3.1 Screening results of active compounds and targets in Sophora japonica
Enter the drug name in the TCMSP database, And according to OB≥30, DL≥ the condition of 0.18, the active components of the drug were obtained. There were 13 active compounds of E. officinalis, Six active compounds, Fructus Aurantii active compounds 5, There are 36 active compounds in Scutellaria baicalensis, There are 11 active compounds, Gardenia 5 active compounds, Radix Paeoniae Alba active compounds 13, A total of 64 active compounds were obtained after mapping, A total of 247 corresponding targets.
3.2 Results of intersection of active compounds in the treatment CRC action targets of Sophora japonica
The 858 CRC related potential targets were retrieved in the G enecards database, and 114 intersection targets were obtained by mapping them with 247 targets corresponding to Huaijiao Diyu decoction, as shown in figure 1.
3.3 Screening results of targets for the treatment of CRC by active compounds in Sophora flavescens decoction
To construct the network map of Chinese medicine - active compound - CRC- target by introducing CRC Chinese medicine, active compound, target and disease into the software, The network diagram consists of 172 nodes and 491 edges, Common goals and diseases containing 58 ingredients and 114 drugs. 114 common targets are considered as potential targets for the treatment of CRC by huajiao diyu decoction (figure 2). degree represents the total number of routes that other nodes in the network connect to that node. The higher the value, The more important the corresponding component or target is. After analysis using the circuit network analyzer plug-in in the Cytoscape software, Tables 1 and 2 list 10 major active components and 10 major targets, respectively. Table 1 shows, The highest degree component is PTGS2(degree 48).
Table. 1 Core targets of the top 10 in the network of Degree CRC- targets
Gene
|
Degree
|
TopologicalCoefficient
|
NeighborhoodConnectivity
|
Radiality
|
PTGS2
|
48
|
0.078683036
|
9.8125
|
0.989747095
|
PTGS1
|
38
|
0.094976077
|
11.44736842
|
0.987924356
|
HSP90AA1
|
38
|
0.097054563
|
11.57894737
|
0.987772461
|
AR
|
24
|
0.139823718
|
15.54166667
|
0.984886459
|
PRSS1
|
23
|
0.142140468
|
15.7826087
|
0.984734564
|
NOS2
|
21
|
0.200746965
|
11.23809524
|
0.976380345
|
ESR1
|
13
|
0.211538462
|
10.30769231
|
0.974101921
|
PPARG
|
11
|
0.21991342
|
24.09090909
|
0.98306372
|
PGR
|
10
|
0.136363636
|
5.5
|
0.971671603
|
CASP3
|
10
|
0.236792453
|
26.1
|
0.98306372
|
(Notes:PTGS2prostaglandin-endoperoxide synthase 2,) Annex PTGS1prostaglandin-endoperoxide synthase 1, HSP90AA1heat shock protein 90alpha family class A member 1, AR androgen receptor, PRSS1serine protease 1, NOS2nitric oxide synthase 2, ESR1Estrogen Receptor 1, PPARG peroxisome proliferator activated receptor gamma, PGR progesterone receptor, CASP3caspase 3)
Table. 2 Core Compounds with Top 10 Values in the Network of Degree CRC- Targets
MOL ID
|
Molecule Name
|
Degree
|
Source
|
MOL000098
|
quercetin
|
77
|
diyu,huaijiao,jingjie,zhizi
|
MOL000006
|
luteolin
|
40
|
jingjie
|
MOL000422
|
kaempferol
|
28
|
baishao,diyu,huaijiao,zhizi
|
MOL000173
|
wogonin
|
27
|
huangqin
|
MOL002714
|
baicalein
|
23
|
huaijiao,huangqin
|
MOL005828
|
nobiletin
|
21
|
zhike
|
MOL001689
|
acacetin
|
18
|
huangqin
|
MOL004328
|
naringenin
|
18
|
zhike
|
MOL002928
|
oroxylin
|
12
|
huangqin
|
MOL002933
|
5,7,4'- Trihydroxy-8-methoxyflavone
|
12
|
huangqin
|
3.4 Building protein interaction networks
Use the string database (https://string-db.org/) to obtain target interaction relationships, Homo sapiens", by selecting species Check "Hidden Network Interrupt Node hide disconnected nodes in the network", And set the "minimum required interaction score minimum required interaction score">0.9 to get the protein interaction network diagram and the core genes of the network (Fig .3), According to the number of connections with adjacent genes, the top 30 core genes were obtained by R software (Fig .4). Import data cytoscape database to draw protein interaction network. Network Analysis plug-in analysis of the network diagram found, sorted by Degree values from high to low, AKT1, is the top 10 MAPK1, MAPK3, HSP90AA1, JUN, MAPK14, ESR1, CCND1, RB1, IL6( Fig .5).
3.5 Analysis of biological functions and pathways
GO function analysis shows, 114 intersection targets predicted 145 enrichment results (pvalueCutoff <0.05, qvalueCutoff <0.05). DNA-binding transcription factor binding, included RNA polymerase II-specific DNA-binding transcription factor binding, ubiquitin-like protein ligase binding and transcription coactivator binding, Based on the results of screening the top 20 of the P values, See figure 6.
KEGG enrichment analysis of Sophora flavescens decoction (p <0.05), The results show that 164 pathways were obtained, PI3K-Akt signaling pathway, MAPK signaling pathway and TNF signaling pathway were the representatives.. The top 20pathways in KEGG enrichment analysis was demonstrated according to P value (Fig.8) Furthermore, they were mainly involved in antiviral, immunomodulatory and anti-inflammatory effects according to the function of these top 20pathways (Table 3).
Table. 3 Information of potential targets and signaling pathways
ID
|
Description
|
pvalue
|
Count
|
hsa04151
|
PI3K-Akt signaling pathway
|
3.73E-20
|
34
|
hsa05161
|
Hepatitis B
|
2.23E-30
|
33
|
hsa05167
|
Kaposi sarcoma-associated herpesvirus infection
|
3.71E-25
|
31
|
hsa05163
|
Human cytomegalovirus infection
|
9.96E-21
|
29
|
hsa05166
|
Human T-cell leukemia virus 1infection
|
6.46E-20
|
28
|
hsa05215
|
Prostate cancer
|
8.43E-29
|
27
|
hsa05169
|
Epstein-Barr virus infection
|
1.01E-19
|
27
|
hsa04210
|
Apoptosis
|
3.99E-23
|
26
|
hsa05205
|
Proteoglycans in cancer
|
2.05E-18
|
26
|
hsa05206
|
MicroRNAs in cancer
|
5.60E-14
|
26
|
hsa05165
|
Human papillomavirus infection
|
2.64E-13
|
26
|
hsa01522
|
Endocrine resistance
|
1.28E-25
|
25
|
hsa05162
|
Measles
|
1.49E-21
|
25
|
hsa05160
|
Hepatitis C
|
3.43E-20
|
25
|
hsa05170
|
Human immunodeficiency virus 1infection
|
7.11E-16
|
24
|
hsa04010
|
MAPK signaling pathway
|
1.11E-12
|
24
|
hsa05022
|
Pathways of neurodegeneration -multiple diseases
|
2.22E-08
|
24
|
hsa04933
|
AGE-RAGE signaling pathway in diabetic complications
|
1.84E-22
|
23
|
hsa04218
|
Cellular senescence
|
7.86E-18
|
23
|
hsa05225
|
Hepatocellular carcinoma
|
4.31E-17
|
23
|
3.6 Molecular docking
Molecular docking results show, There were 12 pairs of quercetin, luteolin, kaempferol, baicalin and other four core components < the Vina score of 5.0, indicating that the binding ability of PTGS1、PTGS2、AR and core components is strong, The Vina value (binding energy) of the interaction between PTGS1 protein and core components is the best, kcal/mol,-7.9 The specific docking results are shown in Table 4. Combined with figure 10, quercetin form hydrogen bonds ARG376、GLN374 the amino acid residues of the PTGS2 protein, luteolin small molecules form hydrogen bonds THR206 the amino acid residues of the PTGS1 protein, wogonin small molecules form hydrogen bonds LYS93、TYR92 the amino acid residues of the AR protein.
Table. 4 Binding energies of key active compounds with core targets
Mol ID
|
Molecule Name
|
PTGS1
|
PTGS2
|
HSP90AA1
|
AR
|
MOL000098
|
quercetin
|
-5.4
|
-7.9
|
-4.2
|
-7.5
|
MOL000006
|
luteolin
|
-6.3
|
-7.9
|
-4.2
|
-7.3
|
MOL000422
|
kaempfero
|
-5.4
|
-7.9
|
-4.2
|
-7.1
|
MOL000173
|
wogonin
|
-6.2
|
-7.9
|
-4.2
|
-7.5
|