UHPLC-Q-TOF-MS/MS and Network Pharmacology Analysis to Reveal Quality Markers of Xinjiang Cydonia oblonga Mill. for Antiatherosclerosis

Cydonia oblonga Mill. (COM), mature fruit of genus Rosaceae, is consumed as a kind of traditional Chinese medicinal herb. Previous studies have shown that the components in COM extract have antioxidant, anti-inflammatory, blood pressure-lowering, blood lipid-lowering, antithrombotic, and other biological activities. However, the quality markers (Q-markers) of atherosclerosis (AS) have not been elucidated. The Q-marker is based on the five core principles of traceability, transferability, specificity, measurability, validity, and prescription dispensing. In this study, the quality markers of quince were investigated by applying the ultraperformance liquid chromatography-time-of-flight mass spectrometry (UHPLC/Q-TOF-MS/MS) method and network pharmacology method to highlight the three core elements which are, respectively, traceability transmission, measurability, and validity. At the first step, 72 components were identified by applying the ultraperformance liquid chromatography-time-of-flight mass spectrometry (UHPLC/Q-TOF-MS/MS) method. In the next step, 46 candidate components of COM anti-AS were obtained by network pharmacology, and then, 27 active components were filtered with the molecular docking assay. Finally, the 27 active components were intersected with 10 active components obtained by mass transfer and traceable quality markers. Four anti-AS Q-markers of COM were identified, including caffeic acid, chlorogenic acid, ellagic acid, and vanillic acid, which provided a reference for the quality control of quince. The methods and strategies can also be applied to other traditional Chinese medicines and their compound preparations, providing new ideas on the quantitative evaluation and identification of quality markers.


Introduction
Cydonia oblonga Mill. (COM), a traditional Chinese medicine (TCM) with Xinjiang characteristics, is effective in reducing blood pressure, lowering blood fat, supplementing blood volume and improving brain health, relieving cough, resolving phlegm, and appetizing, as well as relieving diarrhea symptoms, etc. It is mostly used in treating cardiovascular diseases and gastrointestinal diseases and is most efficient in relieving cough and diarrhea [1,2]. COM is rich in various active components which present potential medical therapeutical value. The domestic research data on the chemical composition analysis of Xinjiang COM is limited. Parida Abuliz et al. demonstrated that Xinjiang COM fruits contain polysaccharides, phenolic acids, flavonoids, alkaloids, tannins, organic acids, volatile oils, triterpenoids, and other components by mutual validation of two methods: systematic pretesting and special pretesting [3]. Our previous studies have shown that COM extract has hypotensive effects, as well as biological activities such as antioxidant, anti-inflammatory, hypolipidemic, hypoglycemic, and antithrombotic [4][5][6][7], which are important pathological factors in the pathogenesis and development of atherosclerosis (AS). It is the main pathological basis of cardiovascular diseases, on which plaque growth, rupture, and thrombosis can lead to acute cardiovascular events and seriously endanger human health.
The quality markers (Q-markers) in a TCM are the core concept of quality evaluation and quality control of Chinese medicine, proposed by Academician Liu Chang-xiao, based on the five core principles of traceability and transferability, specificity, measurability, efficacy, and prescription dispensing [8][9][10]. The Q-marker is a marker representing the characteristics of a certain Chinese medicine, which are of clear origin, related to the therapeutic use, a combination of compounds reflecting the quality characteristics of Chinese medicine, associated with the functional properties of Chinese medicine, revealing the integrity and also effectiveness of Chinese medicine, and providing a scientific basis for quality control of Chinese medicine. Therefore, it is necessary to further define the Q-marker of COM for quality control of COM.
The non-targeted metabolism of TCM components emphasizes the in vivo study, which is very similar to the principles of the holistic and dynamic nature of TCM. UHPLC Q-TOF-MS/MS has been widely used for the multicomponent analysis of drugs for its sharp specificity, fast speed, and strong selectivity [11]. Therefore, in this study, UHPLC Q-TOF-MS/MS is applied to identify the chemical composition of the extract, and then, potential Q-markers of COM are predicted for treating AS, based on the designed specific quality control methods mentioned above. The design of this experiment is shown in Figure 1.

Preparation of Total
Flavonoids from COM. The COM extraction process referenced previous methods mentioned: the carefully washed COM fruit was sliced into pieces, dried in the shade, and crushed through 20-mesh sieves to gather the powder. Powder was soaked with 10 times the amount of 60% ethanol for 4 hours, then using ultrasonic extraction 2 times 40 min each time, combined with filtrate, vacuum concentration to extract. A certain amount of extract was dissolved in water and filtered, and AB-8 macroporous adsorption resin was used on the filtrate. After full adsorption, the water-soluble polar components of protein carbohydrates were eluted with distilled water and then eluted with 50% ethanol and 90% ethanol. The liquid was combined and concentrated, and the total flavonoid powder of COM fruit was obtained after freeze-drying. The content of total flavonoids in COM extract was determined by the UV spectrophotometer with Rutin as the standard substance, with a content of 38.61%. The powder was placed in a dryer and was stored in a cool place for further use.

UHPLC-Q-TOF-MS/MS Analysis of Chemical
Constituents from COM Extract 2.3.1. Preparation of Test Solution. Take a certain amount of COM extract with pure water prepared in constant volume, COM extract solution (4 mg/ml); take a certain amount of solution and centrifuge in 13000 rpm for 10 min; take out supernatant and filter with a 0.22 μm microporous membrane for injection.

Mass Spectrometer
Conditions. AB 5600 Triple TOF mass spectrometer is based on IDA function for primary and secondary mass spectrometry data acquisition. Bombardment energy is 40 eV, collision energy difference is 20 V, and there are 15 secondary spectra per 50 ms. ESI ion source parameters are as follows: atomization pressure (GS1): 55 Psi, auxiliary pressure: 55 Psi, curtain pressure: 35 Psi, temperature: 550°C, and spray voltage: 5500 V (positive ion mode) or -4000 V (negative ion mode). In each data acquisition cycle, the strongest and more than 100 molecular ions were selected to collect the corresponding secondary mass spectrometry data. The original mass spectrometry was imported by Progenesis QI software. The retention time correction, peak recognition, extraction, and integration were carried out by MAPS software. The peaks of MS/MS data were identified by the Biotree self-built secondary mass spectrometry database and the corresponding pyrolysis law matching method.  Collection of AS Targets. The term "atherosclerosis" was used in the TCMIP V2.0 disease-related molecular library. The DisGeNET database (http://www.disgenet.org) (with "UMLS CUI: C0004153" as the limiting word) and GeneCards database (http://www.genecards.org/) were searched, and the Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/) (with "marker/mechanism and therapeutic" as the limiting word) was combined to retrieve and screen known AS disease targets and delete repeated targets, to obtain known targets in the pathogenesis of AS. The AS disease target was mapped to the action target of COM active components to obtain the action target of COM against AS.

Construction of Protein Interaction Network (PPI).
In String (http://genemania.org/), species selection "Homo sapiens," and " minimum required interaction score " ≥ 0:999, the rest were used to analyze the interaction between each target by default parameters. In the construction of protein-protein interaction (PPI), delete isolated nodes to get the initial network. The intersection of "Node1" and "Node2" was obtained, and the intersection target was a target with strong interaction among proteins, which was used as a candidate target for COM treatment of AS. The candidate targets of COM anti-AS and the COM components acting on this candidate were used to construct a visualized association integration network of "Candidate Component-C Candidate Target, CC-CT" by Cytoscape software. The common target network of COM-AS was calculated by the Cytoscape plugin Network Analyzer, and the target was the main target of COM in the treatment of AS. The possible quality markers of COM intervention AS in Xinjiang COM were analyzed and excavated, and the preliminary screening of quality markers of COM intervention AS in Xinjiang was completed.
2.6. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis. "Effectiveness" is the core element of quality marker determination. Because the control of the effectiveness of TCM is the fundamental purpose of quality control, the discovery of disease-related chemical components of traditional Chinese medicine as candidate quality markers from the network diagram of "traditional Chinese medicine-composition-target-pathway" by network pharmacology is the key idea for effective efficacy research. Gene function (GO) and pathway enrichment analysis of COM anti-AS candidate targets were performed using DAVID (version 6.8, https://david.ncifcrf.gov/) with the error detection rate (FDR) as screening criteria. GO gene functions include biological process (BP), molecular function (MF), and cellular component (CC) analysis. The smaller the FDR value, the higher the enrichment of GO and KEGG. Ranked by FDR values, the top-down pathway is the key pathway. Finally, the "Candidate Compound-Candidate Target-Candidate Pathway, CC-CT-CP" network diagram was constructed by Cytoscape software, and the targets and their active components involved in the key signaling pathways were visually analyzed by degree to screen the main components.

Determination of Quality Markers Based on Component
Transfer and Traceability. In this study, the TCMIP V2.0 database was used to sort out the physical and chemical properties of the chemical components that might be preliminarily screened as quality markers, including absorption level and solubility, and further confirm the quality markers of Xinjiang COM intervention in AS combined with its common clinical medication and extraction process.

Determination of Quality Markers Based on Molecular
Docking. In the determination process of Q-markers, the chemical composition "measurability" is the necessary prerequisite. The candidate targets used the CytoNCA (2.1.6) plug-in to simplify the network with the median ≥ 2 of topological properties such as betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), degree centrality (DC), local average connectivity-based method (LAC), and network centrality (NC) of initial network nodes as screening criteria, and the obtained targets were used as potential targets for COM in the treatment of AS.
The preliminary screening components of the integrated pharmacology platform were sorted out through molecular docking technology. The main targets obtained under "2.5.3" and "2.6" and the potential targets obtained by the above network topology parameters such as BC, CC, EC, DC, LAC, and NC are mapped to the intersection core targets as receptors. In the "CC-CT" and "CC-CT-CP" networks obtained under "2.5.3" and "2.6," respectively, the components screened by degree were intersected, and the main components of the intersection were used as ligands for molecular docking. Receptors and ligands are docked via Autodock Vina (version 1.2, http://vina.scripps.edu/ index.html). COM components were confirmed by Pub-Chem (https://www.ncbi.nlm.nih.gov) and Chemical Book database (https://www.chemicalbook.com/). Files in mol format are saved through the Chemical Book database. The structural components were loaded into the AutoDock Tools 1.5.6 program, and atomic charges were added to distribute atomic types. All flexible bonds were rotated by default as docking ligands. AutoTools software was used to pretreat the protein crystal structure of the core targets, remove the redundant protein chains and ligands, remove water molecules by hydrogenation, and calculate the Gasteiger charge as the receptor for molecular docking. Then, Autodock Vina software was used for docking small molecules with proteins. Finally, we complete the conformation analysis and mapping.
Binding energy (BE) is used to determine the matching degree between COM components and core targets. When the ligand and receptor conformation is stable, the lower the energy, the greater the possibility of interaction. In general, BE ≤ −4:25 kcal/mol indicates that the active ingredient has certain binding energy with the target, BE ≤ −5:00 kcal/ mol indicates that the active ingredient has stable binding energy with the target, and BE ≤ −7:00 kcal/mol indicates that the active ingredient has strong binding energy. In this paper, BE ≤ −5:00 kcal/mol was selected as the standard, and the components with good docking with the main target of COM in the treatment of AS were finally obtained as the 4 BioMed Research International Q-marker of COM in the treatment of AS. Finally, the Qmarker was crossed with the Q-marker obtained under "2.5.3" and "2.6," and the common Q-marker was used as the main quality marker for COM intervention in AS.  Table 1.

Preliminary
Screening of COM Intervention on AS Quality Markers. The above 72 components collected by TCMIP, TCMSP, and SwissTargetPrediction database were obtained, respectively, 243, 133, and 1017 targets. After deleting repetitive targets, 1202 targets were obtained. The TCMIP, CTD, DiSGeNET, and GeneCards databases were used to search for 65, 62, 2044, and 4329 disease targets, respectively, related to the pathogenesis of AS, and 737 common targets were obtained by crossing with 1202 COM targets, as shown in Figure 3(a). A total of 737 drug-disease common targets were screened by String data using "Homo sapiens" and " minimum required interaction score " ≥ 0:999 as criteria, and 46 candidate targets were screened. The interaction between the targets is shown in Figure 3(b), and there is a correlation between the two targets. The network contains 46 nodes and 518 edges ( Figure 3(c)). 46 candidate targets were connected to COM components, and the componenttarget network diagram was constructed by Cytoscape software. The network consists of 110 nodes (including 64 components and 46 candidate targets) and 437 edges. The edges between components (red quadrangle) and candidate targets (yellow circle) represent interactions ( Figure 3(d)). Nodes represent proteins, and edges represent the correlation of functions. The more lines represent the greater correlation. In addition, the degree of COM-AS common target network was calculated by the Cytoscape plug-in network analyzer with a degree as the screening condition. The top 10 targets with higher degrees include estrogen receptor (ESR1, degree = 36), epidermal growth factor receptor (EGFR, degree = 36), cell division protein kinase 2 (CDK2, degree = 32), glycogen synthase kinase-3 beta (GSK3B, degree = 26), protooncogene tyrosine-protein kinase SRC (SRC, degree = 24), peroxisome proliferator-activated receptor gamma (PPARG, degree = 2), cell division control protein 2 homolog (CDK1, degree = 20), insulin-like growth factor 1 receptor (IGF1R, degree = 19), insulin receptor (INSR, degree = 18), and integrin alpha-L (ITGAL, degree = 18). It can be the main target of COM for the treatment of AS.
The initial screening of the 64 components of COM was performed according to the screening criterion of degree ≥ 6, and the obtained components were used as the initial screening results for the COM intervention AS quality markers. As shown in Table 2, 46 components, such as ellagic acid (degree = 18), pectolinarigenin (degree = 14), eupatilin (degree = 14), acacetin (degree = 13), and ursolic acid (degree = 12), were obtained as the outcome of the initial screening of COM intervention AS quality markers.

GO and KEGG Enrichment Analysis
Results. The results of GO gene function analysis of 46 candidate targets by DAVID showed that a total of 426 items were obtained, including 301 BP, 80 MF, and 45 CC, and the top 20 results were sorted according to the corrected FDR pairs as shown in Figure 4.
The top 10 biological functions induced by the 64 COM components include enzyme binding, kinase activity, protein binding, transcription factor binding, insulin receptor substrate binding, nuclear hormone receptor binding, chromatin binding, identical protein binding, protein phosphatase binding, and protein heterodimerization activity ( Figure 4(c)). The top 10 cell components listed include cytosol, nucleoplasm, nucleus, receptor complex, perinuclear region of cytoplasm, cytoplasm, cyclin-dependent protein kinase holoenzyme complex, nuclear chromatin, transcription factor complex, and plasma membrane (Figure 4(b)). The top 10 biological processes include negative regulation of apoptotic process, positive regulation of cell proliferation, protein autophosphorylation, ERBB2 signaling pathway, positive regulation of protein phosphorylation, positive regulation of nitric oxide biosynthetic process, positive regulation of transcription from RNA polymerase II promoter, signal transduction, response to drug, positive regulation of transcription, and DNA-templated ( Figure 4(a)).
To further determine the quality markers identified for COM treatment of AS, a component-target-pathway network map was constructed by Cytoscape software, which    The edges between components (red quadrangles), candidate targets (yellow circles), and signaling pathways (green diamonds) represent interactions. The larger the size of the circles, the larger the degrees of the components, targets, and pathways represented, the more important is the network.
The 63 components of COM were screened according to the screening criterion of degree ≥ 5, and the obtained components were used as rescreening results for the COM intervention AS quality markers. As shown in Table 3, 49 components were obtained for ellagic acid (degree = 16), acacetin (degree = 12), ursolic acid (degree = 11), acacetin (degree = 10), and eupatilin (degree = 10).

Quality Marker Results
Based on Quality Transfer and Traceability. The common physicochemical properties of the above components such as oil-water partition coefficients (data sources are available in the TCMIP V2.0 chemical composition database) were summarized in conjunction with the TCMIP V2.0 chemical composition database, and the specific parameters are shown in Table 4. Molecular solubility and ADMET absorption level of the compounds were calculated by the TCMIP using Pipeline Pilot software (version 7.5), representing the molecular solubility and its absorption level in vivo, respectively. The optimal lg P value for a drug is −1 < lg P < 2 for better absorption [13]. In this paper, lg P values of −1 < lg P < 2 and ADMET absorption level > 0 were used as criteria for screening to obtain vanillic acid, chlorogenic acid, cryptochlorogenic acid, isolariciresinol-4-O-Î ′ -D-glucopyranoside, ellagic acid, luteolin-4 ′ -Oglucoside, isochlorogenic acid B, sophoricoside, caffeic acid, and eucommin A, which can be used as quality markers for COM intervention in AS.  Table 5. The "CC-CT" and "CC-CT-CP" obtained under "3.2" and "3.3," respectively, in the "CC-CT" and "CC-CT-CP" networks, and the top 10 candidate targets with higher degrees of each network were intersected with 31 potential targets, and the obtained intersected targets were used as receptors for molecular docking. As can be seen in Figure 5(b), the core targets of the intersection include EGFR (PDBID=5GTY), ESR1 (PDBID=4XI3), CDK1 (PDBID=6GU6), and CDK2 (PDBID=1B39).
In the "CC-CT" and "CC-CT-CP" networks obtained under "3.2" and "3.3," respectively, network, the 46 candidate components obtained according to degree screening were intersected with 49 candidate components, which can be seen in Figure 5(a), and 46 core components were obtained, and the obtained core components were used as ligands for molecular docking with 4 core targets, as shown in Tables 6 and 7. In this paper, with BE ≤ −5:00 kcal/mol, 27 of the 46 core components had good binding activity to the 4 core targets, shown in Figure 5(c).
The 27 core components that dovetailed well with the 10 components obtained based on "3.4" were intersected to obtain 4 components as the main Q-markers for COM for AS, which are shown in Figure 5(d), including caffeic acid, chlorogenic acid, ellagic acid, and vanillic acid. The specific binding molecular models of the four main Qmarkers to the four core targets, including EGFR, ESR1, CDK1, and CDK2.

Discussion
AS is a chronic progressive lesion of the aorta caused by disorders of lipid metabolism and inflammatory response, which is the pathological basis of several cardiovascular diseases. The previous research of our research group mainly focused on the pharmacodynamic effects of COM on cardiovascular diseases; however, the material basis and active components of AS prevention and treatment are not studied in depth. The nontarget metabolomics of TCM plays a leading effect on the modernization research of TCM, such as the discovery of TCM components, the study of the mechanism of action of prescriptions, and the discovery of drug targets, and the elucidation of metabolic pathways and mechanisms of action of TCM components are an important way for TCM to face and develop with the world. Currently, the development of UHPLC-Q-TOF-MS/MS analysis technology provides technical support for the study of mixed components of TCM [14,15]. In this study, 72 components of COM and their 1202 action targets were obtained by UHPLC-Q-TOF-MS/MS technique, and this drug target intersected with AS disease targets, and 737 common drug-disease targets were obtained, and 46 candidate targets of COM against AS were obtained by PPI analysis. 46 candidate targets were screened by network topology parameters to obtain 31 potential targets. 46 candidate targets were enriched by network topology parameters to obtain 46 potential targets. The enrichment analysis of the 46 candidate targets yielded 426 GOs and 83 signaling pathways. The common candidate components obtained from "CC-CT" and "CC-CT-CP" were used as ligands, and 31 potential targets were used as receptors for docking, and the criteria of BE ≤ − 5:00 kcal/mol were used for the 46 core components. The 27 active ingredients were intersected with 10 active ingredients obtained by mass transfer and traceability of quality markers to obtain four Q-markers of COM anti-AS, consisting of caffeic acid, chlorogenic acid, ellagic acid, and vanillic acid, all of which belong to phenolic acids. Phenolic acid components have strong anti-inflammatory, antioxidant, anti-free radical, and hypolipidemic effects [16,17]. The present study predicts an anti-AS effect mainly by acting on EGFR, ESR1, CDK1, and CDK2.
Caffeic acid (CA) is a natural phenolic compound belonging to the family of polyphenolic phenolic acids with a hydroxybenzoic acid structure that is most abundant in fruits, vegetables, and beverages and has biological activities such as antioxidant, anti-inflammatory, antibacterial, and cytostatic [18]. CA modulates the reninangiotensin-aldosterone endocrine axis in vitro [19], and in cyclosporine-induced hypertension experiments in Sprague-Dawley rats, CA significantly reduced plasma angiotensin-converting enzyme (ACE) activity [20]. Studies have shown the ability of CA to inhibit ACE activity and prevent the formation of advanced glycosylation end products (AGE) and its antioxidant, reducing, and chelating activities [21]. CA prevents the oxidation of LDL and BioMed Research International the increase in calcium concentration, thus preventing apoptosis, and thus, CA may act as an inhibitor of LDL oxidation and may treat atherosclerosis [22]. However, the exact mechanism of action has not been clarified.
The results of the present study suggest that CA can form stable hydrogen bonds and π interactions with amino acid residues of EGFR, ESR1, CDK1, and CDK2, the core targets associated with the pathogenesis of AS. Activation of the epidermal growth factor receptor (EGFR) is closely linked to the physiology and pathophysiology of the cardiovascular system, and inhibition of EGFR activity is emerging as a potential therapeutic strategy for the treatment of diseases such as hypertension, cardiac hypertrophy, renal fibrosis, and abdominal aortic aneurysms [23]. Studies have shown that enhanced expression and plasma secretion of heparin-binding epidermal growth factor (HB-EGF), a ligand of EGFR, and enhanced arterial EGFR activation were observed in animal models of diet-induced atherosclerosis. Stanic et al. demonstrated for the first time that a causal relationship between EGFR activation and NADPH oxidase expression was studied and identified EGF-like ligands as a potential modulator of atherosclerosis [24]. In contrast, CA, the active component of COM anti-AS, can inhibit EGF-induced EGFR autophosphorylation, as well as the ability to inhibit oxidized low-density lipoprotein-(OxLDL-) induced cellular oxidative stress and participate in the activation of epidermal growth factor receptors [25]. The accuracy of the predicted results in this paper was further determined.
Estrogen receptor alpha (ESR1) is an important gene transcriptional regulator known to mediate the effects of estrogen. Alterations in ESR1 expression and its function affect the atheroprotective effects of circulating estrogen in insulinogenic atherosclerosis [26]. ESR1 plays an important role in the regulation of adipose tissue VEGFA, which is known to enhance vascular generation, reduce  [27]. In contrast, whether CA regulates the expression of ESR1 and thus the anti-AS effect has not been reported in the study, which is the next step to be validated by our group.
Cycle protein-dependent kinase 1 (CDK1) is one of the important kinases that drive cell entry into mitosis and is involved in cell cycle M phase, G2/M transition point, and G1 phase activities, as well as being a key protein for cell cycle passage through G1/S and G2/M phase restriction

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BioMed Research International points. CDK1 inhibits microtubule-associated protein hydrolysis and regulates centrosome segregation and also promotes spindle assembly and centrosome maturation. Its overactivation, abnormal regulation of mitotic entry and progression, and deranged centrosome segregation accelerate cell proliferation [28]. CDK2 is a cell cycle proteindependent kinase that remains at relatively constant levels throughout the cell cycle. Inhibition of CDK2 expression can significantly inhibit cell growth, causing cells to stay in the G1 phase while undergoing division, thus blocking cell proliferation. Blocking the vicious cycle of apoptosis and proliferation in AS can inhibit or even reverse the develop-ment of AS [29]. Caffeic acid phenethyl ester (CAPE) treatment, a natural derivative of CA, induces cell cycle arrest and growth inhibition in desmoplastic resistant prostate cancer (CRPC) cells by regulating Skp2, p53, p21Cip1, and p27Kip1 [30]. Among them, p27kip1 belongs to CDK1 which has a broad-spectrum inhibitory effect on CDK. SKP2 is a cell cycle G1/S transition and S-phase promotion through ubiquitination and degradation of CDK1. p53 and p21Cip1 are inhibitors of CDK2. In recent years, the mechanism of action of active ingredients of traditional Chinese medicine through blocking the cell cycle and thus inhibiting tumor growth has gradually become clear. It has been found  that drugs that inhibit cell cycle progression have a therapeutic effect on AS [31]. Combining the above research reports with the present study, it can be speculated that whether CA, the active ingredient of COM, inhibits the cell cycle and thus exerts anti-AS effects needs to be further investigated.
Chlorogenic acid (CGA) is a phenolic compound composed of CA and quinic acid. CGA increased the mRNA levels of peroxisome proliferator-activated receptor gamma (PPARγ), Liver X Receptor α (LXRα), ATP Binding Cassette Subfamily A Member 1 (ABCA1), and ATP Binding Cassette Subfamily G Member 1 (ABCG1) as well as the transcriptional activity of PPARγ, resulting in an effective reduction of ApoE -/development of atherosclerosis in mice and promote cholesterol efflux from RAW264.7 macrophages [32,33]. Lysophosphatidylcholine (LPC) is the main atherogenic compound that oxidizes LDL. CGA protects endothelial cells from LPC damage and thus inhibits atherosclerosis [34]. CGA inhibits MCF-7 cell proliferation by downregulating CyclinD1 expression and blocking cells in the G0/G1 phase. It can be speculated that whether CGA inhibits the cell cycle and thus exerts an anti-AS effect needs to be further investigated. However, studies on the effects of  BioMed Research International CGA on EGFR and ESR1 are less reported, and further studies are needed. Ellagic acid (EA) a polyphenolic compound extracted from pomegranate fruit extract reduced the lipid accumulation and expression of key adipogenic genes peroxisome proliferator-activated receptor γ (PPARγ), CCATT/ enhancer-binding protein α (C/EBPα), sterol regulatory element-binding protein-1c (SREBP-1c), acetyl coenzyme A carboxylase (ACC), and fatty acid synthase (FAS) lipid accumulation and expression levels [35]. It also inhibits the gene expression of LPL mRNA and decreases the con-centration of TC and TG in the blood and increases HDL, which may reduce the incidence of cardiovascular disease [36]. Wang et al. showed that EA inhibits the EGFR signaling pathway and thus the migration, invasion, and proliferation of melanoma cells [37]. However, whether EA exerts an anti-AS effect by inhibiting the EGFR signaling pathway is thus to be further investigated. It was shown that EA inhibits the activity of key transcription factors by inhibiting lipogenic inducers to induce the proliferation of pre-and post-3T3-L1 adipocytes, reducing the expression of cell cycle-related proteins 21 BioMed Research International [38]. It can be speculated as to whether CGA inhibits the CDK1 and CDK2 cell cycle and thus exerts anti-AS effects, which needs to be further investigated. However, studies on the role of CGA on ESR1 are less reported, and further studies are needed.
Vanillic acid (VA) is a phenolic compound in the oxidized form of vanillin, and VA has analgesic, antioxidant and anti-inflammatory, and neuroprotective effects on nuclear factor-κB activation, proinflammatory cytokine production, oxidative stress, and acetylcholinesterase [39,40]. Studies have shown that VA acts through the regulation of adhesion processes, E-selectin, and VEGF production, thus treating AS [41]. According to the results of this study, it can be speculated as to whether VA exerts its anti-AS effect through proteins such as EGFR, ESR1, CDK1, and CDK2, thus providing some research ideas for later studies. shown as 3D diagrams and 2D diagrams.

Conclusions
In summary, this study targeted three core elements of traceability and deliverability, measurability, and efficacy, and applied the UHPLC-Q-TOF-MS/MS assay and network pharmacology methods to study the quality markers of COM, and finally identified that caffeic acid, chlorogenic acid, ellagic acid, and vanillic acid were identified as potential Q-markers for COM anti-AS, providing a reference for COM quality control. The method and strategy can also be extended and applied to compound preparation of other traditional Chinese medicines, as well as to provide new ideas for the quantitative evaluation and identification of quality markers. shown as 3D diagrams and 2D diagrams.

Data Availability
The data used to support the findings of this study are included within the article.