A total of 963 DEGs, including 538 upregulated genes and 425 downregulated, were obtained from DCM-expression microarray dataset GSE19303, which was statistical significance between normal controls and DCM patients. The TOP 20 genes, both in the upregulated and downregulated groups, are shown in the heatmap and all the DEGs through volcano plot filtering (Additional file 1: Fig. 1). A detailed list of the 936 genes and the corresponding statistics is shown in Additional file 1.
Network Pharmacology-Based Analysis of TCM
Identification of Bioactive Components in the SFLGZGD
A total of 636 ingredients in SFLGZGD were obtained from the TCMSP database. Among the 636 components in SFLGZGD, 324 (50.9%) met the requirement of OB ≥30% and 93 (14.6%) met the requirements of OB ≥30%, and DL index ≥0.18. The whole components list manifest in Table 1, and the detailed list of the 93 candidate Bioactive Components as shown in Additional file 2.
Compound-Target Network and PPI network
Herb-Compound-Compound Target Network
Exploring the molecular basis of TCM is very crucial for the modernization of TCM, and understanding the targets of TCM is momentous. In the present study, the compound-target network of SFLGZGD on DCM was constructed (Fig.2), which was composed of 28 nodes (10 for bioactive components and 18 for potential targets). These potential targets, including ACHE, MMP3, EGFR, CDKN1A, MMP1, ICAM1, PTGER3, HSPB1, MGAM, COL1A1, ABCG2, PSMD3, COL3A1, CLDN4, CTSD, IGFBP3, MTTP, and ND6, associated with the 10 bioactive components. Except for the MOL000098 derived from PS and HMM, and the rest of bioactive ingredients comes from HMM, CR, ALRP, AMK, and PS, respectively, and the detailed information was shown in Table 2, which signify that these five TCMs and these targets in the network play a significant effect in the process of SFLGZGD treating DCM.
PPI Network Analysis
The PPI network was constructed by the Bisogenet plugin. A total of 1939 genes obtained, the degree ranged from 1 to 933 (Additional file: Fig. 2). The topologically essential genes screened by CytoNCA, a total of 249 genes selected by the parameter “Without weight” and DC min value greater than 81, then, we generated the sub-network based on the previously filtered data, which was shown in the Fig.3A. Besides, we constructed the network by the parameter “Without weight” and BC min value greater than 600 based on the sub-network of previous 249 genes, a total of 16 genes were screened out (Fig.3B). Finally, we also generated the sub-network, these genes in the network may account for the significantly essential therapeutic effects of SFLGZGD on DMC, especially the high-degree protein targets, such as EGFR (degree=933), NTRK1 (degree=674), and HSPB1(degree=415). The detailed PPI information was shown in Additional file 3.
Gene ontology enrichment and KEGG pathway analysis
To identify the biological characteristics of presumptive targets of SFLGZGD on DCM in detail, the GO and KEGG pathway enrichment analyses of involved targets were conducted using several R-packages by R software. We obtained 129 terms on the GO analysis, including 94 BP, 25 CC, and 10 MF, respectively, Count ≥ 2 and FDR<0.05 were as cutoffs (Fig. 4). The detailed GO information was manifested in Additional file 4. The top 20 significantly enriched terms in BP, CC, and MF categories were manifested in Fig. 5. There are 6 genes fall into the category of extracellular structure organization (FDR = 1.15E-03), 5 genes fall into the category of response to toxic substance (FDR = 7.71E-03), multicellular organismal homeostasis (FDR = 6.39E-03), response to oxidative stress (FDR = 5.53E-03), response to steroid hormone (FDR = 3.75E-03), and extracellular matrix organization (FDR = 2.65E-03), respectively, which were the top 6 terms of BP having the largest number of genes enriched (Fig. 5A). The top 3 terms of CC annotation showed that the gene products mainly enriched in the extracellular matrix (7 genes), collagen-containing extracellular matrix (5 genes) and endoplasmic reticulum lumen (4 genes) (Fig. 5B). As to MF annotation, integrin binding, growth factor binding, and serine hydrolase activity were the most enriched term with 3 genes hits, respectively (Fig. 5C).
The KEGG enrichment analysis revealed that 6 pathways were significantly associated with targets of SFLGZGD on DCM as shown in Fig. 6 and Table 3. Among these potential pathways, “Relaxin signaling pathway” was considered the most significant one with the highest degree value (Fig. 6A). Among these potential targets, EGFR, CDKN1A, MMP1, COL1A1, COL3A1, MMP3, ICAM1, and HSPB1 were identified as relatively high-degree targets, which played a crucial role in the development of DCM and were considered as the key markers of SFLGZGD treatment on DCM (Fig. 6B). From the incorporated drug target prediction, GO, and pathway enrichment as well as network analyses, we speculated that the effects of SFLGZGD on DCM might be associated with the roles of its key targets including EGFR, MMP1, COL1A1, COL3A1, CDKN1A, MMP3, ICAM1, and HSPB1 in regulating myocardial cell function.