Identification of DEGs and DEMs
To detect the DEGs in GSE54536 and GSE100054, we set a cutoff of P<0.05 and a fold change of ≥1.5, and for the DEMs in GSE100054, P<0.05 and a fold change of 2. GEO2R analysis indicated the presence of 1790 and 967 DEGs in GSE54536 and GSE100054 respectively (Fig. 2a and b). In GSE54536 and GSE100054, there are 125 genes that co-exist in the DEGs. Among them, 17 genes are upregulated and 14 genes are downregulated compared to controls. We then screened out 42 DEMs from GSE100054 using the GEO2R analysis system.
Functional enrichment and KEGG pathway analysis
For the DEGs, the most significantly enriched GO terms on biological processes (BP) are Cell adhesion, Blood coagulation, G-protein coupled purinergic nucleotide receptor signaling, Inflammatory response, and B cell receptor signaling (Fig. 3a). We listed all the BP, cellular components (CC) (Fig. 3b), molecular functions (MF) (Fig. 3c), and KEGG pathways according to the GO terms (P < 0.05). In addition, the KEGG pathway analysis showed that DEGs were enriched in Aldosterone synthesis and secretion, Gap junctions, Platelet activation, Rap1 signaling, and Estrogen signaling (Fig. 3d).
PPI network and identification of hub genes
125 overlapping DEGs between GSE54536 and GSE100054 were used to establish the protein-protein (PPI) network by STRING, which composed 125 nodes and 93 edges. Subsequently, we analyzed the STRING results using CytoScape, and 20 genes in the PPI network were identified as hub genes in PD. Among others, these included ITGAX, TLR5, SLC11A1, SRC, CLEC7A, CD79A, LCK, and NLRP3 (Fig. 4a). Degree cutoff, node score cutoff, and k-core were set to 2, and max depth was set to 100.
We selected 3 clusters from the PPI network using MCODE, with the most significant cluster consisting of 5 nodes and 10 edges. MCODE analysis also showed that each cluster contained one “seed” gene. NRLRP3, XCL2, and NT5E are the seed genes in the three clusters respectively (Fig. 4b, c and d). The BP of hub genes were involved in Platelet activation, Inflammatory response, Innate immune response, B cell receptor signaling, Stimulatory C-type lectin receptor signaling, Lipopolysaccharide response, Leukocyte migration, and Regulation of cell proliferation (P<0.01).
Construction of a miRNA target regulatory network
In the GSE100054 dataset, there are 42 differentially expressed pre-miRNAs. We examined the mature expression of these miRNA using miRBase. All mature miRNAs were uploaded to DIANA TOOLS (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=microT_CDS/index) to predict the targets of each. The overlapping mRNAs between the DIANA TOOLS predictions and the DEGs in GSE54536 and GSE100054 were then used to construct the regulatory network. After determining the intersection between the 125 DEGs and the predicted target genes of the DEMs, the miRNAs with no target genes or a negative regulatory relationship were discarded. That left 12 mRNAs and 17 miRNAs remaining, making up 34 miRNA-mRNA pairs. The relationships between mRNAs and miRNAs is shown in Fig. 5. Diamond-shaped nodes represent target genes, and rectangular nodes represent miRNAs. Green rectangles represent downregulation of miRNAs in GSE100054, while red nodes represent upregulation miRNAs. Green diamonds show the co-downregulated mRNAs in GSE54536 and GSE100054, and red diamonds show the co-upregulated mRNAs in these two datasets.
Among the 34 miRNA-mRNA pairs, only one miRNA was found to be increased in PD peripheral blood, while the other miRNAs were found to be reduced. Hsa-miR-3690 was elevated, and has a negative regulatory relationship with SLC30A4. We used Cytoscape APP centiscape 2.2 to analyze the network, and found that GNAQ and TMTC2 played important roles (centiscape2.2 degree = 10), and has-miR-142 was the most significant (centiscape2.2 degree = 4). GNAQ was negatively regulated by multiple miRNAs, including hsa-miR-142, hsa-miR-15b, hsa-miR-29b-2, hsa-miR-3136, hsa-miR-4659a, hsa-miR-548f, hsa-miR-548 g, hsa-miR-5582, hsa-miR-5692c, and hsa-miR-582. TMTC2 was negatively regulated by multiple miRNAs including hsa-miR-142, hsa-miR-29b-2, hsa-miR-4324, hsa-miR-4477a, hsa-miR-4659a, hsa-miR-548f, hsa-miR-548 g, hsa-miR-5692c, hsa-miR-582, and hsa-miR-590. Finally, BEND2 was negatively regulated by miRNAs including hsa-miR-142, hsa-miR-4659a, hsa-miR-4795, and hsa-miR-590. Given these observations, hsa-miR-142 is the most versatile and significant miRNA regulator in the network, and can impact GNAQ, TMTC2, BEND2 and KYNU (Fig. 5).