Investigation of the Effects of Glabridin on the Proliferation, Apoptosis, and Migration of the Human Colon Cancer Cell Lines SW480 and SW620 and Its Mechanism Based on Reverse Virtual Screening and Proteomics

Colon cancer is a relatively common malignant tumor of the digestive tract. Currently, most colon cancers originate from adenoma carcinogenesis. By screening various licorice flavonoids with anticancer effects, we found that glabridin (GBN) has a prominent anticolon cancer effect. First, we initially explored whether GBN can inhibit proliferation, migration, and invasion and induce apoptosis in SW480 and SW620 cells. Next, we exploited reverse virtual and proteomics technologies to screen out closely related target pathways on the basis of a drug and target database. At the same time, we constructed the structure of the GBN target pathway in colon cancer. We predicted that GBN can regulate the phosphatidylinositol 3-kinase (PI3K)–protein kinase B (AKT)–mammalian target of the rapamycin pathway (mTOR) pathway to fight colon cancer. Finally, through Western blot analysis and qRT-PCR, we verified that the expression levels of the PI3K, AKT, and mTOR proteins and genes in this pathway were significantly reduced after GBN administration. In short, the promising discovery of the anticolon cancer mechanism of GBN provides a reliable experimental basis for subsequent new drug development.


Introduction
Colorectal cancer (CRC) is one of the common malignant tumors of the digestive tract. It ranks third among all malignant tumors in terms of incidence [1]. This disease is mainly located in the colon or rectum [2]. Thus far, the molecular mechanism of its pathogenesis has not been completely elucidated. Current scientific research shows that the main mechanisms of this malignancy involve the instability of the genome [3], the loss of calcium receptors in colonic epithelial cells [4], the effect of cell adhesion [5], and the abnormal expression of epidermal growth factors [6]. Moreover, many risk factors, including age, high-fat and high-protein diets, genetic factors, polyps, and adenomas, can lead to colon cancer [7]. At present, CRC is mainly treated through surgery combined with radiotherapy, chemical, and targeted drug therapy. However, these therapies can cause serious adverse reactions. Traditional Chinese medicine for the treatment of colon cancer involves tumor prevention and treatment; this approach can reduce the side effects or adverse reactions, such as gastrointestinal reactions, caused by radiotherapy and chemotherapy [8,9].
Glabridin (GBN) is a flavonoid and one of the main active ingredients of Glycyrrhiza glabra L [10]. At present, GBN has a variety of pharmacological activities, including antiatherosclerosis [11], blood lipid reduction [12], nervous system protection [13], and anti-inflammatory [14] actions. In addition, it is commonly used to eliminate free radicals [15] and melanin [16]. GBN has been reported to have effects against liver cancer [17,18], cervical cancer [19], and breast cancer [20]. Our research team found for the first time that GBN has a prominent anticolon cancer effect. On this basis, we used a variety of modern technologies to elucidate comprehensively the mechanism of GBN against colon cancer.
Computer reverse virtual technology can predict potential targets and signal pathways by scoring the binding energy of compounds and proteins [21]. The differential proteins of drug action can be found through proteomics technology. The signaling pathways involving differential proteins are identified to verify the target pathways screened by using virtual technology. In recent years, a growing number of scholars have successfully used these two techniques to study the mechanism of action of biology and drugs [22], which has provided great enlightenment for our research.
In this study, we combine the above two technologies to outline a pathway that is closely related to GBN's anticolon cancer activity. The mechanism of action of GBN may be related to the inhibition of the phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT)-mammalian target of the rapamycin pathway (mTOR) signaling pathway. Finally, we experimentally verify the key indicators of proteomics and computer virtual technology results to clarify the mechanism of GBN in the treatment of colon cancer.

Materials and Methods
2.1. Reagents and Chemicals. GBN standard products were acquired from Shanghai Yuanye Biotechnology Co., Ltd. (batch number G006171216). Ammonia, iodoacetamide, and triethylammonium bicarbonate buffer were all procured from Sigma. Acetone and sodium lauryl sulfate were purchased from Shanghai Sinopharm. TMT 16Plex, Protein Ladder, Protease Inhibitor Cocktail, Pierce™ BCA Protein Assay Kit, NUPAGE 10% BT GEL 1.0MM 12 W, and Bond-Breaker™ TCEP Solution (TCEP) were produced by Thermo Fisher Scientific. Acetonitrile, methanol, formic acid, and other reagents required for the experiments were all of analytical grade. American Sigma Company provided dimethyl sulfoxide reagents. Fetal bovine serum (FBS) was sourced from Gibco, United States (10099-141). Beijing Soleibao Technology Co., Ltd., provided 0.25% trypsin-EDTA digestion solution and EDTA-free trypsin digestion solution.
2.2. Cell Culture. The human colon cancer cell lines SW480 and SW620 were purchased from Shanghai Luyu Biotechnology Co., Ltd. HT29 and HCT116 were provided by Procell Life Science & Technology Co. Ltd. All cells were cultured in L15 medium with 10% FBS. Double antibodies (100 μg/mL streptomycin and 100 units/mL penicillin) were added to the incubator at 37°C and 5% CO 2 . The medium was changed every 2 days, and the cells were passaged at 1 : 3. Cells in the logarithmic growth phase were used in the experiment. The cells were digested and pipetted with 0.25% trypsin digestion solution (Beijing Soleibao Technology Co., Ltd., China). Glycyrrhizin was dissolved in dimethyl sulfoxide solution (Sigma, USA). The medium was diluted to the concentrations of 40, 20, 10, and 5 μg/mL. The experiments were conducted in quadruplicate. In the blank experiment, dimethyl sulfoxide was used instead of glycyrrhizin.

Cell Proliferation Analysis.
First, different colon cancer cells (SW480, SW620, HT29, and HCT116) in the logarithmic growth phase were digested and resuspended. Second, GBN was added at different concentrations (100, 50, 25, and 12.5 μmol/L −1 ). After 24 h, 10 μL of CCK-8 solution was added to each well, and the cells were cultured for 3.5 h. Finally, the absorbance value at 450 nm was measured with a microplate reader for calculation. The calculation formula was as follows: cell survival rate = ½ðAs − AbÞ/ðAc − AbÞ × 100% (As: experimental well OD value; Ac: control well OD value; Ab: blank well OD value).

Cell Apoptosis
Analysis. The cell suspension (without trypsin-EDTA) was treated with different concentrations of GBN (20 and 40 μg/mL). At the same time, 5 μL of Annexin V-FITC was added. Subsequently, the cells were washed with PBS and suspended. Then, the cells were incubated with Annexin V-FITC/PI (Shanghai Beibo Biotechnology Co., Ltd.) at 37°C for 15 min in the dark. All data were analyzed by using a C6 flow cytometer (BD Biosciences, San Diego, CA, USA).

Nuclear Morphology Analysis.
A cover glass was placed in a six-well plate, and colon cancer cells were implanted. The cells were treated with 40 μg/mL GBN. After fixation and washing, 0.5 mL of Hoechst 33258 staining solution (Nanjing KGI Biotechnology Development Co., Ltd.) was added, and the cells were stained for 5 min. After sealing with a sheet, staining was observed with a fluorescence microscope (Olympus IX73), and images were collected.
2.6. Cell Migration Experiment. The cells were plated and incubated when their density reached 80%. Subsequently, the back of the plate was scratched, and the cells in treated medium containing 40 μg/mL GBN were added. After the cells were cultured at 37°C for 0 and 24 h, photographs were taken and recorded to determine the healing distance of the scratches. Each experiment was repeated three times. 2.10.2. Experimental Method. The target crystal structure, including 2119 active sites of 140 targets, corresponding to the disease genes was completely resolved by using the whole target library of the reverse virtual screening platform. The target structure corresponding to the disease gene was analyzed by using the part target library. These structures included 5487 active sites of 506 targets. The structural formula file of GBN was obtained by searching the DrugBank databank. Then, virtual molecular docking was carried out. The binding ability between the compound and protein was scored by applying the reverse virtual screening platform, and the top 200 results were returned. A high score indicated that the drug is bound to the target stably. Finally, target prediction and pathway analysis were carried out by comparing the results of the ProteinBank and KEGG databases.
2.11. Proteomics Analysis 2.11.1. Protein Preparation and TMT Labeling. Tumor cells were lysed in lysis buffer at 70 Hz for 90 s. The lysed tissue sample was centrifuged, and the supernatant was collected. A BCA protein quantification kit (Thermo Fisher Scientific) was used to detect the protein content of the total protein extract of each group of tumor cells. After quantification, the protein sample was reductively alkylated with 10 mmol/L TCEP reducing agent (Sigma) and 40 mmol/L iodoacetamide (Sigma). Subsequently, the TMT 16Plex kit (Thermo Fisher Scientific) was used for labeling. The peptides in each group were labeled with different TMT labels (Table 1). Eventually, all the labeled samples were mixed, vacuum dried, and stored at −20°C for further analysis.

High-pH Liquid Phase Separation and LC-MS/MS
Analysis. The TMT-labeled peptides were dissolved in UPLC loading buffer. A reversed-phase C 18 column ACQUITY UPLC BEH C18 Column (1.7 μm, 2:1 mm × 150 mm, Waters, USA) was used for high-pH liquid phase separation.
Easy-nLC 1200 combined with a Q Exactive HF-X mass spectrometer was used to identify proteins. Peptides were dissolved in mass spectrometry loading buffer and separated on a C 18 chromatographic column (75 μm × 25 cm, Thermo, USA) after 120 min with a volume flow rate of 300 μL/min. The EASY-nLC liquid phase was used as the gradient elution. Buffer phase A comprised 2% acetonitrile and 0.1% formic acid, and buffer phase B consisted of 80% acetonitrile and 0.1% formic acid. MS and MS/MS acquisitions were automatically switched. MS was performed with a full scan (m/z 350-1500). The acquisition mode was tDDA, and the cycle time was 2 s. Finally, TurboTMT was used to improve the resolution of the ion isotope.
2.11.4. Bioinformatic Analysis. Gene Ontology (GO: http:// geneontology.org/) was selected for the functional annotation of all differential proteins from the three aspects of biological process, cell composition, and molecular function. The Kyoto Encyclopedia of Genes and Genomes (KEGG: http://www.genome.jp/kegg//) pathway database was used to analyze the metabolic and signal pathways involving differentially expressed proteins.  Table 2 shows all of the primers used for PCR and sequencing. First-strand cDNA was synthesized by using an RT kit (Thermo Fisher Scientific Technology Company). In accordance with the protocol provided in the instructions, 2 μg of total RNA was extracted for reverse transcription, and the final reaction volume was 20 μg. The PCR mixture contained 4 μL of 5× reaction buffer, 1 μL of Ribolock RNase Inhibitor (20 U/μL), 2 μL of 10 mm dNTP Mix, and 1 μL of RevertAid M-MuLV RT (200 U/μL) (Novazin Biotechnology Co., Ltd., China). All the PCR analyses were performed under the same conditions. The specificity of amplicons was verified through melting curve analysis (10 s at 95°C and 30 s at 60°C) after

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Oxidative Medicine and Cellular Longevity 40 cycles. The expression of the target gene relative to that of the internal control was calculated by using the 2 −△△ Ct method. Finally, the relative quantitative detection of the gene to be determined was performed.
2.12.2. Western Blot Analysis. Proteins were extracted from human colon cancer cells and quantified as described in Section 2.11.3. An equal amount (20 μg) of total protein in each sample was separated by using 10% SDS-PAGE. Then, the bands were transferred to a PVDF membrane (Millipore, MA, USA). Next, the membranes were blocked with 5% with skimmed milk powder at 37°C for 2 h and incubated with anti-PI3K (diluted 1 : 100), anti-Akt (diluted 1 : 100), anti-mTOR (diluted 1 : 100), and anti-β-actin (dilution 1 : 2000) antibodies overnight at 4°C. Hereafter, the membranes were washed with 0.5% Tween-20/PBS for 4 × 10 min, then incubated with the horseradish peroxidase-labeled goat antirabbit secondary antibody (1 : 2000) for 1 h. After four washes with PBST, the strips were exposed in a fully automated chemiluminescence instrument and stored. Protein   Oxidative Medicine and Cellular Longevity expression level was defined as the gray value quantified with ImageJ software (National Institutes of Health, USA) and normalized to β-actin expression.
2.13. Statistical Analysis. All the experimental data were expressed as the mean ± SEM. Statistical analyses were performed with SPSS software (version 26.0, SPSS Inc., Chicago, IL, USA). The t -test (if σ1 2 ≠ σ2 2 , then, the t-test was used) was performed to compare the means between the groups of samples. One-way ANOVA test (if σ1 2 ≠ σ2 2 , then, Dunnett's t -test was used) was conducted to compare the means between multiple groups of samples, and P < 0:05 indicated that the difference was significant.

GBN Inhibited the Proliferation of SW480 and SW620
Cells. Figure 1(a) shows the chemical structure of GBN.
The human colon cancer cell lines SW480, SW620, HT29, and HCT116 were selected to study the inhibitory effects of different concentrations of GBN (100, 50, 25, and 12.5 μmol/L) on tumor cell proliferation. The results showed that different concentrations of GBN had a significant inhibitory effect on colon cancer cells (P < 0:05; P < 0:01). The findings in Table 3 confirm that the human colon cancer cell lines SW480 and SW620 were more sensitive to GBN than other lines and can thus be used for follow-up experiments. The survival rates of the SW480 cells treated with 40 μg/mL GBN for 24 and 48 h were 43:75% ± 2:38% and 45:48% ± 0:23%, respectively (P < 0:001). The survival rates of each group of SW620 cells after 24 h of treatment with 20 and 40 μg/mL GBN were 50:29% ± 1:73% and 22:16% ± 2:08%, respectively (P < 0:001). Cell proliferation in each treatment group significantly inhibited cell proliferation by varying degrees (Figures 1(b) and 1(c)) after 24 h of treatment relative to that in the control group. The difference among groups was statistically significant (P < 0:001). On the basis of these results, the follow-up mechanisms of action and proteomics experiments were performed with 20 and 40 μg/mL GBN for apoptosis experiments and the action time of 24 h.

Effect of GBN on the Migration of SW480 and SW620
Cells. The cell scratch experiment revealed that the migration rates of SW480 and SW620 cells under 40 μg/mL GBN treatment were 34:10% ± 2:34% and 25:11% ± 0:73%, respectively. GBN significantly inhibited the migration of the human colon cancer cell lines SW480 and SW620 and decelerated the healing of scratches (P < 0:001) (Figure 3(a)).

3.4.
Effect of GBN on the Invasive Ability of SW480 and SW620 Cells. The number of invasive SW480 cells in the GBN group was 51:00 ± 6:48. The number of invasive SW620 cells in the GBN group was 51:00 ± 6:56. GBN can significantly inhibit the invasive ability of SW480 and SW620 cells and significantly reduced the number of invasive cells compared with the control treatment (P < 0:01) (Figure 3(b)).

Effects of GBN on the Expression Levels of Proteins and
Genes in SW480 and SW620 Cells. In SW48 and SW6200 cells, the protein expression levels of Bax, cleaved-caspase 7 Oxidative Medicine and Cellular Longevity 3, and cleaved-caspase 9 increased significantly after GBN drug intervention (P < 0:05, P < 0:001, and P < 0:001). Meanwhile, Bcl-2 and MMP9 protein expression decreased (P < 0:01) (Figure 4(a)). Figure 4(b) also illustrates that the MMP2 gene level in SW480 cells significantly decreased (P < 0:01). In SW620 cells, the levels of Bcl-2 and MMP2 genes obviously decreased (P < 0:001). These results confirmed that GBN can promote the migration and invasion of SW480 and SW620 cell apoptosis through the caspase apoptosis pathway.
3.6. Establishment of a Disease Target Database to Screen the Target Results. A total of 18 potential targets were found in accordance with the virtual screening scoring results and the KEGG database analysis of the main related pathways of GBN involved in colon cancer (Table 4). KEGG database analysis revealed that GBN had six possible targets, namely, ABL1, INS, SRC, IRAK4, CHEK1, and KDR. This result confirmed that the virtual binding proteins of GBN and colon cancer were relatively concentrated in the PI3K-AKT, mTOR, and MAPK signaling pathways.

Candidate
Target Determination for GBN. The screened GBN targets were compared with the CRC-related protein data obtained from a tumor genomics database (http:// www.cbioportal.org). The results showed that GBN could upregulate 64 related proteins in CRC cells (Table 5). By comparing these 64 proteins with those in Table 4, we found that 13 candidate targets upregulated proteins in CRC cells and acted on signal pathways (Table 6). In addition, by using the above database, we found five candidate genes for the anticolon cancer effect of GBN, namely, INS, KDR, ABL1, SRC, and CHEK1. These proteins were mainly involved in the PI3K-AKT, mTOR, MAPK, and Wnt pathways.

Comparison of Screened GBN Targets with Genes
Expressed in SW480 and SW620 Cells. First, we obtained the genetic information of SW480 and SW620 cell lines by using the Cancer Cell Line Encyclopedia. Then, through      Table 5, we confirmed that the information of the five target genes in SW480 cells, including PTPN1, SEC14L2, PDE3B, PPARD, and TGFBR1, was consistent. Among these genes, PPARD and TGFBR1 were the same as the screened candidate target genes of GBN. Similarly, in the SW620 cell line, HSP90AB1 was the same as the screened candidate target gene of GBN. Finally, we confirmed that these target genes were involved in the Wnt, TGF-β, MAPK, and PI3K-AKT signaling pathways.

Proteomics Research and Analysis.
We conducted proteomics analysis to obtain a global overview of GBN regulatory proteins and to further understand the mechanism of GBN's anticolon cancer action. High-pH RPLC analysis revealed that 7-20 amino acids were distributed in most of the peptides. This distribution pattern conformed to the general rules based on enzymatic hydrolysis and HCD fragmentation. By searching the NCBInr database, we identified 57 965 peptides and 6750 proteins. A protein with a fold difference that had increased by 1.2 times or decreased by 0.83 Table 7: GBN_620 vs. c_SW620 differential protein GO classification statistics' table.

Bioinformatic Analysis of Differentially Expressed
Proteins. We first performed GO enrichment analysis to clarify the biological importance of the differentially expressed proteins in c_SW620 and c_SW480 under GBN treatment. The functions of these proteins were annotated in accordance with cell composition, molecular functions, and biological processes ( Table 7). The GO annotation analysis of differentially expressed proteins between GBN_620 and the control group c_SW620 showed that 19 proteins were involved in biological processes,   Figure 6: (a) GBN vs. c_SW620 differential protein KEGG enrichment analysis diagram; (b) GBN vs. c_SW480 differential protein KEGG enrichment analysis diagram. Note: (1) In the column chart, the abscissa represents the pathway name, and the ordinate represented the enrichment rate (the greater the ratio, the greater the degree of enrichment). (2) The color gradient of the column indicated the significance of enrichment, and the darker the color indicated the higher the enrichment degree of the KEGG term ( * * * P < 0:001, * * P < 0:01, and * P < 0:05).
The enriched string diagram showed the corresponding relationship between the target protein and the KEGG pathway. The left side was the protein, and the order from top to bottom showed log 2FC. The greater the log 2FC, the difference in the expression of the upregulated protein was the greater. On the right was the KEGG pathway name and z score of the target protein. On the right was the KEGG pathway name and z score of the target protein. Among them, count represented the total number of target proteins involved in this pathway. Up represented the number of upregulated proteins involved in this pathway. Down represented the number of downregulated proteins involved in this pathway. If z score was greater than zero, it meant that there were more upregulated proteins than downregulated proteins involved in this pathway, and this pathway was more likely to be activated. Conversely, z score was less than zero, which meant that this pathway is more likely to be inhibited.
two were involved in cell components, and eight were involved in molecular functions. GO enrichment analysis revealed that the differentially expressed proteins had the highest degrees of enrichment in biological processes and molecular functions. When the enrichment of the differentially expressed proteins was corrected at P < 0:05, the enrichment rate was 100% ( Figure 5(b)). GO annotation analysis was performed on the differentially expressed proteins between the GBN_480 and c_SW480 group. These proteins were involved in 16 biological processes, two cell components, and seven molecular functions. GO enrichment analysis demonstrated that the differentially expressed proteins were enriched in the biological process function at the highest degree. The enrichment rate after concentration correction at P < 0:05 was approximately 40% (Figure 5(c)).

KEGG Functional Analysis of Differentially Expressed
Proteins. By using the KEGG database, we can classify differentially expressed proteins in accordance with the pathways they participate in or the functions they perform. The analysis between groups revealed that in SW620 cells under GBN action, the differentially expressed proteins participated in KEGG pathways (the first 20 are listed in Table 8). Table 9 illustrates that in secondary classification, differential protein pathways were mainly distributed in metabolic pathways, such as amino acids and nucleotides, translation, signal transduction, cell growth and death, and cancerrelated pathways. We discovered that the highly enriched pathways with P < 0:05 were the unsaturated fatty acid biosynthesis pathway, p53 signaling pathway, mTOR signaling pathway, and other pathways (Figure 6(a)).
The analysis of GBN_480 and c_SW480 KEGG annotations showed that the differentially expressed proteins participated in 83 KEGG pathways (the first 20 are listed in Table 10). Secondary classification revealed that the differential proteins mainly concentrated in pathways related to energy metabolism, lipid metabolism, translation, signal transduction, transportation and catabolism, cell growth and death, cancer-related pathways, gene folding and degradation, and infectious diseases (Table 11). KEGG enrichment analysis illustrated that the pathways with P < 0:05, such as apoptosis and platinum drug resistance, were highly enriched ( Figure 6(b)).

Differential Pathway and Protein Expression Analysis.
We mainly investigated the metabolic pathways involving the key proteins to analyze the functional effects of differential proteins as a whole. We found a total of 382 differential proteins in c_SW620 administered with GBN. These proteins were involved in 260 KEGG metabolic pathways that were distributed in six major categories and 44 subcategories. In addition, pathway classification showed that the 29 differential proteins with the largest proportion were distributed in the signal transduction pathway in the environmental information processing category (Figure 7(a)). Next, through KEGG pathway analysis, we finally confirmed that in c_SW620 administered with GBN, the largest number of proteins participated in the two signaling pathways of mTOR and PI3K-Akt. Through the cluster analysis of 29 differential proteins, we found that the administration of GBN had a good callback effect on most signaling pathways (Figure 7(b)). Protein levels changed significantly in c_SW480 cells administered with GBN. In the c_SW480 cell group, we found 348 differential proteins, which were involved in 225 KEGG metabolic pathways. The statistical results of pathway classification indicated that these pathways were distributed in six categories and 41 subcategories (Figure 7(c)). Among the differentially expressed proteins, 27 were distributed in the signal transduction pathway in the environmental information processing category. KEGG analysis indicated that that the largest number of proteins was involved in the two signaling pathways of cancer and PI3K-Akt. Cluster analysis indicated that the main downregulated proteins were serine protein kinase (P30154), guanine nucleotide-binding protein subunit-4 (Q9HAV0), fibronectin (P02751), 14-3-3β/α protein (P31946), Cyclin-dependent kinase CDK-4 (P11802), adhesion protein (P04004), 3-phosphoinositide-dependent protein kinase 1 (Q15530) telomere length regulatory protein TEL2 homolog (Q9Y4R8), V-type proton ATPase (O75348), and RhoA (P61586) (Figure 7(d)).
In short, the use of KEGG pathway enrichment analysis demonstrated that the largest number of proteins was involved in cancer, PI3K-Akt, mTOR, and other signaling pathways. After GBN acted together on the signal transduction pathway in SW620 and SW480 cells, the proteins that showed significant differences included NCSTN, V-ATP, CTNNA, CDK4, DUSP9, MKP4, ATP1B, and CD298.

Discussion
Computer reverse virtual screening technology is often used to predict the signaling pathways and main targets of smallmolecule compounds [23]. It has the advantages of low cost, fast speed, and high efficiency [24]. Our research group uses a system that is based on independent disease target databases (whole and partial) and molecular docking methods   Figure 7: (a) Statistical histogram of pathway classification in SW620 cell administration group; (b) clustering heat map of 29 differential proteins in c_SW620 cell signaling pathway; (c) statistical histogram of pathway classification in SW480 cell administration group; (d) clustering heat map of 29 differential proteins in c_SW480 cell signaling pathway. Note: 7a and 7c represented 6 major categories and 44 subcategories of KEGG metabolic pathways of differential proteins common to the drug-administered group and the control group. 7b and 7d showed that each column represented a sample, and each row represented a protein. On the left was a dendrogram of protein clusters, and on the right was the name of the protein. The closer the two protein branches were, the closer their expression levels were.
(AutoDock Vina). We also established a reverse virtual screening system with the help of the SDF format in the DrugBank database [25]. This system has been used by numerous researchers to verify the accuracy and practicality of anticancer targets [26][27][28]. Proteomics can detect the full set of proteins expressed by the complete genome in a cell [29,30]. This technology can visually identify the regulation of drugs at the cellular protein level [31], then find targets and related pathways [32]. Therefore, on the basis of our experimental results, we confirmed for the first time that GBN can inhibit the apoptosis, migration, and invasion of SW620 and SW480 cells. At the same time, through computer reverse virtual screening technology and proteomics research, we predicted that GBN may inhibit the occurrence of colon cancer through the PI3K-AKT-mTOR pathway.
The PI3K-AKT-mTOR is closely related to the occurrence and development of CRC [33,34]. PI3K is a key factor in this pathway [35]. Activated PI3K promotes the phosphorylation of PIP2 on the cell membrane into PIP3. The THr308 and Ser473 in the AKT protein are phosphorylated to bind with PIP3 [36]. PI3K activation can affect a variety of downstream effector molecules that can lead to reduced apoptosis [37], stimulate cell growth [38], and promote proliferation [39]. Activated Akt can also mediate apoptosis by regulating the expression of genes, such as Bcl-2 and caspase 3 [40]. mTOR, an important target gene downstream of Akt, is considered to be a member of the PI3K-related protein kinase family [41]. Activated mTOR can phosphorylate the translation repressor molecule eIF4E binding protein 1 and ribosomal protein p70S6K [33]. This process can inhibit tumor cell apoptosis [42]. Furthermore, the activated mTOR signaling pathway can increase the expression of matrix metalloproteinases to enhance extracellular matrix degradation. Therefore, this process can enhance tumor cell invasion and promote tumor cell migration [43]. Similarly, a large number of studies have reported that the PI3K-AKT-mTOR pathway can inhibit the growth, proliferation, and invasion of colon cancer cells [44]. Therefore, through

Oxidative Medicine and Cellular Longevity
Western blot analysis and qRT-PCR experiments, our team verified that GBN can exert its mechanism of drug action through the PI3K-AKT-mTOR pathway.

Conclusions
GBN has a certain application value because of its various anti-inflammatory, antibacterial, antitumor, anticardiovascular disease, and nervous system protective effects. Although it is currently not used as a clinical drug, it has an important curative effect without obvious side effects. Therefore, it is a highly promising potential drug. In summary, we used the combination of computer reverse virtual and proteomics technologies to study the mechanism underlying GBN's anticolon cancer effect. GBN can inhibit the proliferation of the human colon cancer lines SW480 and SW620 in vitro and induce tumor cell apoptosis. Its mechanism of action may be related to the inhibition of the PI3K-AKT-mTOR signaling pathway. Our research results demonstrated that the combination of computer reverse virtual and proteomics technologies is an effective tool for evaluating and exploring the efficacy and mechanism of action of Chinese medicine. At the same time, the results of this study provide a new understanding of how GBN inhibits proliferation and migration and promotes apoptosis in the human colon cancer cell lines SW480 and SW620 in vitro and evidence for GBN as a potential candidate drug for colon cancer.

Data Availability
The data that support the findings of this study are available from the corresponding authors upon reasonable request. Some data may not be made available because of privacy or ethical restrictions.

Conflicts of Interest
The authors declare no conflicts of interest.