Introduction: The prevalence of colon cancer remains high across the world. The early diagnosis of colon cancer is challenging. Moreover, patients with colon cancer frequently suffer from poor prognoses. Methods: Differentially expressed genes (DEGs) in colon cancer were acquired based on TCGA-COAD dataset screening. DEGs were input into the Connectivity Map (CMap) database to screen small molecule compounds with the potential to reverse colon cancer pathological function. Glycitein ranked first among the screened small-molecule compounds. We downloaded the main targets of glycitein from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database and constructed protein-protein interaction (PPI) networks of those which were closely related to targets by the Search Tool for the Retrieval of Interaction Gene/Proteins (STRING). Five potential targets of glycitein for treating colon cancer were identified (CCNA2, ESR1, ESR2, MAPK14, and PTGS2). These targets were used as seeds for random walk with restart (RWR) analysis of PPI networks. Then, the interaction network of glycitein-colon cancer-related genes was constructed based on the top 50 genes in affinity coefficients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the potential genes targeted by glycitein in colon cancer treatment and those that were closely bound up with targets. Results: GO analysis demonstrated that the enrichment of these genes was primarily discovered in biological functions including regulation of fibroblast proliferation, response to oxygen levels, and epithelial cell proliferation. The KEGG analysis results illustrated that the signaling pathways where these genes were mostly involved consisted of the mitogen-activated protein kinase signaling pathway, the phosphatidylinositol-3-kinase-Akt signaling pathway, and the p53 signaling pathway. Finally, stable binding of glycitein to five potential targets in colon cancer was verified by molecular docking. Conclusion: This study elucidated the key targets and main pathways of glycitein on the basis of network pharmacology and preliminarily analyzed molecular mechanisms in the treatment of colon cancer. A scientific basis is provided for glycitein application in treating colon cancer.

Colon cancer is characterized by a malignant lesion in the colonic mucosal epithelium, which is commonly found in the digestive tract. In recent years, with the better living standards and altered dietary structure, the incidence of colon cancer has been on the rise. According to the World Cancer Progress Report 2020 issued by the World Health Organization in 2020, colorectal cancer ranks third in incidence and second in mortality worldwide, which seriously threatens human health [1]. Colon cancer has an insidious onset with no obvious clinical manifestations in the early stage. The disease progresses slowly and most cases are in the advanced stage when obvious symptoms occur [2]. While surgery is the mainstay of treatment for colon cancer, it alone is not enough to slow disease recurrence or tumor metastasis [3]. Therefore, it is very important to explore novel drugs and targets for clinical practice in colon cancer treatment.

Compared with traditional anticancer drugs, drugs derived from natural products are more affordable, more effective, and have fewer side effects. As an O-methylated isoflavone, glycitein takes up for 5–10% of total isoflavones originated from soy foods and plays a significant part in human diseases [4, 5]. Zhang et al. [6] found that the dietary phytoestrogen glycitein butyl greatly hindered SKBR-3 cell growth and DNA synthesis in breast cancer. Glycitein has also been found to attenuate cell proliferation in breast cancer and prostate cancer and to exert a cytotoxic effect on gastric cancer cells [7]. Decreased glycitein levels may have a connection with an increased risk of ovarian cancer [8]. Soybeans are rich in isoflavones, such as genistein, daidzein, glycitein (40,7-dihydroxy-6-methoxy isoflavone), and isoflavone glycosides [9]. Epidemiological and animal studies have revealed that a healthy diet of soy products may be conducive to reducing the risk of breast and prostate cancer [10‒13]. Isoflavones possess notable anticancer effects, such as biochanin A and genistein, with good anticancer activity [14]. Other studies have shown that glycitein has a potent suppressive effect on the invasion of MDA-MB-231 (breast cancer cells) [15, 16]. However, no study has investigated how glycitein works in treating colon cancer so far. Therefore, it is important to analyze the application of glycitein in the treatment of colon cancer.

Network pharmacology is a novel research subfield developed constructed by analyzing network models and systems biology [17]. Based on systems biology, the combination of a highly integrated data analysis method and visualization technology paves the way for further diving into traditional Chinese medicine (TCM) theories through network pharmacology. The drug-target-disease network is mapped at the biological level, so as to explore the interaction between the body and drugs and analyze the complexity of drug and protein, as well as protein-protein interactions (PPI) [18‒21]. At present, there have been reports about the TCM in combatting colon cancer based on network pharmacology. For example, Zhang et al. [22] applied network pharmacology and molecular docking to demonstrate the key components of ginger and its mechanism in colon cancer treatment. Sun et al. [23] revealed the molecular mechanism of Qizhen Decoction in combatting colon cancer through network pharmacology. Feng et al. [24] investigated the effect of Danggui Buxue Decoction on treating metastatic colon cancer through network pharmacology and experimentally verified the accuracy of network pharmacological screening results. Therefore, network pharmacology was selected as a mean to explore the relationship between glycitein and colon cancer in this study.

Glycitein has an essential part in cancer therapy, and currently there are few reports on the clinical usage of glycitein in the treatment of colon cancer. We assumed that glycitein could influence the development of colon cancer. Our study predicted that glycitein had potential value in treating colon cancer through the Connectivity Map (CMap) database, analyzed the key targets and main pathways of glycitein based on network pharmacology, and preliminarily explored the molecular mechanism of glycitein acting on colon cancer, giving reference information for subsequent studies on the treatment of colon cancer with glycitein.

Data Downloading

From The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database, the colon cancer mRNA expression dataset TCGA-COAD (normal: 41, tumor: 473) was accessed.

Differential Expression Analysis of Colon Cancer-Related Genes and Prediction of Small Molecule Drugs

mRNA differential expression analysis (|logfold change [FC]| > 2.0, false discovery rate [FDR] <0.05) was performed on TCGA-COAD dataset using the R package edgeR [25] to screen for colon cancer-related differentially expressed genes (DEGs). By using the CMap (https://clue.io/) database, DEGs were compared with a reference dataset, with human colon cancer cells (HT29) selected for cell types, pert type selected for perturbation types, and trt cp selected for compounds. According to the enrichment of DEGs in the reference gene expression profile, a connectivity score was obtained. Negative correlation analysis was performed to predict small molecule drugs capable of reversing the pathology of the disease. Small molecule drug targets were analyzed using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://www.tcmspw.com/tcmsp.php) to find the target of glycitein.

Construction of PPI Networks

The MalaCards database (http://www.malacards.org) was utilized to find genes involved in colon cancer. PPI analysis was conducted on colon cancer-related genes and the targets of glycitein by using the Search Tool for the Retrieval of Interaction Gene/Proteins database (STRING, https://string-db.org/). The interactions with confidence scores ≥0.7 were selected to construct PPI networks.

Random Walk with Restart (RWR) Analysis

Taking the intersection of drug targets and colon cancer-related genes as seeds. R package dnet [26] was utilized to perform RWR analysis on the PPI network of colon cancer-related genes and drug targets. The restart probability was set to 0.85, and the adjacency matrix of the network graph was normalized by the Laplacian method. The affinity coefficient between each gene and seed was obtained after RWR analysis. The 50 nodes with the highest affinity coefficients were chosen for subsequent function analysis. Then, Cytoscape software was utilized to construct the drug-gene interaction network. The R package and the selected parameters used in this paper were uploaded onto GitHub (https://github.com/Striverzhou/Network-Pharmacology-And-RWR).

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analyses

The R package clusterProfiler [27] was employed for GO and KEGG enrichment analyses on genes with the top 50 affinity coefficients. Using the R package GOplot (https://infectious.day-project.org/Web/packages/goplot/index.HTML), the enrichment results were visualized to obtain key signaling pathways of glycitein in colon cancer treatment.

Molecular Docking Simulation

We performed molecular docking studies on glycitein and potential targets of colon cancer to verify the reliability of these targets. The pdb files of potential target proteins were downloaded from the Research Collaboratory for Structural Bioinformatics Protein Data Bank Protein Structure Database (https://www.rcsb.org/). Then, non-protein molecules and ligands were removed, and polar hydrogen was added and charged. From the PubChem Organic Small Molecule Biological Dynamic Database (https://pubchem.ncbi.nlm.nih.gov/), sdf files of the small molecule structure of glycitein were downloaded and then converted into mol2 format with OpenBabel software. Finally, SwissDock (http://www.swissdock.ch/docking), an online molecular docking tool, was utilized to perform interaction simulations between target proteins and glycitein.

Differential Expression Analysis of Colon Cancer-Related Genes and Small Molecule Drug Prediction

mRNA differential expression analysis based on the TCGA-COAD dataset showed that there were 2,064 DEGs (1,162 upregulated and 902 downregulated genes) in tumor tissues (Fig. 1). DEGs were input into the CMap database to predict small molecule compounds that may reverse colon cancer pathology by connectivity (online suppl. Table S1; for all online suppl. material, see www.karger.com/doi/10.1159/000527124). Among the significantly negatively correlated small molecule compounds, glycitein showed the potential of reversing colon cancer pathology.

Fig. 1.

Volcano plot of DEGs in colon cancer. Red represents significantly upregulated genes in colon cancer and blue represents significantly downregulated genes in colon cancer.

Fig. 1.

Volcano plot of DEGs in colon cancer. Red represents significantly upregulated genes in colon cancer and blue represents significantly downregulated genes in colon cancer.

Close modal

The PPI Networks of Targets of Glycitein and Colon Cancer-Related Genes

Through downloading targets of glycitein from the TCMSP database, 23 targets were obtained (online suppl. Table S2). To better understand the mechanism of glycitein-related colon cancer, 99 colon cancer-related genes were found through the MalaCards database (online suppl. Table S3). The PPI networks were constructed by inputting colon cancer-related genes and targets of glycitein into the STRING database (Fig. 2). The network included 105 nodes and 722 interaction lines.

Fig. 2.

PPI network diagram of targets of glycitein and colon cancer-related genes. Node color represents the number of protein associations. Line color represents the type of evidence for interactions, and line thickness represents the support strength of evidence between proteins.

Fig. 2.

PPI network diagram of targets of glycitein and colon cancer-related genes. Node color represents the number of protein associations. Line color represents the type of evidence for interactions, and line thickness represents the support strength of evidence between proteins.

Close modal

The Potential Targets and Key Genes of Glycitein Affecting Colon Cancer Are Mined by RWR

A total of 99 colon cancer-related genes and glycitein target genes were intersected to obtain 5 potential target genes of glycitein (CCNA2, ESR1, ESR2, MAPK14, and PTGS2) in treating colon cancer as seeds (Fig. 3). RWR analysis on PPI networks was conducted to obtain the affinity score of each node gene to the seed (online suppl. Table S4). We considered genes with the top 50 affinity scores as potential targets and key genes of glycitein acting on colon cancer. Therefore, genes with the top 50 affinity scores were chosen to construct a drug-gene interaction network (Fig. 4).

Fig. 3.

Venn diagram showing seed genes. Intersection of colon cancer-related genes and targets of glycitein.

Fig. 3.

Venn diagram showing seed genes. Intersection of colon cancer-related genes and targets of glycitein.

Close modal
Fig. 4.

Glycitein-gene interaction network. Blue positive hexagon represents glycitein. Circular nodes represent genes that may respond to drugs. Node color from deep to light represents affinity scores from high to low.

Fig. 4.

Glycitein-gene interaction network. Blue positive hexagon represents glycitein. Circular nodes represent genes that may respond to drugs. Node color from deep to light represents affinity scores from high to low.

Close modal

Apoptotic Signaling Pathways of Glycitein Acting on Colon Cancer

GO and KEGG enrichment analyses were conducted on potential targets and key genes of glycitein acting on colon cancer. GO analysis results exhibited that the primary enrichment of these genes was found in biological functions including regulation of fibroblast proliferation, response to oxygen levels, regulation of epithelial cell proliferation, epithelial cell proliferation, regulation of response to hypoxia, apoptotic signaling pathway, and positive regulation of the cell cycle (Fig. 5a). The KEGG analysis results illustrated that the signaling pathways where these genes were mostly involved consisted of the FoxO signaling pathway, the mitogen-activated protein kinase (MAPK) signaling pathway, the phosphatidylinositol-3-kinase-Akt (PI3K-Akt) signaling pathway, the ErbB signaling pathway, the IL-17 signaling pathway, and the p53 signaling pathway (Fig. 5b).

Fig. 5.

Potential targets of glycitein acting on colon cancer and functional enrichment of key genes. a GO enrichment analysis results. b KEGG enrichment analysis results.

Fig. 5.

Potential targets of glycitein acting on colon cancer and functional enrichment of key genes. a GO enrichment analysis results. b KEGG enrichment analysis results.

Close modal

Molecular Docking Is Used to Verify the Binding of Glycitein to Potential Targets

By conducting network pharmacological analysis, five targets of glycitein underlying colon cancer were screened in this study. To understand the binding of glycitein and potential targets, we performed molecular docking simulations of glycitein and five potential targets and output a three-dimensional model after glycitein-target docking (Fig. 6). The interaction between small molecules and proteins is determined by Gest. Estimated ΔG < 0 suggests the spontaneous binding possibility of the ligand molecules to the receptor protein. The results manifested that the ∆G of all five targets was less than 0, indicating that they all spontaneously bound to the receptor protein glycitein. The values of ∆G and FullFitness were −8.62 kcal/mol and −2,352.00 kcal/mol and they all were the smallest, indicating that PTGS2 had the strongest and tightest binding to glycitein. From the molecular docking results, it could be seen that glycitein could stably bind to all five potential targets (Table 1).

Table 1.

Molecular docking results

 Molecular docking results
 Molecular docking results
Fig. 6.

Schematic diagram of the interaction of glycitein and related targets. a Schematic diagram of interaction between target protein CCNA2 and glycitein. b Schematic diagram of interaction between target protein ESR1 and glycitein. c Schematic diagram of interaction between target protein ESR2 and glycitein. d Schematic diagram of interaction between target protein MAPK14 and glycitein. e Schematic diagram of interaction between target protein PTGS2 and glycitein.

Fig. 6.

Schematic diagram of the interaction of glycitein and related targets. a Schematic diagram of interaction between target protein CCNA2 and glycitein. b Schematic diagram of interaction between target protein ESR1 and glycitein. c Schematic diagram of interaction between target protein ESR2 and glycitein. d Schematic diagram of interaction between target protein MAPK14 and glycitein. e Schematic diagram of interaction between target protein PTGS2 and glycitein.

Close modal

In this study, according to the DEGs of colon cancer, the small molecule drug prediction was carried out through the CMap database, and the small molecule compound – glycitein, which can reverse the pathology of colon cancer, was identified. It has been demonstrated that glycitein can facilitate the emergence of MAPK-induced ROS, which mediates apoptosis and cell cycle arrest in the G0/G1 phase of AGS cells, and inhibits the activation of STAT3/NF-κB signaling pathway and asparaginase cascade. STAT3/NF-κB signaling pathway facilitates apoptosis and cell cycle arrest of gastric cancer cells mainly through activation by ROS, which in turn impedes the development of gastric cancer [7]. Glycitein can also suppress the invasive ability of malignant glioma by inhibiting the DNA binding and transcriptional activity of AP-1 and NF-kappaB and inhibiting MMP-3 or MMP-9 gene expression [28]. Therefore, this study inferred that glycitein may potentiate management of colon cancer.

Both in vivo and in vitro assays demonstrated that polysaccharide-rich extracts were able to facilitate colon cancer cell apoptosis by inducing NF-κB, PI3K/Akt, and MAPK signaling pathways [29]. Dysregulation of Wnt pathway components is frequently discovered in most cases of colon cancer, leading to aberrant Wnt/β-catenin signaling. In addition, activation of Wnt/β-catenin signaling promotes colon cancer development through its downstream cancer-related targets (such as cyclin D1, c-myc, MMPs, Cox-2, VEGF, uPAR, etc.) [30].

Isoflavones remarkably hinder colorectal cancer cell proliferation, and their core target genes validated in colorectal cancer are MCL1, APP, and KDR [31]. Genistein is an isoflavone present in soybeans, and Christian’s study [32] found that genistein can induce apoptosis of primary cancer and embryonal carcinoma cells, induce the expression of anti-migration proteins p38 and p53 in cell lines, and upregulate the steady-state levels of CYCLIN A and B, thus exerting an anti-cancer role. Its key genes include DXXR, NQO1 and SCD. As one of the isoflavones originated from soybean, glycitein is also extensively investigated regarding its effect on cell apoptosis and antioxidant. In vitro experiments by Winzer et al. [33] showed that glycitein could reduce the expression of IL-6 and RANKL in osteoclasts and its inhibiting and facilitating effects on differentiation and apoptosis, respectively, in osteoclasts were similar to that of genistein. Therefore, it was speculated that glycitein exerted a useful role in bone in vivo. Kang et al. [34] studied the cytoprotective properties of glycitein and found that glycitein, by suppressing reactive oxygen species (ROS) production and JNK activation, had an inhibiting effect on H2O2-induced cell death in V79-4 cells. In this study, five potential targets of glycitein (CCNA2, ESR1, ESR2, MAPK14, PTGS2) for the treatment of colon cancer were obtained by network pharmacological analysis, revealing the potential of glycitein in the clinical treatment of colon cancer. CCNA2 is a cyclin, and its expression is bound up closely with the malignant progression of colon cancer, liver cancer, and lung cancer [35]. Both ESR1 and ESR2 encode estrogen receptors: ESR1 encodes estrogen receptor α, and ESR2 encodes estrogen receptor β. Studies have shown that estrogen receptor α and estrogen receptor β are associated with the development of various tumors such as gastric cancer and papillary thyroid carcinoma [36‒38]. Estrogen receptor α is activated in cancer tissue and accelerates cancer progression by enhancing cancer cell proliferation and inhibiting apoptosis, whereas estrogen receptor β acts primarily against cancer by inducing cancer cell differentiation and pro-apoptosis [39]. MAPK14 is a mitogen-activated protein kinase that can regulate various important pathophysiological processes such as cell growth, differentiation, and stress adaptation to the environment, and inflammatory response [40‒42]. It has been reported that reactive oxygen species-mediated activation of MAPK14-related pathways is able to promote HCT116 cell arrest in G2/M phase [43]. PTGS is a key enzyme in prostaglandin biosynthesis, and it has been confirmed that PTGS2 can activate PGE2. PTGS2 plays an imperative role in tumor cell immune inflammation, cell proliferation, and cycle arrest [44]. Thus, the target genes of glycitein screened in this study are important for colon cancer treatment and are promising as therapeutic targets for colon cancer.

RWR analysis of the PPI networks reveals that many of the potential targets and key genes of glycitein acting on colon cancer are associated with tumors. FOS and JUN are immediate early genes, and their expressed proteins, c-jun and c-fos, can bind to the AP-1 promoter binding site as heterodimers or homodimers to induce oncogene expression [45, 46]. CCNB1 overexpression can promote hepatoma cell proliferation [47]. Elevated CAV1 expression can contribute to the research progress of colon cancer [48]. High expression of CCND1 and HIF1A has a close connection with poor prognosis and recurrence in cancer patients [49, 50]. Aberrant PARP1 expression is implicated in the progression of various malignancies [51]. VEGFA belongs to the VEGF family, and it has been found that VEGF jointly promotes tumor angiogenesis in response to stimulation by EGFR [52]. Oncogenes such as ERBB2, EGFR, VEGFA, CCND1, and MYC promote abnormal proliferation by covering G-S, G-M, and M checkpoints and preventing apoptosis and allowing cells to survive under adverse conditions [53]. CASP3 and CASP8 belong to the CASP family, which has been shown to cause cell death through nuclear envelope breakdown, DNA fragmentation, and chromatin condensation. Genes controlling the apoptosis or cell cycle have been found to form apoptotic genetic polymorphisms that increase the risk of human malignancies [54‒56]. HSP90AA1 is a highly conserved chaperone protein involved in tumor cell proliferation, differentiation, and angiogenesis [57]. In summary, the drug target genes and genes closely related to the targets screened in this study can instill new insights into the study of colon cancer progression as well as the molecular mechanism of treatment.

We conducted functional enrichment analysis on potential targets and key genes of glycitein acting on colon cancer and obtained key pathways of glycitein for treating colon cancer. There is also a close link between the various signaling pathways. The FoxO signaling pathway is mainly nucleated by the transcription factor FoxO to initiate the transcription of downstream target genes, while the MAPK signaling pathway and the PI3K-Akt signaling pathway related to FoxO can affect the proliferation and apoptosis of ovarian cancer cells by regulating the expression of FoXO protein [58‒60]. Potential targets and key genes of glycitein acting on colon cancer are partially involved in ErbB signaling, such as EGFR, which encodes the tyrosine kinase receptor HER1 of the ErbB family, and ERBB2, which encodes HER2 and facilitates the progression of various malignancies if ErbB receptors are overactivated [61]. The PI3K/Akt/mTOR pathway can promote the growth, proliferation, and survival of tumor cells by negatively regulating autophagy, while PI3K/Akt, as a downstream pathway of EGFR, has a role in affecting the invasive ability of colon cancer [62]. Therefore, the signaling pathway obtained in this study may be the main pathway of glycitein affecting colon cancer progression.

In summary, we obtained glycitein, a small molecule drug with potential for the treatment of colon cancer, through bioinformatics screening and revealed the potential targets and key pathways of glycitein in colon cancer treatment through network pharmacology. In addition, we verified the direct binding of glycitein into potential targets through molecular docking and clarified the molecular mechanism of glycitein in treating colon cancer. These findings pave the way for the subsequent clinical application of glycitein in colon cancer treatment.

The manuscript does not involve animal or human experiments and does not use patient data, so ethical approval is not required.

All the authors declare that they have no potential conflicts of interest.

This research has not received any funding.

Tao Xiang and Weibiao Jin contributed to data analysis, drafting, and revising the article, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work.

All data generated or analyzed during this study are included in this article and its online supplementary material. Further enquiries can be directed to the corresponding author.

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