Investigation of molecular biomarker candidates for diagnosis and prognosis of chronic periodontitis by bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus (GEO).

BACKGROUND: Chronic periodontitis (CP) is a multifactorial inflammatory disease. For the diagnosis of CP, it is necessary to investigate molecular biomarkers and the biological pathway of CP. Although analysis of mRNA expression profiling with microarray is useful to elucidate pathological mechanisms of multifactorial diseases, it is expensive. Therefore, we utilized pooled microarray gene expression data on the basis of data sharing to reduce hybridization costs and compensate for insufficient mRNA sampling. The aim of the present study was to identify molecular biomarker candidates and biological pathways of CP using pooled datasets in the Gene Expression Omnibus (GEO) database.
METHODS: Three pooled transcriptomic datasets (GSE10334, GSE16134, and GSE23586) of gingival tissue with CP in the GEO database were analyzed for differentially expressed genes (DEGs) using GEO2R, functional analysis and biological pathways with the Database of Annotation Visualization and Integrated Discovery database, Protein-Protein Interaction (PPI) network and hub gene with the Search Tool for the Retrieval of Interaction Genes database, and biomarker candidates for diagnosis and prognosis and upstream regulators of dominant biomarker candidates with the Ingenuity Pathway Analysis database.
RESULTS: We shared pooled microarray datasets in the GEO database. One hundred and twenty-three common DEGs were found in gingival tissue with CP, including 81 upregulated genes and 42 downregulated genes. Upregulated genes in Gene Ontology were significantly enriched in immune responses, and those in the Kyoto Encyclopedia of Genes and Genomes pathway were significantly enriched in the cytokine-cytokine receptor interaction pathway, cell adhesion molecules, and hematopoietic cell lineage. From the PPI network, the 12 nodes with the highest degree were screened as hub genes. Additionally, six biomarker candidates for CP diagnosis and prognosis were screened.
CONCLUSIONS: We identified several potential biomarkers for CP diagnosis and prognosis (e.g., CSF3, CXCL12, IL1B, MS4A1, PECAM1, and TAGLN) and upstream regulators of biomarker candidates for CP diagnosis (TNF and TGF2). We also confirmed key genes of CP pathogenesis such as CD19, IL8, CD79A, FCGR3B, SELL, CSF3, IL1B, FCGR2B, CXCL12, C3, CD53, and IL10RA. To our knowledge, this is the first report to reveal associations of CD53, CD79A, MS4A1, PECAM1, and TAGLN with CP.

Background

Chronic periodontitis (CP) is a multifactorial inflammatory disease caused by genetic, immune, environmental, and microbiological factors and lifestyle habits [1-3]. CP is characterized by destruction of periodontal tissues, especially gingival tissue inflammation and alveolar bone resorption. Many previous studies of multiple gene interactions and pathways have not completely elucidated the biological mechanisms of CP.

Development of high-throughput experimental methods in biological studies has yielded extensive omics data. Additionally, transcriptomic studies using microarray analysis have advanced our understanding of the expression landscape for biological mechanisms of multifactorial diseases. Integration of multiple microarray datasets has generated disease-associated mRNA profiles for screening. While the experimental condition of each dataset is clinically and technically different, common differentially expressed genes (DEGs) related to CP among multiple datasets may identify key genes as potential targets for CP diagnosis and prognosis.

At present, data sharing and integration of omics data for investigating mechanisms of multifactorial diseases have gained attention. Registration of biological experimental data in public databases has also been recommended to help facilitate data sharing. Use of pooled microarray gene expression datasets is a method to reduce hybridization costs and compensate for insufficient amounts of mRNA sampling [4-9]. Many studies utilizing microarray analysis to investigate mechanisms underlying periodontitis have been conducted [10-25].

The National Center for Biotechnology Information developed the Gene Expression Omnibus (GEO) database to promote pooling and sharing of publically available transcriptomic data to facilitate biomedical research [26-30]. ArrayExpress is a public database for high-throughput functional genomic data that consists of two parts: the ArrayExpress Repository, which is the Minimum Information About a Microarray Experiment supportive public archive of microarray data, and the ArrayExpress Data Warehouse, which is a database of gene expression profiles selected from a repository that is consistently reannotated [31].

In this study, we focused on gene expression in gingival tissue from CP patients. We selected and analyzed three pooled microarray platform datasets in the GEO database. The aims of the present study were to identify biomarker candidates for CP diagnosis and prognosis based on functional and molecular analyses by evaluating DEGs in gingival tissue between healthy control and CP groups.

Methods

In the present study, we selected microarray datasets of gingival tissue from CP patients in the GEO database and investigated clinical biomarker candidates for CP diagnosis and prognosis based on functional and molecular pathway analyses of DEGs. We selected three datasets of gingival tissue with CP, GSE10334, GSE16134, and GSE23586, using the following keywords: “chronic periodontitis,” “Homo sapiens,” “gingival tissue,” and “microarray platform GPL570: Affymetrix Human Genome U133 plus 2.0 Array.” These three datasets were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/). A summary of the individual studies is shown in Table 1.

Table 1

Summary of individual studies of chronic periodontitis

GEO gene set ID	GSE10334	GSE16134	GSE23586
Platform	GPL570: Affymetric Human Geneme U133 plus 2.0 Array
Number of Healthy Control Persons vs. Chronic Periodontitis Persons	64 vs. 63	69 vs. 65	3 vs. 3
Clinical Data
Healthy Control	PD ≤ 4 mm, AL ≤ 2 mm, BoP-	PD ≤ 4 mm, AL ≤ 2 mm, BoP-	PD ≤ 2 mm, AL = 0, BoP-, GI = 0
Chronic Periodontitis	PD > 4 mm, AL ≥ 3 mm, BoP+	PD > 4 mm, AL ≥ 3 mm, BoP+	PD ≥ 5 mm, AL ≥ 5 mm, BoP+, GI ≥ 1
Diabetes	Not	Not	Not
Pregnant	Not	Not	Not
Smoking	Not	Not	Not
	No systemic antibiotics or anti-inflammatory drugs for ≥6 months	No systemic antibiotics or anti-inflammatory drugs for ≥6 months	No systemic antibiotics or anti-inflammatory drugs for ≥6 months
PubMed ID	18,980,520	19,835,625	21,382,035
		24,646,639

PD Probing Depth, AL Attachment Level, BoP Bleeding on Probing, GI Gingival Index

Identification of up/downregulated DEGs

Up- or downregulated DEGs in the three selected datasets were identified using GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/). GEO2R is an interactive web tool and an R-based web application for comparing two groups of datasets in the GEO database, which we used to compare normal healthy control and CP groups. Common up- or downregulated DEGs in the three selected datasets were extracted. We set p < 0.05 and |fold change (FC)| > 2 as the cut-off criteria.

Functional analysis of DEGs

Functional analysis of DEGs was carried out using the Gene Ontology (GO) database. Signaling pathways of DEGs were investigated based on the Kyoto Encyclopedia of Genes and Genomes (KEGG). GO and KEGG analyses were performed using the Database for Annotation Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/). We set p < 0.05 and false discovery rate (FDR) < 5% as the cut-off criteria.

Protein-protein interaction (PPI) network construction and hub gene identification

The PPI network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database (http://string-db.org/), which is an online repository that imports PPI data from published literature. We used default function in STRING. We calculated degrees of each protein node, and the top 12 genes were identified as hub genes.

Common molecular biomarker candidates and molecular pathways

Common molecular biomarker candidates for CP diagnosis and prognosis among the three datasets were investigated using Biomarker Analysis in QIAGEN’s Ingenuity Pathway Analysis (IPA) software (http://www.ingenuity.com). Applicable biomarkers were selected based on IPA-biomarkers analysis. We set p < 0.05 and |FC| > 2 as the cut-off criteria.

Upstream regulators of dominant biomarker candidates

Upstream regulators of dominant biomarker candidates and molecular pathways were analyzed using Comparison Analysis in IPA software. We set p < 0.05 and FDR < 5% as the cut -off criteria. We then illustrated molecular pathways including upstream regulators and dominant biomarker candidates.

Functional and pathway enrichment analyses of upstream regulators

Upstream regulators of each dominant biomarker candidate were analyzed based on GO and KEGG databases using DAVID. We set p < 0.05 and FDR < 5% as the cut-off criteria.

Results

We selected three gene expression microarray datasets with CP in the GEO database and investigated molecular function, PPI, hub genes, molecular pathways, and upstream regulators using DEGs to identify clinical biomarker candidates for CP diagnosis and prognosis.

One hundred and twenty-three common DEGs among GSE10334, GSE16134, and GSE23586 between normal healthy control and CP groups were identified using GEO2R. Specifically, 81 DEGs were significantly upregulated and 42 DEGs were significantly downregulated (Tables 2 and 3).

Table 2

Common upregulated DEGs (p < 0.05, FC > 2) in chronic periodontitis

Gene Symbol	Gene Description	Probe
ARHGAP9	pho GTPase activating protein 9	224451_x_at
ATP2A3	ATPase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 3	207522_s_at
BHLHA15	basic helix-loop-helix family member A15	235965_at
C3	complement component 3	217767_at
CCL18	C-C motif chemokine ligand 18	209924_at
CD19	CD19 molecule	206398_s_at
CD53	CD53 molecule	203416_at
CD79A	CD79a molecule	1555779_a_at
CECR1	adenosine deaminase 2	219505_at
CHST2	carbohydrate sulfotransferase 2	203921_at
CLDN10	claudin 10	205328_at
COL15A1	collagen type XV alpha 1	203477_at
COL4A1	collagen type IV alpha 1	211981_at
COL4A2	collagen type IV alpha 2	211964_at
CSF2RB	colony stimulating factor 2 receptor beta	205159_at
CSF3	colony stimulating factor 3	207442_at
CXCL12	chemokine (C-X-C motif) ligand 12	203666_at
CXCL8	chemokine (C-X-C motif) ligand 8	202859_x_at
CYTIP	cytohesin 1 interacting protein	209606_at
DENND5B	DENN domain containing 5B	228551_at
DERL3	derlin 3	229721_x_at
EAF2	ELL associated factor 2	219551_at
ENPP2	ectonucleotide pyrophosphatase phosphodiesterase 2	209392_at, 210839_s_at
ENTPD1	ectonucleoside triphosphate diphosphohydrolase 1	207691_x_at, 209474_s_at
EVI2B	ecotropic viral integration site 2B	211742_s_at
FABP4	fatty acid binding protein 4	203980_at
FAM30A	family with sequence similarity 30 member A	206478_at
FCGR2B	Fc fragment of IgG, low affinity IIb, receptor (CD32)	210889_s_at
FCGR3B	Fc fragment of IgG, low affinity IIIb, receptor (CD16b)	204007_at
FCN1	ficolin 1	205237_at
FCRL5	Fc receptor like 5	224405_at
FCRLA	Fc receptor like A	235372_at
FKBP11	FKBP prolyl isomerase 11	219117_s_at
FPR1	formyl peptide receptor 1	205119_s_at
HCLS1	hematopoietic cell-specific Lyn substrate 1	202957_at
ICAM2	intercellular adhesion molecule 2	213620_s_at, 204683_at
ICAM3	intercellular adhesion molecule 3	204949_at
IGHM	immunoglobulin heavy constant mu	209374_s_at
IGKC	immunoglobulin kappa constant	216207_x_at, 215217_at
IGKV1OR2–118	immunoglobulin kappa variable 1/OR2–118	217480_x_at
IGLC1	immunoglobulin lambda constant 1	211655_at
IGLJ3	immunoglobulin lambda joining 3	216853_x_at
IGLL5	immunoglobulin lambda like polypeptide 5	217235_x_at
IGLV1–44	immunoglobulin lambda variable 1–44	216430_x_at, 216573_at
IKZF1	IKAROS family zinc finger 1	227346_at
IL10RA	interleukin 10 receptor, alpha	204912_at
IL1B	interleukin 1 beta	205067_at
IL2RG	interleukin 2 receptor subunit gamma	204116_at
IRF4	interferon regulator factor 4	204562_at
ITGAL	integrin subunit alpha L	1554240_a_at
ITM2C	integral membrane protein 2C	221004_s_at
JCHAIN	joining chain of multimeric IgA and IgM	212592_at
KLHL6	kelch like family member 6	228167_at
LAX1	lymphocyte transmembrane adaptor 1	207734_at
MME	membrane metalloendopeptidase	203434_s_at
MMP7	metallopeptidase 7	204259_at
MS4A1	4-domains A1	228592_at
NEDD9	neural precursor cell expressed developmentally down regulated 9	1560706_at
P2RY8	P2Y receptor family member 8	229686_at
PECAM1	adhesion molecule 1	208981_at, 208982_at, 208983_s_at
PIM2	pim-2 proto-oncogene serine/threonine inase	204269_at
PIP5K1B	phosphatidylinositol-4-phosphate 5-kinase type 1 beta	205632_s_at
PLPP5	phospholipid phosphatase 5	226150_at
PROK2	prokineticine	232629_at
RAB30	RAB30, member RAS oncogene family	228003_at
RAC2	Rac family small GTPase 2	213603_s_at
RGS1	Regulator of G protein signaling 1	216834_at
SAMSN1	SAM domain, SH3 domain and nuclear localization signals 1	220330_s_at
SEL1L3	SEL1L family member 3	212314_at
SELL	selectin L	204563_at
SELM	selenoprotein M	226051_at
SLAMF7	SLAM family member 7	219159_s_at, 234306_s_at
SPAG4	sperm associated antigen 4	219888_at
SRGN	serglycin	201858_s_at, 201859_at
ST6GAL1	ST6 beta-galactoside alpha-2, 6-sialyltransferase 1	201998_at
STAP1	signal transducing adaptor family member 1	220059_at
TAGAP	T cell activation RhoGTPase activating protein	229723_at, 242388_x_at, 1552542_s_at, 234050_at
TAGLN	transgelin	205547_s_at
THEMIS2	thymocyte selection associated family member 2	210785_s_at
TNFRSF17	TNF superfamily member 17	206641_at
ZBP1	Z-DNA binding protein 1	242020_s_at

Table 3

Common downregulated DEGs (p < 0.05, FC < −2) in chronic periodontitis

Gene Symbol	Gene Description	Probe
AADAC	arylacetamide deacetylase	205969_at
AADACL2	arylacetamide deacetylase like 2	240420_at
ABCA12	ATP binding cassette subfamily A member 12	215465_at
AHNAK2	AHNAK nucleoprotein 2	1558378_a_at
ARG1	arginase 1	206177_s_at
ATP6V1C2	ATPase H+ transporting V1 subunit C2	1552532_a_at
BPIFC	BPI fold containing family C	1555773_at
CALML5	calmodulin like 5	220414_at
CLDN20	claudin 20	1554812_at
CWH43	cell wall biogenesis 43 C-terminal homolog	220724_at
CYP2C18	cytochrome P450 family 2 subfamily C member 18	215103_at
CYP3A5	cytochrome P450 family 3 subfamily A member 5	205765_at
DSC1	desmocollin 1	207324_s_at
DSC2	desmocollin 2	204750_s_at
ELOVL4	ELOVL fatty acid elongase 4	219532_at
EPB41L4B	erythrocyte membrane protein band 4.1 like 4B	220161_s_at
EXPH5	exophilin 5	213929_at, 214734_at
FLG	filaggrin	215704_at
FLG2	filaggrin family member 2	1569410_at
FOXN1	forkhead box N1	1558687_a_at
FOXP2	forkhead box P2	1555647_a_at, 235201_at, 1555516_at
GJA3	gap junction protein alpha 3	239572_at
KRT10	keratin 10	207023_x_at
LGALSL	galectin like	226188_at
LOR	loricrin	207720_at
LY6G6C	lymphocyte antigen 6 family member G6C	207114_at
MAP 2	microtubule associated protein 2	225540_at
MUC15	mucin 15, cell surface associated	227241_at, 227238_at
NEFL	neurofilament light	221916_at, 221805_at
NEFM	neurofilament medium	205113_at
NOS1	nitric oxide synthase 1	239132_at
NPR3	natriuretic peptide receptor 3	219789_at
NSG1	neuronal vesicle trafficking associated 1	209570_s_at
POF1B	POF1B actin binding protein	219756_s_at, 1555383_a_at
PTGER3	prostaglandin E receptor 3	213933_at
RORA	RAR related orphan receptor A	210426_x_at, 210479_s_at, 235567_at, 226682_at
RPTN	repetin	1553454_at
SH3GL3	SH3 domain containing GRB2 like 3, endophilin A3	205637_s_at
SLC16A9	solute carrier family 16 member 9	227506_at
SPAG17	sperm associated antigen 17	233516_s_at
WASL	Wiskott-Aldrich syndrome like	205809_s_at
YOD1	YOD1 deubiquitinase	227309_at

Functional and pathway enrichment analyses of DEGs

The results of functional enrichment analysis of up- or downregulated DEGs in gingival tissue analyzed based on GO Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) and pathway enrichment analyzed based on the KEGG pathway using DAVID are shown in Tables 4 and 5.

Table 4

Functional and pathway enrichment analyses of upregulated genes in chronic periodontitis

Category	Term	Genes	p-value	FDR (%)
GOTERM_BP_FAT	GO:0006955~immune response	CSF3, ITGAL, ST6GAL1, IGLV1–44, ENPP2, C3, TNFRSF17, SLAMF7, IGHM, CXCL12, CCL18, RGS1, FCGR2B, LAX1, FCN1, MS4A1, IL1B, IL2RG, CD79A, IGKC, FCGR3B, IGLC1	1.50E-12	2.31E-09
GOTERM_BP_FAT	GO:0046649~lymphocyte activation	ITGAL, IKZF1, LAX1, MS4A1, IRF4, CD79A, SLAMF7, CXCL12	1.69E-05	0.025995389
GOTERM_BP_FAT	GO:0001775~cell activation	ITGAL, IKZF1, LAX1, MS4A1, IRF4, CD79A, SLAMF7, ENTPD1,	2.19E-05	0.033613816
		CXCL12
GOTERM_BP_FAT	GO:0006935~chemotaxis	PROK2, RAC2, ENPP2, FPR1, IL1B, CXCL12, CCL18	4.96E-05	0.07634361
GOTERM_BP_FAT	GO:0042330~taxis	PROK2, RAC2, ENPP2, FPR1, IL1B, CXCL12, CCL18	4.96E-05	0.07634361
GOTERM_BP_FAT	GO:0002684~positive regulation of immune system process	CD19, IKZF1, C3, LAX1, IL1B, IL2RG, CD79A, CXCL12	5.32E-05	0.081770006
GOTERM_BP_FAT	GO:0045321~leukocyte activation	ITGAL, IKZF1, LAX1, MS4A1, IRF4, CD79A, SLAMF7, CXCL12	5.91E-05	0.090857027
GOTERM_BP_FAT	GO:0048584~positive regulation of response to stimulus	CD19, C3, LAX1, IL1B, FABP4, CD79A, CXCL12	4.14E-04	0.635505695
GOTERM_BP_FAT	GO:0007155~cell adhesion	ITGAL, SELL, ICAM2, ICAM3, PECAM1, COL15A1, NEDD9,	5.28E-04	0.809495344
		CLDN10, SLAMF7, ENTPD1, CXCL12
GOTERM_BP_FAT	GO:0022610~biological adhesion	ITGAL, SELL, ICAM2, ICAM3, PECAM1, COL15A1, NEDD9,	5.34E-04	0.818570769
		CLDN10, SLAMF7, ENTPD1, CXCL12
GOTERM_BP_FAT	GO:0007626~locomotory behavior	PROK2, RAC2, ENPP2, FPR1, IL1B, CXCL12, CCL18	9.08E-04	1.387461056
GOTERM_BP_FAT	GO:0050863~regulation of T cell activation	IKZF1, LAX1, IL1B, IL2RG, IRF4	0.00138016	2.10243667
GOTERM_BP_FAT	GO:0050778~positive regulation of immune response	CD19, C3, LAX1, IL1B, CD79A	0.00301947	4.54592016
GOTERM_BP_FAT	GO:0051249~regulation of lymphocyte activation	IKZF1, LAX1, IL1B, IL2RG, IRF4	0.00325016	4.885167435
GOTERM_CC_FAT	GO:0005576~extracellular region	CSF3, COL4A2, ST6GAL1, COL4A1, IGLV1–44, ENPP2, C3, MMP7, CECR1, COL15A1, IGHM, CXCL12, CCL18, PROK2, FCN1, PECAM1, IL1B, FCRLA, IGKC, ENTPD1, FCGR3B, IGLC1, SRGN	3.24E-04	0.37812111
GOTERM_CC_FAT	GO:0044421~extracellular region part	CSF3, COL4A2, COL4A1, C3, MMP7, CECR1, COL15A1, CXCL12, CCL18, FCN1, PECAM1, IL1B, ENTPD1, SRGN	5.83E-04	0.680822917
GOTERM_MF_FAT	GO:0003823~antigen binding	IGLV1–44, FCN1, IGKC, IGHM, IGLC1	0.00152847	1.82616636
KEGG_PATHWAY	hsa04060:Cytokine-cytokine receptor interaction	CSF3, IL10RA, CSF2RB, IL1B, TNFRSF17, IL2RG, CXCL12, CCL18	0.00241736	2.314177036
KEGG_PATHWAY	hsa04514:Cell adhesion molecules (CAMs)	ITGAL, SELL, ICAM2, ICAM3, PECAM1, CLDN10	0.00244312	2.338574426
KEGG_PATHWAY	hsa04640:Hematopoietic cell lineage	CSF3, CD19, MS4A1, IL1B, MME	0.00329177	3.139362079

GO Gene Ontology, BP Biological Process, CC Cellular Component, MF Molecular Function

KEGG Kyoto Encyclopedia of Genes and Genomes

Table 5

Functional and pathway enrichment analyses of downregulated genes in chronic periodontitis

Category	Term	Genes	p-value	FDR (%)
GOTERM_BP_FAT	GO:0008544~epidermis development	LOR, FLG, FOXN1, AHNAK2, KRT10, CALML5	4.02E-05	0.05644221
GOTERM_BP_FAT	GO:0007398~ectoderm development	LOR, FLG, FOXN1, AHNAK2, KRT10, CALML5	5.84E-05	0.082008699
GOTERM_BP_FAT	GO:0030216~keratinocyte differentiation	LOR, FLG, FOXN1, AHNAK2	3.70E-04	0.519046373
GOTERM_BP_FAT	GO:0009913~epidermal cell differentiation	LOR, FLG, FOXN1, AHNAK2	4.78E-04	0.67019525
GOTERM_BP_FAT	GO:0030855~epithelial cell differentiation	LOR, FLG, FOXN1, AHNAK2	0.003062052	4.219242042
GOTERM_CC_FAT	GO:0005856~cytoskeleton	LOR, NOS1, FLG, RPTN, MAP 2, KRT10, WASL, EPB41L4B, NEFL, NEFM, SPAG17	4.42E-04	0.488280071
GOTERM_CC_FAT	GO:0001533~cornified envelope	LOR, FLG, RPTN	0.001040385	1.146550038
GOTERM_MF_FAT	GO:0005198~structural molecule activity	LOR, FLG, MAP 2, FLG2, KRT10, CLDN20, EPB41L4B, NEFL, NEFM	1.15E-04	0.127152153
GOTERM_MF_FAT	GO:0005200~structural constituent of cytoskeleton	LOR, EPB41L4B, NEFL, NEFM	7.83E-04	0.865579593

Upregulated genes were significantly enriched in BP related to immune response and cell adhesion. Downregulated genes were significantly enriched in epidermis and ectoderm development and keratinocyte, epidermal cell, and epithelial cell differentiation.

Significantly enriched KEGG pathways of upregulated genes included cytokine-cytokine receptor interaction, adhesion molecules, and hematopoietic cell lineage. The pathways of downregulated genes were not significantly enriched.

PPI network construction and hub gene identification

PPI networks of the identified DEGs were constructed using STRING, which consisted of 130 edges and 76 nodes (Fig. 1). The nodes with the higher degrees were screened as hub genes including cluster of differentiation (CD) 19 (CD19), interleukin (IL)-8 (IL8), CD79A, Fc fragment of IgG receptor (FCGR) IIIb (FCGR3B), selectin L (SELL), colony stimulating factor 3 (CSF3), IL-1 beta (IL1B), FCGR IIb (FCGR2B), C-X-C motif chemokine ligand 12 (CXCL12), complement component 3 (C3), CD53, and IL-10 receptor subunit alpha (IL10RA) (Table 6).

Fig. 1

Protein-protein interaction of upregulated genes in chronic periodontitis. Network stats: number of nodes is 76, number of edges is 130. This network involves 12 hub genes, CD19, IL8, CD79A, FCGR3B, SELL, CSF3, IL1B, FCGR2B, CXCL12, C3, CD53, and IL10RA, and edges

Table 6

Top 12 hub genes with higher degrees of connectivity in chronic periodontitis

Gene symbol	Gene description	Degree	Connected genes
CD19	CD19 molecule	21	C3, CD79A, CSF3, CXCL12, ENTPD1, FCGR2B, FCGR3B, FCRLA, ICAM3, IGLL5, IKZF1, IL10RA, IL1B, IL2RG, IL8, IRF4, ITGAL, MME, MS4A1, SELL, TNFRSF17
IL8	Interleukin 8	18	C3, CCL18, CD19, CD79A, CSF3, CXCL12, FABP4, FCGR2B, FCGR3B, FPR1, ICAM3, IL1B, IL2RG, ITGAL, MME, MMP7, SELL, SRGN
CD79A	CD79a molecule	16	C3, CD19, CSF3, FCGR2B, FCGR3B, FCRLA, HCLS1, IGJ, IGLL5, IL1B, IL8, IRF4, MME, MS4A1, SEL, TNFRSF17
FCGR3B	Fc fragment of IgG, low affinity IIIb, receptor (CD16b)	14	C3, CD19, CD79A, CSF3, CXCL12, ICAM3, IGLL5, IL10RA, IL1B, IL8, ITGAL, MME, SELL, SKAMF7
SELL	Selectin L	14	CD19, CD79A, CHST2, CSF3, CXCL12, FCGR2B, FCGR3B, ICAM2, ICAM3, IKZF1, IL10RA, IL1B, IL8, ITGAL
CSF3	Colony stimulating factor 3	13	CD19, CD79A, CSF2RB, CXCL12, FCGR2B, FCGR3B, IL10RA, IL1B, IL2RG, IL8, MME, PROK2, SELL
IL1B	Interleukin 1 beta	11	C3, CD19, CD79A, CSF3, CXCL12, FCGR2B, FCGR3B, IL8, MMP7, SELL, SRGN
FCGR2B	Fc fragment of IgG, low affinity IIb, receptor (CD32)	10	C3, CD19, CD79A, CSF3, IGLL5, IL10RA, IL1B, IL8, ITGAL, SELL
CXCL12	Chemokine (C-X-C motif) ligand 12	9	C3, CCL18, CD19, CSF3, FCGR3B, FPR1, IL1B1, IL8, SELL
C3	Complement component 3	8	CD9, CD79A, CXCL12, FCGR2B, FCGR3B, FPR1, IL1B, IL8
CD53	CD53 molecule	8	CYTIP, EV12B, HCLS1, IL10RA, RAC2, SAMSN1, SRGN, THEMIS2
IL10RA	Interleukin 10 receptor, alpha	8	CD19, CD53, CSF3, FCGR2B, FCGR3B, HCLS1, SELL, THEMIS2

Common molecular biomarker candidates for diagnosis, prognosis, and other processes were identified using IPA software (Table 7). Among them, CSF3, CXCL12, IL1B, and transgelin (TAGLN) were identified as common biomarker candidates for CP diagnosis, and CXCL12, IL1B, membrane spanning 4-domains A1 (MS4A1), and platelet and endothelial cell adhesion molecule 1 (PECAM1) were identified as candidates for CP prognosis. Molecular pathways of biomarker candidates are shown in Additional file 1: Figure S1, Additional file 2: Figure S2, Additional file 3: Figure S3, Additional file 4: Figure S4, Additional file 5: Figure S5 and Additional file 6: Figure S6.

Table 7

Common molecular biomarker candidates for chronic periodontitis diagnosis, prognosis, and other processes

Gene symbol	Gene description	Up- or Down-regulated Gene (p-value)	Biomarker applications
ALOX5	arachidonate 5-lipoxygenase	upregulated gene (p < 0.01)	diagnosis, efficacy
APOC1	apolipoprotein C1	upregulated gene (p < 0.05)	prognosis, unspecified application
ARHGDIB	Rho GDP dissociation inhibitor beta	upregulated gene (p < 0.05)	diagnosis
BDNF	brain derived neurotrophic factor	downregulated gene (p < 0.01)	efficacy, response to therapy
CCL19	C-C motif chemokine ligand 19	upregulated gene (p < 0.01)	disease progression, unspecified application
CCR7	C-C motif chemokine receptor 7	upregulated gene (p < 0.05)	diagnosis, efficacy
CSF3	colony stimulating factor 3	upregulated gene (p < 0.05), logFc> 1	diagnosis
CXCL12	C-X-C motif chemokine ligand 12	upregulated gene (p < 0.05), logFc> 1	diagnosis, efficacy, prognosis, unspecified application
CXCR4	C-X-C motif chemokine receptor 4	upregulated gene (p < 0.05)	diagnosis
CYGB	cytoglobin	upregulated gene (p < 0.05)	diagnosis
EIF4E	eukaryotic translation initiation factor 4E	downregulated gene (p < 0.05)	prognosis
EREG	epiregulin	downregulated gene (p < 0.05)	prognosis, response to therapy
ESR1	estrogen receptor 1	upregulated gene (p < 0.01)	diagnosis, disease progression, efficacy, prognosis, response to therapy, unspecified application
IGH	immunoglobulin heavy locus	upregulated gene (p < 0.01)	diagnosis, prognosis
IL1B	interleukin 1 beta	upregulated gene (p < 0.05), logFc> 1	diagnosis, efficacy, prognosis
KDR	kinase insert domain receptor	upregulated gene (p < 0.05)	disease progression, efficacy, prognosis, response to therapy, safety
LCK	LCK proto-oncogene, Src family tyrosine kinase	upregulated gene (p < 0.05)	diagnosis
LCP1	lymphocyte cytosolic protein 1	upregulated gene (p < 0.01)	disease progression
LGALS1	galectin 1	upregulated gene (p < 0.05)	diagnosis, prognosis
LYVE1	lymphatic vessel endothelial hyaluronan receptor 1	upregulated gene (p < 0.05)	disease progression
MMP9	matrix metallopeptidase 9	upregulated gene (p < 0.05)	diagnosis, disease progression, efficacy, prognosis, unspecified application
MS4A1	membrane spanning 4-domains A1	upregulated gene (p < 0.05), logFc> 1	efficacy, prognosis, unspecified application
PAPPA	pappalysin 1	upregulated gene (p < 0.05)	diagnosis
PDGFRB	platelet derived growth factor receptor beta	upregulated gene (p < 0.05)	prognosis, response to therapy, unspecified application
PECAM1	platelet and endothelial cell adhesion molecule 1	upregulated gene (p < 0.05), logFc> 1	disease progression, efficacy, prognosis
PRKCB	protein kinase C beta	upregulated gene (p < 0.05)	diagnosis, efficacy, unspecified application
PTPRC	protein tyrosine phosphatase, receptor type C	upregulated gene (p < 0.01)	diagnosis, efficacy, unspecified application
SERPINA1	serpin family A member 1	upregulated gene (p < 0.01)	diagnosis, unspecified application
SFRP2	secreted frizzled related protein 2	upregulated gene (p < 0.05)	diagnosis
STRA6	stimulated by retinoic acid 6	upregulated gene (p < 0.05)	diagnosis
TAGLN	transgelin	upregulated gene (p < 0.01), logFc> 1	diagnosis
TIMP4	TIMP metallopeptidase inhibitor 4	upregulated gene (p < 0.05)	diagnosis, prognosis
TNFSF13B	TNF superfamily member 13b	upregulated gene (p < 0.05)	efficacy, response to therapy
TPM1	tropomyosin 1	upregulated gene (p < 0.01)	diagnosis
VIM	vimentin	upregulated gene (p < 0.05)	diagnosis, efficacy, prognosis, unspecified application

Upstream regulators of dominant biomarker candidates are shown in Table 8. Among them, tumor necrosis factor (TNF) and fibroblast growth factor 2 (FGF2) were identified as upstream regulators of dominant biomarker candidates for CP diagnosis such as CSF3, CXCL12, IL1B, and TAGLN (Fig. 2). IL1B, which is a biomarker candidate for CP diagnosis and prognosis, is an upstream regulator of CSF3 and CXCL12.

Table 8

Upstream regulators of dominant biomarker candidates in chronic periodontitis

Dominant Biomarker Candidate	Upstream Regulator
CSF3	ABCG1,ADAM17,ANKRD42,ARNT,BIRC2,BIRC3,BMP4,C3AR1,C5,C5AR1,CARD9,CARM1,CD40,CEACAM1,CEBPA,CEBPB,CLEC4M,CLEC7A,CSF2,CTNNB1EP300,ETS2,EZH2,FGF2,FLI1,FOS,FOSL1GLI2,IFNG,IL10,IL15,IL17A,IL17F,IL17RA,IL1B,IL2,IL25,IL3,IL36GIL37,IL4,ITGB2JAK3,KRAS,KRT17,LECT2,LEP,LILRA2MAP3K8MYD88,NFKBIA,NFKBIE,NR1H2,OSM,PPARGPRDM1,PRKCE,PTGS2,RARA,RBPJ,SIRPA,SOCS1,STAT3,TCF4,TGM2,TLR2,TLR3,TLR4,TLR5,TLR9,TNF,TNFRSF1A,TNFRSF25,TNFSF11,TRAF6,VEGFA,WDR77,WNT5A
CXCL12	ACVRL1,ADAM10,APP,AR,BMP2,BSG,CCL11,CCR2,CCR5,CD14,CD40,CHUK,CREBBP,CSF3,CSF3R,CTNNB1,CXCL12,CXCR4,EBF1,EGFR,EPO,ERBB2,ERBB3,ERBB4,ESR1,ESR2,ETV5,F2R,FGF2,FHL2,GDF2,HIF1A,HMOX1,HRAS,IFNG,IFNGR1,IKBKB,IKBKG,IL10,IL15,IL17A,IL17RA,IL18,IL1A,IL1B,IL1R1,IL2,IL22,ITGA9,LTBR,MKL1,MMP1,MMP9,MYD88,NFKB2,NFKBIA,NQO1,OSM,PARP1,PRKAA1,PRKAA2,PRKCD,PTGS2,PTH,RARB,RBPJ,RELB,SNAI2,SP1,SPP1,TGFB1,TNC,TNF,TNFRSF1B,TRAF3,TWIST1,VCAN,VEGFA,VHL,WNT5A,YY1
IL1B	ABCG1,ACTN4,ADM,ADORA2B,AGER,AGT,AHR,AIMP1,ALB,ANKRD42,ANXA1,APOE,APP,ATF3,ATG7,B4GALNT1,BCL2,BCL2L1,BCL3,BCL6,BGN,BID,BIRC3,BMP7,BRAF,BRD2,BSG,BTG2,BTK,BTRC,C3,C3AR1,C5,C5AR1,C7,C9,CAMP,CARD9,CBL,CCL11,CCL2,CCL3,CCR2,CD14,CD200,CD28,CD36,CD40,CD40LG,CD44,CD69,CDK5R1,CEBPB,CEBPD,CHUK,CLEC10A,CLEC7A,CNR2,COCH,CR1L,CR2,CREB1,CRH,CSF1,CSF2,CST3,CTNNB1,CTSG,CXCL12,CXCL8,CYBB,CYP2J2,CYR61,DICER1,DUSP1,EGF,EGFR,EGLN1,ELANE,ELN,EPHX2,ERBB2,ESR1,ESR2,F2,F2R,F2RL1,F3,FAS,FASLG,FBXO32,FCGR2A,FGF2,FN1,FOSL1,FOXO1,GAS6,GHRHR,GLI2,GNRH1,HGF,HIF1A,HMOX1,HRAS,HSPD1,HTR7,ICAM1,IFNAR1,IFNB1,IFNG,IFNGR1,IGF1,IGFBP3,IGHM,IKBKB,IKBKG,IL10,IL10RA,IL11,IL12A,IL12B,IL13,IL17A,IL17RA,IL18,IL1A,IL1B,IL1R1,IL1RN,IL2,IL22,IL25,IL26,IL27,IL27RA,IL3,IL32,IL33,IL36A,IL36B,IL36RN,IL37,IL4,IL4R,IL6R,INSR,IRAK1,IRAK2,IRAK4,IRF3,IRF4,IRF6,IRF8,ITCH,ITGA4,ITGA5,ITGA9,ITGAM,ITGAX,ITGB1,ITGB3,JAG2,JAK2,JUN,KLF2,KNG1,KRAS,KRT17,LBP,LCN2,LECT2,LEP,LGALS1,LGALS9,LIF,LILRB4,LPL,LTA,LY6E,LYN,MAP 2 K3,MAP 3 K7,MAP 3 K8,MAPK12,MAPK14,MAPK7,MAPK8,MAPK9,MAPKAPK2,MEFV,MET,MIF,MTOR,MVP,MYD88,NCOR2,NFKB1,NFKBIA,NFKBIB,NLRC4,NOS1,NOS2,NR1H2,NR3C1,NR3C2,NT5E,OSM,P2RX4,PARP1,PDE5A,PDK2,PDPK1,PDX1,PELI1,PF4,PIK3R1,PIM3,PLA2G2D,PLAT,PLAU,PLG,PPARG,PRDM1,PRKCD,PRKCE,PROC,PSEN1,PTAFR,PTGER4,PTGES,PTGS2,PTPN6,PTX3,RAC1,RARB,RBPJ,RC3H1,RELA,RELB,RETNLB,RGS10,RHOA,RIPK1,RORA,RUNX3,S1PR3,SCD,SELP,SELPLG,SERPINE2,SFRP5,SFTPD,SGPP1,SIRT1,SMAD3,SMAD4,SMAD7,SMARCA4,SOCS1,SOCS6,SOD2,SP1,SPHK1,SPI1,SPP1,SREBF1,ST1,ST8SIA1,STAT1,STAT3,STK40,SYK,TAC1,TAC4,TARDBP,TCF3,TCL1A,TGFB1,TGFBR2,TGIF1,TGM2,THBD,TICAM1,TICAM2,TIRAP,TLR10,TLR2,TLR3,TLR4,TLR5,TLR6,TLR7,TLR9,TNC,TNF,TNFAIP3,TNFRSF1A,TNFRSF9,TNFSF10,TNFSF11,TNFSF12,TP63,TPSAB1/TPSB2,TRAF3,TRAF6,TREM1,TSC22D1,TSC22D3,TWIST1,TXN,TYROBP,UCN,VCAN,VEGFA,WNT5A,WT1,WWTR1,XDH,YY1,ZC3H12A,ZFP36
MS4A1	BCOR,GATA1,IL4,IRF4,IRF8,POU2F2,SPI1,TFE3,TGFB3,TXN
PECAM1	APLN,ATG7,CD44,CYR61,ENG,ERG,FAS,FGFR3,GATA1,GATA2,GATA6,HBB,HMOX1,IFNG,IL12A,IL17A,IL2,IL6,JAK2,KLF2,KLF4,KRAS,LEP,LIF,MAP 2 K1,MAPK14,MOG,MTOR,NAMPT,PIM3,PLCG1,PLG,PPARG,RELA,SOX2,SOX4,STAT1,STAT3,TGFA,TGFB1,TGFB2,THBD,TLR3,TNF,VEGFA,WT1
TAGLN	ACVRL1,ADAMTS12,APP,BMP2,BMP4,CREBBP,ELK1,ERBB2,F2R,FGF2,FHL2,FN1,FOXA1,FOXA2,GATA6,GNA15,HDAC1,HDAC3,HDAC4,HMGA1,HOXC8,HOXD3,HRAS,HTT,KLF4,MAPK14,MDK,MKL1,MKL2,MMP1,NOTCH1,PDLIM2,PPARG,RHOA,ROCK2,RUNX2,S1PR3,SMAD3,SMAD7,SMARCA2,SMARCA4,SP1,SP3,SPHK1,STAT3,TAZ,TGFB1,TGFB2,TGFB3,TGFBR2,TNF,TP63,VHL,YAP1,YY1

Fig. 2

Biomarker candidates and upstream regulators in chronic periodontitis. The pathway shows relationships between biomarker candidates CSF3, CXCL12, IL1B, MS4A1, PECAM1, and TAGLN and their upstream regulators TNF, FGF2, and IL1B

The results of functional and pathway enrichment analyses are shown in Additional file 7: Table S1, Additional file 8: Table S2, Additional file 9: Table S3, Additional file 10: Table S4, Additional file 11: Table S5 and Additional file 12: Table S6.

In BP, upstream regulators of CSF3 were significantly enriched in positive regulation of the biosynthetic process and the macromolecule metabolic process (Additional file 7: Table S1). Upstream regulators of CXCL12 were significantly enriched in positive regulation of the biosynthetic process, the cellular biosynthetic process, and the nitrogen compound metabolic process (Additional file 8: Table S2). Upstream regulators of IL1B were significantly enriched in response to wounding, regulation of programmed cell death, regulation of cell death, defense response, and inflammatory response (Additional file 9: Table S3). Upstream regulators of MS4A1 were significantly enriched in regulation of gene-specific transcription, regulation of transcription from RNA polymerase II promoter, and positive regulation of gene-specific transcription (Additional file 10: Table S4). Upstream regulators of PECAM1 were significantly enriched in positive regulation of the macromolecule metabolic process, the biosynthetic process, and signal transduction (Additional file 11: Table S5). Additionally, TAGLN was significantly enriched in positive regulation of the macromolecule biosynthetic process, the nucleobase, nucleoside, nucleotide and nucleic acid metabolic process, and the biosynthetic process (Additional file 12: Table S6).

In KEGG pathways, upstream regulators of CSF3 were significantly enriched in cytokine-cytokine receptor interaction and the Toll-like receptor signaling pathway (Additional file 7: Table S1). Upstream regulators of CXCL12 were significantly enriched in cytokine activity, growth factor activity, and cytokine binding (Additional file 8: Table S2). Upstream regulators of IL1B were significantly enriched in the Toll-like receptor signaling pathway and cytokine-cytokine receptor interaction (Additional file 9: Table S3). Upstream regulators of MS4A1 were significantly enriched in the intestinal immune network for IgA production (Additional file 10: Table S4). Upstream regulators of PECAM1 were significantly enriched in cytokine activity, growth factor activity, and transcription regulator activity (Additional file 11: Table S5). Additionally, upstream regulators of TAGLN were significantly enriched in the transforming growth factor beta (TGF-β) signaling pathway (Additional file 12: Table S6).

Discussion

CP is a multifactorial disease associated with genetic, environmental, and microbiological factors, lifestyle habits, and systemic diseases. The pathological mechanisms of CP are complex and have not yet been fully delineated.

Microarray analysis of mRNA expression is a powerful tool to elucidate screening profiles and is capable of efficiently narrowing down candidate genes associated with multifactorial diseases and investigating underlying mechanisms of diseases and biomarkers for diagnosis and prognosis [4–9, 32, 33]. Furthermore, the clinical application of biomarkers at an early stage is important for global health [32].

In this study, we focused on mRNA expression data in gingival tissue from CP patients using pooled datasets in the GEO database to elucidate characteristics of DEGs and biomarker candidates for CP diagnosis and prognosis.

Eighty-one common upregulated DEGs and 42 downregulated DEGs were found. Upregulated genes were enriched in processes associated with immunity in GO BP, which comprise immune response, regulation of the immune response, regulation of the immune system process, and positive regulation of the immune system process and cytokine-cytokine receptor interaction, cell adhesion molecules, and hematopoietic cell lineage in the KEGG pathway. Downregulated genes were enriched in epidermis and ectoderm development and keratinocyte, epidermal cell, and epithelial cell differentiation, and no KEGG pathway was significant. The association between immunity and CP was assumed.

Our analysis also suggested that CD19, IL8, CD79A, FCGR3B, SELL, CSF3, IL1B, FCGR2B, CXCL12, C3, CD53, and IL10RA are hub genes for the pathological pathway of CP.

Guo et al reported several hub genes of periodontitis using microarray analyses [5]. Similar to their report, we also identified SLAMF7, CD79A, MMP7, IL1B, LAX1, IGLJ3, CSF3 and TNFRSF17 as DEGs. Common results of GO enrichment analysis were immune response, chemotaxis, and taxis. Common KEGG pathways included cytokine-cytokine receptor interaction and cell adhesion molecules (CAMs). Common hub genes were IL8, IL1B, CXCL12, CSF3, CD79A, and SELL.

Song et al reported several DEGs and functional enrichment analysis of inflammation and bone loss process in periodontitis. With comparing the results of our present study to them [12], common DEGs were CD19, formyl peptide receptor 1 (FPR1), interferon regulatory factor 4 (IRF4), and IL1B. Common results of GO enrichment analysis in upregulated DEGs were cell activation, positive regulation of immune system process, extracellular region, extracellular region part, and antigen binding, while those in downregulated DEGs were epidermis development, keratinocyte differentiation, epidermal cell differentiation, structural molecule activity, and structural constituent of the cytoskeleton. Common KEGG pathways of upregulated DEGs were cytokine-cytokine receptor interaction, hematopoietic cell lineage, and CAMs appear to be related to inflammation and bone loss process in periodontitis.

We also identified CSF3, CXCL12, IL1B, and TAGLN as biomarker candidates for CP diagnosis and CXCL12, IL1B, MS4A1, and PECAM1 as biomarker candidates for CP prognosis. CSF3, CXCL12, IL1B, and MS4A1 are related to immune response. CXCL12 and MS4A1 are related to lymphocyte activation and cell activation. PECAM1 is related to phagocytosis and endocytosis. TAGLN is a TGF-β1-inducible gene [34].

Furthermore, TNF and FGF2 are common upstream regulators of all biomarker candidates for CP diagnosis. Mitogen-activated protein kinase 1 (ERK, MAPK1) is a common upstream regulator of all biomarker candidates for CP prognosis. Additionally, IL1B is one of the upstream regulators of CSF3 and CXCL12. Furthermore, vascular endothelial growth factor A and prostaglandin-endoperoxide synthase 2 are upstream regulators of CSF3, CXCL12, and IL1B.

Among biomarker candidates and hub genes, the association of CD53, CD79A, MS4A1, PECAM1, and TAGLN with CP has not been previously reported. Potential reason is that biological information in databases for bioinformatics analysis is continuously updated as omics data become available and developed functions of software improves. CD53 plays a role in the regulation of growth. CD79A encodes the Ig-alpha protein of the B-cell antigen component. MS4A1 plays a role in the development and differentiation of B-cells into plasma cells. PECAM1 is a member of the immunoglobulin superfamily and involved in leukocyte migration. Furthermore, CD53, CD79A, MS4A1, and PECAM1 are associated with immune responses to infection by microorganisms. Lastly, TAGLN is a member of the calponin family and expressed in vascular smooth muscle [34].

Biomarker candidates such as CSF3, CXCL12, IL1B, MS4A1, and PECAM1, upstream regulators such as TNF and FGF2, and hub genes such as CD53, CD79A, MS4A1 and PECAM1 are related to immune response and inflammation.

Conclusions

In summary, our study, which analyzed pooled omics datasets with distinct clinical and experimental baselines, provided new clues for elucidating common genetic factors of multifactorial diseases such as CP. Data mining and integration with sharing and using pooled omics data could be useful tools to investigate biomarker candidates for diagnosis and prognosis of diseases in clinical practice and to understand complicated underlying molecular mechanisms. We also identified key genes related to CP pathogenesis such as CSF3, CXCL12, IL1B, TAGLN, CD19, IL8, and CD79A and upstream genes of biomarker candidates such as TNF and FGF2, which could provide potential targets for CP diagnosis. For clinical application, a combination of biomarkers would likely be necessary for CP diagnosis or prognosis. Bioinformatics analysis of pooled microarray datasets is useful for screening to investigate biomarker candidates of CP. Further validation of these predicted molecular biomarkers obtained from bioinformatics analysis using experimental research approaches such as qRT-PCR is necessary.

Figure S1. Most relevant genetic network related to common biomarker candidate gene CSF3 analyzed by IPA. (PDF 354 kb)

Figure S2. Most relevant genetic network related to common biomarker candidate gene CXCL12 analyzed by IPA. (PDF 503 kb)

Figure S3. Most relevant genetic network related to common biomarker candidate gene IL1B analyzed by IPA. (PDF 420 kb)

Figure S4. Most relevant genetic network related to common biomarker candidate gene MS4A1 analyzed by IPA. (PDF 407 kb)

Figure S5. Most relevant genetic network related to common biomarker candidate gene PECAM1 analyzed by IPA. (PDF 442 kb)

Figure S6. Most relevant genetic network related to common biomarker candidate gene TAGLN analyzed by IPA. (PDF 398 kb)

Table S1. Functional and pathway enrichment analyses of upstream regulators of CSF3. (XLSX 41 kb)

Table S2. Functional and pathway enrichment analyses of upstream regulators of CXCL12. (XLSX 45 kb)

Table S3. Functional and pathway enrichment analyses of upstream regulators of IL1B. (XLSX 98 kb)

Table S4. Functional and pathway enrichment analyses of upstream regulators of MS4A1. (XLSX 11 kb)

Table S5. Functional and pathway enrichment analyses of upstream regulators of PECAM1. (XLSX 37 kb)

Table S6. Functional and pathway enrichment analyses of upstream regulators of TAGLN. (XLSX 41 kb)