To identify all of the metabolites present in icaritin, we utilized the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (https://tcmsp-e.com/). Oral bioavailability (OB) and “drug-likeness” (DL) are the essential pharmacokinetic qualities in absorption, distribution, metabolism, and excretion methodology. OB refers to the pace and extent of absorption of an oral medication into the circulatory system from the gastrointestinal tract. DL refers to herbal compounds that are related structurally to recognized medicines. Substances with OB ≥30% and DL ≥0.18 were investigated as active ingredients in the present study (8).

By attaching to certain molecular processes at transcriptome and protein levels, chemicals can have an impact upon biosynthesis. The TCMSP database was used to find chemical–target interactions. The “Ensemble” dataset within the UniProt database (www.uniprot.org/) was used to harmonize gene names. After deleting duplicate targets, the targets of icaritin were identified.

We used the phrase “UCEC” to search the GeneCards database (www.genecards.org/) and the Online Mendelian Inheritance in Man (OMIM) database (www.omim.org/) and Therapeutic Target Database (https://db.idrblab.net/) to search for UCEC-related targets. The GeneCards database includes complete data about annotated human genes. Using the program “Venny 2.1.0”, the overlap between the putative target genes of icaritin and UCEC-related targets was found (http://bioinfogp.cnb.csic.ed/tools/venny/). These were considered to be targets in icaritin for UCEC intervention, as shown in a Venn diagram.

We used the target genes of icaritin and the treatment targets of UCEC from Venny 2.1.0, which was the overlap between them. Then, using Cytoscape 3.8.0 (https://cytoscape.org/), we merged the details from the ingredient, genetic, and disease data to create a C–G–D network (9).

To locate the genes that overlapped, we utilized the target genes of icaritin and the therapy targets of UCEC from either of the three datasets. The created PPI network, which itself was based on the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://string-db.org/), was displayed in Cytoscape. With a level of precision, the “degree” within Cytoscape was used to identify the top-11 domains that were plausible hub candidates (10).

Enrichment analyses were employed to provide further information about the biological activities of icaritin that occur in UCEC. We used the Metascape database (https://metascape.org/) for 135 key target genes. Then, in the GO database, biological process (BP), cellular component (CC), and molecular function (MF) terms were used to identify the top-10 GO keywords to obtain gene functions. The KEGG database was employed to identify the signaling pathways that were enriched. The Bioinformatics databases (www.bioinformatics.com.cn/) was used to conduct mapping analysis.

Icaritin (purity = 99.27%) was purchased from MedChemExpress (catalog number: HY-N0678; Monmouth Junction, NJ, USA). Icaritin powder was dissolved in dimethyl sulfoxide to a high concentration (10 mM) and stored in the dark at −20°C. At the time of use, icaritin solutions were diluted with culture medium to the required concentrations.

The HEC-1-A cell line was obtained from ProCell (Santa Ana, CA, USA). The cell line was cultured in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum, 1% penicillin–streptomycin solution, 1% alanyl-glutamine in an atmosphere of 5% CO₂ at 37°C.

During the logarithmic growth phase, HEC-1-A cells were plated into 96-well plates (1×10⁴ cells in a suspension of 100 μL), with three replicates per treatment. When cells showed adherent growth, the supernatant was replaced with medium containing icaritin. After 24h,48 h,72h 10 μL of Cell Counting Kit-8 (CCK-8) working solution (Servicebio, Wuhan, China) was added, and cells were cultured for an additional 2 h. The absorbance at 450 nm was measured. Cell-growth curves were drawn based on the average absorbance of each sample.

HEC-1-A cells were seeded in 12-well plates at 250 cells/well. When cells showed adherent growth, medium containing icaritin was added, followed by incubation at 37°C for 2 weeks, during which time the medium was changed every 3 days. Colonies were fixed with methanol, stained with 0.1% crystal violet, and counted.

Straight lines at a distance of 0.5 cm from each other were marked on the underside of six-well plates. Logarithmic-growth phase cells were seeded into dishes. Nicks were made 0.5-cm apart on the inside bottom of the dish along with the labeled lines using a 1000 μL sterile pipette tip when cells were close to confluence. Then, cell cultures were washed thrice with sterile phosphate-buffered saline and incubation in serum-free medium for 24 h was undertaken. The scratch width was photographed under phase contrast on an inverted microscope (BX51; Olympus, Tokyo) with a digital camera (100× magnification; DP70; Olympus). This experiment was repeated three times.

Cells were seeded into six-well plates and cultured for 48 h. Flow cytometry was used to detect apoptosis in different groups according to manufacturer (BD Biosciences, San Jose, CA, USA) instructions. This experiment was repeated three times.

Proteins extracted from HEC-1-A cells were prepared using RIPA buffer containing the phosphatase inhibitor phenylmethylsulfonyl fluoride (RIPA:phenylmethylsulfonyl fluoride = 100:1). Proteins were quantified with a bicinchoninic acid protein assay kit (Servicebio, Wuhan, China). Then, protein samples were fractionated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis on 10% gels and transferred to polyvinylidene difluoride (PVDF) membranes. PVDF membranes were incubated with primary antibodies against PI3K (1:500 dilution; Proteintech, Chicago, IL, USA), phosphorylated (p)-PI3K (1:1000; BIOSS, Woburn, MA, USA), Akt (1:20000; Proteintech), p-Akt (1:20000; Proteintech), and glyceraldehyde 3-phosphate dehydrogenase (1: 50000; Proteintech). After washing with Tris-buffered saline and Tween 20, PVDF membranes were incubated with the corresponding secondary antibody for 1.5 h at 25°C. An electrochemiluminescence detection reagent (Beyotime Institute of Biotechnology, Shanghai, China) was used to visualize protein bands.

Experimental data were analyzed using SPSS 24.0 (IBM, Armonk, NY, USA). Repeated-measures analysis of variance was used to compare differences among groups. The unpaired Student’s t-test was employed to compare mean values between two groups. Univariate analysis of variance was used to compare data between groups. Data are the mean ± standard deviation. P < 0.05 was considered significant.

We searched the TCMSP database for 23 ingredients based on OB and DL values ( Table 1 ).

After deleting duplicates, according to TCMSP and UniProt databases, icaritin had 206 targets. After repetitions in the GeneCards database had been identified, 2551 genes related to UCEC were identified. The intersection of 206 icaritin endpoints and 2551 UCEC-related keywords was established using Venny 2.1.0. Venn diagrams revealed 135 candidate genes related to UCEC and icaritin ( Figure 1 ).

The components of traditional Chinese medicine (TCM) formulations interact with a vast array of moieties to create a multitude of therapeutic actions. However, network pharmacology is an essential method for comprehending the basic mechanisms of how TCM formulations work. Cytoscape was used to create a C–G–D system for icaritin with regard to UCEC ( Figure 2 ). The C–G–D system revealed 160 nodes and 293 edges.

To further identify the genetic basis of the pharmacologic activities of icaritin, candidate genes were added to the STRING dataset to create a PPI network. The nodes and edges of the PPI network indicate proteins and protein–protein interactions. After omission of disconnected locations, a PPI network was formed with 91 notes and 266 edges ( Figure 3 ). The top-11 key targets for potential anti-UCEC effects were actin-beta (ACTB), acyl-coenzyme a oxidase 1 (ACOX1), acetyl-coenzyme a acetyltransferase 1 (ACAT1), acyl coenzyme a dehydrogenase very long chain (ACADVL), acyl coenzyme A dehydrogenase (ACADM), adenosine kinase (ADK), actin-alpha 1 (ACTA1), adenosine triphosphate citrate lyase (ACLY), long-chain specific acyl-coa dehydrogenase (ACADL), and actinin alpha 1 (ACTN1) ( Figure 4 ).

The targets of icaritin stated above were inputted into the Metascape database to examine the vast diversity of biological properties of potential targets for UCEC therapy. The most enriched BP were “response to inorganic substance”, “response to metal ion”, and “response to reactive oxygen species.” The most enriched CC were “membrane raft”, “membrane microdomain”, and “plasma membrane raft”. The most enriched MF were “DNA-binding transcription factor binding”, “RNA polymerase II-specific DNA-binding transcription factor binding”, and “transcription factor binding” ( Figure 5 ).

The 135 endpoints were entered into the Metascape database for analysis of enrichment of signaling pathways. A mixture of enrichment scores and p-values was used to determine the top-10 signaling pathways that were enriched ( Table 2 ); “Pathways in cancer”, “PI3K-Akt signaling pathway”, and “MAPK signaling pathway” were among the signaling pathways enriched ( Figure 6 ).

Addition of various concentrations of icaritin to HEC-1-A cells led to strong impairment of their viability ( Figure 7A ). Besides, the formation of colonies of HEC-1-A cells was also repressed when icaritin (25 μM) was added and incubation allowed to proceed for 48 h ( Figure 7B ). In addition, the number of apoptotic HEC-1-A cells increased after the addition of icaritin (25 μM) and incubation being allowed to proceed for 48 h ( Figure 7C ). These results indicated that icaritin could impede the growth and enhance the apoptosis of HEC-1-A cells.

To ascertain the biological function of icaritin in UCEC, we added medium containing icaritin (25 μM) to HEC-1-A cells and allowed incubation to proceed for 24 h. Icaritin inhibited the migration of HEC-1-A cells significantly according to the wound-healing assay ( Figure 8 ). This result suggested that icaritin could impair the migration ability of HEC-1-A cells.

Analyses of signaling-pathway enrichment using the KEGG database suggested that the PI3K-Akt signaling pathway was extremely important. The PI3K-Akt signaling pathway is important in tumor progression. To unravel the molecular mechanisms involved, we focused on this pathway. Interestingly, western blotting illustrated that when icaritin (25 μM) was added to HEC-1-A cells and incubation allowed to proceed for 48 h, expression of p-PI3K and p-Akt declined. Simultaneously, expression of PI3K and Akt was unchanged ( Figure 9 ). These results illustrated that icaritin modulated activation of PI3K/Akt to promote the survival of HEC-1-A cells.