The expression profile analysis of microarray data
We identified 2303 DEGs, including 1076 up-regulated genes and 1227 down-regulated genes. The heatmap of DEGs was shown in Fig. 1A. The volcano plot (Fig. 1B) showed the distribution of all genes, the results showed that red plots represented up-regulated genes, green plots represented down-regulated genes and the symbol of DEGs, whose fold change (FC) beyond 8 folds were labeled. The GO analysis was performed to identify the function of DEGs. The Fig. 1C showed that several terms were enriched, immune related functions such as neutrophil activation, neutrophil activation involved in immune response, neutrophil mediated immunity, neutrophil degranulation, regulation of leukocyte activation, T cell activation, positive regulation of cytokine production, lymphocyte differentiation and T cell differentiation were primarily enriched. KEGG analysis was conducted to identify the key pathways of DEGs. The Fig. 1D showed that human T-cell leukemia virus 1 infection, Epstein-Barr virus infection, Th17 cell differentiation, Th1 and Th2 cell differentiation and PD-L1 expression and PD-1 checkpoint pathway in cancer were significantly enriched. These pathways are immune-related pathways may play a crucial role in IA. In addition, the results of GSEA analysis (Fig. 1E) indicated that MAPK signaling pathway, NOD-like receptor signaling pathway and regulation of actin cytoskeleton were up-regulated, while Herpes simplex virus 1 infection, RNA transport and Spliceosome were down-regulated.
Construction Of Co-expression Network
The WGCNA analysis was conducted on 2303 DEGs. Softpower 18 was chose to construct the co-expression network. The results were shown in Fig. 2A, we showed the all modules and the merged modules, which were merged because of similarity of modules. After modules were merged, a total of 8 modules including green, midnightblue, cyan, lightcyan, black, grey60, salmon and grey were obtained. The grey module was removed in analysis, because of these genes not belonging to any module. Correlations between modules and IA were shown in Fig. 2B. Correlation analysis showed that the black module was the mostly significant correlation with IA (r = 0.62). As shown in Fig. 2C, the results of cluster also indicated that black module is the highest relationship with IA, followed by salmon (r = 0.53) and grey60 (r = 0.52). Meanwhile, the module expression patterns of black, salmon and grey60 were shown in Fig. 2D. In addition, Fig. 2E showed the relationship among module eigengenes.
Go Analysis Was Applied To Analyze Black Module
We analyzed the black module, which was the highest relationship with IA, using GO analysis. The results showed that there several terms were same as the results of total DEGs GO results (Fig. 3A). However, there were still some immune-related terms were uniquely enriched, such as regulation of inflammatory response, myeloid cell differentiation, negative regulation of cytokine production, myeloid leukocyte differentiation and macrophage activation. Figure 3B showed gene expression of immune-related functions. The genes of immune-related functions were almost all up-regulated, while down-regulated genes were in the minority (Fig. 3C). As shown in Fig. 3D GO-GSEA analysis was further performed, the black module genes were mainly enriched in secretory granule, secretory vesicle, cytoplasmic vesicle part, intracellular vesicle and cytoplasmic vesicle. Finally, the cluster results were shown in Fig. 3E.
KEGG Analysis Was Applied To Analyze Black Module
We also performed the KEGG analysis for black module. Histogram and bubble charts of pathway enrichment were shown in Fig. 4A. Immune-related pathways such as Human T-cell leukemia virus 1 infection, NOD-like receptor signaling pathway, TNF signaling pathway, Acute myeloid leukemia and Fc epsilon RI signaling pathway were significantly enriched. Overlapped top ten pathways were shown in Fig. 4B. The top ten pathways were shown in circle type (Fig. 4C), gene expression of these pathways was also presented. The heatmap of enriched pathways was shown in Fig. 4D. Almost all genes were up-regulated, while FCER1A and HLA-DQA1 were significantly down-regulated. In order to identify which diseases were related to the black module, DOSE analysis was conducted. As was shown in Fig. 4E, Pneumonia, Lung diseases, Carotid Atherosclerosis, Periodontitis and Juvenile arthritis were significantly enriched. Remarkably, Pneumonia and Lung were the top two enriched diseases. They may be correlated with IA. In addition, the string database [21] was used to identify key genes, which may play an important role in IA. Eight genes were screened as key genes, including MMP9, TLR8, TLR2, CYBB, ITGAX, ITGAM and MPO (Fig. 4F).
Hub genes in black module and ROC curves of biomarkers
We identified a total of 621genes in black module using WGCNA in R. 87 genes (r > 0.6) were significantly related with IA. Then, hub genes were identified by degree values, the results were shown in Fig. 5A. TLR4, TP53I3/PIG3, TMTC1, CYSTM1, FAR1, MKNK1, GAS7 and ITGAM were identified as the hub genes. The hub genes expression and statistical analysis between hematological patients with IA and normal samples were conducted (Fig. 5B). As was shown in Fig. 5C, the hub genes expression and statistical analysis between hematological patients with IA and non-IA. Finally, TLR4, TP53I3/PIG3 and TMTC1 were identified as potential biomarkers for IA, because they were significantly differential expression between hematological patients with IA and non-IA. Moreover, to explore if TLR4, TP53I3/PIG3 and TMTC1 could be outstanding biomarkers. ROC curve analysis was performed (Fig. 5D). The results revealed that the area under curve (AUC) for the three genes was beyond 0.7. At the optimal cut-off value of TLR4 (cut‐off value = 8.109), sensitivity was 78.3% and specificity was 72.7%. The results for TP53I3/PIG3 (cut‐off value = 4.470), sensitivity was 91.3% and specificity was 54.5%. In addition, for TMTC1 (cut‐off value = 2.898), sensitivity and specificity were 78.3% and 81.8%, respectively. Then, the three genes were validated if they were high expression in hematological patients with bacterial infections. Surprisingly, TLR4 and TP53I3/PIG3 were high expression in hematological patients with bacterial infections (Fig. 5E). However, the expression levels of TP53I3/PIG3 were obviously higher in hematological patients with bacterial infections than hematological patients with IA.