499
    ENSG00000146047.4 protein_coding HIST1H2BA Down 0.005162611
    ENSG00000146232.10 protein_coding NFKBIE Down 0.020469032
    ENSG00000146828.13 protein_coding SLC12A9 Down 0.042442305
    ENSG00000146833.11 protein_coding TRIM4 Up 0.001031314
    ENSG00000146856.10 protein_coding AGBL3 Down 0.044096515
    ENSG00000147419.12 protein_coding CCDC25 Up 0.027233589
    ENSG00000148187.13 protein_coding MRRF Up 0.01456252
    ENSG00000148335.10 protein_coding NTMT1 Down 0.033476509
    ENSG00000148602.5 protein_coding LRIT1 Down 0.038378276
    ENSG00000148737.11 protein_coding TCF7L2 Down 0.03027628
    ENSG00000148835.9 protein_coding TAF5 Up 0.017608599
    ENSG00000149115.9 protein_coding TNKS1BP1 Down 0.031874191
    ENSG00000149503.8 protein_coding INCENP Down 0.039618744
    ENSG00000149792.4 protein_coding MRPL49 Down 0.014313227
    ENSG00000149823.3 protein_coding VPS51 Down 0.034228234
    ENSG00000149932.12 protein_coding TMEM219 Down 0.042059762
    ENSG00000149968.7 protein_coding MMP3 Down 0.007039867
    ENSG00000151093.3 protein_coding OXSM Up 0.032113374
    ENSG00000151490.9 protein_coding PTPRO Down 0.0169847
    ENSG00000151631.7 pseudogene AKR1C6P Down 0.004333285
    ENSG00000151651.11 protein_coding ADAM8 Down 0.034809828
    ENSG00000151726.9 protein_coding ACSL1 Down 0.040278414
    ENSG00000152207.3 protein_coding CYSLTR2 Up 0.031075114
    ENSG00000152382.5 protein_coding TADA1 Down 0.034551617
    ENSG00000153140.4 protein_coding CETN3 Up 0.028301839
    ENSG00000153207.10 protein_coding AHCTF1 Down 0.014567315
    ENSG00000153266.8 protein_coding FEZF2 Down 0.036977261
    ENSG00000153487.11 protein_coding ING1 Down 0.015188879
    ENSG00000154370.9 protein_coding TRIM11 Down 0.018777551
    ENSG00000154447.10 protein_coding SH3RF1 Down 0.009674391
    ENSG00000154639.14 protein_coding CXADR Down 0.00685789
    ENSG00000154781.11 protein_coding CCDC174 Down 0.049642675
    ENSG00000154839.5 protein_coding SKA1 Down 0.046702852
    ENSG00000154978.8 protein_coding VOPP1 Down 0.030307149
    ENSG00000155438.7 protein_coding MKI67IP Down 0.03414805
    ENSG00000155918.3 protein_coding RAET1L Down 0.047085308
    ENSG00000156172.5 protein_coding C8orf37 Up 0.015241022
    ENSG00000156508.13 protein_coding EEF1A1 Down 0.029958858
    ENSG00000157227.8 protein_coding MMP14 Down 0.018623306
    ENSG00000157326.14 protein_coding DHRS4 Down 0.047099568
    ENSG00000157456.3 protein_coding CCNB2 Down 0.012184904
    ENSG00000157637.8 protein_coding SLC38A10 Down 0.026642558
    ENSG00000158014.10 protein_coding SLC30A2 Down 0.026315721
    ENSG00000158483.11 protein_coding FAM86C1 Down 0.00975309
    ENSG00000158497.2 protein_coding HMHB1 Down 0.040327646
    ENSG00000158555.10 protein_coding GDPD5 Down 0.034837228
    ENSG00000159069.9 protein_coding FBXW5 Down 0.01907369
    ENSG00000159885.9 protein_coding ZNF222 Up 0.01199585
    ENSG00000160111.8 protein_coding CPAMD8 Down 0.049299496
    ENSG00000160179.14 protein_coding ABCG1 Up 0.022299138
    ENSG00000160180.14 protein_coding TFF3 Down 0.042914253
    ENSG00000160214.8 protein_coding RRP1 Down 0.049738569
    ENSG00000160796.12 protein_coding NBEAL2 Down 0.035908182
    ENSG00000160883.6 protein_coding HK3 Down 0.044846812
    ENSG00000160888.6 protein_coding IER2 Down 0.027407148
    ENSG00000161640.11 protein_coding SIGLEC11 Down 0.042121328
    ENSG00000161791.9 protein_coding FMNL3 Down 0.011008036
    ENSG00000162086.10 protein_coding ZNF75A Up 0.019226079
    ENSG00000162302.8 protein_coding RPS6KA4 Down 0.048212217
    ENSG00000162377.4 protein_coding SELRC1 Up 0.047979072
    ENSG00000162413.12 protein_coding KLHL21 Down 0.048454407
    ENSG00000162522.6 protein_coding KIAA1522 Down 0.016306649
    ENSG00000162783.8 protein_coding IER5 Down 0.021919966
    ENSG00000162913.9 protein_coding C1orf145 Down 0.018777551
    ENSG00000162927.9 protein_coding PUS10 Up 0.04033551
    ENSG00000162931.7 protein_coding TRIM17 Down 0.023650474
    ENSG00000163162.4 protein_coding RNF149 Down 0.025666014
    ENSG00000163239.8 protein_coding TDRD10 Down 0.043592404
    ENSG00000163389.6 protein_coding POGLUT1 Up 0.022081661
    ENSG00000163947.7 protein_coding ARHGEF3 Up 0.010264753
    ENSG00000164105.3 protein_coding SAP30 Up 0.046694723
    ENSG00000164342.8 protein_coding TLR3 Up 0.003277358
    ENSG00000164402.9 protein_coding 8-Sep Down 0.024177765
    ENSG00000164411.6 protein_coding GJB7 Down 0.026747052
    ENSG00000165029.11 protein_coding ABCA1 Up 0.010492577
    ENSG00000165188.9 protein_coding RNF183 Down 0.010089847
    ENSG00000165406.11 protein_coding 8-Mar Up 0.047180108
    ENSG00000165512.4 protein_coding ZNF22 Up 0.033208827
    ENSG00000165782.6 protein_coding TMEM55B Down 0.022652362
    ENSG00000165886.4 protein_coding UBTD1 Down 0.014531961
    ENSG00000165915.9 protein_coding SLC39A13 Down 0.024410393
    ENSG00000166265.7 protein_coding CYYR1 Down 0.037657919
    ENSG00000166363.4 protein_coding OR10A5 Up 0.018183215
    ENSG00000166446.10 protein_coding CDYL2 Down 0.011853205
    ENSG00000166451.9 protein_coding CENPN Up 0.040214671
    ENSG00000166452.7 protein_coding AKIP1 Up 0.048924644
    ENSG00000166938.8 protein_coding DIS3L Up 0.036638539
    ENSG00000166974.8 protein_coding MAPRE2 Up 0.048488455
    ENSG00000167110.12 protein_coding GOLGA2 Down 0.023994018
    ENSG00000167186.6 protein_coding COQ7 Up 0.000296612
    ENSG00000167333.8 protein_coding TRIM68 Up 0.039246931
    ENSG00000167394.8 protein_coding ZNF668 Down 0.015855291
    ENSG00000167414.4 protein_coding GNG8 Down 0.0489265
    ENSG00000167555.9 protein_coding ZNF528 Up 0.002098542
    ENSG00000167604.9 protein_coding NFKBID Down 0.04908962
    ENSG00000167657.7 protein_coding DAPK3 Down 0.045503987
    ENSG00000168066.16 protein_coding SF1 Down 0.047265519
    ENSG00000168310.6 protein_coding IRF2 Up 0.044253405
    ENSG00000168594.11 protein_coding ADAM29 Down 0.003444856
    ENSG00000168803.10 protein_coding ADAL Up 0.021101681
    ENSG00000168818.5 protein_coding STX18 Up 0.022001045
    ENSG00000168967.10 pseudogene PMCHL1 Down 0.008435479
    ENSG00000169105.6 protein_coding CHST14 Down 0.024636798
    ENSG00000169379.11 protein_coding ARL13B Up 0.010209654
    ENSG00000169393.4 protein_coding ELSPBP1 Down 0.014495593
    ENSG00000169429.6 protein_coding IL8 Down 0.028420528
    ENSG00000170265.7 protein_coding ZNF282 Down 0.042671468
    ENSG00000170369.3 protein_coding CST2 Down 0.041604924
    ENSG00000170445.8 protein_coding HARS Down 0.029725289
    ENSG00000170779.10 protein_coding CDCA4 Down 0.028547735
    ENSG00000170837.2 protein_coding GPR27 Down 0.035077639
    ENSG00000170891.6 protein_coding CYTL1 Down 0.032834474
    ENSG00000171130.13 protein_coding ATP6V0E2 Down 0.039784296
    ENSG00000171223.4 protein_coding JUNB Down 0.038177458
    ENSG00000171357.5 protein_coding LURAP1 Down 0.033100787
    ENSG00000171467.11 protein_coding ZNF318 Down 0.044327394
    ENSG00000171530.9 protein_coding TBCA Down 0.009876223
    ENSG00000171570.6 protein_coding RAB4B-EGLN2 Down 0.010162577
    ENSG00000171657.5 protein_coding GPR82 Up 0.002688119
    ENSG00000171766.11 protein_coding GATM Up 0.035375608
    ENSG00000171827.6 protein_coding ZNF570 Up 0.016772935
    ENSG00000171861.6 protein_coding RNMTL1 Down 0.041110394
    ENSG00000171942.3 protein_coding OR10H2 Down 0.01466908
    ENSG00000172208.3 protein_coding OR4X2 Up 0.038143037
    ENSG00000172322.9 protein_coding CLEC12A Up 0.008850366
    ENSG00000172375.8 protein_coding C2CD2L Down 0.012079432
    ENSG00000172428.6 protein_coding MYEOV2 Down 0.025783713
    ENSG00000173020.6 protein_coding ADRBK1 Down 0.043055525
    ENSG00000173153.9 protein_coding ESRRA Down 0.008541886
    ENSG00000173653.3 protein_coding RCE1 Down 0.030649495
    ENSG00000173715.11 protein_coding C11orf80 Down 0.030649495
    ENSG00000173812.6 protein_coding EIF1 Down 0.0170157
    ENSG00000173846.8 protein_coding PLK3 Down 0.015509349
    ENSG00000174600.9 protein_coding CMKLR1 Up 0.044214317
    ENSG00000174886.8 protein_coding NDUFA11 Down 0.022192837
    ENSG00000175130.6 protein_coding MARCKSL1 Down 0.038863502
    ENSG00000175728.2 protein_coding C11orf44 Up 0.045296195
    ENSG00000175874.5 protein_coding CREG2 Down 0.01537001
    ENSG00000176485.6 protein_coding PLA2G16 Down 0.024862828
    ENSG00000176563.5 protein_coding CNTD1 Down 0.016145384
    ENSG00000176619.6 protein_coding LMNB2 Down 0.030268302
    ENSG00000176624.9 protein_coding MEX3C Up 0.029845716
    ENSG00000176714.9 protein_coding CCDC121 Down 0.022706601
    ENSG00000176749.4 protein_coding CDK5R1 Down 0.04550598
    ENSG00000176845.8 protein_coding METRNL Down 0.010677297
    ENSG00000176894.5 protein_coding PXMP2 Down 0.013611729
    ENSG00000176974.13 protein_coding SHMT1 Up 0.049830843
    ENSG00000177051.5 protein_coding FBXO46 Down 0.047542187
    ENSG00000177169.5 protein_coding ULK1 Down 0.016389135
    ENSG00000177666.11 protein_coding PNPLA2 Down 0.043015592
    ENSG00000178229.7 protein_coding ZNF543 Up 0.010459153
    ENSG00000178951.4 protein_coding ZBTB7A Down 0.019056001
    ENSG00000178980.10 protein_coding SEPW1 Down 0.016556027
    ENSG00000179029.10 protein_coding TMEM107 Down 0.005945219
    ENSG00000180008.8 protein_coding SOCS4 Up 0.039599073
    ENSG00000180332.5 protein_coding KCTD4 Down 0.022125509
    ENSG00000180346.2 protein_coding TIGD2 Up 0.037693259
    ENSG00000180539.4 protein_coding C9orf139 Down 0.01622825
    ENSG00000180725.4 pseudogene AC015871.1 Down 0.042511409
    ENSG00000181378.9 protein_coding CCDC108 Down 0.038235948
    ENSG00000181791.1 pseudogene AC009041.1 Down 0.0167024
    ENSG00000182118.5 protein_coding FAM89A Down 0.002426708
    ENSG00000182333.10 protein_coding LIPF Down 0.018324226
    ENSG00000182368.4 protein_coding FAM27A Down 0.03035551
    ENSG00000182541.13 protein_coding LIMK2 Down 0.041747303
    ENSG00000182782.7 protein_coding HCAR2 Down 0.046508935
    ENSG00000182841.8 pseudogene RRP7B Down 0.04819365
    ENSG00000183019.3 protein_coding C19orf59 Down 0.031283921
    ENSG00000183020.9 protein_coding AP2A2 Down 0.000692239
    ENSG00000183087.10 protein_coding GAS6 Down 0.026705611
    ENSG00000183648.5 protein_coding NDUFB1 Down 0.00863385
    ENSG00000183696.9 protein_coding UPP1 Down 0.021571741
    ENSG00000183709.7 protein_coding IFNL2 Up 0.001410201
    ENSG00000183723.8 protein_coding CMTM4 Down 0.047938821
    ENSG00000183779.5 protein_coding ZNF703 Down 0.033061055
    ENSG00000184110.10 protein_coding EIF3C Down 0.008205594
    ENSG00000184517.7 protein_coding ZFP1 Up 0.045781579
    ENSG00000184619.3 protein_coding KRBA2 Down 0.025225822
    ENSG00000184897.4 protein_coding H1FX Down 0.014748614
    ENSG00000184922.9 protein_coding FMNL1 Down 0.01035533
    ENSG00000184990.8 protein_coding SIVA1 Down 0.039061925
    ENSG00000185641.5 pseudogene CTD-2287O16.1 Down 0.043078899
    ENSG00000185787.10 protein_coding MORF4L1 Down 0.047667026
    ENSG00000185972.4 protein_coding CCIN Down 0.044995407
    ENSG00000186106.7 protein_coding ANKRD46 Up 0.042420836
    ENSG00000186111.4 protein_coding PIP5K1C Down 0.042436011
    ENSG00000186281.8 protein_coding GPAT2 Down 0.049178673
    ENSG00000186594.8 lincRNA MIR22HG Down 0.017511035
    ENSG00000186635.10 protein_coding ARAP1 Down 0.015438232
    ENSG00000186795.1 protein_coding KCNK18 Down 0.020856507
    ENSG00000187630.10 protein_coding DHRS4L2 Down 0.047099568
    ENSG00000187688.10 protein_coding TRPV2 Down 0.003137583
    ENSG00000188379.5 protein_coding IFNA2 Down 0.004976237
    ENSG00000189042.9 protein_coding ZNF567 Up 0.015774858
    ENSG00000189114.6 protein_coding BLOC1S3 Down 0.028793638
    ENSG00000189332.4 protein_coding RP11-113D6.10 Down 0.029127001
    ENSG00000196152.6 protein_coding ZNF79 Up 0.002879344
    ENSG00000196331.5 protein_coding HIST1H2BO Down 0.025404112
    ENSG00000196358.6 protein_coding NTNG2 Down 0.042750195
    ENSG00000196371.2 protein_coding FUT4 Down 0.011093343
    ENSG00000196415.5 protein_coding PRTN3 Down 0.035149627
    ENSG00000196417.8 protein_coding ZNF765 Up 0.005727824
    ENSG00000196652.7 protein_coding ZKSCAN5 Up 0.016188909
    ENSG00000196668.3 processed_transcript LINC00173 Down 0.003213639
    ENSG00000196693.10 protein_coding ZNF33B Up 0.010400288
    ENSG00000196843.11 protein_coding ARID5A Down 0.023473729
    ENSG00000196917.4 protein_coding HCAR1 Down 0.023288331
    ENSG00000196981.2 protein_coding WDR5B Up 0.010645113
    ENSG00000197016.7 protein_coding ZNF470 Up 0.021502777
    ENSG00000197312.7 protein_coding DDI2 Up 0.049559262
    ENSG00000197321.10 protein_coding SVIL Down 0.029323831
    ENSG00000197919.3 protein_coding IFNA1 Down 0.011997429
    ENSG00000198055.6 protein_coding GRK6 Down 0.018617852
    ENSG00000198081.6 protein_coding ZBTB14 Up 0.03828373
    ENSG00000198324.10 protein_coding FAM109A Down 0.015387399
    ENSG00000198445.3 protein_coding CCT8L2 Down 0.008263414
    ENSG00000198464.9 protein_coding ZNF480 Up 0.04695864
    ENSG00000198471.1 protein_coding RTP2 Up 0.026862674
    ENSG00000198551.5 protein_coding ZNF627 Up 0.007529781
    ENSG00000198754.5 protein_coding OXCT2 Down 0.028382633
    ENSG00000198830.6 protein_coding HMGN2 Down 0.034053765
    ENSG00000198881.8 protein_coding ASB12 Down 0.042963309
    ENSG00000198900.5 protein_coding TOP1 Up 0.036803174
    ENSG00000198914.2 protein_coding POU3F3 Down 0.040875916
    ENSG00000198945.3 protein_coding L3MBTL3 Up 0.040353636
    ENSG00000198954.4 protein_coding KIAA1279 Up 0.014789739
    ENSG00000198967.3 protein_coding OR10Z1 Down 0.02892736
    ENSG00000203326.5 protein_coding ZNF525 Up 0.019737784
    ENSG00000203684.5 protein_coding IBA57-AS1 Down 0.018777551
    ENSG00000204807.1 protein_coding FAM27E2 Down 0.03035551
    ENSG00000204983.8 protein_coding PRSS1 Down 0.022866585
    ENSG00000205022.5 protein_coding PABPN1L Down 0.031277467
    ENSG00000205409.3 protein_coding OR52E6 Up 0.039983429
    ENSG00000212916.3 protein_coding MAP10 Down 0.04462334
    ENSG00000213799.6 protein_coding ZNF845 Up 0.019737784
    ENSG00000213828.1 protein_coding AC017028.1 Down 0.026268434
    ENSG00000213888.2 protein_coding AC005003.1 Down 0.030588957
    ENSG00000214253.4 protein_coding FIS1 Down 0.036530674
    ENSG00000215612.5 protein_coding HMX1 Down 0.030423062
    ENSG00000215695.1 protein_coding RSC1A1 Up 0.049559262
    ENSG00000217930.3 protein_coding PAM16 Down 0.014976017
    ENSG00000220848.4 pseudogene RPS18P9 Down 0.024449402
    ENSG00000221837.4 protein_coding KRTAP10-9 Down 0.028181439
    ENSG00000221840.2 protein_coding OR4A5 Down 0.013014151
    ENSG00000221949.2 protein_coding C12orf61 Down 0.031578862
    ENSG00000221954.2 protein_coding OR4C12 Down 0.010582213
    ENSG00000224474.2 protein_coding AL355490.1 Down 0.026396768
    ENSG00000228300.9 protein_coding C19orf24 Down 0.046090199
    ENSG00000228336.1 pseudogene OR9H1P Down 0.031871552
    ENSG00000230257.1 antisense NFE4 Down 0.021397338
    ENSG00000232973.7 antisense CYP1B1-AS1 Up 0.031113526
    ENSG00000233016.2 antisense SNHG7 Down 0.048765474
    ENSG00000236773.1 pseudogene RP11-365O16.1 Up 0.015477893
    ENSG00000237541.3 protein_coding HLA-DQA2 Up 0.020285026
    ENSG00000238184.1 processed_transcript AC129929.5 Down 0.040824827
    ENSG00000240720.3 protein_coding LRRD1 Up 0.006393288
    ENSG00000240970.1 pseudogene RPL23AP64 Down 0.000986343
    ENSG00000241360.1 protein_coding PDXP Down 0.04634582
    ENSG00000244623.1 protein_coding OR2AE1 Up 0.001031314
    ENSG00000245680.5 protein_coding ZNF585B Up 0.029339876
    ENSG00000247077.2 protein_coding PGAM5 Down 0.03099048
    ENSG00000251357.4 protein_coding AP000350.10 Down 0.044557673
    ENSG00000254521.2 protein_coding SIGLEC12 Up 0.008757157
    ENSG00000255769.3 pseudogene RP11-152F13.3 Down 0.031901455
    ENSG00000255804.1 protein_coding OR6J1 Up 0.039222956
    ENSG00000256235.1 protein_coding SMIM3 Down 0.036368455
    ENSG00000256294.3 protein_coding ZNF225 Up 0.046416681
    ENSG00000256453.1 protein_coding DND1 Down 0.023504581
    ENSG00000256632.3 protein_coding RP13-672B3.2 Down 0.013611729
    ENSG00000256683.2 protein_coding ZNF350 Up 0.021879236
    ENSG00000257341.1 protein_coding CRIP1 Down 0.049720545
    ENSG00000257702.3 antisense LBX2-AS1 Down 0.035045107
    ENSG00000258405.5 protein_coding ZNF578 Down 0.021484975
    ENSG00000258472.4 protein_coding RP11-192H23.4 Down 0.047286987
    ENSG00000259571.1 protein_coding BLID Down 0.02508268
    ENSG00000261713.2 processed_transcript SSTR5-AS1 Down 0.0167024
    ENSG00000263002.3 protein_coding ZNF234 Up 0.035012293
    ENSG00000263620.1 protein_coding RP11-599B13.6 Down 0.04868739
    ENSG00000263809.1 protein_coding RP11-849F2.7 Down 0.025225822
    ENSG00000264735.1 lincRNA RP11-498C9.17 Down 0.038254904
    ENSG00000267022.1 protein_coding ZNF223 Up 0.005281393
    ENSG00000267059.2 protein_coding UQCR11 Down 0.01521352
    ENSG00000267360.2 protein_coding CTC-454I21.3 Up 0.036745114
    ENSG00000267545.1 protein_coding AC005779.2 Down 0.028793638
    ENSG00000267699.2 protein_coding RP11-729L2.2 Up 0.041545876
    ENSG00000268614.1 protein_coding CTD-2207O23.10 Down 0.021055407
    ENSG00000268797.1 protein_coding CTC-490E21.12 Down 0.003100426
    ENSG00000269220.1 lincRNA LINC00528 Down 0.026695365
    ENSG00000269636.1 protein_coding AC010441.1 Down 0.036368455
    ENSG00000269858.1 protein_coding EGLN2 Down 0.003100426
    ENSG00000271810.1 protein_coding RP11-426L16.10 Down 0.02868524
    ENSG00000271959.1 antisense CTD-3064M3.7 Down 0.036335334
    ENSG00000272906.1 lincRNA RP11-533E19.7 Down 0.011437491
    ENSG00000273006.1 lincRNA RP11-314C9.2 Up 0.031113526
     | Show Table
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    Figure 2.  The gene expression levels of the differentially expressed genes across samples. The expression values are scaled by genes. The samples with high and low BMD are represented by the bands with pink and green, respectively. The premenopausal and postmenopausal samples are represented by the bands with green and red, respectively.
    DownLoad: Full-Size Img PowerPoint

    3.3.   GO and KEGG enrichment analysis

    Next, the differentially expressed mRNAs were subjected to GO and KEGG analyses (Figure 2). GO analysis indicated that the upregulated genes were mainly involved in regulating nucleobase metabolic process, regulation of organelle assembly, regulation of plasma lipoprotein particle levels, response to interferon-gamma, snRNA metabolic process, ncRNA transcription and ncRNA processing (Figure 3A). Furthermore, the downregulated genes were mainly enriched in categories associated with regulation of sequence-specific DNA binding transcription factor activity, neutral lipid metabolic process, cytokinesis, nucleoside triphosphate metabolic process, regulation of cell-cell adhesion, nucleoside monophosphate metabolic process, and glycoprotein metabolic process (Figure 3B). The above pathways may therefore participate in regulating the bone miner density.

    Figure 3.  GO and KEGG pathway enrichment analysis for the differentially expressed mRNAs. The enriched GO terms and KEGG pathways for up- and down-regulated genes are presented in (A and B), and (C and D), respectively. The height of each bar represents the -log10-transformed P-value. The GO terms and pathway names are listed below the bars.
    DownLoad: Full-Size Img PowerPoint

    KEGG pathway analysis revealed that upregulated genes were primarily enriched in pathways associated with the glycine, serine and threonine metabolism, ABC transporters, arginine and proline metabolism, Jak-STAT signaling pathway, RNA degradation, and herpes simplex infection (Figure 3C). In accordance with the enriched GO terms, the up-regulated genes were significantly enriched in inflammation-related pathways. Downregulated genes were mainly associated with adipocytokine signaling pathway, metabolic pathways, oxidative phosphorylation, cytosolic DNA-sensing pathway, and glycerophospholipid metabolism (Figure 3D). The GO and KEGG enrichment analysis of the down-regulated genes suggested that metabolic pathways above may function by maintaining the bone miner density.

    3.4.   PPI network construction for DE-mRNAs

    To investigate the interactions between the proteins encoded by the DEGs, we mapped the DEGs to protein-protein interaction network (Figure 4). The PPI network for the upregulated genes contained 38 nodes and 33 edges, and the hub nodes with the higher connectivity degree were SHMT1, MYLIP, MKRN1 and EXOSC8 (connectivity degree = 4, Figure 4A). The PPI network for the downregulated genes contained 198 nodes and 348 edges, and the hub nodes with the higher connectivity degree were IL8 (connectivity degree = 28), NFKBIA (connectivity degree = 13), HADC5 (connectivity degree = 12) and HDAC4 (connectivity degree = 12) (Figure 4B).

    Figure 4.  The PPI network of differentially expressed genes. The PPI network for up- and down-regulated genes are presented in (A) and (B), and colored by red and green, respectively.
    DownLoad: Full-Size Img PowerPoint

    Furthermore, we also performed a module analysis of the network using MCODE plugin. With degree cut-off ≥3, four up-regulated proteins, including MEX3C, MKRN1, TRIM4 and MYLIP, were identified as key proteins/genes of a module in the up-regulated PPI network (Figure 5A). The high connectivity and components of a module suggested that MKRN1 and MYLIP may play important roles in regulating bone miner density. Similarly, the down-regulated proteins, including JDP2, HADC4, HDAC5, CDYL2, ACADVL, ACSL1 and BRD4, were identified as key proteins/genes in the down-regulated PPI network (Figure 5B). Moreover, IL8, CCL25, CNR1, HCAR2, HCAR1, GNG13 and GNG8 were also identified as a module in the down-regulated PPI network (Figure 5C). All these proteins except IL8 participated in G-alpha (ⅰ) signaling events, indicating that this pathway may be important for bone mineral homeostasis.

    Figure 5.  The PPI subnetwork for up- and down-regulated genes by module analysis. The up- and down-regulated genes are colored by red and green.
    DownLoad: Full-Size Img PowerPoint

    3.5.   Co-expression network analysis of differently expressed mRNAs and lncRNAs in osteoporosis

    To predict the potential functional roles of the differentially expressed lncRNAs, the Pearson correlation coefficient for lncRNA-DE-mRNA pairs was first calculated according to their expression value. The co-expressed mRNA-lncRNA pairs with an absolute value of their Pearson correlation coefficient of ≥0.8 were selected. As presented in Figure 6A, the network included 7 differentially expressed lncRNAs and 70 DE-mRNAs.

    Figure 6.  Prediction of biological function of lncRNAs by lncRNA and mRNA co-expression analysis. A. Co-expression network of lncRNAs and mRNAs. The diamonds, red circles and green circles represent the lncRNAs, up-regulated mRNAs, and down-regulated mRNAs. B. The predicted biological functions for lncRNAs. The height of each bar represents the -log10-transformed P-value. Each lncRNA is represented by one specific color. The GO terms are listed below the bars.
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    To characterize the biological function of the 7 differentially expressed lncRNAs, we performed gene set enrichment analysis on the highly correlated DE-mRNAs. Based to the GO terms by enrichment analysis, we successfully annotated 6 of the 7 DE-lncRNAs (P < 0.05, Figure 6B). Specifically, AC009041.1 and SSTR5-AS1 were primarily enriched in DNA conformation change and DNA recombination. Particularly, RP11-498C9.17 was enriched in pathways involved in epigenetic regulation, such as macromolecule deacylation, epigenetic regulation of gene expression, chromatin remodeling, chromatin assembly or disassembly and protein-DNA complex subunit organization. Other lincRNAs may participate in pathways such as cell-cell signaling by Wnt, response to extracellular stimulus, autophagy, and cell cycle checkpoint.

    4.   Discussion

    Osteoclastogenesis and bone resorption play a fundamental role in osteoporosis pathogenesis. Better understanding the regulation of osteoclastogenesis is very important for the discovery of therapeutic targets for the treatment of osteoporosis.

    In this study, we aimed to analyze the key mRNAs and lncRNAs in osteoporosis. To explore the molecular mechanism of regulating bone miner density, we collected gene expression datasets of peripheral blood monocytes (PBMs) from low or high BMD subjects. To identify the probes on the array corresponding to long non-coding RNA (lncRNA) sequences, we re-annotated all probes using GENCODE gene annotation, and identified a total of 830 long non-coding RNAs.

    To investigate the biological differences between samples with low and high BMD, we performed differential expression analysis of the gene expression data from the discovery datasets, and identified a total of 496 differentially expressed genes (DEGs), including 120 up-regulated and 376 down-regulated in low BMD subjects, of which, 24 were long non-coding RNAs differentially expressed between samples with low and high BMD. Subsequently, the differentially expressed mRNAs were subjected to GO and KEGG analyses. The up-regulated mRNAs were significantly enriched in inflammation-related pathways, which was consistent with the conclusion that inflammatory condition could contribute to the differentiation from monocyte to osteoclast by previous study [25]. In contrast, the downregulated genes were mainly associated with adipocytokine signaling pathway, metabolic pathways, oxidative phosphorylation, cytosolic DNA-sensing pathway, and glycerophospholipid metabolism, suggesting that metabolic pathways may function by maintaining the bone miner density.

    To investigate the interactions between the proteins encoded by the DEGs, we mapped the DEGs to protein-protein interaction network. We observed that the down-regulated proteins, including JDP2, HADC4, HDAC5, CDYL2, ACADVL, ACSL1 and BRD4, were key components in the down-regulated PPI network (Figure 5B). It is well established that the downregulation of histone deacetylases can promote osteoclastogenesis [26,27], indicating that the downregulation of histone deacetylases and cofactors, such as, HDAC4, HDAC5 and JDP2 may be critical regulators in osteoclastogenesis. Moreover, IL8, CCL25, CNR1, HCAR2, HCAR1, GNG13 and GNG8 were also identified as a module in the down-regulated PPI network (Figure 5C). All these proteins except IL8 participated in G-alpha (ⅰ) signaling events, which could inhibit cAMP dependent pathway [28]. Previous study [29] also reported that cAMP-PKA could regulate osteoclast differentiation, indicating that this pathway may be important for bone mineral homeostasis.

    To predict the potential functional roles of the differentially expressed lncRNAs, the Pearson correlation coefficient for lncRNA-DE-mRNA pairs was calculated to construct the co-expression network for mRNAs and lncRNAs. Particularly, RP11-498C9.17 was highly correlated with epigenetic regulators, such as HDAC4, MORF4L1, HMGA1 and DND1. As we described above, the downregulation of histone deacetylases can promote osteoclastogenesis, indicating that the lncRNA RP11-498C9.17 may play a key role in bone mineral homeostasis via controlling osteoclastogenesis.

    In conclusion, our integrative analysis revealed key mRNAs and lncRNAs, involved in bone mineral homeostasis and osteoclastogenesis. The results not only broaden our insights into lncRNAs in bone mineral homeostasis and osteoclastogenesis, but also improved our understanding of molecular mechanism.

    Acknowledgements

    This work were financially supported by the Doctoral Fund of Inner Mongolia Normal University under Grant No. 100900091719, the Scientific research projects of the Inner Mongolian higher educational system(NJZY19025)and Provincial Nature Science Research Project of Anhui Colleges (KJ2018A0331). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

    Conflict of interest

    The authors declare that they have no competing interests.



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