Sage Journals: Discover world-class research

Abstract

Purpose

The objective of this research was to employ network pharmacology to analyze and identify the key active components and target points of action of Aidi injection in relation to colorectal cancer. Additionally, this study aimed to experimentally validate the mechanism of action of hinokinin in treating colorectal cancer.

Methods

This study employed a network pharmacology methodology to identify the primary components and action targets of Aidi injection in public databases; a similar approach was used to identify effective targets for colorectal cancer treatment. The protein‒protein interaction network, along with gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, facilitated the prediction of central targets and pathways by which Aidi injection combats colorectal cancer. Molecular docking techniques were harnessed to sift through drugs and their targets for high binding affinity. Experimental validation was carried out to corroborate the findings derived from network pharmacology.

Results

We identified 37 active constituents of Aidi injection (ADI), 701 potential targets, 768 colorectal cancer (CRC) targets, and 111 overlapping targets. The anti-CRC efficacy of ADI seems to be associated with several pathways: cancer pathways, EGFR tyrosine kinase inhibitor resistance, proteoglycans in cancer, endocrine resistance, central carbon metabolism in cancer, chemo-oncogenic receptor initiation, cancer-specific microRNAs, and signaling pathways such as PI3K-Akt, mitogen-activated protein kinase, prolactin, FoxO, and Ras. Predominantly, ADI exhibited activity against key markers such as EGFR, ERBB2, HSP90AA1, mTOR, HIF1A, CCND1, JUN, AKT1, SRC, and STAT3 to mitigate CRC. Furthermore, Hinokinin has been shown to curtail the proliferation of colorectal cancer cells, amplify apoptosis, and modulate the expression of the mTOR protein.

Conclusion

Through network pharmacology, we identified 13 shared targets associated with colorectal cancer. Subsequent experimental validation revealed that hinokinin can curtail the proliferation of colorectal cancer cells and enhance their apoptosis, primarily by modulating mTOR expression.

Keywords

Aidi injection colorectal cancer network pharmacology Hinokinin mTOR

Introduction

Colorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide. Morbidity and mortality are approximately 25% lower in women than in men. These rates show regional variation, and the highest rates occur in the most developed countries. As the number of CRC cases continues to increase in developing countries, the global incidence of CRC is expected to reach 2.5 million new cases in 2035.¹ Approximately 10% of all cancer types and cancer-related deaths occur worldwide every year.² Thus, drugs for controlling and preventing CRC need to be developed.^3,4

Aidi injection (ADI) is a traditional Chinese medicine that contains components of cantharis, ginseng, astragalus, and acanthopanax. It can inhibit tumor growth by improving the immunity of individuals and is often used as an adjuvant drug in cancer chemotherapy. Several studies have shown that the ADI can substantially improve the overall remission rate in patients with CRC, liver cancer,⁵ breast cancer, or lung cancer. The ADI is also widely used to treat CRC.⁶

Great progress has been made in the comprehensive treatment of multiple diseases in recent years. TCM plays a very important role in cancer treatment. Patients often have adverse reactions to Western medicine, which decreases the efficacy of treatment. Network pharmacology is a novel approach in which a network is used to identify effector mechanisms of herb-component-target multimolecule synergy at the systems level.⁷ In this study, we predicted the targets and signaling pathways of ADI related to the inhibition of CRC using network pharmacology. We analyzed the effector targets that are associated with the anti-CRC effects of ADI. We also used molecular docking technology and conducted cell experiments to elucidate the mechanism underlying the inhibitory effects of ADI on CRC.

Materials and Methods

Schematic Diagram

A schematic illustration of the study design and workflow is presented in Figure 1. Network pharmacology, molecular docking, and experimental validation were used to determine the mechanism underlying the inhibitory effects of ADI on CRC.

Figure 1.

The study design and workflow.

Network Pharmacology

Screening of major active ingredients and targets of ADI

The list of compounds present in cantharis (Mylabris), ginseng (Panax ginseng C. A. Mey.), astragalus (Hedysarum Multijugum Maxim.), and acanthopanax (Eleutherococcus senticosus (Rupr. & Maxim.) Maxim.) were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP)⁸ and the Heritage Board (HERB) database.⁹ Oral bioavailability (OB) and drug similarity (DL) are important indicators for determining the absorption, distribution, metabolism, and excretion of drugs through drug metabolism kinetics.¹⁰ We screened the active ingredients of the target compounds with an OB ≥ 30% and a DL ≥ 0.18 and obtained the targets of action of the corresponding compounds from the TCMSP database. Those compounds that met the screening criteria but did not have corresponding targets were screened in the TCMSP database, and their canonical simplified molecular input line entry system numbers were screened from the PubChem database. Homo sapiens was selected, and targets with a probability greater than 0 were added to the Swiss Target Prediction database.¹¹ After screening for drug action targets, duplicate targets were removed, and the data were normalized using the UniProt database.¹²

Identification of the targets of CRC

We searched the GeneCards database,¹³ DrugBank database,¹⁴ Online Mendelian Inheritance in Humans (OMIM) database,¹⁵ and DisGeNET database¹⁶ using “colorectal cancer” as the keyword. We removed duplicate targets and created target data for the disease.

Identifying the potential targets of ADI for treating CRC

Venn diagrams were generated using the bioinformatics online website (http://www.bioinformatics.com.cn). Cross-targets were obtained by comparing the targets of action of the active ingredients of ADI with the targets of CRC. These cross-target effects were the potential targets of ADI for treating CRC.

Construction of the ADI active ingredient-CRC-target interaction network

We removed the active ingredients from the ADI that did not intersect with the disease targets and then imported the remaining active ingredients and cross-targets into Cytoscape 3.9.1 software.¹⁷ We constructed the topology of the ADI-regulated CRC network according to the drug-compound-target division. The node sizes were set to map continuously based on the degree of connectivity. Each node represents a drug, compound, or target. Connections between the nodes represented interactions between them. The degree of the nodes indicates the number of connected nodes.

Enrichment analysis

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on cross-targets using the DAVID website.¹⁸ Additionally, the entries of the GO and KEGG enrichment analyses were plotted based on the screening conditions of P < .01 and FDR < 0.01. We also identified the top 20 cellular component (CC), biological process (BP), molecular function (MF), and signaling pathway terms involved in cross-targeting. The pathway enrichment was visualized and analyzed on the bioinformatics online website.

Construction of protein–protein interaction networks

The cross-target protein‒protein interaction (PPI) networks were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://cn.string-db.org/) with the lowest interaction score ≥0.4. The “Homo sapiens” sequence was used as a screening criterion.¹⁹ The data were exported in TSV format and imported into Cytoscape 3.9.1 software for further analysis.

Core target screening

The PPI networks were filtered using the CytoHubba²⁰ plugin. The top 20 core genes were subsequently selected based on degree, maximal clique centrality (MCC), edge-percolated component (EPC), closeness, and radiality. The top 20 core genes were selected, and crossover was performed to obtain the key targets. The genes were then ranked in descending order of degree, as the degree value reflects the importance of the target.

Molecular docking

We downloaded the 3-dimensional (3D) structures of the core targets from the Protein Data Bank (PDB)²¹ and obtained the SDF structure files of the compounds from the PubChem database^.²² We also used PyMoL 2.5.4 software²³ to convert the SDF files to PDB files, dehydrate the receptor protein, remove the ligand and retain the single chain, and interchange the ligand by rotating the 2 rotamers around the C-glycosidic bond using Chem3D 19.0.0.22 software^.²⁴ We used the AutoDock tool²⁵ to modify the receptor protein via hydrogenation and charge balancing. The protein processing data were analyzed using AutoDock Vina 1.1.2 for molecular docking.

Statistical analysis

The data were summarized using office software (version 2303 Build 16.0.16227.20202) and the WPS Office (version 11.1.0.14036). The PPI data were analyzed using Cytoscape 3.9.1.

Experimental Validation

Drug preparation and reagents

We prepared a 5-mM solution of hinokinin by dissolving and diluting 1 mg of hinokinin (MedChemExpress, CAS: 26543-89-5) in DMSO and stored it at −20 °C.

PBS (Biosharp, BL550A), DMEM (basal medium; Procell, PM150210), fetal bovine serum (FBS; Gibco, 10099), EDTA-treated trypsin (Solarbio, 9002-9007-7), penicillin‒streptomycin mixture (PS; Solarbio, P1400), CCK-8 (Dojindo, CK04), antibody GAPDH (Abcam, ab8245), antibody mTOR (Wan Class Biotechnology, WL02477), goat antirabbit IgG-HRP (Imab, EM35111-01), and goat antimouse IgG-HRP (Imab, EM35110-01) were used in this study.

Cell culture

The human CRC cell line SW480 was purchased from the Institute of Chinese Academy of Sciences. The SW480 cells were cultured in DMEM supplemented with 10% FBS and 1% PS. The cells were grown in an incubator with 5% CO₂ at 37 °C.

Cell viability assay

The human CRC cell line SW480 was seeded in a 96-well plate at a density of 5000 cells/well and cultured overnight. Then, different concentrations of hinokinin were added for 48 h. Subsequently, the CCK-8 solution was added to each well, and after 60 min of incubation at 37 °C, the optical density was measured at 450 nm using a microplate reader.

CCK-8 experiment

The cells were seeded at a density of 5 × 10³ cells/well in 96-well microplates with 100 µL of medium per well. Then, the cells were treated with different concentrations of Hinokinin (0, 40, and 80 µM). After each treatment for 0, 24, 48, or 72 h, 10 µL of the CCK-8 reagent was added to each well and incubated for 1 h. The absorbance was analyzed at 450 nm using a microplate reader.

Flow cytometry assay

The SW480 cells were grown in DMEM supplemented with 10% FBS in 6-well plates for 6 h. Then, the cells were treated with different concentrations of hinokinin (0, 40, and 80 µM) for 24 h. The treated cells were harvested and washed with cold PBS. Then, the cells were resuspended in binding buffer and stained with 5 µL of Annexin V-FITC and 5 µL of propidium iodide (BD 556547 Annexin V-FITC/PI Cell Apoptosis Double dye Kit) at 4 °C for 30 min in the dark. The stained cells were washed thrice with binding buffer to remove excess dye and then resuspended in 500 µL of binding buffer. The percentage of apoptotic cells was analyzed within 1 h by flow cytometry.

Western blotting assay

Total protein was extracted from the treated cells (Beyotime) using RIPA buffer containing 1 mmol/L PMSF, and the protein concentration was quantified using a BCA protein assay kit (Beyotime).²⁶ The protein bands were separated via SDS‒PAGE and subsequently transferred onto a polyvinylidene difluoride (PVDF) membrane (Millipore). After blocking with 5% bovine serum albumin in phosphate-buffered saline with Tween for 1 h, the PVDF membrane was incubated with the primary antibody overnight at 4 °C. After washing 3 times, the membrane was incubated with horseradish peroxidase-conjugated secondary antibody for 1 h at room temperature. Finally, the enhanced chemiluminescence technique was used.

Mathematical and statistical analysis

All the statistical analyses were conducted using GraphPad 9.1.1. All the data are reported as the means ± SD. The data were analyzed using an unpaired Student t test for pairwise comparisons. All results were considered to be statistically significant at P < .05.

Results

Screening of Active Ingredients and Targets in ADI

The pinyin names of the herbal ingredients Banmao, Renshen, and Huangqi were entered into the TCMSP, and the name Ciwujia was entered into the HERB database. The parameters were set as OB ≥ 30% and DL ≥ 0.18. The Swiss Target Prediction database was used to predict the targets of the screened ingredients. The targets with a probability > 0 were selected. In total, 51 Chinese herbal ingredients, including 1 cantharis, 22 ginseng, 20 Astragalus, and 10 acanthopanax components, were obtained. After excluding the components that did not match, 37 effective ingredients were obtained, including one cantharis, 17 ginseng, 11 Astragalus, and 10 acanthopanax components. Kaempferol was found to be a common component of ginseng and Astragalus, while quercetin was a common component of Astragalus and Acanthopanax (Table 1). After removing duplicates, the UniProt database was used for mapping IDs, and 701 gene targets were mapped from 696 UniProt IDs.

Table 1.

The Active Ingredients Present in Aidi Injection.

Chinese herb	Mol ID	Molecule name	OB (%)	DL
cantharis	MOL001851	3-Phenyl-4-azafluorene	32.9	0.23
ginseng	MOL002879	Diop	43.59	0.39
ginseng	MOL000449	Stigmasterol	43.83	0.76
ginseng	MOL000358	beta-sitosterol	36.91	0.75
ginseng	MOL003648	Inermin	65.83	0.54
ginseng	MOL000422	kaempferol	41.88	0.24
ginseng	MOL004492	Chrysanthemaxanthin	38.72	0.58
ginseng	MOL005314	Celabenzine	101.88	0.49
ginseng	MOL005317	Deoxyharringtonine	39.27	0.81
ginseng	MOL005318	Dianthramine	40.45	0.2
ginseng	MOL005320	arachidonate	45.57	0.2
ginseng	MOL005321	Frutinone A	65.9	0.34
ginseng	MOL005344	ginsenoside rh2	36.32	0.56
ginseng	MOL005356	Girinimbin	61.22	0.31
ginseng	MOL005357	Gomisin B	31.99	0.83
ginseng	MOL005376	Panaxadiol	33.09	0.79
ginseng	MOL005384	suchilactone	57.52	0.56
ginseng	MOL000787	Fumarine	59.26	0.83
astragalus	MOL000211	Mairin	55.38	0.78
astragalus	MOL000239	Jaranol	50.83	0.29
astragalus	MOL000033	(3S,8S,9S,10R,13R,14S,17R)-10,13-dimethyl-17-[(2R,5S)-5-propan- 2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H- cyclopenta [a]phenanthren-3-ol	36.23	0.78
astragalus	MOL000354	isorhamnetin	49.6	0.31
astragalus	MOL000371	3,9-di-O-methylnissolin	53.74	0.48
astragalus	MOL000378	7-O-methylisomucronulatol	74.69	0.3
astragalus	MOL000387	Bifendate	31.1	0.67
astragalus	MOL000392	formononetin	69.67	0.21
astragalus	MOL000417	Calycosin	47.75	0.24
astragalus	MOL000422	kaempferol	41.88	0.24
astragalus	MOL000098	quercetin	46.43	0.28
acanthopanax	MOL000098	Quercetin	46.43	0.28
acanthopanax	MOL009047	pinoresinol dimethyl ether	33.29	0.62
acanthopanax	MOL001558	sesamin	56.55	0.83
acanthopanax	MOL001987	β-sitosterol	33.94	0.7
acanthopanax	MOL001002	ellagic acid	43.06	0.43
acanthopanax	MOL009009	(+)-medioresinol	87.19	0.62
acanthopanax	MOL002005	Hinokinin	56.5	0.64
acanthopanax	MOL002776	Baicalin	40.12	0.75
acanthopanax	MOL005922	Acanthoside B	43.35	0.77
acanthopanax	MOL001689	acacetin	34.97	0.24

Abbreviation: OB, oral bioavailability.

Identification of Potential Targets of ADI for the Treatment of CRC

We searched the GeneCards database (top 500 targets), DrugBank database, DisGeNET database, and OMIM database and found 834 disease targets related to CRC. After removing duplicates, 768 targets remained. The targets that intersected with the active regulatory components of ADI and the disease-related genes of CRC were considered potential targets of ADI for the treatment of CRC. The targets corresponding to the active components of the drug intersected with the disease targets. In total, 111 intersecting targets were obtained, which constituted the potential targets of ADI for the treatment of CRC (Figure 2). After removing duplicates, 37 active components corresponding to these targets remained.

Figure 2.

The potential targets of ADI for CRC treatment. (A) A total of 768 targets in the CRC group and 701 targets in the ADI group were identified, with 111 intersecting targets. ADI, Aidi injection; CRC, colorectal cancer.

Construction of the ADI Active Ingredient-CRC-Target Interaction Network

A topology network of the active ingredients and targets of ADI was constructed using Cytoscape 3.9.1 software. The network consisted of 152 nodes and 551 edges; the size of the nodes was proportional to their degree (Figure 3).

Figure 3.

The ADI component-CRC-target interaction network. ADI, Aidi injection; CRC, colorectal cancer.

Enrichment Analysis

The 111 intersecting targets were analyzed by GO and KEGG pathway enrichment analysis using the DAVID online analysis tool. The filtering criteria were set at P < .01 and FDR < 0.01; based on these criteria, 206 enriched terms were obtained for GO-BP, 26 for GO-CC, 55 for GO-MF, and 142 for KEGG analysis. The top 20 enriched terms for each category were selected and are shown in a bubble chart (Figure 4).

Figure 4.

The top 20 items of GO and KEGG enrichment analyses. (A) BP; (B) CC; (C) MF; (D) KEGG. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; CC, cellular component; MF, molecular function; MAPK, mitogen-activated protein kinase; ERK, extracellular regulated MAP kinase.

The results of the GO-BP analysis showed that the targets were involved mainly in the positive regulation of kinase activity, the transmembrane receptor protein tyrosine kinase signaling pathway, peptide tyrosine phosphorylation, protein autophosphorylation, negative regulation of apoptosis, and protein phosphorylation (Figure 4A). The results of the GO-CC analysis showed enrichment in the receptor complex, macromolecular complex, and cyclin-dependent protein kinase holoenzyme complex, among others (Figure 4B). The results of the GO-MF analysis revealed enrichment of transmembrane receptor protein tyrosine kinase activity, protein tyrosine kinase activity, protein kinase activity, and ATP binding (Figure 4C).

In CRC, the potential target genes of ADI were found to be primarily enriched in cancer pathways; EGFR tyrosine kinase inhibitor resistance; protein glycosylation in cancer; endocrine resistance; central carbon metabolism in cancer; chemical carcinogenesis; receptor activation; microRNAs in cancer; and signaling pathways, such as the PI3K-Akt, mitogen-activated protein kinase (MAPK), prolactin, FoxO, and Ras pathways (Figure 4D). Each pathway was associated with different target genes (Table 2).

Table 2.

Analysis of the First 20 Pathways by KEGG.

ID	Pathway	Genes
hsa05200	Pathways in cancer	RET, ALK, GSK3B, FLT4, FGF2, IGF1R, CASP9, EDNRA, CASP8, CCND2, CCND1, CASP3, AKT1, EP300, JAK2, PDGFRB, PDGFRA, MAP2K1, HSP90AA1, MMP1, MMP2, MMP9, TGFBR1, TGFBR2, CCNA2, AR, PIK3CA, KIT, PPARG, RAF1, MET, HDAC1, XIAP, PIK3R1, PTGS2, HIF1A, EGFR, MAPK8, TERT, ERBB2, ABL1, MAPK1, NTRK1, JUN, STAT3, BRAF, ESR1, MTOR, ESR2, NFKB1, IL2, PTK2, VEGFA, CDK6, CDK4, CDK2, BCL2, MDM2, FGFR3, FGFR1, BCL2L1
hsa04151	PI3K-Akt signaling pathway	GSK3B, FLT1, FLT4, PIK3R1, FGF2, EGFR, IGF1R, CASP9, CCND2, CCND1, ERBB4, ERBB2, KDR, AKT1, MAPK1, JAK2, MCL1, PDGFRB, NTRK1, PDGFRA, MAP2K1, HSP90AA1, IL2, MTOR, PTK2, NFKB1, VEGFA, CDK6, PIK3CA, CDK4, KIT, CDK2, BCL2, MDM2, RAF1, MET, FGFR3, TLR4, FGFR1, BCL2L1
hsa05215	Prostate cancer	GSK3B, PIK3R1, EGFR, IGF1R, CASP9, CCND1, PLAU, ERBB2, AKT1, MAPK1, EP300, PDGFRB, PDGFRA, MAP2K1, HSP90AA1, BRAF, MMP9, MTOR, NFKB1, AR, PIK3CA, CDK2, BCL2, MDM2, RAF1, FGFR1
hsa01521	EGFR tyrosine kinase inhibitor resistance	PDGFRB, PDGFRA, GSK3B, MAP2K1, SRC, STAT3, BRAF, PIK3R1, FGF2, EGFR, MTOR, IGF1R, VEGFA, PIK3CA, ERBB2, KDR, BCL2, AKT1, MAPK1, JAK2, RAF1, MET, FGFR3, BCL2L1
hsa05205	Proteoglycans in cancer	SRC, PIK3R1, FGF2, HIF1A, TNF, EGFR, IGF1R, CCND1, PLAU, ERBB4, CASP3, ERBB2, KDR, AKT1, MAPK1, MAP2K1, MMP2, STAT3, PTPN11, BRAF, MAPK14, MMP9, ESR1, MTOR, PTK2, VEGFA, PIK3CA, MDM2, RAF1, MET, TLR4, FGFR1
hsa01522	Endocrine resistance	MAP2K1, JUN, SRC, MMP2, BRAF, PIK3R1, MAPK14, ESR1, MMP9, EGFR, MTOR, PTK2, ESR2, IGF1R, MAPK8, PIK3CA, CCND1, CDK4, ERBB2, MDM2, BCL2, AKT1, MAPK1, RAF1
hsa05212	Pancreatic cancer	MAP2K1, STAT3, BRAF, PIK3R1, EGFR, TGFBR1, MTOR, NFKB1, TGFBR2, VEGFA, CASP9, MAPK8, CDK6, PIK3CA, CCND1, CDK4, ERBB2, AKT1, MAPK1, RAF1, BCL2L1
hsa05161	Hepatitis B	SRC, PIK3R1, TNF, CASP9, MAPK8, CASP8, CASP3, AKT1, MAPK1, EP300, JAK2, JUN, MAP2K1, STAT3, BRAF, MAPK14, MMP9, TGFBR1, NFKB1, TGFBR2, CCNA2, PIK3CA, CDK2, BCL2, RAF1, TLR4
hsa04010	MAPK signaling pathway	FLT1, FLT4, FGF2, TNF, EGFR, IGF1R, RPS6KA3, MAPK8, ERBB4, CASP3, ERBB2, KDR, AKT1, MAPK1, MAP3K8, PDGFRB, NTRK1, PDGFRA, JUN, MAP2K1, PLA2G4A, BRAF, MAPK14, TGFBR1, NFKB1, TGFBR2, VEGFA, KIT, RAF1, MET, FGFR3, FGFR1
hsa05230	Central carbon metabolism in cancer	PDGFRB, NTRK1, RET, PDGFRA, MAP2K1, IDH1, PIK3R1, HIF1A, EGFR, MTOR, PIK3CA, KIT, ERBB2, AKT1, MAPK1, RAF1, MET, FGFR3, FGFR1
hsa05207	Chemical carcinogenesis - receptor activation	SRC, XIAP, PIK3R1, FGF2, EGFR, RPS6KA3, CCND1, CYP1B1, AKT1, MAPK1, JAK2, JUN, MAP2K1, HSP90AA1, VDR, STAT3, ESR1, MTOR, ESR2, NFKB1, VEGFA, AR, PIK3CA, CYP1A1, BCL2, RAF1, UGT2B7
hsa05206	MicroRNAs in cancer	ABCB1, HDAC1, PIK3R1, PTGS2, EGFR, CCND2, CCND1, PLAU, CASP3, ERBB2, ABL1, CYP1B1, MAPK1, EP300, MCL1, PDGFRB, PDGFRA, ABCC1, MAP2K1, STAT3, MMP9, MTOR, NFKB1, VEGFA, CDK6, PIK3CA, BCL2, MDM2, RAF1, MET, FGFR3
hsa05417	Lipid and atherosclerosis	GSK3B, SRC, PIK3R1, TNF, CASP9, MAPK8, CASP8, CASP3, CASP1, AKT1, MAPK1, JAK2, JUN, HSP90AA1, MMP1, STAT3, MAPK14, MMP9, PTK2, NFKB1, CYP2C9, PIK3CA, CYP1A1, BCL2, PPARG, TLR4, BCL2L1
hsa05167	Kaposi sarcoma-associated herpesvirus infection	GSK3B, SRC, PIK3R1, PTGS2, FGF2, HIF1A, CASP9, MAPK8, CASP8, CCND1, CASP3, AKT1, MAPK1, EP300, JAK2, JUN, MAP2K1, STAT3, MAPK14, MTOR, NFKB1, VEGFA, CDK6, PIK3CA, CDK4, RAF1
hsa04510	Focal adhesion	GSK3B, FLT1, SRC, FLT4, XIAP, PIK3R1, EGFR, IGF1R, MAPK8, CCND2, CCND1, ERBB2, KDR, AKT1, MAPK1, PDGFRB, PDGFRA, JUN, MAP2K1, BRAF, PTK2, VEGFA, PIK3CA, BCL2, RAF1, MET
hsa04917	Prolactin signaling pathway	GSK3B, MAP2K1, SRC, STAT3, PIK3R1, MAPK14, ESR1, ESR2, NFKB1, CYP17A1, MAPK8, CCND2, PIK3CA, CCND1, AKT1, MAPK1, JAK2, RAF1
hsa04068	FoxO signaling pathway	MAP2K1, PLK1, STAT3, BRAF, PIK3R1, MAPK14, PRKAB1, EGFR, TGFBR1, TGFBR2, IGF1R, CCNB1, MAPK8, CCND2, PIK3CA, CCND1, CDK2, MDM2, AKT1, EP300, MAPK1, RAF1
hsa04014	Ras signaling pathway	FLT1, FLT4, PIK3R1, FGF2, EGFR, IGF1R, MAPK8, KDR, ABL1, AKT1, MAPK1, PDGFRB, NTRK1, PDGFRA, MAP2K1, PLA2G2A, PLA2G4A, PTPN11, NFKB1, VEGFA, PIK3CA, KIT, RAF1, MET, FGFR3, FGFR1, BCL2L1
hsa05218	Melanoma	PDGFRB, PDGFRA, MAP2K1, BRAF, PIK3R1, FGF2, EGFR, IGF1R, CDK6, PIK3CA, CCND1, CDK4, MDM2, AKT1, MAPK1, RAF1, MET, FGFR1
hsa05219	Bladder cancer	MAP2K1, MMP1, SRC, MMP2, BRAF, MMP9, EGFR, VEGFA, CCND1, CDK4, ERBB2, MDM2, MAPK1, RAF1, FGFR3

Abbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes; MAPK, mitogen-activated protein kinase.

Construction of the PPI Network

Using the Bioinformatics Online website (http://www.bioinformatics.com.cn), we constructed a Venn diagram to identify the intersecting targets between ADI and CRC. In total, 111 common targets were obtained (Figure 2). The intersecting targets were examined via PPI network analysis using the STRING database, with “Homo sapiens” as the species and a minimum interaction score of ≥0.4 as the filtering criterion. A PPI network diagram was generated with the STRING database, where each edge represents a PPI (Figure 5A). The top 20 core genes were selected based on “degree,” “MCC,” “EPC,” “closeness,” and “radiality” (Table 3). A Venn diagram was constructed using the Bioinformatics Online website to visualize the intersection of the top 20 genes selected using CytoHubba (degree, MCC, EPC, closeness, and radiality) (Figure 5B). In total, 13 key targets were ultimately obtained (Figure 5B) and were ranked in descending order by degree (Table 3). The key targets included EGFR, VEGFA, CCND1, JUN, AKT1, CASP3, HSP90AA1, SRC, STAT3, ERBB2, mTOR, HIF1A, and BCL2L1. These 13 key targets were further used to construct a core PPI network (Figure 5C), which consisted of 13 nodes and 78 edges. The network followed a gradient from red to yellow based on the descending degree values, with red indicating higher rankings. EGFR had the highest ranking, and BCL2L1 had the lowest ranking.

Figure 5.

PPI network of ADI and CRC. (A) String; (B) Cytoscape 3.9.1; (C) Screening the top 20 genes by CytoHubba and taking the intersection; (D) The PPI core network of the 13 key targets. PPI, protein‒protein interaction; ADI, Aidi injection; CRC, colorectal cancer; MCC, maximal clique centrality; EPC, edge-percolated component; EGFR, epidermal growth factor receptor; VEGFA, vascular endothelial growth factor A; CCND1, cyclin D1; JUN, transcription factor Jun; AKT1, AKT serine/threonine kinase 1; CASP3, caspase-3; HSP90AA1, heat shock protein 90 alpha family class A member 1; SRC, SRC proto-oncogene; STAT3, signal transducer and activator of transcription 3; ERBB2, Erb-B2 receptor tyrosine kinase 2; MTOR, mechanistic target of rapamycin kinase; HIF1A, hypoxia-inducible factor 1 subunit alpha; BCL2L1, BCL2 like 1.

Table 3.

CytoHubba (Degree, MCC, EPC, Closeness, and Radiality) Screening of the Top 20 Genes.

Rank	Degree	MCC	EPC	Closeness	Radiality
1	EGFR	CASP3	CCND1	EGFR	EGFR
2	VEGFA	JUN	VEGFA	VEGFA	VEGFA
3	ESR1	HIF1A	STAT3	ESR1	ESR1
4	CCND1	VEGFA	HSP90AA1	CCND1	CCND1
5	JUN	MTOR	AKT1	JUN	JUN
6	AKT1	HSP90AA1	ESR1	AKT1	AKT1
7	CASP3	CCND1	MTOR	CASP3	CASP3
8	HSP90AA1	STAT3	EGFR	HSP90AA1	HSP90AA1
9	SRC	SRC	JUN	SRC	SRC
10	STAT3	BCL2L1	ERBB2	STAT3	STAT3
11	ERBB2	CASP8	HIF1A	ERBB2	ERBB2
12	MTOR	MAP2K1	CASP3	MTOR	HIF1A
13	HIF1A	MMP9	SRC	HIF1A	MTOR
14	TNF	ERBB2	PIK3CA	TNF	TNF
15	PIK3CA	CASP9	MDM2	PIK3CA	PIK3CA
16	BCL2L1	AKT1	BCL2L1	BCL2L1	BCL2L1
17	MAPK1	PTGS2	TNF	MAPK1	MAPK1
18	MDM2	EGFR	MCL1	MDM2	MDM2
19	MMP9	MCL1	CASP8	PTGS2	PTGS2
20	PTGS2	IGF1R	MAPK1	MMP9	CASP8

Abbreviations: MAPK, mitogen-activated protein kinase; MCC, maximal clique centrality; EPC, edge-percolated component.

Molecular Docking

Molecular docking was performed using the core targets in the PPI network. We found that 10 core targets could bind to the corresponding small-molecule drugs. We analyzed the stability of the binding conformation and the required binding energy. A binding energy ≤ –4.25 kcal/mol indicates weak binding activity, ≤ –5.0 kcal/mol indicates excellent binding activity, and ≤–7.0 kcal/mol indicates strong binding activity.²⁷ Molecular docking was performed with EGFR, CCND1, AKT1, SRC, mTOR, HSP90AA1, STAT3, ERBB2, HIF1A, and JUN and their corresponding compounds. In total, 36 successful docking pairs were obtained (Table 4). Subsequently, the docking data were visualized using PyMoL 2.5.4 software to create 3D docking illustrations.

Table 4.

Results of the Molecular Docking of Some Key Targets.

Target	Entry	PDB ID	Molecular name	Docking score (kcal/mol)
EGFR	P00533	6WXN	Baicalin	−9.2
			Quercetin	−8.8
			Ellagic acid	−8.4
			Jaranol	−7.9
			Isorhamnetin	−7.9
			Kaempferol	−7.7
			Formononetin	−7.7
			Calycosin	−7.7
			7-O-methylisomucronulatol	−7.5
			Deoxyharringtonine	−7.3
			Gomisin B	−5.4
ERBB2	P04626	3PP0	Deoxyharringtonine	−9.3
			Ellagic acid	−8.2
HSP90AA1	P07900	3O0I	Hinokinin	−9.1
			Frutinone A	−8.7
JUN	P05412	4Z9L	Bifendate	−5.4
MTOR	P42345	4O2L	Hinokinin	−8.7
			Inermin	−7.8
			Gomisin B	−6.3
SRC	P12931	4DGG	Isorhamnetin	−7.2
			Kaempferol	−7
			Deoxyharringtonine	−6.7
STAT3	P40763	1Q1M	Suchilactone	−7.9
			3,9-di-O-methylnissolin	−6.6
AKT1	P31749	1UNQ	Ellagic acid	−6
			Jaranol	−5.6
			Quercetin	−5.5
			Kaempferol	−5.4
			Isorhamnetin	−5.3
CCND1	P24385	2W96	Inermin	−7.4
			Suchilactone	−7.2
			Ellagic acid	−6.7
			3,9-di-O-methylnissolin	−6.3
			Gomisin B	−5.6
HIF1A	Q16665	3HQU	Sesamin	−7.5
			Acanthoside B	−4.5

Abbreviation: PDB, Protein Data Bank.

We selected the top 10 docking results based on the binding affinity (Figure 6). The molecular docking results showed that ERBB2 could bind to deoxyharringtonine and ellagic acid, with binding energies of −9.3 kcal/mol and −8.2 kcal/mol, respectively, indicating strong binding affinity. The potential binding sites included THR-862, LYS-753, ASN-85, MET-801, and CYS-805. Both compounds formed at least one hydrogen bond with amino acids, and the hydrogen bond distances were relatively short, averaging 3 Å (Figure 6A and 6H). Specifically, ellagic acid formed 5 hydrogen bonds with the active residues of the ERBB2 receptor, indicating a stronger binding affinity with the protein. EGFR could bind to baicalin, quercetin, ellagic acid, jaranol, and isorhamnetin, with binding energies of −9.2 kcal/mol, −8.8 kcal/mol, −8.4 kcal/mol, −7.9 kcal/mol, and −7.9 kcal/mol, respectively, indicating that it could bind to different types of compounds. The binding sites included ASN-842, LYS-745, ALA-722, ARG-841, MET-793, and CYS-797. All 5 compounds formed at least one hydrogen bond with amino acids, and the hydrogen bond distances were relatively short, averaging 3 Å (Figure 6B, 6D, 6G, 6I, and 6J). Baicalin formed 5 hydrogen bonds with the active residues of the EGFR receptor, indicating a strong binding affinity for the protein. HSP90AA1 could bind to hinokinin and frutinone A, with binding energies of −9.1 kcal/mol and −8.7 kcal/mol, respectively. The potential binding sites included THR-184 and TYR-139 (Figure 6C and 6E). Similarly, mTOR can bind to hinokinin, with a binding energy of −8.7 kcal/mol. The binding sites included mainly ASN-119, SER-16, and THR-38. This compound formed 5 hydrogen bonds with amino acids, and the hydrogen bond distances were relatively short, averaging 3.1 Å (Figure 6F).

Figure 6.

Molecular docking diagrams of the top 10 molecular docking maps with low binding energies. The protein active site, binding distance, and molecular docking model between the protein and the main active ingredient. (A) ERBB2-Deoxyharringtonine (−9.3 kcal/mol); (B) EGFR-Baicalin (−9.2 kcal/mol); (C) HSP90AA1-Hinokinin (−9.1 kcal/mol); (D) EGFR-Quercetin (−8.8 kcal/mol); (E) HSP90AA1-Frutinone-A (−8.7 kcal/mol); (F) MTOR-Hinokinin (−8.7 kcal/mol); (G) EGFR-Ellagic acid (−8.4 kcal/mol); (H) ERBB2-Ellagic acid (−8.2 kcal/mol); (I) EGFR-Jaranol (−7.9 kcal/mol); (J) EGFR-Isorhamnetin (−7.9 kcal/mol).

Experimental Validation: The Effects of Hinokinin on the Growth, Apoptosis, and Expression of the mTOR Protein in CRC Cells

In this study, SW480 cells were treated with different concentrations (10⁻³, 10⁻², 10⁻¹, 1, 10, and 100 µM) of hinokinin for 48 h, and the viable cell concentration was determined based on the results of the CCK-8 assay to determine the effective concentrations (10, 20, 40, and 80 µM). Subsequently, 40 µM and 80 µM hinokinin were selected for further investigation (Figure 7A and B). The SW480 cells were treated with drugs at concentrations of 0, 40, or 80 µM, and cell viability was evaluated by the CCK-8 assay at 0, 24, 48, and 72 h. The results showed that the group treated with 80 µM drug exhibited significantly greater inhibition of the growth of SW480 cells than the group treated with 40 µM drug (Figure 7C). To evaluate the effect of the drug on cell apoptosis, SW480 cells were treated with 0 µM, 40 µM, or 80 µM for 24 h, after which apoptosis was detected via flow cytometry. The results showed a significant increase in the number of apoptotic cells in the 40 µM and 80 µM treatment groups relative to the number of apoptotic cells in the 0 µM treatment group; the 80 µM treatment group exhibited a greater effect (Figure 7D). The results of immunoblotting experiments showed that the expression of the mTOR protein significantly increased in the 40 µM and 80 µM treatment groups compared to that in the 0 µM treatment group; the 80 µM treatment group showed a significantly greater increase (Figure 7E).

Figure 7.

Hinokinin inhibited cell growth, promoted cell apoptosis, and increased the protein expression of mTOR in the colorectal cancer cell line SW480. (A) Effective drug concentrations were selected through gradient administration of drugs; (B) The optimal drug concentration was determined; (C) The effect of hinokinin on the growth of SW480 cells; (D) The effect of hinokinin on the apoptosis of SW480 cells; (E) The effect of hinokinin on the growth of SW480 cells; (F) The effect of hinokinin on the expression of the mTOR protein in SW480 cells. The data are presented as the means ± SD of 3 independent experiments. *P < .05, **P < .01, ***P < .001, #P < .0001.

Discussion

In this study, we used modern bioinformatics tools to construct an ADI modulation network for CRC, analyzed the mechanism underlying the effect of ADI on CRC, performed a PPI analysis of 111 targets that intersect with CRC, and obtained core targets using the plugin CytoHubba. The results indicated that EGFR, VEGFA, CCND1, JUN, AKT1, CASP3, HSP90AA1, SRC, STAT3, ERBB2, mTOR, HIF1A, and BCL2L1 played key roles in the PPI network.

As a commonly used adjuvant drug in clinical cancer treatment, it is necessary to explore its mechanism of action in CRC. Related studies have reported that ADI can be combined with gemcitabine, cisplatin, paclitaxel, vinorelbine, and other chemotherapy drugs to improve the prognosis of patients with non-small cell lung cancer.^28–30 ADI can be combined with doxorubicin to fight liver cancer.³¹ In addition, ADI can induce HCC cell death through the mitochondrial pathway.³² ADI can inhibit EMT and angiogenesis in human esophageal squamous cell carcinoma to exert its antimetastatic effects.³³ Recently, the role of ADI in CRC has been noted, and it has been reported in the literature that ADI combined with FOLFOX4 chemotherapy can improve the quality of life of patients with advanced CRC and reduce some toxicities associated with chemotherapy.³⁴ The aim of this study was to explore the role of the main components of ADI in CRC. Together with our results, several of the key compounds of ADI can act strongly on the corresponding target proteins. The main active components of ADI include kaempferol, quercetin, ellagic acid, isrhampsin, and cypress, but the mechanism of action of the specific components is unclear. Through a literature review, we found that the main active component of ADI, ellagic acid, induces cell cycle arrest and apoptosis in human colon cancer HCT116 cells through the TGF-β1/Smad3 signaling pathway.³⁵ Baicalin induces apoptosis, inhibits migration, and enhances antitumor immunity in CRC cells through the TLR4/NF-κB signaling pathway.³⁶ Quercetin inhibits CRC by preventing progression of the cell cycle, angiogenesis, and metastasis and by promoting apoptosis.³⁷ Isorhamnetin inhibits the growth of colon cancer cells through the PI3K-Akt-mTOR pathway.³⁸ Hinokinin is a natural compound with several biological activities.³⁹ It has been reported that Hinokinin induces G2/M arrest and contributes to the antiproliferative effect of doxorubicin on breast cancer cells.⁴⁰ It has antioxidant and anti-inflammatory properties and can inhibit the growth and spread of cancer cells. Even in the early stages of research in the field of CRC treatment, Hinokinin has shown potential clinical application. We combined the molecular docking data to investigate whether hinokinin could affect CRC function and mTOR protein expression.

The mTOR protein is a serine/threonine kinase and acts as a master regulator of cellular metabolism.⁴¹ It strongly regulates many fundamental cellular processes, ranging from protein synthesis to autophagy, and dysregulated mTOR signaling are associated with cancer progression.⁴² Additionally, mTOR negatively regulates tumor suppressors, facilitating the induction of autophagy and inhibiting the initiation of cancer.⁴³

Based on the above findings, we selected hinokinin for experimental verification. We found that hinokinin can suppress the growth of CRC cells and promote apoptosis, and it likely exerts these effects by affecting the expression of the mTOR protein. Although we experimentally verified the main component (hinokinin) and mTOR targets in this study, we did not investigate all the effective components and targets; thus, further experimental validation of the mechanism by which ADI affects CRC is needed.

This study has several limitations. First, this study qualitatively predicted only the drug composition and target, and the clear pharmacological effects need to be verified by animal experiments or even clinical trials. In addition, this experiment was only performed at the cellular level, and when applied to animal or clinical experiments, the entry of various components of the drug into the body after metabolism requires further study. Second, only the CRC cell line SW 480 was selected for experimental validation, which also increased the uncertainty of the application of drug components in other cell lines. Finally, the network pharmacology method is based on database prediction and computational results, and the research results are universal. A large number of constantly updated basic research results are needed as support, and further experimental verification of the molecular biology of these plants is needed.

Conclusions

In summary, in this study, network pharmacological analysis and experimental validation were performed to determine the effects of ADI and hinokinin on CRC. We identified 13 common targets of CRC via network pharmacology. Further validation of the experimental results indicated that hinokinin can inhibit the growth of CRC cells and promote their apoptosis by regulating the expression of mTOR. Our findings might lead to the development of new strategies for treating CRC-related diseases. However, further investigations are needed to elucidate other mechanisms underlying the effects of ADI on CRC.

Supplemental Material

sj-xlsx-1-npx-10.1177_1934578X241239169 - Supplemental material for Network Pharmacology Approach and Partial Experimental Validation of Aidi Injection Solution for the Treatment of Colorectal Cancer

Supplemental material, sj-xlsx-1-npx-10.1177_1934578X241239169 for Network Pharmacology Approach and Partial Experimental Validation of Aidi Injection Solution for the Treatment of Colorectal Cancer by Wentao Yu, Yinhua Weng, Jiawei Wang, Yifei Gao and Yaqi Li, Chichu Xie, Zhiyuan Jian in Natural Product Communications

Footnotes

Authors’ Note

All data are available in the manuscript, and they are shown in figures, tables, and supplementary files.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Specific Research Project of Guangxi for Research Bases and Talents, The University-level Master's Research Program of Guilin Medical University, (grant number No. AD19110097, GYYK2022019).

ORCID iDs

Wentao Yu

Yinhua Weng

Jiawei Wang

Yaqi Li

Chichu Xie

Zhiyuan Jian

Supplemental Material

Supplemental material for this article is available online.

References

Bray

Ferlay

Soerjomataram

Siegel

Torre

Jemal

. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424. doi:10.3322/caac.21492

Arnold

Sierra

Laversanne

Soerjomataram

Jemal

Bray

. Global patterns and trends in colorectal cancer incidence and mortality. Gut. 2017;66(4):683-691. doi:10.1136/gutjnl-2015-310912

de Sousa e Melo

Kurtova

Harnoss

, et al. A distinct role for Lgr5(+) stem cells in primary and metastatic colon cancer. Nature. 2017;543(7647):676-680. doi:10.1038/nature21713

Shimokawa

Ohta

Nishikori

, et al. Visualization and targeting of LGR5(+) human colon cancer stem cells. Nature. 2017;545(7653):187-192. doi:10.1038/nature22081

Zhang

, et al. Synergistic antitumor effects of compound-composed optimal formula from Aidi injection on hepatocellular carcinoma and colorectal cancer. Phytomedicine. 2022;103:154231. doi:10.1016/j.phymed.2022.154231

Yang

Shen

Zhu

, et al. Chinese patent medicine Aidi injection for cancer care: an overview of systematic reviews and meta-analyses. J Ethnopharmacol. 2022;282:114656. doi:10.1016/j.jep.2021.114656

Nogales

Mamdouh

List

Kiel

Casas

Schmidt

. Network pharmacology: curing causal mechanisms instead of treating symptoms. Trends Pharmacol Sci. 2022;43(2):136-150. doi:10.1016/j.tips.2021.11.004

Wang

, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13. doi:10.1186/1758-2946-6-13

Fang

Dong

Liu

, et al. HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine. Nucleic Acids Res. 2021;49(D1):D1197-D1206. doi:10.1093/nar/gkaa1063

10.

Zhang

Chen

Jiang

, et al. Integrated strategy for accurately screening biomarkers based on metabolomics coupled with network pharmacology. Talanta. 2020;211:120710. doi:10.1016/j.talanta.2020.120710

11.

Gfeller

Michielin

Zoete

. Shaping the interaction landscape of bioactive molecules. Bioinformatics. 2013;29(23):3073-3079. doi:10.1093/bioinformatics/btt540

12.

Coudert

Gehant

de Castro

, et al. Annotation of biologically relevant ligands in UniProtKB using ChEBI. Bioinformatics. 2023;39(1). doi:10.1093/bioinformatics/btac793

13.

Safran

Chalifa-Caspi

Shmueli

, et al. Human gene-centric databases at the weizmann institute of science: geneCards, UDB, CroW 21 and HORDE. Nucleic Acids Res. 2003;31(1):142-146. doi:10.1093/nar/gkg050

14.

Wishart

Feunang

Guo

, et al. Drugbank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074-D1082. doi:10.1093/nar/gkx1037

15.

Amberger

Bocchini

Scott

Hamosh

. OMIM.Org: leveraging knowledge across phenotype-gene relationships. Nucleic Acids Res. 2019;47(D1):D1038-D1043. doi:10.1093/nar/gky1151

16.

Piñero

Ramírez-Anguita

Saüch-Pitarch

, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2019;48(D1):D845-D855. doi:10.1093/nar/gkz1021

17.

Demchak

Otasek

Pico

, et al. The cytoscape automation app article collection. F1000Res. 2018;7:800. doi:10.12688/f1000research.15355.1

18.

Sherman

Hao

Qiu

, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50(W1):W216-w221. doi:10.1093/nar/gkac194

19.

von Mering

Huynen

Jaeggi

Schmidt

Bork

Snel

. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 2003;31(1):258-261. doi:10.1093/nar/gkg034

20.

Liu

Chen

. Identification of important genes related to ferroptosis and hypoxia in acute myocardial infarction based on WGCNA. Bioengineered. 2021;12(1):7950-7963. doi:10.1080/21655979.2021.1984004

21.

Berman

Westbrook

Feng

, et al. The protein data bank. Nucleic Acids Res. 2000;28(1):235-242. doi:10.1093/nar/28.1.235

22.

Kim

Chen

Cheng

, et al. Pubchem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021;49(D1):D1388-D1395. doi:10.1093/nar/gkaa971

23.

Seeliger

de Groot

. Ligand docking and binding site analysis with PyMOL and autodock/vina. J Comput Aided Mol Des. 2010;24(5):417-422. doi:10.1007/s10822-010-9352-6

24.

Frank

Powder-George

Ramsewak

Reynolds

. Variable-temperature ¹H-NMR studies on two C-glycosylflavones. Molecules. 2012;17(7):7914-7926. doi:10.3390/molecules17077914

25.

Forli

Huey

Pique

Sanner

Goodsell

Olson

. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 2016;11(5):905-919. doi:10.1038/nprot.2016.051

26.

Mishra

Tiwari

Gomes

. Protein purification and analysis: next generation Western blotting techniques. Expert Rev Proteomics. 2017;14(11):1037-1053. doi:10.1080/14789450.2017.1388167

27.

Zhao

Liu

Song

, et al. Network pharmacology study on the mechanism of Qiangzhifang in the treatment of panic disorder. Ann Transl Med. 2021;9(16):1350. doi:10.21037/atm-21-4090

28.

Wang

Zheng

Chen

, et al. The optimal adjuvant strategy of Aidi injection with gemcitabine and cisplatin in advanced non-small cell lung cancer: a meta-analysis of 70 randomized controlled trials. Front Pharmacol. 2021;12:582447. doi:10.3389/fphar.2021.582447

29.

Xiao

Jiang

Wang

, et al. Clinical efficacy and safety of Aidi injection combination with vinorelbine and cisplatin for advanced non-small-cell lung carcinoma: a systematic review and meta-analysis of 54 randomized controlled trials. Pharmacol Res. 2020;153:104637. doi:10.1016/j.phrs.2020.104637

30.

Xiao

Wang

Zhou

, et al. Clinical efficacy and safety of Aidi injection plus paclitaxel-based chemotherapy for advanced non-small cell lung cancer: a meta-analysis of 31 randomized controlled trials following the PRISMA guidelines. J Ethnopharmacol. 2019;228:110-122. doi:10.1016/j.jep.2018.09.024

31.

Wang

Zhu

Wang

, et al. Cell metabolomics study on synergistic anti-hepatocellular carcinoma effect of Aidi injection combined with doxorubicin. Biomed Chromatogr. 2022;36(10):e5451. doi:10.1002/bmc.5451

32.

Lan

Liu

, et al. Aidi injection induces apoptosis of hepatocellular carcinoma cells through the mitochondrial pathway. J Ethnopharmacol. 2021;274:114073. doi:10.1016/j.jep.2021.114073

33.

Shi

Diao

Jin

Ding

. Anti-metastatic effects of Aidi on human esophageal squamous cell carcinoma by inhibiting epithelial-mesenchymal transition and angiogenesis. Mol Med Rep. 2018;18(1):131-138. doi:10.3892/mmr.2018.8976

34.

Wang

Nan

Zhang

Wang

Zhang

. Aidi injection combined with FOLFOX4 chemotherapy regimen in the treatment of advanced colorectal carcinoma. J Cancer Res Ther. 2014;10(Suppl 1):52-55. doi:10.4103/0973-1482.139760

35.

Zhao

Wei

, et al. Ellagic acid induces cell cycle arrest and apoptosis via the TGF-β1/Smad3 signaling pathway in human colon cancer HCT-116 cells. Oncol Rep. 2020;44(2):768-776. doi:10.3892/or.2020.7617

36.

Song

Zhu

Liu

Zhang

Liang

. Baicalin triggers apoptosis, inhibits migration, and enhances anti-tumor immunity in colorectal cancer via TLR4/NF-κB signaling pathway. J Food Biochem. 2022;46(3):e13703. doi:10.1111/jfbc.13703

37.

Darband

Kaviani

Yousefi

, et al. Quercetin: a functional dietary flavonoid with potential chemo-preventive properties in colorectal cancer. J Cell Physiol. 2018;233(9):6544-6560. doi:10.1002/jcp.26595

38.

Yang

Chen

Cai

. Isorhamnetin suppresses colon cancer cell growth through the PI3K-Akt-mTOR pathway. Mol Med Rep. 2014;9(3):935-940. doi:10.3892/mmr.2014.1886

39.

Marcotullio

Pelosi

Curini

. Hinokinin, an emerging bioactive lignan. Molecules. 2014;19(9):14862-14878. doi:10.3390/molecules190914862

40.

Cunha

Teixeira

Martins

, et al. (-)-Hinokinin induces G2/M arrest and contributes to the antiproliferative effects of doxorubicin in breast cancer cells. Planta Med. 2016;82(6):530-538. doi:10.1055/s-0042-101761

41.

Kim

Guan

. mTOR: a pharmacologic target for autophagy regulation. J Clin Invest. 2015;125(1):25-32. doi:10.1172/jci73939

42.

Rakesh

PriyaDharshini

Sakthivel

Rasmi

. Role and regulation of autophagy in cancer. Biochim Biophys Acta Mol Basis Dis. 2022;1868(7):166400. doi:10.1016/j.bbadis.2022.166400

43.

Yun

Lee

. The roles of autophagy in cancer. Int J Mol Sci. 2018;19(11):3466. doi:10.3390/ijms19113466

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.01 MB