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Article

Exploratory Analysis of Differentially Expressed Genes for Distinguishing Adipose-Derived Mesenchymal Stroma/Stem Cells from Fibroblasts

by
Masami Kanawa
1,*,
Katsumi Fujimoto
2,
Tania Saskianti
3,
Ayumu Nakashima
4,5 and
Takeshi Kawamoto
1,6
1
Natural Science Center for Basic Research and Development, Hiroshima University, Hiroshima 734-8533, Japan
2
Department of Molecular Biology and Biochemistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8533, Japan
3
Department of Pediatric Dentistry, Faculty of Dental Medicine, Universitas Airlangga, Surabaya 60132, Indonesia
4
Department of Stem Cell Biology and Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8533, Japan
5
Department of Nephrology, Graduate School of Medicine, University of Yamanashi, Yamanashi 409-3898, Japan
6
Writing Center, Hiroshima University, Higashi-Hiroshima 739-8512, Japan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(18), 9881; https://doi.org/10.3390/app15189881
Submission received: 12 August 2025 / Revised: 3 September 2025 / Accepted: 8 September 2025 / Published: 9 September 2025
Browse Figures
Versions Notes

Abstract

Adipose-derived mesenchymal stromal/stem cells (AT-MSCs) can be typically isolated from adipose tissue using a minimally invasive procedure. However, since AT-MSCs are usually obtained from subcutaneous tissue, there is a risk of contamination with fibroblasts (FBs), which can reduce the differentiation potential of AT-MSCs. To avoid this contamination, it is crucial to identify specific markers to effectively distinguish AT-MSCs from FBs. Analysis of microarray data obtained from three studies (GSE9451, GSE66084, GSE94667, and GSE38947) revealed 123 genes expressed at levels more than 1.5-fold higher in AT-MSCs compared to FBs. Using STRING, a protein-protein interaction (PPI) network consisting of 80 nodes and 197 edges was identified within the 123 genes. Further investigation using Molecular Complex Detection in Cytoscape identified a module of 12 genes: COL3A1, FBN1, COL4A1, COL5A2, POSTN, CTGF, SPARC, HSPG2, FSTL1, LAMA2, LAMC1, COL16A1. Gene Ontology analysis revealed that these genes were enriched in extracellular region (GO: 0005576). Additionally, these 12 genes corresponded to the top 12 of the 15 hub genes calculated using the Maximal Clique Centrality algorithm. The results of this study suggest that these 12 genes may serve as markers for distinguishing AT-MSCs from FBs, offering potential applications in regenerative medicine.

1. Introduction

Mesenchymal stromal/stem cells (MSCs), which can differentiate into multiple cell types such as osteocytes, chondrocytes, adipocytes, myocytes, and nerve cells [1,2,3], have garnered considerable attention in recent years as promising sources for regenerative medicine. MSCs can be isolated from various tissues, including bone marrow, placenta, umbilical cord blood, synovium, dental pulp, and adipose tissue [1,4,5,6,7]. Subcutaneous adipose tissue is a readily accessible source for obtaining substantial quantities of adipose-derived MSCs (AT-MSCs) through a minimally invasive procedure [8]. However, since adipose tissue is isolated from subcutaneous tissue, a risk of contamination with fibroblasts (FBs) exists [9]. MSCs are morphologically indistinguishable from FBs. These cells share similar surface immunophenotype and proliferation potential, although FBs have little differentiation capacities [10]. Contamination of MSC cultures with fibroblasts has been reported to impair therapeutic efficacy; the presence of fibroblasts as a cell contaminant in the MSC preparation abolished therapeutic effects of stem cell transplantation [11]. Therefore, the identification of reliable biomarkers to distinguish AT-MSCs from FBs is essential to ensure the purity of MSC preparations and to safeguard the clinical effectiveness of MSC-based therapies.
Differences between MSCs and FBs are not fully understood. The International Society for Cellular Therapy defines MSCs as positive for the MSC markers CD105, CD73, and CD90, and negative for hematopoietic lineage markers [12]. Recently, Alt et al. compared the surface antigen markers of AT-MSCs and FBs and found that the MSC markers CD44, CD73, and CD105 were positive in both AT-MSCs and FBs, whereas the endothelial cell marker CD31 and hematopoietic lineage markers CD14 and CD45 were negative in both cell types [13]. Zanata et al. also reported that AT-MSCs and FBs showed similar immunophenotypic profiles [14]. These findings suggest that distinguishing between AT-MSCs and FBs based on surface antigen differences is challenging.
Bioinformatics analysis of differentially expressed genes (DEGs) using a protein-protein interaction (PPI) network has recently been used to identify accurate and reliable markers. For example, in studies of various cancers, such as hepatocellular carcinoma, colorectal cancer, breast cancer, gastric cancer, and glioblastoma, PPI network analysis of DEGs has led to the identification of gene markers for diagnosis and prognosis [15,16,17,18,19]. In addition, similar approaches have been used to identify key genes involved in the osteogenic and chondrogenic differentiation of MSCs [20,21]. However, no studies have yet analyzed the differences between MSCs and FBs using PPI network analysis.
In a previous study, we analyzed the difference in gene expression between bone marrow-derived MSCs (BM-MSCs) and FBs using DNA microarray analysis and identified several DEGs that could serve as gene markers to distinguish BM-MSCs from FBs [22]. However, whether these markers can distinguish AT-MSCs from FBs remains unexplored.
In this study, we identified 123 DEGs that were expressed at >1.5-fold higher levels in AT-MSCs than in FBs by comparing microarray data. Using STRING and MCODE in Cytoscape (v3.10.3), we identified 80 nodes and 197 edges along with a module consisting of 12 genes within the PPI networks of the 123 DEGs. Gene Ontology (GO) analysis revealed that the 12 genes were enriched in the extracellular region of the cellular component (CC). Our findings suggest that the 12 genes comprising the module identified may serve as markers for distinguishing AT-MSCs from FBs.

2. Materials and Methods

2.1. Sample Collection

Microarray data for AT-MSCs, BM-MSCs, and FBs were obtained from three studies (Table 1). Gene expression profiles (GSE9451 [23,24], GSE66084 [24], GSE94667 [25], and GSE38947 [26]) were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/, accessed on 5 September 2025). The GSE9451 and GSE66084 microarray datasets were based on the GPL574 platform (Affymetrix Human Genome U133 Plus 2.0Array) (Affymetrix, Inc., Santa Clara, CA, USA). The GSE94667 dataset was based on the GPL16043 platform (GeneChip® PrimeView Human Gene Expression Array) (Affymetrix, Inc.). The GSE38947 dataset was based on the GPL14550 platform (Agilent-028004 SurePrint G3 Human GE 8 × 60 K Microarray) (Agilent Technologies, Santa Clara, CA, USA). These datasets contained nine samples each of AT-MSCs, BM-MSCs, and FBs.

2.2. Analysis of Gene Expression Patterns and Identification of DEGs

A unified standardized expression set of 7616 genes was created from cross-platform expression data of all 27 samples on the Subio platform (Subio, Inc., Kagoshima, Japan). To identify gene expression patterns across different cell types, hierarchical clustering was performed using Pearson’s correlation. DEGs with higher expression in AT-MSCs than in FBs were screened, using an adjusted p value < 0.05 and fold change > 1.5 as cut-off criteria (Supplementary Table S1).

2.3. PPI Network and GO Analysis

The PPI network of DEGs was constructed by using the STRING online database (version 12.0) (https://string-db.org/) [27,28]. Interactions with a combined score > 0.4 were considered statistically significant. The constructed PPI network was then imported into Cytoscape (version 3.7.1) to visualize the molecular interaction networks [29]. The most significant module was identified using the plugin Molecular Complex Detection (MCODE) (version 1.5.1) [30], with the following selection criteria: MCODE score > 5, degree cutoff = 2, node score cutoff = 0.2, maximum depth = 100, and k-score = 2. Hub genes were calculated based on the Maximal Clique Centrality (MCC) algorithm [31] using the Cytoscape plugin CytoHubba (version 0.1). GO analysis was performed using g:Profiler (version e109_eg56_p17_773ec798) [32] (Supplementary Table S2).

3. Results

3.1. Hierarchical Clustering Analysis

The DNA microarray data of 27 samples (9 AT-MSCs, 9 BM-MSCs, and 9 FBs) were extracted from three studies (Table 1), and a standardized expression set of 7616 genes was created using the Subio platform. The 7616 genes were successfully categorized into three cell types, AT-MSCs, BM-MSCs, and FBs, using hierarchical clustering analysis (Figure 1). Six distinct clusters were identified: Cluster 1 consisted mainly of genes expressed at high levels in BM-MSCs but not in AT-MSCs and FBs. Cluster 2 contained genes expressed at high levels in FBs, but not in AT-MSCs and BM-MSCs. Cluster 3 consisted of genes expressed at low levels in AT-MSCs but not in BM-MSCs and FBs, whereas clusters 4a, 4b, and 4c contained genes expressed at high levels in AT-MSCs but not in BM-MSCs and FBs. Cluster 5 primarily comprised genes expressed at low levels in FBs, but not in AT-MSCs and BM-MSCs. Cluster 6 consisted of genes expressed at low levels in BM-MSCs but not in AT-MSCs and FBs.

3.2. DEG Selection Between AT-MSCs and FBs and Construction of PPI Networks

Next, we sought to identify the DEGs between AT-MSCs and FBs. From the three studies, DEGs expressed at >1.5-fold higher levels in AT-MSCs than in FBs were selected. Using volcano plots, 685 DEGs in Study A, 904 DEGs in Study B, and 1565 DEGs in Study C were identified (Figure 2a). As shown in Figure 2b, the three studies shared 123 DEGs (Supplementary Table S1).
The list of 123 DEGs was uploaded to STRING in order to construct the PPI networks. A PPI network containing 80 nodes and 197 edges was identified (Figure 3a). Furthermore, using MCODE in Cytoscape, a significant module (Module A) with 12 nodes and 55 edges was identified (Figure 3b). The 12 nodes represented the following genes: COL3A1, FBN1, COL4A1, COL5A2, POSTN, CTGF, SPARC, HSPG2, FSTL1, LAMA2, LAMC1, and COL16A1.

3.3. GO Analysis

To investigate the functions of the 12 identified genes, we performed GO analysis using g:Profiler. We identified 13 molecular function (MF), 37 biological process (BP), and 16 CC terms for the 12 genes (p < 0.05) (Figure 4a and Supplementary Table S2). Notably, all 12 genes were enriched in the extracellular region (GO: 0005576) of the CC (Figure 4b). Additionally, most of the genes were enriched in three key terms: extracellular matrix structural constituent (GO: 0005201) of MF, collagen-containing extracellular matrix (GO: 0062023) of CC, and extracellular matrix (GO: 0031012) of CC. Furthermore, COL3A1, COL16A1, and CTGF (CCN2) were enriched in terms associated with integrins, such as integrin binding (GO: 0005178) in MF and integrin-mediated signaling pathway (GO: 0007229) in BP.

3.4. Selection and Analysis of Hub Genes

To identify hub genes in the PPI network identified, the 80 nodes were calculated using the MCC algorithm. The top 15 genes with high MCC scores were considered hub genes: COL3A1, FBN1, COL4A1, COL5A2, POSTN, CTGF (CCN2), SPARC, HSPG2, FSTL1, LAMA2, LAMC1, COL16A1, SERPINE1, TAGLN, and SLCLA5 (Table 2). Interestingly, the top 12 of the 15 hub genes were identical to the 12 genes comprising Module A, suggesting that these 12 genes play crucial roles in the PPI network. The MCC scores of the remaining three genes, SERPINE1, TAGLN, and SLCLA5, were relatively low compared with those of the 12 genes. Notably, three of the top four genes were collagen genes: COL3A1, COL4A1, and COL5A2.
Next, we compared the expression levels of the 12 genes in AT-MSCs and FBs (Table 2). The expression levels of POSTN and CTGF in AT-MSCs were found to be at least 10-fold higher than those in FBs, suggesting that the genes in Module A may function as markers for distinguishing AT-MSCs from FBs.

4. Discussion

In this study, we compared MSCs and FBs, which share a high degree of similarity in terms of morphology and surface antigens at the molecular level. By analyzing 123 genes with higher expression levels in AT-MSCs compared to FBs, we constructed PPI networks and identified a module consisting of 12 genes (COL3A1, FBN1, COL4A1, COL5A2, POSTN, CTGF, SPARC, HSPG2, FSTL1, LAMA2, LAMC1, and COL16A1) (Module A). The MCC algorithm suggested that these 12 genes function as hub genes in the PPI network identified in this study.
Interestingly, COL3A1 exhibited the highest MCC score among the 12 genes in Module A, suggesting that COL3A1 may play a crucial role in maintaining the differentiation potential of MSCs. Type III collagen is primarily located in the medial layer of the arterial wall [33] and is also involved in the tenogenic differentiation ability of tendon stem/progenitor cells (TSPCs) [34]. Notably, the tenogenic differentiation ability of TSPCs derived from aged donors is significantly lower compared to those from young donors, with tendon-related markers, including COL3A1, being significantly downregulated in aged TSPCs [34]. Rong et al. also reported a significant decrease in COL3A1 expression in senescent human MSCs [33]. COL3A1 expression in senescent MSCs was restored by the overexpression of the embryonic transcription factor NANOG [33], which was reported to be sufficient to reverse cellular aging and restore the differentiation ability of aged MSCs [35,36]. Together, these findings indicate that COL3A1 may play an essential role in maintaining the differentiation ability of AT-MSCs. However, to date, knockdown or overexpression studies of COL3A1 in AT-MSCs have not been reported, and such experiments will be necessary in the future to clarify its specific role in AT-MSC differentiation potentials.
Although COL3A1 had the highest MCC score among the 12 genes (13,544), its expression ratio in AT-MSCs compared to FBs was ranked eighth (2.66-fold). In contrast, the expression ratios of POSTN and CTGF in AT-MSCs to FBs were both 10 times or higher, and the MCC scores of these two genes were also exceptionally high, exceeding 10,000. Furthermore, Ragelle et al. performed a proteomic characterization of extracellular matrices derived from AT-MSCs, BM-MSCs, and FBs and reported that high protein expression of CTGF and POSTN was detected in AT-MSC-derived matrices [25]. Given the expression ratios and MCC scores of POSTN and CTGF, they were considered effective markers. PPI network analysis also indicated strong associations between these two genes and the remaining ten genes in Module A. Therefore, we propose that these 12 genes could serve as gene markers for distinguishing AT-MSCs from FBs.
CTGF has been shown to regulate BM-MSC lineage fate through extracellular matrix interactions, particularly via modulation of the CTGF-VEGF complex by RhoA/ROCK signaling [37]. Activation of this pathway promotes myofibroblast differentiation of BM-MSC, whereas its inhibition facilitates endothelial differentiation. These findings suggest that CTGF functions as a key regulator of lineage specification, linking extracellular matrix cues to distinct differentiation outcomes in MSCs. Postn enhances AT-MSC adhesion, migration, and therapeutic efficiency in a murine ischemia model [38], and POSTN expression also decreases during adipogenic differentiation of AT-MSCs [39]. Together, these reports underscore the roles of CTGF and POSTN as ECM molecules influencing lineage specification, stemness, and regenerative capacity. However, despite these functional associations, experimental manipulation of POSTN expression in AT-MSCs has not been investigated, and further studies will be important to elucidate its precise function in stemness and differentiation.
In a previous study, we identified ten gene markers, including CTGF, for distinguishing between BM-MSCs and FBs [22]. The expression of CTGF in BM-MSCs was 15-fold higher than that in FBs. In the samples used in this study, the BM-MSC to FB expression ratio was 9.15-fold (Supplementary Table S3), while the ratio for AT-MSCs to FBs was 10.46-fold. Therefore, CTGF is considered a common marker of AT-MSCs and BM-MSCs.
GO analysis revealed that all 12 genes identified in this study were enriched in the extracellular region (GO:0005576). The extracellular matrix, which forms a stem cell niche, is essential for cell proliferation and differentiation [40]. Using LC-MS/MS, Soteriou et al. analyzed feeders for human embryonic stem (ES) cell culture and identified 42 extracellular proteins [41], 6 of which were encoded by the genes identified in this study: COL3A1, FBN1, COL4A1, COL5A2, HSPG2, and LAMC1. Interestingly, Soteriou et al. successfully cultured ES cells in dishes coated with fibrillin-1 (FBN1) alone or perlecan (HSPG2) in combination with fibronectin, without losing the undifferentiated characteristics of the cells. These findings suggest that some of the genes identified in this study play crucial roles in maintaining the stemness of both AT-MSCs and ES cells.
Our GO analysis also revealed that COL3A1, COL16A1, and CTGF (CCN2), identified in this study, were enriched in integrin-related terms such as integrin binding (GO:0005178) and the integrin-mediated signaling pathway (GO:0007229). Given that integrins play critical roles in stem cell functions [40], these findings suggest that COL3A1, COL16A1, and CTGF (CCN2), together with integrins, are involved in the stem cell characteristics of AT-MSCs, including their high differentiation potential. Indeed, in a previous study, we identified the integrin subunit alpha 5 gene as a marker predicting the adipogenic differentiation potential of BM-MSCs [42], although no integrin gene was identified in this study.
As mentioned earlier, AT-MSCs cannot be easily distinguished from FBs based on phenotypic profiles, such as surface CD markers [14]. If AT-MSC cultures are contaminated with FBs, it becomes difficult to identify the contamination. Therefore, markers are required to distinguish between AT-MSCs and FBs. In the attempt to establish such markers, Melo et al. used RNA-seq and identified 910 genes highly expressed in AT-MSCs compared to FBs [43]. Similarly, Jääger et al. reported 59 genes more highly expressed in AT-MSCs than in FBs [44]. Among the 12 genes identified in this study, 9 genes were included in the list reported by Melo et al. [43]: COL3A1, COL4A1, COL16A1, CTGF, FBN1, LAMA2, LAMC1, POSTN, and SPARC. Additionally, the 59 genes reported by Jääger et al. contained 4 genes identified in this study: COL3A1, COL16A1, POSTN, and SPARC, all of which were included in the above 9 genes. These findings collectively suggest that these nine genes are particularly important for distinguishing AT-MSCs from FBs.
This study has several limitations. First, it relies on publicly available DNA microarray datasets rather than RNA-seq, which has recently become the mainstream approach for transcriptome analysis. Our laboratory has used DNA microarray platforms to study differences between MSCs and FBs, including BM-MSCs [22,23,24], even before RNA-seq became widely available. For this reason, we integrated array datasets from multiple laboratories to identify genes that consistently distinguish AT-MSCs from FBs across studies. Although RNA-seq could provide greater depth and resolution, we did not identify publicly available datasets containing matched samples of AT-MSCs, BM-MSCs, and FBs with sufficient replicates. Second, this study lacks experimental validation of the identified candidate genes. Future work should expand sample sizes and employ absolute quantification methods such as digital PCR to validate the expression levels of the 12 genes identified here. In addition, it will be important to correlate gene expression with AT-MSC differentiation capacity, to establish functional links between marker expression and cell fate.

5. Conclusions

In summary, hierarchical clustering analysis identified gene clusters that may distinguish AT-MSCs from FBs. In addition, we have identified a key module of 12 genes differentially expressed between AT-MSCs and FBs by constructing a PPI network. All 12 genes were enriched in the extracellular region categories, suggesting their involvement in the stem cell niche. Our findings suggest that these 12 genes could serve as markers to distinguish AT-MSCs from FBs and may play a role in maintaining the differentiation potential and stemness of AT-MSCs. These insights offer a novel approach for advancing regenerative medicine.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/app15189881/s1: Table S1: Differentially expressed 123 genes between AT-MSCs and FBs among 3 studies. Table S2: GO analysis for Module A. Table S3: Differentially expressed 96 genes between BM-MSCs and FBs among 3 studies.

Author Contributions

M.K. conducted the research, while M.K. and T.K. were responsible for drafting the manuscript. K.F. and A.N. were involved in the study’s conception and design. M.K., T.K., and T.S. took part in revising and editing the manuscript. They all agreed to accept responsibility and accountability for the article’s content and to collectively address any inquiries regarding the accuracy or integrity of the published work. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by JSPS KAKENHI Grant Number JP21K097920A.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets analyzed in the study could be found in the GEO portal (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 24 May 2022)).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AT-MSCsAdipose-derived Mesenchymal Stromal/Stem Cells
BM-MSCsBone Marrow-derived Mesenchymal Stromal/Stem Cells
DEGsDifferentially Expressed Genes
ES CellsEmbryonic Stem Cells
FBsFibroblasts
GOGene Ontology
GEOGene Expression Omnibus
MCCMaximal Clique Centrality
MCODEMolecular Complex Detection

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Applsci 15 09881 g001
Figure 1. Heat map of hierarchical clustering of 7616 genes from AT-MSCs, BM-MSCs, and FBs. Each column represents a sample, and each row represents a gene. Dendrograms at the top and to the left show the relatedness of expression patterns for individual samples and individual probe sets (genes), respectively. Red circles mark identified clustering nodes. Expression pattern grouping is shown on the right of the heat map.
Figure 1. Heat map of hierarchical clustering of 7616 genes from AT-MSCs, BM-MSCs, and FBs. Each column represents a sample, and each row represents a gene. Dendrograms at the top and to the left show the relatedness of expression patterns for individual samples and individual probe sets (genes), respectively. Red circles mark identified clustering nodes. Expression pattern grouping is shown on the right of the heat map.
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Figure 2. Identification of differentially expressed genes (DEGs) from three studies. (a) Volcano plots of the three studies. Red plots represent DEGs expressed at higher levels in AT-MSCs than in FBs with the criteria of p value < 0.05 and fold change > 1.5. (b) Venn diagram showing the overlap in the number of DEGs among three studies.
Figure 2. Identification of differentially expressed genes (DEGs) from three studies. (a) Volcano plots of the three studies. Red plots represent DEGs expressed at higher levels in AT-MSCs than in FBs with the criteria of p value < 0.05 and fold change > 1.5. (b) Venn diagram showing the overlap in the number of DEGs among three studies.
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Figure 3. Identification of the module genes. (a) PPI network of 123 genes constructed by Cytoscape. (b) The identified module with 12 nodes and 55 edges (Module A).
Figure 3. Identification of the module genes. (a) PPI network of 123 genes constructed by Cytoscape. (b) The identified module with 12 nodes and 55 edges (Module A).
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Figure 4. GO analysis of 12 genes of Module A in g:Profiler. (a) Manhattan plot showing enriched terms of GO molecular functions (GO: MF), biological processes (GO: BP), or cellular components (GO: CC) in 12 genes. The black line indicates −log10(Padj) = 3. (b) Detailed results depicting 28 enriched terms with Padj < 0.001.
Figure 4. GO analysis of 12 genes of Module A in g:Profiler. (a) Manhattan plot showing enriched terms of GO molecular functions (GO: MF), biological processes (GO: BP), or cellular components (GO: CC) in 12 genes. The black line indicates −log10(Padj) = 3. (b) Detailed results depicting 28 enriched terms with Padj < 0.001.
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Table 1. Microarray datasets used in this study.
Table 1. Microarray datasets used in this study.
StudyAcronymSource NameGEO
Series		GEO SampleDonor No.
Study-AAT-MSCsadipose tissue mesenchymal stem cellsGSE66084GSM16140571
AT-MSCsadipose tissue mesenchymal stem cellsGSM16140562
AT-MSCsadipose tissue mesenchymal stem cellsGSM16140583
BM-MSCsiliac mesenchymal stem cellsGSE9451GSM2397134
BM-MSCsiliac mesenchymal stem cellsGSM2397155
BM-MSCsiliac mesenchymal stem cellsGSM2397226
FBsskin fibroblastsGSM2397667
FBsskin fibroblastsGSM2397698
FBsgingival fibroblastsGSM2398019
Study-BAT-MSCsAdipose-derived mesenchymal stem cellsGSE94667GSM248056810
AT-MSCsAdipose-derived mesenchymal stem cellsGSM248056711
AT-MSCsAdipose-derived mesenchymal stem cellsGSM248056912
BM-MSCsBone marrow-derived mesenchymal stem cellsGSM248055613
BM-MSCsBone marrow-derived mesenchymal stem cellsGSM248055514
BM-MSCsBone marrow-derived mesenchymal stem cellsGSM248055715
FBsneonatal dermal fibroblastsGSM248054416
FBsneonatal dermal fibroblastsGSM248054517
FBsneonatal dermal fibroblastsGSM248054618
Study-CAT-MSCsadipose stromal cellsGSE38947GSM95262019
AT-MSCsadipose stromal cellsGSM95262120
AT-MSCsadipose stromal cellsGSM95262221
BM-MSCsbone marrow mesenchymal stem cellsGSM95261722
BM-MSCsbone marrow mesenchymal stem cellsGSM95261923
BM-MSCsbone marrow mesenchymal stem cellsGSM95261824
FBsforeskin fibroblastsGSM95262325
FBsforeskin fibroblastsGSM95262426
FBsforeskin fibroblastsGSM95262527
GEO: Gene Expression Omnibus.
Table 2. Hub genes identified by MCC (Maximal Chique Centrality) algorithm.
Table 2. Hub genes identified by MCC (Maximal Chique Centrality) algorithm.
Gene SymbolMCC ScoreModule AFold Difference ± SD (AT-MSCs/FBs)
1COL3A113,544Yes2.66 ± 1.34
2FBN113,536Yes3.54 ± 1.93
3COL4A113,232Yes5.57 ± 4.90
4COL5A213,152Yes2.11 ± 0.23
5POSTN11,982Yes12.12 ± 15.05
6CTGF (CCN2)10,506Yes10.46 ± 6.42
7SPARC10,202Yes2.03 ± 0.42
8HSPG26486Yes1.91 ± 0.26
9FSTL15192Yes1.94 ± 0.09
10LAMA22881Yes4.89 ± 3.11
11LAMC11561Yes2.68 ± 0.39
12COL16A11446Yes2.16 ± 0.41
13SERPINE1252No4.58 ± 1.53
14TAGLN243No3.13 ± 1.76
15SLC1A582No2.18 ± 0.81
Module A indicates the significant module identified in this study (Figure 3b).
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Kanawa, M.; Fujimoto, K.; Saskianti, T.; Nakashima, A.; Kawamoto, T. Exploratory Analysis of Differentially Expressed Genes for Distinguishing Adipose-Derived Mesenchymal Stroma/Stem Cells from Fibroblasts. Appl. Sci. 2025, 15, 9881. https://doi.org/10.3390/app15189881

AMA Style

Kanawa M, Fujimoto K, Saskianti T, Nakashima A, Kawamoto T. Exploratory Analysis of Differentially Expressed Genes for Distinguishing Adipose-Derived Mesenchymal Stroma/Stem Cells from Fibroblasts. Applied Sciences. 2025; 15(18):9881. https://doi.org/10.3390/app15189881

Chicago/Turabian Style

Kanawa, Masami, Katsumi Fujimoto, Tania Saskianti, Ayumu Nakashima, and Takeshi Kawamoto. 2025. "Exploratory Analysis of Differentially Expressed Genes for Distinguishing Adipose-Derived Mesenchymal Stroma/Stem Cells from Fibroblasts" Applied Sciences 15, no. 18: 9881. https://doi.org/10.3390/app15189881

APA Style

Kanawa, M., Fujimoto, K., Saskianti, T., Nakashima, A., & Kawamoto, T. (2025). Exploratory Analysis of Differentially Expressed Genes for Distinguishing Adipose-Derived Mesenchymal Stroma/Stem Cells from Fibroblasts. Applied Sciences, 15(18), 9881. https://doi.org/10.3390/app15189881

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