Bioinformation analysis
Based on cibersort way of 22 immune cells type, there were available to get 10 MS samples (p<0.05), and 15 normal samples.GSE81279 contained 6 MS untreated monocyte and 11 monocyte samples after treated as showed. The percentage of the infiltrated immune cell showed Monocytes, NK cells resting, T-cell gamma delta, B cell naive were the top 4 abundant immune infiltrates in normal people(Fig1 a). Compared with normal group, B cells naive(p= 0.001), T cells CD4 naive(p= 0.019), T cells gamma delta(p= 0.016), Eosinophils(p= 0.001), Neutrophils (p= 0.017)differed in MS group. Respectively, T-cell gamma delta and monocyte showed a trend toward reduction. In the MS group.It indicates the correlation between these differentially expressed types of immune cells. The five types of immune cells were weakly to moderately correlated. Monocytes were negatively correlated with T cells CD4 naive and Dendritic cells activated, Neutrophils, Eosinophils, B cell naive (r = − 0.26 and r = − 0.49, r = − 0.41, r = − 0.42,r = − 0.09). T cells gamma delta were also negatively correlated with T cells CD4 naive and Dendritic cells activated, Neutrophils, Eosinophils, B cell naive (r =−0.56 and r =− 0.27,r =− 0.16,r = − 0.3 8,r = − 0.50), which indicated that the function of monocytes and T cells gamma delta may be antagonistic in MS .T cells CD4 naive and Dendritic cells activated Neutrophils, Eosinophils cells was synergistic.T cells CD4 naive were negatively correlated with T cells CD8, T-cells gamma delta(r = − 0.71 and r = − 0.54), were positively correlated with Neutrophils(r = 0.43), Eosinophils(r =0.5), T cells CD4 memory resting(r =0.41), B cells naive(r = 0.53)(Fig1 b,c,d).
GSEA analysis
Through the GO biological process, the top 10 most significantly enriched GO terms were presented in Table 1,2..GO terms of treatment were primarily associated with "Regulation Of Peptidyl Serine Phosphorylation Of Stat Protein, Response To Uv B, Negative Regulation Of Translational Initiation, Regulation Of Protein Kinase C Signaling, Positive Regulation Of Creb Transcription Factor Activity, Antibacterial Humoral Response, Extracellular Matrix Assembly, Digestive System Development, Positive Regulation Of Transcription By Rna Polymerase I, Modulation By Host Of Viral Process, Positive Regulation Of Carbohydrate Metabolic Process"(p<0.0001, p<0.0001, p<0.0001,p<0.0001,p<0.0001,p<0.0001, p<0.0001, p<0.0001, P=0.018, P=0.019 )were gene sets of MS.14 gene sets were significantly enriched in the untreated group(p<0.05), and 6 gene sets were significantly enriched (p<0.05) of the treatment group. The details were shown in Fig 2.
The connection between the most prominent GO terms is shown. The network-presented numerous genes, such as HBB,GATA2,NAA35,TCL1A, SECISBP2L,CLC,AGPAT5,CCR3,LTF,MALAT1,MS4A3 and some other genes were significantly differentially expressed in MS.The two genes (PWP1, GGNBP2) expressed differently in the "Regulation Of Peptidyl Serine Phosphorylation Of Stat Protein" gene sets for GSEA.RELA,XPC,HMGN1,CDKN1A,MME,DDB2 expressed significantly in "Response To Uv B"gene sets for GSEA. Gene set enrichment result was presented in the figure. The enrichment showed that the gene set was enriched at the front of the sequence (ES = -0.65). We obtained the list of all core genes, such as CDKN1A, DDB2, MME HMGN1, XPC, RELA for subsequent analysis.
Table1 GSEA-based GO analysis of top 10 biological process terms
NAME
|
SIZ
E
|
ES
|
NES
|
NOM p-val
|
FDR q-val
|
FWER p-val
|
REGULATION_OF_PEPTIDYL_SERINE_PHOS
PHORYLATION_OF_STAT_PROTEIN
|
20
|
-0.55770963
|
-1.8629167
|
0
|
0.2439
|
0.29
|
RESPONSE_TO_UV_B
|
17
|
-0.64523363
|
-1.6230046
|
0
|
1
|
0.98
|
MYOBLAST_PROLIFERATION
|
19
|
-0.50449675
|
-1.5804042
|
0.02040
|
1
|
1
|
NEGATIVE_REGULATION_OF_TRANSLATIO
NAL_INITIATION
|
17
|
-0.53856456
|
-1.57758
|
0
|
1
|
1
|
REGULATION_OF_PROTEIN_KINASE_C_SIG
NALING
|
15
|
-0.61808467
|
-1.571803
|
0
|
1
|
1
|
POSITIVE_REGULATION_OF_CREB_TRANSC
RIPTION_FACTOR_ACTIVITY
|
18
|
-0.6728891
|
-1.5715604
|
0.01818
|
1
|
1
|
ANTIBACTERIAL_HUMORAL_RESPONSE
|
40
|
-0.62112427
|
-1.5605229
|
0
|
1
|
1
|
SERINE_PHOSPHORYLATION_OF_STAT_PR
OTEIN
|
24
|
-0.4843511
|
-1.5514377
|
0.07317
|
1
|
1
|
EMBRYONIC_HINDLIMB_MORPHOGENESIS
|
28
|
-0.44431925
|
-1.5426135
|
0.04255
|
1
|
1
|
POSITIVE_REGULATION_OF_MESONEPHRO
S_DEVELOPMENT
|
22
|
-0.53573346
|
-1.5345056
|
0.02083
|
1
|
1
|
Table2. The top 10 GSEA-based kegg analysis terms.
NAME
|
SIZE
|
ES NES NOM p-val FDR q-val
|
PRIMARY_IMMUNODEFICIENCY
|
35
|
-0.5699937 -1.3643178 0.076923081
|
LINOLEIC_ACID_METABOLISM
|
24
|
-0.42973483 -1.2812052 0.08 1
|
GLYCINE_SERINE_AND_THREONINE_METABOLISM
|
30
|
-0.4299071 -1.2570243 0.232558151
|
LYSINE_DEGRADATION
|
43
|
-0.3849916 -1.2218008 0.1754386 1
|
TASTE_TRANSDUCTION
|
44
|
-0.25822443 -1.1707219 0.2244898 1
|
OLFACTORY_TRANSDUCTION
|
111
|
-0.23009725 -1.1693118 0.209302320.8867015
|
ABC_TRANSPORTERS
|
43
|
-0.36049527 -1.1056648 0.3043478 0.9979592
|
RENIN_ANGIOTENSIN_SYSTEM
|
16
|
-0.38465297 -1.0639392 0.326086971
|
RIG_I_LIKE_RECEPTOR_SIGNALING_PATHWAY
|
68
|
-0.33164003 -1.0420119 0.461538460.9984609
|
NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY
|
62
|
-0.3369027 -0.9907027 0.530612231
|
Phosphorylation Of Stat Protein.C. representated kegg pathway:Primary Immunodeficiency.D.It representates Linoleic Acid Metabolism pathway.The depth of the inner arc area decreasing or increasing of the biological process of the six pictures and shows the running ES score and positions of geneset members on the rank ordered List.E.heat map Heat Map of the top 50 features for each phenotype in 1 collapsed tosymbols.
KEGG analysis
There were 40 main KEGG pathways(Fig 3). The MS patients showed pathways of "Primary Immunodeficiency, Linoleic Acid Metabolism, Glycine Serine And Threonine Metabolism, Lysine Degradation, Taste Transduction, Olfactory Transduction, Abc Transporters, Renin-Angiotensin System, Rig I Like Receptor Signaling Pathway, Nod Like Receptor Signaling Pathway", the results indicated that the activation of signaling pathways in MS was related to immune, linoleic and amino acid metabolism, TNFRSF13B, PTPRC, CIITA, IL7R, CD40LG, DCLRE1C, BTK, JAK3, IKBKG, RFXAP, TNFRSF13C, ICOS, TAP2, RFX5, CD19, CD40, CD79A, BLNK were in Primary Immunodeficiency pathways. Meanwhile,25 gene sets are upregulated in phenotype MS.
Discovery of core genes
Six types of immune infiltrating cells are shown in Fig 4. A total of 41 genes showed a tight link with immune infiltrating cells. Genes such as CD79A, IGHD, IGHM, IGKC, IL4R, MS4A1, SELL, TCL1A were negatively correlated with B cells naive. CCR7, IL7R, ITK, SELL, TRAC, TRBC1 were negatively related to T cells CD4 naive. BCL2A1,CCR3,CLC,FOSB,NCF2,P2RY14 and C5AR1,CXCR2 ,
FCGR3B, FPR1, NCF2,SELL were negatively related to Eosinophils and Neutrophils respectively.CCL5,CD3D,CST7,GZMA,GZMK, IL2RB,KLRB1,PRF1 TRDC were positively correlated with T cells gamma delta ,CLEC7A, FCN1,HCK,MNDA, MS4A6A,S100A12 were positively correlated with monocytes. As we knew above, Monocyte and T cell gamma-delta were negatively correlated with the other 4 immune-cells.
Validation of core genes
GSE81279 shows the clinical information and the core gene list.We analysis between core gene list and clinical information CD79A,IL4R,MS4A1,SELL,CCR7, CD3D,FAIM3,IL7R,LCK,CCL5,GZMA,GZMK,IL2RB,KLRB1,PRF1,CLEC7A,FPR 1,MNDA,NCF2 worked in MS. Among them, CLEC7A, SELL, MS4A1 were positively correlated with the treated MS patients, it also indicated that these core genes had a close connection with immune cell infiltration and clinical manifestation. And, GZMA, IL7R, IL4R, GZMK, CD79A, IL2RB, PRF1, KLRB1,CD3D were positively correlated with untreated patients.