Open Access

An lncRNA‑miRNA‑mRNA ceRNA network for adipocyte differentiation from human adipose‑derived stem cells

  • Authors:
    • Zhen Guo
    • Yali Cao
  • View Affiliations

  • Published online on: March 21, 2019
  • Pages: 4271-4287
  • Copyright: © Guo et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Human adipose tissue‑derived stromal stem cells (HASCs) represent a promising regenerative resource for breast reconstruction and augmentation. However, the mechanisms involved in inducing its adipogenic differentiation remain to be fully elucidated. The present study aimed to comprehensively investigate the expression changes in mRNAs, microRNAs (miRNAs) and long non‑coding (lnc)RNAs during the adipogenic differentiation of HASCs, and screen crucial lncRNA‑miRNA‑mRNA interaction axes using microarray datasets GSE57593, GSE25715 and GSE61302 collected from the Gene Expression Omnibus database. Following pretreatment, differentially expressed genes (DEGs), miRNAs (DE‑miRNAs) or lncRNAs (DE‑lncRNAs) between undifferentiated and differentiated HASCs were identified using the Linear Models for Microarray data method. A protein‑protein interaction (PPI) network was constructed for the DEGs based on protein databases, followed by module analysis. The ‘lncRNA‑miRNA‑mRNA’ competing endogenous RNA (ceRNA) network was constructed based on the interactions between miRNAs and mRNAs, lncRNAs and miRNAs predicted by the miRWalk and lnCeDB databases. The underlying functions of mRNAs were predicted using the clusterProfiler package. In the present study, 905 DEGs, 36 DE‑miRNAs and 577 DE‑lncRNAs were screened between undifferentiated HASCs and differentiated adipocyte cells. PPI network analysis demonstrated that LEP may be a hub gene, which was also enriched in significant module 5. LEP was predicted to be involved in the Janus kinase‑signal transducer and activator of transcription signaling pathway, and the regulation of inflammatory response. The upregulation of LEP was regulated by downregulated hsa‑miRNA (miR)‑130b‑5p and hsa‑miR‑23a‑5p (or hsa‑miR‑302d‑3p). These miRNAs also respectively interacted with RP11‑552F3.9 (or RP11‑15A1.7), ultimately forming the ceRNA axes. In conclusion, the present study revealed that the RP11‑552F3.9 (RP11‑15A1.7)‑hsa‑miR‑130b‑5p/hsa‑miR‑23a‑5p (hsa‑miR‑302d‑3p)‑LEP interaction axes may be crucial for inducing the adipogenic differentiation of HASCs via involvement in inflammation.


Breast reconstruction and augmentation are frequently performed surgical procedures worldwide due to the high prevalence of breast cancer (1) and cosmetic demand. Autologous fat transfer to the subcutaneous tissue is the most commonly used technique in these plastic and reconstructive surgical procedures as it appears to be relatively inexpensive, readily obtainable, safe and complication-free compared with artificial implants (2). However, the long-term replacement outcomes may not be satisfactory, which may be, in part, attributed to low graft survival and poor vascularization (3). Therefore, it is necessary to further improve the autologous fat grafting technique to overcome the above limitations.

Human adipose-derived stem cells (HASCs) are a population of pluripotent cells, which have a high proliferation capacity, possess preferential potential to differentiate into adipocytes and can secrete angiogenic growth factors. Therefore, the addition of HASCs to lipoaspirate may prevent graft volume loss and enhance blood vessel generation in the grafts. This hypothesis has been confirmed in previous clinical trials (46). However, the use of autologous HASCs has not been Food and Drug Administration-approved; this may be due to the fact that the reconstructive mechanism of HASCs remains to be fully elucidated. Therefore, it is essential to investigate the molecular mechanisms that induce the transition of HASCs towards adipocytes and attempt to develop a more effective combination to improve the efficacy of HASC therapy for breast reconstruction and augmentation (7).

Currently, several genes have been identified to be associated with adipogenesis for HASCs. Cytokine interleukin-1α (IL-1α) is demonstrated to evidently inhibit the proliferation and adipogenic differentiation of HASCs through the activation of nuclear factor (NF)-κB and extracellular signal-regulated kinase 1/2 pathways; and subsequent upregulation of pro-inflammatory cytokines, including interleukin (IL)-8, IL-6, C-C motif chemokine ligand 2 and IL-1β, in adipose-derived stem cells (8). A study by Strong et al (9) analyzed the overall cytokine profile of HASCs undergoing adipogenic differentiation and also found a decrease in the expression of IL-1, but reported increases in IL-12, IL-17 and intercellular adhesion molecule-1. By transcriptome profile analysis, Satish et al (10) identified several novel genes and signaling pathways involved in regulating adipogenesis, including periostin, protein phosphatase 1 regulatory inhibitor subunit 1A and fibroblast growth factor 11. MicroRNAs (miRNAs) are a class of small RNAs that are important for the regulation of cellular processes by downregulating gene expression via binding to the 3′-untranslated region. There is also evidence to indicate the roles of miRNAs in adipogenic differentiation. The levels of miRNA (miR)-27a and miR-27b have been found to be downregulated following the adipogenic induction of HASCs. The overexpression of miR-27a or miR-27b inhibits adipocyte differentiation by downregulating the expression of prohibitin; and the target association between miR-27a/b and prohibitin was confirmed using a luciferase reporter assay (11). miR-17-5p and miR-106a were shown to promote the adipogenic lineage commitment of HASCs by directly targeting bone morphogenetic protein 2 and subsequently increasing adipogenic CCAAT enhancer binding protein α (C/EBPα) and peroxisome proliferator activated receptor (PPAR)γ (12). In addition to miRNAs, long non-coding RNAs (lncRNAs) have emerged as important factors contributing to adipocyte differentiation in HASCs. Nuermaimaiti et al (13) demonstrated that the knockdown of HOXA11-AS1 inhibited adipocyte differentiation, leading to the suppression of adipogenic-related gene transcription in addition to decreased lipid accumulation in HASCs. The knockdown of MIR31HG also inhibited adipocyte differentiation, whereas the overexpression of MIR31HG promoted adipogenesis in vitro and in vivo (14). However, the adipogenic differentiation-related genes, miRNAs and lncRNAs of HASCs have received limited investigation.

Several scholars have put forward the competing endogenous RNAs (ceRNAs) hypothesis as an lncRNA-miRNA-mRNA link: LncRNAs may serve as molecular sponges for miRNAs and functionally liberate mRNA-targeted regulated by the aforementioned active miRNAs. Certain adipocyte differentiation-related lncRNA-miRNA-mRNA interaction axes have previously been obtained in bone marrow mesenchymal stem cells (BMSCs) (15,16), but not in HASCs.

The aim of the present study was to screen crucial miRNAs, lncRNAs and mRNAs associated with the adipocyte differentiation of HASCs by constructing the miRNA-lncRNA-mRNA ceRNA regulatory network using microarray data collected from a public database. The results of the present study may improve current understanding of the molecular mechanisms that induce the transition of HASCs towards adipocytes and provide targets for inducing adipogenic differentiation.

Materials and methods

Gene Expression Omnibus (GEO) dataset

The lncRNA, miRNA and mRNA expression profiles of HASCs prior to and following adipocyte differentiation were retrieved from the public GEO database ( under accession nos. GSE57593, GSE25715 and GSE61302 (10), respectively. The GSE57593 microarray dataset (platform: GPL18109, Agilent-038314 CBC Homo sapiens lncRNA + mRNA microarray V2.0) included samples from four undifferentiated HASCs and six differentiated adipocyte cells, which were induced following adipogenic medium culture for 3 and 6 days, with three replicates of each. The GSE25715 non-coding RNA sequencing dataset (platform: GPL9442, AB SOLiD System 3.0, Homo sapiens), included samples from four undifferentiated HASCs [two with adapter set A (from the 5′ to the 3′ end) and two with adapter set B (from the 3′ to the 5′ end)] and eight adipocyte differentiated cells that were induced using adipogenic medium for 3 and 8 days, with two replicates of each and using adapter sets A and B. The GSE61302 microarray dataset (platform: GPL570, Affymetrix Human Genome U133 Plus 2.0 Array) included samples from five undifferentiated HASCs and 10 differentiated adipocyte cells which were induced with adipogenic medium for 7 days (four replicates) and 21 days (six replicates).

Data preprocessing and differential expression analysis

For the microarray data, the raw data were preprocessed using the Robust Multichip Average algorithm (17) as implemented in the Bioconductor R package (version 3.4.1;, including background correction, quantile normalization and median summarization. For the sequencing data, low expression value data (=0, 70%) were filtered.

In consideration of the different differentiated time, the present study only focused on the differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs) between the undifferentiated and differentiated cells. The DEGs, DELs and DEMs were identified using the Linear Models for Microarray data method (18) in the Bioconductor R package (version 3.4.1; The empirical Bayes t-test was used to calculate the p-value, which was subsequently adjusted by the Benjamini-Hochberg (BH) procedure (19). Genes were considered differentially expressed if they met the following conditions: P-value (adjusted) P<0.05 and |logFC(fold change)| >1 (that is, FC>2). A hierarchical cluster heatmap was created using the R package pheatmap (version: 1.0.8; based on the Euclidean distance to observe the ability of the DEGs, DELs and DEMs to distinguish the differentiated from the undifferentiated samples.

Protein-protein interaction (PPI) network

To screen crucial genes, the DEGs were imported into PPI data that were collected from the Search Tool for the Retrieval of Interacting Genes (version 10.0; http://string database (20). The PPIs with combined scores ≥0.4 (medium confidence) were selected to construct the PPI network, which was visualized using Cytoscape software (version 3.4; (21). The network topological features, including the degree (number of interactions per node or protein), betweenness (number of shortest paths that pass through each node), and closeness centrality (average length of the shortest paths to access all other proteins in the network) were determined using the CytoNCA plugin in Cytoscape software ( (22) to rank the nodes in the PPI network and screen hub genes. Modules were identified to be significant with an Molecular Complex Detection (MCODE) score ≥4 and ≥6 nodes.

Furthermore, the MCODE (version:1.4.2, plugin of Cytoscape software was also used to identify functionally related and highly interconnected modules from the PPI network with a degree cut-off of 2, node score cut-off of 0.2, k-core of 2 and maximum depth of 100 (23).

ceRNA regulatory network construction

The DEM-related target genes were predicted using the miRWalk database (version 2.0; (24), which provides the largest collection of predicted and experimentally verified miR-target interactions with various miRNA databases, including miRWalk, miRanda, miRDB, miRMap, RNA22 and TargetScan. The miRNA-target gene interaction pairs were selected if they were predicted in at least five databases. The target genes were then overlapped with the DEGs to screen the DEM (upregulated)-DEG (downregulated) or DEM (downregulated)-DEG (upregulated) interaction pairs.

The miRWalk (version 2.0; (24) and lnCeDB ( (25) databases were used to screen the interactions between DELs and DEMs. The DEL (upregulated)-DEM (downregulated) and DEL (downregulated)-DEM (upregulated) interaction pairs were collected.

The DEL-DEM and DEM-DEG interactions were integrated to construct the lncRNA-miRNA-mRNA ceRNA network, which was visualized using Cytoscape software (version 3.4; (21).

Function enrichment analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler tool (version 3.2.11; to reveal the function of the DEGs in the PPI and the target genes of miRNAs. Adjusted P<0.05 using the BH method was set as the cut-off value (19).


Differential expression analysis

Based on the given threshold (adjusted P<0.05 and |logFC| >1), a total of 925 DEGs were identified from 20,514 mRNAs between the undifferentiated HASCs and differentiated adipocyte cells, including 302 upregulated and 623 downregulated DEGs; 577 DELs were screened from 7,882 lncRNAs between the undifferentiated HASCs and differentiated adipocyte cells, including 323 upregulated and 254 downregulated DELs. A total of 35 DEMs were screened from 499 miRNAs between the undifferentiated HASCs and differentiated adipocyte cells (including 20 upregulated and 15 downregulated), based on the threshold of P<0.05 and |logFC| >1. The top 20 DEGs, DEMs and DELs are shown in Table I. The heatmap indicated that these DEGs (Fig. 1A), DEMs (Fig. 1B) and DELs (Fig. 1C) distinguished the differentiated from the undifferentiated samples.

Table I.

Top 10 upregulated and downregulated differentially expressed lncRNAs, miRNAs and mRNAs.

Table I.

Top 10 upregulated and downregulated differentially expressed lncRNAs, miRNAs and mRNAs.


lncRNAlogFCAdjusted P-valuemiRNAlogFCP-valuemRNAlogFCAdjusted P-value
ZBED3-AS14.74 2.74×10−7hsa-miR-29b-2*2.89 3.08×10−5FGF112.14 3.54×10−7
RP11-95P13.14.95 5.72×10−7 hsa-miR-642a-3p5.28 4.29×10−4DDIT4L3.38 4.11×10−7
AC104654.24.12 1.08×10−5hsa-miR-21142.80 1.31×10−3PKP21.74 4.11×10−7
RP11-196G18.32.19 1.41×10−5hsa-miR-30a*2.54 2.20×10−3GPR1551.57 4.11×10−7
RP11-439A17.92.19 1.41×10−5hsa-miR-34b*4.57 3.85×10−3ZNF582-AS11.20 4.11×10−7
RP5-998N21.42.19 1.41×10−5hsa-miR-6682.29 4.38×10−3PGRMC11.10 4.11×10−7
CTC-564N23.24.65 2.62×10−5hsa-miR-3451.81 4.20×10−3ZNF436-AS12.26 5.34×10−7
CHL1-AS13.53 5.17×10−5hsa-miR-675*2.60 5.68×10−3FAM162A1.14 5.34×10−7
AC104653.12.94 5.17×10−5hsa-miR-34a3.54 8.48×10−3BNIP31.23 1.44×10−6
RP11-696N14.12.15 5.17×10−5hsa-miR-378c3.10 9.65×10−3IGFBP51.74 1.85×10−6
LINC01085−4.82 1.20×10−7hsa-miR-485-3p−3.30 9.05×10−4FOSB−6.71 1.28×10−14
APCDD1L-AS1−3.14 2.45×10−6hsa-miR-3151−2.15 1.80×10−3IER2−1.72 2.51×10−9
RP11-54A9.1−3.45 5.73×10−6hsa-miR-130b*−1.42 6.82×10−3KLF2−2.02 3.82×10−9
CTD-2354A18.1−3.16 7.55×10−6hsa-miR-302d−1.66 8.82×10−3ID1−4.29 1.64×10−8
CTD-2066L21.2−5.09 1.11×10−5hsa-miR-487a−2.39 7.82×10−3SKIL−1.49 4.79×10−8
RP11-114H23.1−1.83 1.11×10−5hsa-miR-411*−1.31 4.96×10−2PRIMA1−2.89 9.17×10−8
RP3-410C9.2−5.035 1.30×10−5hsa-miR-154*−2.12 1.72×10−2RRM2−4.51 9.17×10−8
APOBEC3B-AS1−3.97 1.30×10−5hsa-let-7e−1.35 2.81×10−3EGR3−5.27 9.17×10−8
RP11-30P6.6−3.10 1.41×10−5 hsa-miR-125b-1*−1.54 2.84×10−2C16orf89−2.88 1.73×10−7
LINC00460−3.07 2.39×10−5hsa-miR-23b−1.03 4.18×10−2NFKBIZ−1.84 2.89×10−7

[i] lncRNA, long non-coding RNA; miRNA, microRNA.

PPI network analysis of DEGs to screen hub genes

A PPI network was constructed using the screened DEGs, which included 360 nodes (162 upregulated and 198 downregulated) and 1,381 interaction pairs (Fig. 2). According to the rank of three topological features, JUN, cyclin B1 (CCNB1), C-X-C motif chemokine ligand 10 (CXCL10), enolase 2 (ENO2), enoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase (EHHADH), protein tyrosine phosphatase, receptor type C (PTPRC), Rac family small GTPase 2 (RAC2), leptin (LEP) and kinase insert domain receptor (KDR) were considered as hub genes in the PPI network (Table II). Six significant functionally related and highly interconnected modules were extracted from the whole PPI network (Fig. 3; Table III). Hub gene CCNB1 was enriched in module 1, which was associated with cell cycle (Fig. 3A). Hub gene CXCL10 was enriched in module 2 (Fig. 3B), which was associated with several inflammation pathways, including the chemokine signaling pathway, cytokine-cytokine receptor interaction, IL-17 signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Hub gene ENO2 and PTPRC were respectively enriched into module 3 (Fig. 3C) and 4 (Fig. 3D). Hub gene JUN in module 4 was enriched in the NOD-like receptor signaling pathway or infection. Hub gene LEP in module 5 (Fig. 3E) was important in the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway. Hub gene EHHADH in module 6 (Fig. 3F) was amino acid- or glucose metabolism-related (Table IV; Fig. 4A). As hub gene PTPRC and ENO2 were respectively enriched into module 4 and 3, but they were not included in the pathway-related genes, GO enrichment analysis was also performed. As a result, PTPRC was predicted to be involved in positive regulation of cytosolic calcium ion concentration (Table V; Fig. 4B). The function of ENO2 was not predicted.

Table II.

Topological features of DEGs in the protein-protein interaction network.

Table II.

Topological features of DEGs in the protein-protein interaction network.


[i] DEG, differentially expressed gene.

Table III.

Module analysis results.

Table III.

Module analysis results.

ClusterScoreaNodes (n)Edges (n)Node IDs
  213.001378CXCL10, GAL, CXCL5, CXCL11, C5, P2RY12, CCL28, CNR1, S1PR1, CXCL2, CXCL1, CXCR4, CXCL6
  73.3345PDE2A, PRELP, RRAD, LRRC2
103.0033HSD17B14, ADH4, TP53I3
113.0033TRIM69, RNF19B, TRIM9
123.0033GPAT2, AGPAT5, GPD1
133.0033VLDLR, DAB1, MAP1B
143.0033LAMB3, ITGA7, LAMA2

a Score = density × number of nodes.

Table IV.

Kyoto Encyclopedia of Genes and Genomes pathway enrichment for genes in modules.

Table IV.

Kyoto Encyclopedia of Genes and Genomes pathway enrichment for genes in modules.

ClusterIDDescriptionAdjusted P-valueGenes
1hsa04914 Progesterone-mediated oocyte maturation 1.16×10−3 AURKA/CCNB1/CCNB2
1hsa04110Cell cycle 1.16×10−3 MCM2/CCNB1/CCNB2
1hsa04114Oocyte meiosis 1.16×10−3 AURKA/CCNB1/CCNB2
1hsa04068FoxO signaling pathway 1.16×10−3 PLK4/CCNB1/CCNB2
1hsa03030DNA replication 2.02×10−3MCM2/POLE2
1hsa04115p53 signaling pathway 5.99×10−3CCNB1/CCNB2
1hsa04218Cellular senescence 2.73×10−2CCNB1/CCNB2
2hsa04062Chemokine signaling pathway 1.96×10−9 CXCL10/CXCL5/CXCL11/CCL28/CXCL2/CXCL1/CXCR4/CXCL6
2hsa04060Cytokine-cytokine receptor interaction 2.03×10−8 CXCL10/CXCL5/CXCL11/CCL28/CXCL2/CXCL1/CXCR4/CXCL6
2hsa04657IL-17 signaling pathway 2.28×10−6 CXCL10/CXCL5/CXCL2/CXCL1/CXCL6
2hsa04668TNF signaling pathway 1.60×10−4 CXCL10/CXCL5/CXCL2/CXCL1
2hsa05133Pertussis 1.41×10−3CXCL5/C5/CXCL6
2hsa05323Rheumatoid arthritis 1.94×10−3 CXCL5/CXCL1/CXCL6
2hsa04672Intestinal immune network for IgA production 1.28×10−2CCL28/CXCR4
2hsa05134Legionellosis 1.41×10−2CXCL2/CXCL1
2hsa05132Salmonella infection 0.02.99×10−2CXCL2/CXCL1
2hsa04620Toll-like receptor signaling pathway 3.88×10−2CXCL10/CXCL11
4hsa04080Neuroactive ligand-receptor interaction 7.99×10−4 AVPR1A/P2RY1/EDNRB/PTGFR/HTR2A/HTR2B
4hsa04020Calcium signaling pathway 8.35×10−4 AVPR1A/EDNRB/PTGFR/HTR2A/HTR2B
4hsa05164Influenza A 8.30×10−3 OAS1/JUN/IFIH1/IRF7
4hsa05168Herpes simplex infection 8.30×10−3 OAS1/JUN/IFIH1/IRF7
4hsa05162Measles 3.60×10−2 OAS1/IFIH1/IRF7
4hsa05161Hepatitis B 3.68×10−2JUN/IFIH1/IRF7
4hsa04621NOD-like receptor signaling pathway 4.88×10−2OAS1/JUN/IRF7
5hsa04630Jak-STAT signaling pathway 6.39×10−4 LEP/IL4R/IL10RA/TSLP
5hsa03320PPAR signaling pathway 8.88×10−4 ACSL1/SCD/FABP4
5hsa04060Cytokine-cytokine receptor interaction 1.57×10−3 LEP/IL4R/IL10RA/TSLP
5hsa01212Fatty acid metabolism 9.00×10−3ACSL1/SCD
5hsa04923Regulation of lipolysis in adipocytes 9.10×10−3PNPLA2/FABP4
5hsa00561Glycerolipid metabolism 9.65×10−3DGAT2/PNPLA2
5hsa04920Adipocytokine signaling pathway 0.01.06×10−2ACSL1/LEP
5hsa04152AMPK signaling pathway 2.76×10−2SCD/LEP
5hsa00061Fatty acid biosynthesis 4.36×10−2ACSL1
6hsa00410β-alanine metabolism 1.50×10−6 ABAT/EHHADH/ALDH3A2/ALDH1B1
6hsa00280Valine, leucine and isoleucine degradation 4.60×10−6 ABAT/EHHADH/ALDH3A2/ALDH1B1
6hsa00380Tryptophan metabolism 1.65×10−4 EHHADH/ALDH3A2/ALDH1B1
6hsa00071Fatty acid degradation 1.66×10−4 EHHADH/ALDH3A2/ALDH1B1
6hsa00310Lysine degradation 3.22×10−4 EHHADH/ALDH3A2/ALDH1B1
6hsa00340Histidine metabolism 1.98×10−3 ALDH3A2/ALDH1B1
6hsa00053Ascorbate and aldarate metabolism 2.21×10−3 ALDH3A2/ALDH1B1
6hsa00650Butanoate metabolism 2.21×10−3ABAT/EHHADH
6hsa00640Propanoate metabolism 2.57×10−3ABAT/EHHADH
6hsa00620Pyruvate metabolism 3.44×10−3 ALDH3A2/ALDH1B1
6hsa01212Fatty acid metabolism 4.70×10−3EHHADH/PECR
6hsa00330Arginine and proline metabolism 4.70×10−3 ALDH3A2/ALDH1B1
6hsa05130Pathogenic Escherichia coli infection 5.24×10−3TUBA1A/TUBA4A
6hsa00561Glycerolipid metabolism 6.00×10−3 ALDH3A2/ALDH1B1
6hsa00010 Glycolysis/Gluconeogenesis 6.90×10−3 ALDH3A2/ALDH1B1
6hsa04146Peroxisome 9.55×10−3EHHADH/PECR
6hsa04540Gap junction 1.01×10−2TUBA1A/TUBA4A
6hsa04210Apoptosis 2.27×10−2TUBA1A/TUBA4A
6hsa04145Phagosome 2.58×10−2TUBA1A/TUBA4A
6hsa04530Tight junction 3.03×10−2TUBA1A/TUBA4A
6hsa01040Biosynthesis of unsaturated fatty acids 4.23×10−2PECR

Table V.

GO enrichment for genes in modules.

Table V.

GO enrichment for genes in modules.

ClusterIDDescriptionAdjusted P-valueGenes
1GO:0051310Metaphase plate congression 4.09×10−11 CDT1/CENPF/SPAG5/KIF2C/CDCA8/KIF14/CCNB1
1GO:0051303Establishment of chromosome localization 1.45×10−10 CDT1/CENPF/SPAG5/KIF2C/CDCA8/KIF14/CCNB1
1GO:0050000Chromosome localization 1.45×10−10 CDT1/CENPF/SPAG5/KIF2C/CDCA8/KIF14/CCNB1
1GO:0140014Mitotic nuclear division 1.71×10−10 CDT1/CENPF/SPAG5/KIF2C/CDCA8/KIF14/AURKA/KIF11/CCNB1
1GO:0000280Nuclear division 5.46×10−9 CDT1/CENPF/SPAG5/KIF2C/CDCA8/KIF14/AURKA/KIF11/CCNB1
2GO:0060326Cell chemotaxis 9.21×10−14 CXCL10/CXCL5/CXCL11/C5/CCL28/S1PR1/CXCL2/CXCL1/CXCR4/CXCL6
2GO:0050900Leukocyte migration 9.21×10−14 CXCL10/CXCL5/CXCL11/C5/P2RY12/CCL28/S1PR1/CXCL2/CXCL1/CXCR4/CXCL6
2GO:0002685Regulation of leukocyte migration 9.21×10−14 CXCL10/CXCL5/CXCL11/C5/P2RY12/CCL28/CXCL2/CXCL1/CXCL6
2GO:0050920Regulation of chemotaxis 1.35×10−13 CXCL10/CXCL5/CXCL11/C5/S1PR1/CXCL2/CXCL1/CXCR4/CXCL6
2GO:0030595Leukocyte chemotaxis 2.66×10−13 CXCL10/CXCL5/CXCL11/C5/S1PR1/CXCL2/CXCL1/CXCR4/CXCL6
4GO:0007204Positive regulation of cytosolic calcium ion concentration 6.33×10−7 AVPR1A/P2RY1/EDNRB/PTPRC/HTR2A/TAC1/NMB/HTR2B
4GO:0009615Response to virus 6.33×10−7 OAS1/CCL22/HERC5/OASL/IFIH1/PTPRC/IRF7/IFI44
4GO:0051480Regulation of cytosolic calcium ion concentration 6.33×10−7 AVPR1A/P2RY1/EDNRB/PTPRC/HTR2A/TAC1/NMB/HTR2B
4GO:2000021Regulation of ion homeostasis 8.55×10−7 AVPR1A/EDNRB/PTPRC/HTR2A/TAC1/IFI6/HTR2B
4GO:0007620Copulation 1.45×10−6 AVPR1A/P2RY1/EDNRB/TAC1
5GO:0006641Triglyceride metabolic process 8.23×10−5 ACSL1/DGAT2/PNPLA2/FABP4
5GO:0006639Acylglycerol metabolic process 8.23×10−5 ACSL1/DGAT2/PNPLA2/FABP4
5GO:0006638Neutral lipid metabolic process 8.23×10−5 ACSL1/DGAT2/PNPLA2/FABP4
5GO:0019216Regulation of lipid metabolic process 1.38×10−4 ACSL1/SCD/DGAT2/PNPLA2/LEP
5GO:0035337Fatty-acyl-CoA metabolic process 1.60×10−4 ACSL1/SCD/DGAT2
6GO:0072329Monocarboxylic acid catabolic process 2.81×10−4 ABAT/EHHADH/ALDH3A2/PECR
6GO:0006631Fatty acid metabolic process 2.81×10−4 PRKAR2B/EHHADH/ALDH3A2/PECR/PDHX
6GO:0044282Small molecule catabolic process 2.81×10−4 ABAT/EHHADH/ALDH3A2/ALDH1B1/PECR
6GO:0034308Primary alcohol metabolic process 5.03×10−4 ALDH3A2/ALDH1B1/PECR
6GO:0016054Organic acid catabolic process 8.48×10−4 ABAT/EHHADH/ALDH3A2/PECR

[i] Only the top five terms are listed. GO, Gene Ontology.

DEM-DEG regulatory association analysis

A total of 7,381 target genes were predicted for the 21 upregulated DEMs, and 5,841 were predicted for the 15 downregulated DEMs. Following overlapping with the DEGs, 654 interactions were obtained for the 21 upregulated DEMs and 247 downregulated DEGs, and 197 interactions were obtained for the 14 downregulated DEMs and 96 upregulated DEGs.

The target genes of five upregulated DEMs (hsa-miR-103a-2-5p, hsa-miR-582-5p, hsa-miR-642a-5p, hsa-miR-1292-5p and hsa-miR-30c-5p and) were enriched into 29 KEGG pathways, whereas 36 KEGG pathways were enriched for six downregulated DEMs (hsa-miR-302d-3p, hsa-miR-154-3p, hsa-miR-485-3p, hsa-miR-25-5p, hsa-miR-487a and hsa-miR-411-3p) (Fig. 5A). Among them, hsa-miR-302d-3p regulated hub gene LEP to be involved in neuroactive ligand-receptor interaction; whereas hsa-miR-487a targeted hub gene EHHADH for involvement in amino acid (β-alanine, lysine, valine, leucine and isoleucine) metabolism; hub gene CXCL10 regulated by hsa-miR-411-3p was involved in the IL-17 signaling pathway, Toll-like receptor signaling pathway, and TNF signaling pathway (Table VI).

Table VI.

Kyoto Encyclopedia of Genes and Genomes pathway enrichment for target genes of microRNAs.

Table VI.

Kyoto Encyclopedia of Genes and Genomes pathway enrichment for target genes of microRNAs.

ExpressionClusterIDDescriptionAdjusted P-valueGenes
Up hsa-miR-103a-2-5phsa04921Oxytocin signaling pathway 2.67×10−2 NFATC2/OXTR/GUCY1A3/PTGS2
hsa-miR-582-5phsa05167Kaposi's sarcoma-associated herpesvirus infection 2.16×10−2 PTGS2/ANGPT2/CXCL2/NFATC2
hsa-miR-642a-3phsa05202Transcriptional misregulation in cancer 2.51×10−2NR4A3
hsa-miR-1292-5phsa04625C-type lectin receptor signaling pathway 3.91×10−3PTGS2/IRF1
hsa-miR-1292-5phsa05165Human papillomavirus infection 1.85×10−2PTGS2/IRF1
hsa-miR-1292-5phsa04923Regulation of lipolysis in adipocytes 3.92×10−2PTGS2
hsa-miR-1292-5phsa04370VEGF signaling pathway 3.92×10−2PTGS2
hsa-miR-1292-5phsa04917Prolactin signaling pathway 3.92×10−2IRF1
hsa-miR-1292-5phsa05140Leishmaniasis 3.92×10−2PTGS2
hsa-miR-1292-5phsa05133Pertussis 3.92×10−2IRF1
hsa-miR-1292-5phsa04657IL-17 signaling pathway 3.92×10−2PTGS2
hsa-miR-1292-5phsa04064NF-κB signaling pathway 3.92×10−2PTGS2
hsa-miR-1292-5phsa04668TNF signaling pathway 4.04×10−2PTGS2
hsa-miR-1292-5phsa05160Hepatitis C 4.38×10−2IRF1
hsa-miR-30c-5phsa05161Hepatitis B 2.34×10−2 NFATC2/CCNA1/CCNE2
hsa-miR-30c-5phsa04218Cellular senescence 2.34×10−2 NFATC2/CCNA1/CCNE2
Downhsa-miR-154-3phsa00561Glycerolipid metabolism 5.15×10−3GPAM/ALDH1B1
hsa-miR-154-3phsa00340Histidine metabolism 3.86×10−2ALDH1B1
hsa-miR-154-3phsa00053Ascorbate and aldarate metabolism 3.86×10−2ALDH1B1
hsa-miR-154-3phsa00410β-alanine metabolism 3.86×10−2ALDH1B1
hsa-miR-154-3phsa00620Pyruvate metabolism 3.86×10−2ALDH1B1
hsa-miR-25-5phsa04514Cell adhesion molecules (CAMs) 5.22×10−4 PTPRF/F11R/JAM2
hsa-miR-25-5phsa05120Epithelial cell signaling in Helicobacter pylori infection 4.44×10−3F11R/JAM2
hsa-miR-25-5phsa04670Leukocyte transendothelial migration 8.00×10−3F11R/JAM2
hsa-miR-25-5phsa04530Tight junction 1.37×10−2F11R/JAM2
hsa-miR-25-5phsa00340Histidine metabolism 4.01×10−2ALDH1B1
hsa-miR-302d-3phsa04080Neuroactive ligand-receptor interaction 1.35×10−2 EDNRB/LEP/RXFP1/PTGFR
hsa-miR-485-3phsa04514Cell adhesion molecules (CAMs) 3.30×10−2PTPRF/NLGN4X
hsa-miR-487a-3phsa00410β-alanine metabolism 1.07×10−2ALDH1B1/EHHADH
hsa-miR-487a-3phsa00380Tryptophan metabolism 1.07×10−2ALDH1B1/EHHADH
hsa-miR-487a-3phsa00071Fatty acid degradation 1.07×10−2ALDH1B1/EHHADH
hsa-miR-487a-3phsa00280Valine, leucine and isoleucine degradation 1.07×10−2ALDH1B1/EHHADH
hsa-miR-487a-3phsa00310Lysine degradation 1.15×10−2ALDH1B1/EHHADH
hsa-miR-487a-3phsa00561Glycerolipid metabolism 1.15×10−2GPAM/ALDH1B1
hsa-miR-411-3phsa00061Fatty acid biosynthesis 3.16×10−2OLAH
hsa-miR-411-3phsa04623Cytosolic DNA-sensing pathway 4.34×10−2CXCL10
hsa-miR-411-3phsa04622RIG-I-like receptor signaling pathway 4.34×10−2CXCL10
hsa-miR-411-3phsa04657IL-17 signaling pathway 4.34×10−2CXCL10
hsa-miR-411-3phsa04620Toll-like receptor signaling pathway 4.34×10−2CXCL10
hsa-miR-411-3phsa04668TNF signaling pathway 4.34×10−2CXCL10

[i] miR, microRNA.

Furthermore, GO biological process term enrichment analysis was also performed to predict the functions of DEMs (Fig. 5B). As a result, GO terms were enriched for nine upregulated DEMs (hsa-miR-103-5p, hsa-let-7e-5p, hsa-miR-212-3p, hsa-miR-345-5p, hsa-miR-378a-5p, hsa-miR-642a-3p, hsa-miR-582-3p, hsa-miR-664a-3p and hsa-miR-668-3p) and five downregulated DEMs (hsa-miR-302d-3p, hsa-miR-485-3p, hsa-miR-130b-5p, hsa-miR-23a-5p and hsa-miR-23b-5p). hsa-miR-378a-5p may regulate hub gene RAC2 to be involved in cell-substrate adhesion. hsa-miR-130b-5p, hsa-miR-23a-5p and hsa-miR-302d-3p may regulate hub gene LEP to be involved in regulation of inflammatory response and IL-8 secretion (Table VII).

Table VII.

GO term enrichment for target genes of microRNAs.

Table VII.

GO term enrichment for target genes of microRNAs.

ExpressionClusterIDDescriptionAdjusted P-valueGenes
Up hsa-miR-103a-2-5pGO:0051968Positive regulation of synaptic transmission, glutamatergic 1.18×10−2 OXTR/NLGN1/PTGS2
hsa-miR-103a-2-5pGO:0048661Positive regulation of smooth muscle cell proliferation 1.18×10−2 IL10/NR4A3/IL6R/PTGS2
hsa-miR-103a-2-5pGO:0050807Regulation of synapse organization 2.79×10−2OXTR/IL10/NLGN1/ LRRTM2
hsa-miR-103a-2-5pGO:0048660Regulation of smooth muscle cell proliferation 2.79×10−2 IL10/NR4A3/IL6R/PTGS2
hsa-miR-103a-2-5pGO:0048659Smooth muscle cell proliferation 2.79×10−2 IL10/NR4A3/IL6R/PTGS2
hsa-miR-378a-5pGO:0007162Negative regulation of cell adhesion 3.27×10−3 IRF1/PELI1/ANGPT2/IL10/SMAD7/SEMA3E
hsa-miR-378a-5pGO:0031589Cell-substrate adhesion 6.55×10−3 LIMS1/RAC2/KIF14/ANGPT2/SEMA3E/PEAK1
hsa-miR-378a-5pGO:0050868Negative regulation of T cell activation 8.67×10−3 IRF1/PELI1/IL10/SMAD7
hsa-miR-378a-5pGO:1903038Negative regulation of leukocyte cell-cell adhesion 9.77×10−3 IRF1/PELI1/IL10/SMAD7
hsa-miR-378a-5pGO:0051250Negative regulation of lymphocyte activation 1.53×10−2 IRF1/PELI1/IL10/SMAD7
hsa-miR-582-3pGO:1902043Positive regulation of extrinsic apoptotic signaling pathway via death domain receptors 6.49×10−3SKIL/TIMP3
hsa-miR-582-3pGO:2001238Positive regulation of extrinsic apoptotic signaling pathway 2.23×10−2SKIL/TIMP3
hsa-miR-582-3pGO:1902041Regulation of extrinsic apoptotic signaling pathway via death domain receptors 2.23×10−2SKIL/TIMP3
hsa-miR-582-3pGO:0030512Negative regulation of transforming growth factor β receptor signaling pathway 2.23×10−2SKIL/HTRA4
hsa-miR-582-3pGO:1903845Negative regulation of cellular response to transforming growth factor β stimulus 2.23×10−2SKIL/HTRA4
hsa-miR-642a-3pGO:0048839Inner ear development 4.66×10−2NR4A3/MCOLN3
hsa-miR-642a-3pGO:0043583Ear development 4.66×10−2NR4A3/MCOLN3
hsa-miR-642a-3pGO:0061469Regulation of type B pancreatic cell proliferation 4.66×10−2NR4A3
hsa-miR-642a-3pGO:0061081Positive regulation of myeloid leukocyte cytokine production involved in immune response 4.66×10−2NR4A3
hsa-miR-642a-3pGO:0070486Leukocyte aggregation 4.66×10−2NR4A3
hsa-miR-668-3pGO:0051983Regulation of chromosome segregation 1.74×10−2 KIF2C/MKI67/GEN1
hsa-miR-345-5pGO:0003188Heart valve formation 3.30×10−2HEY2/ERG
hsa-miR-345-5pGO:0007265Ras protein signal transduction 4.24×10−2 CDC42EP2/NGFR/RASAL2/P2RY8
hsa-miR-345-5pGO:0060317Cardiac epithelial to mesenchymal transition 4.65×10−2HEY2/ERG
hsa-miR-345-5pGO:0003179Heart valve morphogenesis 4.65×10−2HEY2/ERG
hsa-miR-345-5pGO:0007266Rho protein signal transduction 4.65×10−2 CDC42EP2/NGFR/P2RY8
hsa-miR-664a-3pGO:0060712Spongiotrophoblast layer development 3.57×10−3LIF/NRK/PHLDA2
hsa-miR-664a-3pGO:0010976Positive regulation of neuron projection development 3.11×10−2 MAP1B/NTRK2/PAK3/SKIL/NLGN1
hsa-miR-664a-3pGO:0033135Regulation of peptidyl-serine phosphorylation 3.11×10−2 LIF/PTGS2/NTRK2/RASSF2
hsa-miR-664a-3pGO:0010770Positive regulation of cell morphogenesis involved in differentiation 3.11×10−2 MAP1B/NTRK2/PAK3/SKIL
hsa-miR-664a-3pGO:0010769Regulation of cell morphogenesis involved in differentiation 3.11×10−2 MAP1B/NTRK2/PAK3/SKIL/NLGN1
hsa-let-7e-5pGO:0010866Regulation of triglyceride biosynthetic process 5.26×10−3THRSP/DGAT2
hsa-let-7e-5pGO:0046890Regulation of lipid biosynthetic process 5.26×10−3 THRSP/SCD/DGAT2
hsa-let-7e-5pGO:0019432Triglyceride biosynthetic process 5.26×10−3THRSP/DGAT2
hsa-let-7e-5pGO:0090207Regulation of triglyceride metabolic process 5.26×10−3THRSP/DGAT2
hsa-let-7e-5pGO:0046460Neutral lipid biosynthetic process 5.26×10−3THRSP/DGAT2
hsa-miR-212-3pGO:0050708Regulation of protein 5.15×10−3 SLC2A1/IL1RL1/GPAM/PDE8B
hsa-miR-212-3pGO:0002791Regulation of peptide secretion 5.15×10−3 SLC2A1/IL1RL1/GPAM/PDE8B
Down hsa-miR-130b-5pGO:0001101Response to acid chemical 2.93×10−2 WNT5A/ACSL1/LEP/PTGFR
hsa-miR-130b-5pGO:0043032Positive regulation of macrophage activation 2.93×10−2WNT5A/IL1RL1
hsa-miR-130b-5pGO:0050727Regulation of inflammatory response 3.66×10−2 WNT5A/LEP/CX3CL1/IL1RL1
hsa-miR-130b-5pGO:0072606Interleukin-8 secretion 3.66×10−2WNT5A/LEP
hsa-miR-130b-5pGO:0001819Positive regulation of cytokine production 3.66×10−2 WNT5A/LEP/CX3CL1/IL1RL1
hsa-miR-23a-5pGO:0006865Amino acid transport 2.87×10−2 LEP/SLC6A6/ATP1A2
hsa-miR-23a-5pGO:0051955Regulation of amino acid transport 2.87×10−2LEP/ATP1A2
hsa-miR-23a-5pGO:0006109Regulation of carbohydrate metabolic process 2.87×10−2 LEP/IGFBP5/PFKFB4
hsa-miR-23a-5pGO:0019229Regulation of vasoconstriction 2.87×10−2LEP/ATP1A2
hsa-miR-23a-5pGO:0046942Carboxylic acid transport 2.87×10−2 LEP/SLC6A6/ATP1A2
hsa-miR-23a-5pGO:0001909Leukocyte mediated cytotoxicity 3.30×10−2LEP/TREM1
hsa-miR-23a-5pGO:0014897Striated muscle hypertrophy 3.80×10−2LEP/IGFBP5
hsa-miR-23a-5pGO:0010906Regulation of glucose metabolic process 3.80×10−2LEP/IGFBP5
hsa-miR-23a-5pGO:0010675Regulation of cellular carbohydrate metabolic process 4.72×10−2LEP/IGFBP5
hsa-miR-23b-3pGO:0007422Peripheral nervous system development 4.39×10−2 ALDH3A2/EDNRB/HOXD10
hsa-miR-302d-3pGO:0010888Negative regulation of lipid storage 3.41×10−2LEP/ABCG1
hsa-miR-302d-3pGO:0032355Response to estradiol 3.41×10−2 LEP/TXNIP/PTGFR
hsa-miR-302d-3pGO:0008203Cholesterol metabolic process 3.41×10−2 VLDLR/LEP/ABCG1
hsa-miR-302d-3pGO:1902652Secondary alcohol metabolic process 3.41×10−2 VLDLR/LEP/ABCG1
hsa-miR-302d-3pGO:0006869Lipid transport 3.41×10−2 VLDLR/LEP/THRSP/ABCG1
hsa-miR-302d-3pGO:1900015Regulation of cytokine production involved in inflammatory response 3.41×10−2C5orf30/LEP
hsa-miR-302d-3pGO:0046890Regulation of lipid biosynthetic process 3.41×10−2 LEP/THRSP/ABCG1
hsa-miR-302d-3pGO:0002534Cytokine production involved in inflammatory response 3.41×10−2C5orf30/LEP
hsa-miR-485-3pGO:0035384Thioester biosynthetic process 2.86×10−2PDHX/SCD
hsa-miR-485-3pGO:0071616Acyl-CoA biosynthetic process 2.86×10−2PDHX/SCD

[i] GO, Gene Ontology; miR, microRNA.

ceRNA network

Using the miRWalk and InCeDB databases, 14 upregulated DEMs were predicted to regulate 60 downregulated DELs and nine downregulated DEMs were predicted to regulate 15 upregulated DELs. An lncRNA-miRNA-mRNA ceRNA network was subsequently established (Fig. 6A and B), in which 366 nodes (23 DEMs: 14 upregulated and nine downregulated; 268 DEGs: 67 upregulated and 201 downregulated; 75 DELs: 15 upregulated and 60 downregulated) and 560 interactions (450 DEL-DEM and 110 DEM-DEG) were present. In this ceRNA, upregulated RP11-552F3.9 (or RP11-15A1.7) may function as a ceRNA to respectively suppress the inhibitory effects of hsa-miR-23a-5p and hsa-miR-302d-3p (or hsa-miR-130b-5p) on LEP, resulting in its upregulated expression; whereas the downregulation of GDNF-AS1 may be insufficient to prevent the inhibitory effects of hsa-miR-378a-5p on hub gene RAC2, leading to its downregulated expression (Fig. 6A and B).


The present study aimed to identify crucial mRNAs, miRNAs and lncRNAs for the adipocyte differentiation of HASCs based on a series of bioinformatics analyses, including PPI network construction, module analysis, miRNA-mRNA regulatory pair prediction, ceRNA network generation and function enrichment. In these analyses, the LEP gene was enriched and was regulated by RP11-552F3.9 (or RP11-15A1.7)-hsa-miR-23a-5p/hsa-miR-302d-3p (or hsa-miR-130b-5p), and involved in the inflammatory response, indicating that the LEP-related ceRNA axis may be important for the differentiation of adipose tissue-derived stem cells into adipocytes.

There is evidence demonstrating that LEP is important in adipocyte differentiation (26). Lee et al (27) observed that leptin treatment can promote lipid droplet formation and adipocyte differentiation, which was evaluated by the activity of glycerol-3-phosphate dehydrogenase activity, of HASCs. Additionally, the effect of leptin on adipocyte differentiation was found to be higher for HASCs than BMSCs (27). Another study indicated that, in BMSCs, leptin may accelerate osteogenic differentiation but inhibit adipocyte differentiation (28). Similarly, leptin was shown to have a suppressive effect on adipogenesis in dental pulp stem cells and periodontal ligament stem cells (29). These findings suggest that leptin may be a specific factor for regeneration of the subcutaneous fat layer using HASCs for tissue engineering. As expected, the LEP gene was also significantly upregulated in differentiated adipocyte samples compared with undifferentiated HASCs in the present study. It was predicted that the downstream of LEP may be involved in the JAK-STAT signaling pathway to mediate the inflammatory response via interaction with certain anti-inflammatory-related factors (IL4R, downregulated; Table IV; Fig. 3E). This prediction appears to have been indirectly verified by previous studies; it has been reported that leptin may have a promoting effect on the astroglial differentiation of stem cells through activation of the JAK-STAT pathway, with JAK-STAT inhibitors decreasing the expression of astrocyte marker leptin (30). STAT6 is reported to inhibit human IL-4 promoter activity in T cells and downregulate the gene expression of IL-4 (31). IL-4/IL4R can inhibit adipocyte differentiation by two mechanisms: Inhibiting adipogenesis via downregulating the expression of PPARγ and C/EBPα; and promoting lipolysis in mature adipocytes via enhancing the activity and translocation of hormone-sensitive lipase to decrease lipid deposits (32). However, the LEP-JAK-STAT-IL-4/IL4R signal pathways in the adipocyte differentiation of HASCs requires further experimental validation. In addition to the downstream pathways, the present study also analyzed the upstream non-coding RNAs of LEP, including miRNAs and lncRNAs, which were previously considered to be crucial for the adipogenic differentiation of HASCs (13,14,3336). The results identified the RP11-552F3.9 (or RP11-15A1.7)-hsa-miR-23a-5p/hsa-miR-302d-3p (or hsa-miR-130b-5p)-LEP ceRNA axes. miR-130 has been shown to affect adipocyte differentiation from preadipocytes, with overexpressing miR-130 impairing adipogenesis and reducing miR-130-enhanced adipogenesis, and its potential target may be adipogenesis-related gene PPARγ (37). In addition, the inhibition of miR-23a has been reported to increase the adipogenic differentiation of BMSCs (38). In line with these findings, the present study found that hsa-miR-130b-5p and has-miR-23a-5p were downregulated in adipocyte differentiated HASCs. There have been no reports on the roles of miR-302d-3p and the above lncRNAs (RP11-552F3.9 and RP11-15A1.7) for adipocyte differentiation, indicating they may be novel targets identified by the present study.

RAC2 was identified as another hub gene that may be involved in the adipocyte differentiation of HASCs by miRNA-mRNA regulatory pair prediction and ceRNA network analysis. RAC2 was related to cell-substrate adhesion. It is well accepted that cell-substrate adhesion can control the fate of stem cells (39). A previous study showed that HASCs differentiated into adipocytes when the substrate stiffness decreased (40). Focal adhesion kinase (FAK) is a central protein involved in cell-substrate adhesion by Cas-Rac-lamellipodin signaling (41). The stimulation of Rac and increase in the activity of FAK enhanced cell tension by maintaining cell shape and matrix adhesion (42), whereas reduced cell stiffness and reduced adhesion strength have been observed in FAK-deficient cells (43). The inhibition of FAK has also been reported to lead to the elevation of adipogenic marker gene LEP and lipid accumulation in HASCs (43). These findings implicate the underlying anti-adipogenic activity of FAK and RAC. In line with these findings, the present study found that RAC2 was downregulated in adipocyte differentiated HASCs. Furthermore, it was predicted that RAC2 may be regulated by GDNF-AS1-hsa-miR-378a-5p. Previous evidence has shown that miR-378 is an adipogenesis-related miRNA in human adipocytes (44). The expression of miR-378a is upregulated in the adipose tissues of high fat diet-induced obese mice, and during the differentiation of preadipocytes (45,46). Investigations of the mechanism have revealed that miR-378 may induce adipogenesis by targeting mitogen-activated protein kinase 1 (45), E2F transcription factor 2 and Ras-related nuclear-binding protein 10 (46). Accordingly, it was hypothesized that hsa-miR-378a-5p may be upregulated in adipocyte differentiated HASCs, which was demonstrated in the present study. However, further experiments are required to confirm the role of this miRNA in HASC differentiation and its targeted interactions with RAC2. There are no previous reports on the roles of GDNF-AS1 in HASC differentiation, indicating it may also be a novel target identified by the present study.

Hub genes CXCL10 in module 2 and EHHADH in module 6 were shown to be respectively regulated by hsa-miR-411-3p and hsa-miR-487a, being involved in inflammatory and amino acid metabolism pathways for HASC differentiation. As reported for LEP above, inflammation promotes the adipocyte differentiation of HASCs, whereas CXCL10 is a well-known pro-inflammatory chemokine (47). Therefore, CXCL10 may be upregulated in adipocyte differentiated HASCs, which was confirmed in the present study. EHHADH has been reported as a downstream target upregulated by PPAR (48). PPAR is an important marker in stimulating adipogenesis (12). EHHADH may also be expressed at a high level in adipocyte differentiated HASCs, which was in consistent with the present study. These two miRNAs regulating CXCL10 and EHHADH have not been demonstrated to be responsible for HASC differentiation, which highlights potential directions in future investigations.

CCNB1, JUN and PTPRC were suggested to be important for adipocyte differentiation from HASCs according to the PPI network analysis. With reference to previous studies, high expression levels of CCNB1 (49) and JUN (50) may be positively associated with the proliferation of stem cells. Generally, the differentiation process can be executed only following weakening of the proliferation ability of stem cells. In the adipocyte differentiation of HASCs, CCNB1 and JUN may be downregulated, which was verified in the present study. PTPRC is also known as CD45, a JAK phosphatase, which negatively regulates cytokine receptor signaling via inhibiting the activity of STAT3 (51,52). According to the findings of LEP above, PTPRC may be downregulated for the adipocyte differentiation of HASCs, which was in line with the results of the present study.

There were some limitations in the present study. First, adipocyte differentiated cells were induced following different culture durations in the GSE57593, GSE25715 and GSE61302 datasets, which may lead to differences in the expression levels of the identified mRNAs, miRNAs and lncRNAs if the same samples were used for their detection. Second, the sample size of each dataset (GSE57593: Four undifferentiated HASCs and six adipocyte differentiated cells; GSE25715: Four undifferentiated HASCs and eight adipocyte differentiated cells: GSE61302: Five undifferentiated HASCs and 10 adipocyte differentiated cells) was small. Additional high-throughput sequencing experiments with larger samples are required to confirm the conclusions. Third, the present study comprised preliminary screening; however, further wet experiments, including quantitative-polymerase chain reaction analysis, western blotting, dual luciferase reporter assays, and knockout or overexpression in vitro and in vivo, are indispensable to confirm the expression levels of the identified target genes and validate the regulatory associations between DEMs and DELs/DEGs.

In conclusion, the present study preliminarily identified several crucial DEGs (LEP, RAC2, CXCL10, EHHADH CCNB1, JUN and PTPRC), DEMs (has-miR-130b-5p and has-miR-23a-5p, has-miR-302d-3p, has-miR-378a-5p, hsa-miR-411-3p and hsa-miR-487a) and DELs (RP11-552F3.9, RP11-15A1.7 and GDNF-AS1) for inducing the adipogenic differentiation of HASCs. Among these, the RP11-552F3.9 (or RP11-15A1.7)-hsa-miR-302d-3p-LEP ceRNA interaction axes may be particularly important and represents a novel mechanism for the adipogenic differentiation of HASCs. Further in vitro and in vivo investigations are required to confirm their roles in breast reconstruction and augmentation.


Not applicable.


No funding was received.

Availability of data and materials

The microarray data GSE57593, GSE25715 and GSE61302 were downloaded from the GEO database in NCBI (

Authors' contributions

ZG and YC conceived and designed the original study. ZG conducted the bioinformatic analysis and drafted the manuscript. YC contributed to the acquisition and interpretation of data and revised the manuscript. Both authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.



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Guo Z and Guo Z: An lncRNA‑miRNA‑mRNA ceRNA network for adipocyte differentiation from human adipose‑derived stem cells. Mol Med Rep 19: 4271-4287, 2019
Guo, Z., & Guo, Z. (2019). An lncRNA‑miRNA‑mRNA ceRNA network for adipocyte differentiation from human adipose‑derived stem cells. Molecular Medicine Reports, 19, 4271-4287.
Guo, Z., Cao, Y."An lncRNA‑miRNA‑mRNA ceRNA network for adipocyte differentiation from human adipose‑derived stem cells". Molecular Medicine Reports 19.5 (2019): 4271-4287.
Guo, Z., Cao, Y."An lncRNA‑miRNA‑mRNA ceRNA network for adipocyte differentiation from human adipose‑derived stem cells". Molecular Medicine Reports 19, no. 5 (2019): 4271-4287.