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. 2024 Mar 29;12(1):22. doi: 10.1007/s40203-024-00192-6

In vitro combination effects of plant-derived quercetin with synthetic bicalutamide on prostate cancer and normal cell lines: in silico comparison

Mary Shobha Rani Inala 1, Kiranmayee Pamidimukkala 1,✉
PMCID: PMC10980673  PMID: 38559707

Abstract

Prostate cancer is the second most frequent and the fifth greatest cause of death in men. Although diet has been connected to the prevalence of cancer in addition to other factors, the relation between cancer and prevention is weak. Treatment options are at risk due to cell resistance. To identify new combinations, we tried plant-derived quercetin with bicalutamide on cell lines. To determine the cytotoxicity and apoptotic potential of plant-derived quercetin and its combination, MTT [3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide] and dual stain assays were performed. In silico protein–ligand interaction was performed to support the in vitro findings. A thin layer, column, and high-performance chromatography were used to purify quercetin along with an authentic sample. In the cytotoxic study, quercetin was minimized by 80% similar to bicalutamide and a combination of quercetin and bicalutamide by 50% when compared to controls by 2%. Quercetin and bicalutamide showed a similar binding affinity for androgen receptors (9.7 and 9.8), hub genes (10.8 and 10.0), and a few other PCa-related genes (9.4 and 9.1). We propose to conclude that the combination of quercetin plus bicalutamide can be used for chemotherapy if additional in vivo studies are conducted. The intake of foods high in polyphenolic compounds can help to prevent prostate cancer. Examination of quercetin on several cell lines will provide a definite conclusion to combat cancers.

Keywords: Apoptosis, Cancer, Diet, Flavonoids, Medicinal plants, Protein–ligand interaction

Introduction

Cancer is one of the second leading causes of death worldwide. Lung and prostate cancers are the most common cancers in men. Prostate cancer develops in the prostate gland and kills one man in nine men (Sung et al. 2021). It can be benign or metastatic and treatment options vary greatly depending on the stage of cancer. Irregular prostate cell proliferation and growth lead to prostate cancer in men of about 66 years and above.

Hormone therapy (androgen deprivation therapy, ADT) is preferred in conjunction with radiation therapy to make treatment more promising. However, long-term ADT treatment is uncertain and has negative consequences (Crawford and Moul 2015). Bicalutamide, the first-generation anti-androgen, is used in androgen deprivation therapy in combination with other chemotherapeutic drugs (Nozawa et al. 2016). Prostate tumors became resistant to bicalutamide, resulting in the growth of castration-resistant prostate cancer (CRPC). Treatment options for CRPC include ADT combined with enzalutamide, abiraterone, docetaxel, and cabazitaxel or radiotherapy (Rice et al. 2019). The available standard CRPC treatments became overburdened to the patients. In many situations, the isolated natural active compound is inefficient, innovating a combination is the better option.

Researchers are trying hard to find ways in cancer treatment by using natural products assisted treatment to improve the drug’s efficiency (Sharma et al. 2023). Plant-derived components and their refined active compounds have been the sole source of cancer treatment (Choudhari et al. 2020). Because of their antioxidant capacity (Zhang et al. 2018), flavonoids are the most investigated components in the anticancer medication development process. Flavonols are a type of flavonoids that are structurally similar to testosterone. As a result, they have high interactions with androgen receptors and anti-androgen activity in PCa. Flavonoids reduce the levels of dihydrotestosterone, a prostate-specific antigen, and prevent the formation of AR in the nucleus (Singh et al. 2017).

Quercetin (3, 3ʹ, 4ʹ, 5, 7- pentahydroxyflavone), a flavonoid, abundant in edible fruits and vegetables, has been identified as a possible anticancer agent (David et al. 2016). Quercetin has anti-cancer, anti-oxidant, and anti-inflammatory effects. It regulates several factors that take part in signaling in cancer cells. It inhibits the phosphorylation of mTOR, and Akt etc., lessens β-catenin, reduces VEGF secretion for increasing apoptosis, and inhibits metastasis (Lotfi et al. 2023).

According to the American Institute of Cancer Research (AICR), fruits and vegetables intake reduces the risk of tumor occurrence (Clinton et al. 2020). Furthermore, the National Academy of Sciences of the United States emphasized the importance of including fruits and vegetables in one’s diet and noted their involvement in cancer prevention (Sari et al. 2021). This has drawn our attention to identify natural sources of flavonoid content from locally available greens, Anethum graveolens L. and Raphanus sativus L. in Southeast Karnataka. The underground radish tuber is widely used, whereas, the leaves are well neglected. Hence, radish leaves were chosen for this study.

Anethum graveolens L. is an annual herb of the Apiaceae family that is used in Unani and traditional medicine for various digestive issues. It is utilized in over 56 Ayurveda formulations. It is widely grown in Eurasia and Karnataka and is widely spread in Bijapur, yielding 646 tons per year (Jana and Shekhawat 2010). Numerous studies have demonstrated that eating A. graveolens seeds and leaves, having high antioxidant potential, can reduce the occurrence of various chronic diseases including cancer. The plant has been reported having antioxidant, anti-inflammatory, and anticancer properties. According to Al-Oqail and Farshori (2021), A. graveolens employed as a good source of natural anticancer via a possible dietary supplement. Raphanus sativus L., an annual vegetable native to Kolar, belongs to the Brassicaceae family, produces 42,276 tons per year on average. The whole plant has traditional importance in many countries and the seed, root, and leaf are said to have medicinal benefits (Manivannan et al. 2019). Though the herb has been used for centuries, our knowledge of flavonol in this plant is inadequate and this led us to concentrate on its activity. Among the most predominant phytochemicals in R. sativus, the renowned are flavonoid for their nutraceutical and pharmacological effects. Rutin, a flavonoid component, found naturally and widely in a variety of fruits and vegetables (Omar et al. 2021). Though the tuber is an edible part, the consumption of leaves and sprouts, on the other hand, is increasing. Salads typically include raw leaves and sprouts. A systematic evaluation of 63 studies on the nutritional and phytochemical properties of radish revealed that nearly 609 phytochemicals, mostly flavonoids, were found in the leaves and sprouts. This information emphasizes the health benefits of radish leaves (Gamba et al. 2021).

According to the published findings, genes of hub, adhesion-associated, ECM-enriched, cell division, migration, mitotic processes, and apoptosis were upregulated (Zhu et al. 2023). As a result, it is critical to understand the binding capacity of drugs to the targets on PCa cells. To better understand this, the current study predicts the binding capacity of bicalutamide, a synthetic drug, and quercetin, a flavonoid, purified from a natural source with targets that are important in prostate cancer progression.

The goal of this study is to extract the bioactive component quercetin from edible greens and investigate the effects of the combination of bicalutamide and quercetin on prostate cancer cell lines with substantiated in silico analysis.

Material and methods

Purchased the seeds of edible greens from a local shop and cultivated them in the herbal garden in a pesticide-free environment. Regular watering was given with a rose can on every alternate day. When the lowermost leaves started senesce, the plants were subjected to three-day water stress, after which, the leaf material was collected from both the plants, washed with running tap water, distilled water, shade dried, and pulverized. The powdered leaf material was stored in an air-tight container and is utilized for quercetin extraction. Bicalutamide (CAS. No. 90357–06-5) and all other chemicals were obtained from Sigma Aldrich.

Extraction of quercetin by column chromatography

Before column chromatography, 25 g of each leaf powder was dissolved separately in 100 ml of ethanol and shaken at 120–130 rpm for 48 h at room temperature (37 °C). After 48 h, the residue was filtered using Whatmann No. 1 filter paper and allowed the solvent to evaporate. The material collected from both plants was stored separately at 4 °C and used for quercetin extraction through column chromatography. In brief, 100 mg of ethanol-dissolved extract was placed onto a silica column, ran with hexane, ethanol, and ethyl acetate and collected the fractions at 2-min interval. The pooled fractions of column chromatography with hexane: ethanol combination yielded a better percentage of compound than ethyl acetate combinations, thence hexane: ethanol as well as standard quercetin (Sigma—CAS. No: 117–39-5) were made in the concentration of 1 mg/ mL in appropriate solvent. About 20 µL of the extract was injected into the HPLC column by following suitable conditions: column temperature – 32 °C, flow rate – 0.8 ml/min, detection time at 360 nm, and run-time – 40 min. The experiment was repeated three times and the results were noted (Chandrappa et al. 2014). Standard quercetin was used only for comparing retention time in HPLC, not to test its efficacy on cell lines. The purpose of this study is to test the combination of natural derivatives with bicalutamide.

Determination of cytotoxicity

The pilot investigation used minimal concentrations from 0 to 50 µg/ml with a 10 µg/ml variance (data not shown). MTT assay was initiated with 50 µg/ml as the extracts showed cytotoxicity at this concentration. Prostate cancer (PCa 3) cell lines were cultured in DMEM and FBS medium (Rahman and Akhtar 2017) for the experiment. In a 96-well plate, 200 μl of cell suspension (2 × 104 cells/ml) was added per well and left to grow for 24 h. Both plant-derived quercetin (quercetin from A. graveolens and R. sativus), bicalutamide and the combination of quercetin with bicalutamide (1:1) at 0.165 μmol/ml, 0.33, 0.5, 0.66 μmol/ml concentrations respectively were made individually in DMSO, added 100 μl into each well and incubated in a 5% CO2 incubator for 24 h, 48 and 72 h. MTT was added to a final concentration of 0.5 mg/ml total volume and incubated further for 2 h. Following the removal of MTT, 100 μl DMSO for solubilization,, measured absorbance at 570 nm, and the IC50 was determined. The same experiment was repeated on normal mouse fibroblast cells (3T3-L1) and results were calculated.

Dual stain method

The protocol differed slightly from (Yashaswee and Trigun 2020). Prostate cancer cell lines were cultured to a concentration of 2 × 104/ ml and 100 μl was seeded in each well in a 96-well plate, and then treated with the combination (equal concentration of quercetin and bicalutamide) for 72 h before being treated with 0.25% trypsin. After treatment, the 25 μl cells were spread on a glass slide, 1 μl dual fluorescent staining solution (100 μg/ml Acridine Orange and 100 μg/ml Ethidium Bromide) was added and the slide was covered with a coverslip. Within 20 min, the morphology of apoptotic cells was examined and counted under a fluorescent microscope. The findings were made for the three replicates.

Drug-ligand interaction study

The crystal structures of ligands (Androgen receptors: 1E3G, 1GS4, 1I37. 1I38, 1R4I, 1T7R, 1XOW, 1Z95, 2AM9, 2AMA, 2PIT, 2PIW, 2PIX, 2YHD, 2YLO, 2YLP, 3BTR, 4HLW; Peroxisome-proliferator-activated receptors: 1K7I, 1KKQ; human retinoic acid receptor gamma 2LBD; Thyroid Hormone Receptor 3GWS and Nuclear Receptor 4IQR, hub genes such as, transcription factors 1NKP, adhesion associated 4ZT1, ECM enrich, and cell proliferation and migration 2YL2, 3HP3, 3ODU, 4ZYO, 5J0A, 5JTH, 5Z07, 6GUE, 6YIP, 7Q86) were retrieved from Protein Data Bank (PDB), drug structures from PubChem, and web servers such as SwissADME, Molsoft L.L.C, Lipinski’s Rule of five and CASTp were used for in silico prediction. The protein–ligand interaction study was carried out with the help of the online free version of CB-Dock 2 (Liu et al. 2022).

Statistical analysis

The mean ± standard deviation was calculated for the data. ANOVA was used to establish the statistical differences of all the treatment groups at different time intervals to compare the combinations of test compound and drug; p-value < 0.05 is considered significant.

Results

Identification of quercetin

The column chromatography fractions were collected. The quercetin concentrations of A. graveolens and R. sativus on dry weight basis were found to be 128 mg and 190 mg respectively. TLC was used to establish the purity and identification of the fractions compared to standard quercetin. The Rf values of standard quercetin, and quercetin from A. graveolens and R. sativus were 0.6, 0.57, and 0.52 respectively (Fig. 1).

Fig. 1.

Fig. 1

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HPLC chromatograms of plant quercetin fractions and standard quercetin

Cytotoxic potential of quercetin extracted from A. graveolens and R. sativus

MTT assay was used to determine cell viability in cell lines treated with plant-derived quercetin and commercial bicalutamide at 24 h, 48, and 72 h time intervals. Figure 2 depicts that plant quercetin showed cytotoxicity in dose and time-dependent manner. The following are the results at a concentration of 250 µg/ml: upon 24 h incubation, the viable cell percentage was 74.5% (quercetin from A. graveolens), 83.11% (quercetin from R. sativus) and 22.5% (bicalutamide); whereas, upon 48 h incubation, it was 54.62% (quercetin from A. graveolens), 51.33% (quercetin from R. sativus) and 10.89% (bicalutamide) and at 72 h incubation, it was 38.6% (quercetin from A. graveolens), 31.92% (quercetin from R. sativus) and 7.79% (bicalutamide). The cytotoxic effect of quercetin derived from both the plant materials was greatest at 72 h time intervals and was further examined to screen for combination with bicalutamide.

Fig. 2.

Fig. 2

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Comparison of cytotoxicity of quercetin and bicalutamide at different time intervals

MTT was performed by combining quercetin extracted from both plants with bicalutamide at different time points (24 h, 48 h and 72 h) and 250 μg/ml concentration, the combination of quercetin from A. graveolens and bicalutamide showed 90% cytotoxicity (10.92% viable cells) after 72 h incubation and the combination of quercetin from R. sativus and bicalutamide showed 85% cytotoxicity (15.44% viable cells) after 72 h incubation (Figs. 2 and 3).

Fig. 3.

Fig. 3

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Cytotoxicity of combination of quercetin and Bicalutamide on prostate cancer cells

The findings show that the combination of compounds has a high cytotoxic effect and quercetin can be an adjuvant to bicalutamide in treating prostate cancer. Normal mouse fibroblast cells were then treated with the same combination for IC50 (Fig. 4). This experiment aims to depict the effect of the combination of plant-derived quercetin and bicalutamide) on normal cells versus the commercial drug bicalutamide.

Fig. 4.

Fig. 4

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Cytotoxicity of combination of quercetin and Bicalutamide on normal mouse fibroblast cells

Quercetin and bicalutamide induced apoptosis in prostate cancer cells

Apoptosis evasion has been recognized as a cancer hallmark; however, quercetin suppresses this evasion by increasing apoptotic activity in PCa3 cells. When compared to the control, 50% early apoptosis was noted in the apoptosis assay and maximum apoptosis was time and concentration-dependent. At 72 h, the combination of plant-derived quercetin with bicalutamide further enhanced the apoptotic pattern. According to the study’s findings, a higher dose trigger apoptosis in prostate cancer cells (Fig. 5).

Fig. 5.

Fig. 5

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Cytotoxic potential of quercetin on normal mouse fibroblast cells (3T3-L1)

The current study used blind docking to determine the binding efficiency of bicalutamide and quercetin targets viz., ARs, PPARs, nuclear receptors, and hub genes. PubChem was used to obtain the 3D structures of bicalutamide and quercetin. The drug targets for this study were chosen from previously published data. The topmost poses were taken for all the targets (Tables 1, 2, and 3).

Table 1.

Various parameters of the targets when bound to bicalutamide and quercetin

Target details Parameters Bicalutamide Quercetin
1I37 Cavity 352 394
Hormone/growth factor  Vina score - 8.8 - 8.4
Chain a Bond pattern Pi pi (1), ionic (1), w (2), hydro (20), h (2) Hydro (9), w (4), h (5)
2.00 Å resolution Contact residues GLU678 GLU681 PRO682 GLY683 VAL684 VAL685 GLN711 LEU712 HIS714 VAL715 TRP718LEU744 MET745 ALA748 TRP751 ARG752 THR755 PRO801 GLN802 PHE804 LEU805 LYS808 LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 GLU872 LEU873 HIS874 PHE876 THR877 LEU880 PHE891
1I38 Cavity 381 437
Hormone/growth factor  Vina score - 8.8 - 9.8
chain a Bond pattern Hydro (15), w (3), h (1), pi pi (1), ionic (1) Hydro (11), h (11), w (2)
2.00 Å resolution Contact residues LEU677 GLU678 ALA679 GLU681 PRO682 GLY683 VAL685 GLN711 HIS714 VAL715 ALA748 MET749 TRP751 ARG752 PRO801 GLN802 PHE804 LEU805 LYS808 LEU701 LEU704 ASN705 LEU707 GLY708 GLU709 GLN711 TRP741 MET742 MET745 VAL746 PHE747 MET749 ARG752 PHE764 MET780 MET787 GLU872 LEU873 HIS874 PHE876 ALA877 LEU880 PHE891 MET895 ILE899
1T7R Cavity 414 486
Hormone/growth factor receptor Vina score - 8.7 - 8.6
Chain a Bond pattern H (2), hydro (15),w (1), ionic (1), pi pi (1) Hydro (10), w (4), h (5)
1.40 Å resolution Contact residues GLU678 GLU681 PRO682 GLY683 VAL685 GLN711 HIS714 VAL715 TRP718 LEU744 MET745 ALA748 TRP751 ARG752 THR755 PRO801 GLN802 PHE804 LEU805 LYS808 LEU701 LEU704 ASN705 LEU707 GLY708 GLU709 GLN711 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 HIS874 PHE876 THR877 PHE878 LEU880 PHE891
2AM9 Cavity 1654 1654
Hormone/growth factor receptor Vina score - 7.2 - 8.4
Chain a Bond pattern Hydro (16), w (1), h (3) H (5), w (2), hydro (9)
1.64 Å resolution Contact residues LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 TRP741 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 PHE876 THR877 PHE891 MET895 ILE899 LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 HIS874 PHE876 THR877 LEU880 PHE891
2AMA Cavity 1760 1760
Hormone/growth factor receptor Vina score - 7.7 - 8.7
Chain a Bond pattern Hydro (19), h (4) H (5), w (4), hydro (10)
1.90 Å resolution Contact residues LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 TRP741 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 PHE876 THR877 LEU880 PHE891 MET895 ILE899 LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 HIS874 PHE876 THR877 LEU880 PHE891
2PIT Cavity 1653 1653
Hormone/growth factor receptor Vina score - 8.4 - 8.5
Chain a Bond pattern Hydro (15), h (5), w (2) Hydro (11), w (4), h (7)
1.76 Å resolution Contact residues LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 TRP741 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 PHE876 THR877 LEU880 MET895 ILE899 LEU701 LEU704 ASN705 LEU707 GLY708 GLU709 GLN711 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 GLU872 LEU873 HIS874 PHE876 THR877 LEU880 PHE891
2PIW Cavity 348 494
Hormone/growth factor receptor Vina score - 8.7 - 8.7
Chain a Bond pattern Hydro (20), h (2), w (1), pi pi (2), cation (1), ionic (1) Hydro (12), h (7), w (1)
2.58 Å resolution Contact residues GLU681 PRO682 GLY683 VAL685 GLN711 HIS714 VAL715 TRP718 LEU744 MET745 ALA748 MET749 TRP751 ARG752 THR755 PRO801 PHE804 LEU805 LYS808 LEU701 LEU704 ASN705 LEU707 GLY708 GLU709 GLN711 TRP741 MET742 MET745 VAL746 ALA748 MET749 ARG752 PHE764 MET780 LEU873 PHE876 THR877 LEU880 PHE891 MET895 ILE899
2PIX Cavity 387 510
Hormone receptor  Vina score - 8.8 - 8.8
chain a Bond pattern Hydro (18), pi pi (3), w (3), h (2) Hydro (14), h (10), w (2)
2.40 Å resolution Contact residues GLU681 PRO682 GLY683 VAL684 VAL685 GLN711 HIS714 VAL715 TRP718 LEU744 MET745 ALA748 MET749 TRP751 ARG752 THR755 PRO801 PHE804 LEU805 LYS808 LEU701 LEU704 ASN705 LEU707 GLY708 GLU709 GLN711 TRP741 MET745 VAL746 MET749 ARG752 PHE764 MET780 LEU873 PHE876 THR877 LEU880 PHE891 MET895 ILE899
2YLO Cavity 2025 2025
Hormone receptor  Vina score - 8.4 - 8.7
Chain a Bond pattern H (5), hydro (16), pi pi (3), ionic (1) Hydro (14), h (5), w (5)
2.50 Å resolution Contact residues GLU681 PRO682 GLY683 VAL685 GLN711 VAL715 TRP718 LEU744 MET745 ALA748 MET749 TRP751 ARG752 THR755 PRO801 PHE804 LEU805 LYS808 LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 TRP741 MET742 MET745 VAL746 PHE747 MET749 ARG752 PHE764 MET780 MET787 LEU873 PHE876 THR877 LEU880 PHE891 MET895 ILE899
2YLP Cavity 425 456
Hormone receptor  Vina score - 8.3 - 8.7
Chain a Bond pattern Hydro (17), h (3), ionic (1), pi pi (1) Hydro (9), h (5), w (4)
2.30 Å resolution Contact residues GLU678 GLU681 PRO682 GLY683 VAL684 VAL685 GLN711 LEU712 HIS714 VAL715 TRP718 LEU744 MET745 ALA748 TRP751 ARG752 THR755 PRO801 GLN802 PHE804 LEU805 LYS808 LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 GLU872 LEU873 HIS874 PHE876 THR877 LEU880
3BTR Cavity 137 137
Hormone   Vina score - 7.7 - 7.9
Chain a Bond pattern W (1), ionic (1), h (6), hydro (9) Hydro (8), h (9), ionic (1), w (1)
2.60 Å resolution Contact residues GLU180 TRP184 GLY224 ARG227 ASN228 TRP231 GLU266 ASP270Chain B: ALA612 GLY613 MET614 THR615 LEU621 LYS622 LYS623 LEU624 GLU180 TRP184 GLY224 TYR225 ARG227 ASN228 TRP231 GLU266 ASP270Chain B: GLY613 MET614 THR615 LEU621 LYS623 LEU624
1E3G Cavity 830 436
Androgen receptor  Vina score - 8.1 - 9
Chain a Bond pattern Hydro (16), h (5), w (2), pi pi (1)  H (8), w (3), hydro (14)
2.40 Å resolution Contact residues LEU677 GLU678 ALA679 GLU681 PRO 682 GLY683 VAL684 ALA748 TRP751 ARG752 ASN756 TYR763 PRO766 PRO801 GLN802 PHE804 LEU805 LEU701 SER702 LEU704 ASN705 LEU707 GLY708 GLN711 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 PHE876 THR877 LEU880 VAL889 PHE891
1GS4 Cavity 7148 531
Androgen receptor  Vina score - 9.1 - 8.8
Chain a Bond pattern Pi pi (3), w (1), ionic (1), h (3), hydro (17)  Hydro (11), w (3), h (10)
1.95 Å resolution Contact residues GLU681 PRO682 GLY683 VAL684 VAL685 GLN711 HIS714 VAL715 TRP718 LEU744 MET745 ALA748 TRP751 ARG752 THR755 PRO801 PHE804 LEU805 LYS808 HIS701 LEU704 ASN705 LEU707 GLY708 GLN711 TRP741 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 PHE876 ALA877 LEU880 PHE891 MET895 ILE899
1R4I Cavity 7603 7603
Transcription/DNA Vina score - 9.5 - 9
chain c and d Bond pattern H (15), w (7), hydro (3), ionic (1), pi pi (12) Pi pi (9), w (7), hydro (2) h (16) ionic (1)
3.10 Å resolution Contact residues LYS592 ASN593 LYS592 ASN593 PRO595 LYS592 ASN593 LYS592 ASN593
1XOW Cavity 384 431
Transcription Vina score - 9 - 8.6
Chain a Bond pattern Pi pi (1), ionic (1), w (2) h (2), hydro (15) Hydro (11), h (7), w (3), pi pi (1)
1.80 Å resolution Contact residues GLU678 GLU681 PRO682 GLY683 VAL684 VAL685 GLN711 LEU712 HIS714 VAL715 TRP718 LEU744 MET745 ALA748 TRP751 ARG752 PRO801GLN802 PHE804 LEU805 LYS808 LEU701 SER702 LEU704 ASN705 LEU707 GLY708 GLN711 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 PHE876 THR877 LEU880 VAL889
1Z95 Cavity 554 554
Transcription regulation, receptor  Vina score - 9.7 - 9
Chain a  Bond pattern Hydro (18), h (3) Hydro (10), w (2), h (5), pi pi (1)
1.80 Å resolution Contact residues LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 GLN738 LEU741 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 HIS874 PHE876 THR877 PHE891 MET895 ILE898 ILE899 VAL903 LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 PHE876 THR877 LEU880 PHE891 MET895 ILE899
2YHD Cavity 1619 1619
Transcription Vina score - 8.6 - 8.8
Chain a Bond pattern Hydro (15), h (3) Hydro (8), w (4), h (5)
2.20 Å resolution Contact residues LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 TRP741 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 PHE876 THR877 PHE891 MET895 ILE899 LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 GLU872 LEU873 HIS874 PHE876 THR877 PHE878 LEU880
4HLW Cavity 386 453
Transcription Vina score - 8.5 - 8.9
Chain a Bond pattern H (4), hydro (19), pi pi (3), w (3), ionic (1) H (5), hydro (8), w (4)
2.50 Å resolution Contact residues GLU681 PRO682 GLY683 VAL684 VAL685 GLN711 HIS714 VAL715 TRP718 LEU744 MET745 ALA748 MET749 TRP751 ARG752 THR755 PRO801 PHE804 LEU805 LYS808 LEU701 LEU704 ASN705 LEU707 GLY708 GLN711 MET742 MET745 VAL746 MET749 ARG752 PHE764 MET780 MET787 LEU873 HIS874 PHE876 THR877 PHE878 LEU880
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Table 2.

Binding properties of quercetin and bicalutamide to hub genes

Target details Parameters Bicalutamide Quercetin
1NKP Cavity 1698 1098
Transcription Vina score - 8.8 - 9.5
Chain a, d and g Bond pattern Hydro (9), w (7), h (15), ionic (2), pi pi (9) Ionic (12), w (5), cation pi (1), h (15), pi pi (5), hydrop (8)
1.80 Å resolution Contact residues MET499 LYS502 ARG503 THR505 HIS506 LEU5097603 GLU910 ARG913 ARG914 ASN915 GLU916 LEU917 LYS918 PHE921 LYS939ARG215 ILE218 LYS219 PHE222 ARG23976032983
2YL2 Cavity 712 2983
Ligase Vina score - 9.5 - 9
Chain a and b Bond pattern Hydro (16), w (3), ionic (1), h (1), pi pi (1) Ionic (3), h (11), w (4), hydro (3), cation pi (1)
2.30 Å  resolution Contact residues ALA407 ILE408 SER409 PHE411 PRO429 ALA430 THR431 ALA433 PRO435 PHE438 GLU439 GLU442 GLN443 VAL446 TYR517 PHE529 GLU530 SER532 ALA533 HIS534 PRO536 PRO538 LYS280 LYS348 GLY352 GLY353 GLY354 GLY355 ILE358 HIS394 GLN418 HIS421 GLN422 LYS423 GLU461 GLU474 ASN476 ARG478 VAL481 GLU482 ARG546 THR647
3HP3 Cavity 3498 3498
Cytokine  Vina score - 8.3 - 7.9
Chain a,b,c,d,e Bond pattern Hydro (12), w (3), h (3), ionic (1) W (3), hydro (4), h (4), ionic (1)
2.20Å resolution Contact residues LYS27 ILE28 LEU29 ASN30 THR31LYS27 ASN30 THR31 PRO32LYS56 GLN59 GLU60 GLU63 ASN67LYS56 GLN59 GLU60 GLU63 LYS27 ILE28 ASN30 THR31LYS27 ILE28 LEU29 ASN30 THR31LYS56 TRP57 GLN59 GLU60 GLU63LYS56 GLN59 GLU60 GLU63
3ODU Cavity 3218 3218
Signalling protein, hydrolase Vina score - 10.5 - 8.3
Chain a, b Bond pattern Hydro (15), h (6), w (3), ionic (2), pi pi (1) Ionic (1), pi pi (1), w (2), h (3), hydro (9)
2.50 Å resolution Contact residues GLU32 ASN33 ASN37 LYS38 LEU41 TYR45 PHE93 TRP94 ASP97 ALA98 TRP102 CYS109 VAL112 HIS113 TYR116 ARG183 CYS186 HIS281 SER285 GLU288 LEU41 TYR45 PHE93 TRP94 ASP97 ALA98 TRP102 VAL112 HIS113 TYR116 ASP171 ARG188 SER285 GLU288
4ZT1 Cavity 2925 129
Cell adhesion Vina score - 7.8 - 7.4
Chain a, b Bond pattern Hydro (12), h (6), w (2) Ionic (3), h (14), hydro (7), w (7)
1.92 Å resolution Contact residues PRO5 PRO6 ILE7 SER8 CYS9 ASN20 LEU21VAL22TRP59PRO5 PRO6 ILE7 SER8 LEU21 VAL22 THR97 SER8 ARG68 THR97 VAL98 THR99 ASP100 ASP137ASN140THR141ILE7 SER8 CYS9 PRO10 GLU13 LYS19 ASN20 LEU21 GLN101
4ZYO Cavity 3063 3063
Oxidoreductase Vina score - 10.6 - 10
Chain a Bond pattern Hydro (10), h (6), pi pi (2), w (1) Hydro (10), h (10), w (1), pi pi (4)
3.25 Å resolution Contact residues ARG74 ASN75 LEU78 MET79 PHE146 GLN147 ASN148 GLU152 TRP153 ASP156 HIS157 PRO170 HIS171 TRP184 LEU185 TYR254 LEU258 THR261 TRP262 ASN265 MET79 PHE146 GLN147 ASN148 TRP153 ASP156 HIS157 HIS160 PRO170 HIS171 TRP184 LEU185 TYR254 LEU258 THR261 TRP262 ASN265
5JTH Cavity 110 508
Transcription Vina score - 7.2 - 7.3
Chain a Bond pattern Pi pi (5), w (1), hydro (6), h (5) H (7), hydro (4), w (2)
1.84 Å   resolution Contact residues THR39 THR40 TYR43 ALA72 ARG75 ASN76 TYR78 ASP212 GLN214 TRP215 HIS218 ASP2 GLN8 GLU11 PHE12 MET72 ALA73 ARG74 MET76 THR79 SER81 THR146 ALA147 LYS148GLN5 ASN9 ARG12 ARG16
5J0A Cavity 710 710
PDZ-binding kinase Vina score - 8.2 - 7.5
Chain a Bond pattern Pi pi (1), h (2), w (3), hydro (6) H (7), hydro (3), w (3)
2.74 Å resolution Contact residues HIS247 ALA270 ALA273 ALA274 THR277 ARG278 PRO280 GLU303LEU21 CYS22 THR24 THR26 ASN45 VAL46 TYR47 HIS247 ILE248 ASN249 ALA273 ALA274 THR277 ARG278 PRO279 PRO280 GLU303VAL20 LEU21 CYS22 TYR47 MET49
5Z07 Cavity 110 103
Cell cycle Vina score - 7 - 6.8
Chain a Bond pattern Hydro (7), h (5), w (1), pi pi (4) Hydro (6), h (10), w (3), cation pi (1)
2.30 Å resolution Contact residues THR39 THR40 TYR43 ALA72 ARG75 ASN76 TYR78 ASP212 GLN214 TRP215 HIS218 LEU58 LEU59 THR60 LEU61 ARG62 ASN63 HIS64 LEU65 ASP66 GLN67 LEU70 GLY98 GLN99 LEU100 LYS101 PRO102 THR103 LEU106
6GUE Cavity 980 525
Cellcycle (CDK2/Cyclin A)  Vina score - 10 - 9.1
Chain a Bond pattern Pi pi (1), ionic (2), hydro (21), w (3), h (3) Pi pi (1), ionic (2), hydro (15), h (10), w (3)
1.99 Å resolution Contact residues ILE10 GLY11 GLU12 TYR15 VAL18 ALA31 LYS33 GLU51 VAL64 PHE80 GLU81 PHE82 LEU83 HIS84 GLN85 ASP86 LYS89 GLN131 ASN132 LEU134 ALA144 ASP145 ILE10 GLU12 GLY13 THR14 VAL18 ALA31 LYS33 GLU51 VAL64 PHE80 GLU81 PHE82 LEU83 HIS84 GLN85 ASP86 LYS89 GLN131 ASN132 LEU134 ALA144 ASP145 PHE146
6YIP Cavity 86 86
CellcycleKinesin-like protein KIF20A  Vina score - 6.6 - 6.2
Chain a, b Bond pattern Hydro (14), cation pi (1), ionic (1) H (5), hydro (9), ionic (1)
1.43 Å resolution Contact residues LEU618 MET621 TYR622 LYS625GLU619 GLU620 TYR622 GLU623 GLU624 LEU626 ASN627 LEU618 MET621 TYR622 GLU624 LYS625LEU618 GLU619 GLU620 MET621 TYR622 GLU623 LEU626
7Q86 Cavity 46647 12409
Oxidoreductase Vina score - 10.8 - 0.8
Chain a, b Bond pattern Ionic (2), w (6), h (6), hydro (18), pi pi (3) W (4), h (11), hydro (4), ionic (1)
2.09  Å resolution Contact residues THR139 GLU140 HIS143 GLY144 THR145 TRP175 TRP176 TYR232 ASN237 PRO416 THR419 PHE420 GLU421 GLU423 TYR303 ARG307 GLN309 ILE321 GLN327 LYS330 ALA395 CYS396 GLY397 GLY398 HIS399 TYR401 TYR303 ARG307 GLN309 ILE321 PHE324 THR326 GLN327 LYS330 MET394 ALA395 CYS396 GLY397 GLY398 HIS399THR139 GLU140 MET141 GLY144 THR145 TRP176 THR419 GLU423
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Table 3.

Other genes involved in binding with bicalutamide and quercetin

Target details Parameters Bicalutamide Quercetin
1KKQ Cavity 7148 7897
Peroxisome proliferator activated receptor  Vina score - 9.1 - 8.5
chain a, b, c, d Bond pattern Hydro (18), h (2), w (1), ionic (1), pi pi (1) Hydro (16), h (3), w (1), pi pi (1)
3.00 Å resolution Contact residues PHE239 ILE241 LEU247 MET249 ALA250 GLU251 THR253 LEU254 ARG271 ILE272 CYS275 CYS276 THR279 VAL332 ALA333 ASN336 GLY337 PHE338 ILE339 ALA268 GLU269 VAL270 ILE272 PHE273 CYS276 GLN277 LEU344 LEU347 PHE351 ILE354 MET355 HIS440 VAL444 ILE447 LYS448 LEU456
2LBD Cavity 625 625
Nuclear receptor Vina score - 7.6 - 9
Chain a Bond pattern Hydro (22), w (2), h (4) Hydro (14), w (4), h (9), cation pi (1)
2.06 Å resolution Contact residues TRP227 PHE230 LEU233 ALA234 THR235 CYS237 ILE238 LEU268 LEU271 MET272 ARG274 ILE275 PHE288 GLY303 PHE304 LEU307 GLY393 ALA394 ARG396 ALA397 LEU400 MET408 ILE412 MET415 LEU416 PHE201 TRP227 PHE230 SER231 LEU233 ALA234 CYS237 LEU268 ILE270 LEU271 MET272 ARG274 ILE275 ARG278 THR287 PHE288 SER289 PHE304 GLY393 ALA394 MET408 ILE412 MET415 LEU416
1K7L Cavity 2646 2646
Transcription  Vina score - 9.2 - 9.1
Chain a and g Bond pattern Hydro (11), h (7) Hydro (7), w (7), h (10)
2.50 Å resolution Contact residues PHE218 ASN219 MET220 CYS275 CYS278 THR279 GLU282 THR283 GLU286 ILE317 MET320 LEU321 VAL324 LEU331 VAL332 ALA333 TYR334 GLY335 TYR214 PHE218 MET220 ASN221 LYS222 THR279 GLU282 THR283 GLU286 MET320 LEU321 SER323 VAL324 LEU331 VAL332 ALA333 TYR334 GLY335 ASP372 ILE375
4IQR Cavity 2593 1385
Transcription/DNA  Vina score - 8.9 - 8.2
Chain a Bond pattern Hydro (19), w (1), h (2) Ionic (4), w (10), h (11), hydro (9)
2.90 Å resolution Contact residues ILE175 ASP177 VAL178 CYS179 SER181 MET182 GLN185 LEU219 LEU220 GLY222 ALA223 ARG226 LEU234 LEU236 GLY237 VAL242 LEU249 MET252 SER253 VAL255 SER256 ILE259 MET342 GLN345 ILE346 ILE349 ILE357 HIS214 GLU217 ILE283 ILE284 ASP287 ASP289 ALA290 LYS291 ARG303 LEU332 GLN336ARG258 GLU262 LEU329 LEU330 LEU331 PRO333 THR334 SER337 GLN341
3GWS Cavity 502 502
Thyroid hormone receptor beta  Vina score - 9.4 - 8.6
Chain a Bond pattern W (5), hydro (19), h (4) Hydro (11), w (6), h (7)
2.20 Å resolution  Contact residues ASN233 PHE269 PHE272 THR273 ILE275 ILE276 ALA279 ARG282 MET310 MET313 SER314 ARG316 ALA317 ARG320 THR329 LEU330 ASN331 GLY332 LEU341 GLY344 GLY345 LEU346 HIS435 MET442 PHE455 PHE269 PHE272 THR273 ILE275 ILE276 ALA279 MET310 MET313 SER314 LEU315 ALA317 LEU330 ASN331 GLN340 LEU341 GLY344 GLY345 LEU346 ILE353 MET442 PHE455
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The molecular weights of bicalutamide (430.37 g/mol) and quercetin (302.24 g/mol) were between 150 and 500 g/mol, indicating that these two drugs might enter the cell. Low molecular weight compounds are better absorbed orally. One will comprehend the balance between the structure and molecule properties, which affect a small molecule’s ability to become an oral drug and recognize similarities between known and unknown drugs (Daina et al. 2017).

Molecular properties of drug-likeness (Figs. 6 and 7) from Molsoft L.L.C. and SwissADME indicated that these drugs fall in Lipinski’s rule of five: < 500 g/mol molecular weight, < 5 log P value, ≤ 140 A˚ Polar Surface Area (PSA), < 10 rotatable bonds and < 10 and < 5 HBA and HBD respectively (Daina et al. 2017).

Fig. 6.

Fig. 6

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Drug likeness properties of quercetin and bicalutamide

Fig. 7.

Fig. 7

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Physicochemical descriptors, predicted ADME parameters, pharmacokinetic properties, drug-like nature medicinal chemistry (A, B) and BOILED egg (C) of Bicalutamide and Quercetin

Entering the smiles in the tool, Molsoft L.L.C, reveals the likeness, hydrogen bond donor and acceptors (HBD, HBA), various log values, and Blood Brain Barrier (BBB). The potential drug candidate must have high potency and bridge the lipophilic and ligand efficacy. Low solubility results in slow absorption. Too many hydrogen bonds and low-fat solubility prevent penetration through the cell membrane. Figure 8 depicts a heat map of vina scores for the entire target populations tested. The highest and the lowest scores reported with AR receptors were 9.7, 7.2 (dark green and red are the highest and lowest) and 9.8, 7.9 with bicalutamide and quercetin respectively. The hub gene interaction with the drugs yielded maximum and minimum scores of 10.8 and 10 and for the other genes, 9.4, 7.6; 9.1, 8.2 for bicalutamide and quercetin respectively.

Fig. 8.

Fig. 8

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Vina score comparison of all the targets when bound to bicatuamide and quercetin. A AR with quercetin and bicalutamide, B Hub genes with quercetin and bicalutamide; C other genes with quercetin and bicalutamide

The Computed Atlas of Surface Topography of Proteins (CASTp) is an online resource for locating, delineating, and measuring the concave area of regions on a protein’s 3D structure. It can also detect the surface pockets and gaps buried within the proteins (Table 4). It analytically calculates and measures the volume and area of the pockets as well as solvent accessibility (http://cast.engr.uic.edu).

Table 4.

The area and volume of the targets to predict the concave areas

Sl.no. Target Area (SA) Å2 Volume (SA) Å3
1 1K7L 13055.436 19194.715
2 1R4I 304.881 338.459
3 1XOW 186.178 116.96
4 1Z95 263.16 141.153
5 2YHD 200.845 133.2
6 4HLW 199.976 131.412
7 4IQR 2161.872 11465.761
8 1I37 179.006 120.588
9 1I38 235.37 159.664
10 1T7R 174.098 164.315
11 2AM9 356.797 190.024
12 2AMA 352.731 191.756
13 2PIT 367.099 194.594
14 2PIW 211.733 148.817
15 2YLO 357.551 189.404
16 2YLP 192.651 129.324
17 3BTR 264.98 390.65
18 3GWS 196.582 223.876
19 1E3G 125.914 119.315
20 1GS4 227.637 161.307
21 1KKQ 14462.674 26940.382
22 2LBD 277.664 141.76
23 1NKP 250.057 501.694
24 2YL2 7106.58 12928.613
25 3HP3 795.909 1238.795
26 3ODU 4394.556 4726.635
27 4ZYO 677.349 470.779
28 4ZT1 1125.081 1350.853
29 5JTH 176.809 94.425
30 5Z07 not found not found
31 7Q86 not found not found
32 4Zt1 1125.081 1350.853
33 6GUE not found not found
34 5J0A 3333.665 6533.184
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Discussion

Involvement of amino acids in various bond patterns

Even though the efficacy of both drugs was nearly identical, quercetin had more hydrogen bonds than bicalutamide when bound to all the receptors studied (Tables 1, 2, and 3). When quercetin and bicalutamide bound with different ligands, the lowest of five and the highest of 16; two, and 15 hydrogen bonds were noted. Other types of bonding found include pi–pi interaction, ionic, hydrophobic, cation pi, and weak interactions. When a cation pi bond is formed during the interaction, the cationic side chains (Lys, Arg) are close to aromatic side chains (Tyr, Trp, and Phe) (Gallivan and Dougherty 1999). This study identified a similar pattern (Tables 1, 2, and 3).

Out of 15 pi-pi interactions in total with AR ligands, only three interactions (1R4I, 1XOW, 1Z95) were observed with quercetin; and this interaction does not contribute significantly to increasing the vina score between the two drugs (Table 1). Four of the 13 pi pi interactions (Table 2), were with quercetin and the others were with bicalutamide, with little difference in vina score. Among all the bond patterns, H-bonds are commonly used to facilitate and enhance protein–ligand binding. H-bonds regulate protein–ligand pairing mechanisms and reduce water competition during interaction. H-bonds boost ligand-receptor interaction, when compared to H2 and O2 bonding with water, depending on whether both have stronger or weaker H-bonding capacities.

In addition to other functions, the ubiquitous H-bonds help in protein folding, interact with ligands and act as catalysts (Chen et al. 2016). Aromatic amino acids play a role in pi-pi and cation-pi interactions, as evidenced by our findings. Hydrophobic contacts are the most common type of bonds in protein–ligand interactions. This interaction is mostly between the receptor’s aliphatic carbon and the ligand’s aromatic atom. Leu followed by Ile, Val, and Ala are the most frequently involved amino acids in hydrophobic interactions. This statement (Sahu et al. 2008) is supported by the results.

According to electro-statistics, the ionic bond-forming residues must be oppositely charged at physiological pH i.e., His, Arg, or Lys interact with Glu or Asp (Tam et al. 2022). In AR, the total number of ionic bonds with bicalutamide and quercetin are 10 and 2 respectively, both of which demonstrated the involvement of the aforementioned amino acids.

Reasons for choosing the receptors

Due to the anomalous behavior of AR, prostate cancer has become the leading cause of death among men worldwide. Non-steroidal anti-androgen, bicalutamide with and without adjuvant or combination therapy to limit endogenous testosterone synthesis is an option in cancer progression treatment. Knowledge of drug binding mechanisms is vital for developing new anti-androgens and AR modulators. After hormone binding, AR behaves differently than the other nuclear receptors, preferring to respond to co-activators with aromatic-rich regions rather than leucine-rich regions. The interaction between AR and the coactivators (with or without leucine-rich regions) promotes prostate cancer and its progression under aberrant settings (Singh et al.2017).

The crucial step for AR’s successive function is the nuclear import based on the ligand (ligand-dependent nuclear import). In the absence of a ligand, AR binds to 5-alpha dihydrotestosterone in the cytoplasm, translocates to the nucleus, and changes the target genes. To redirect AR’s role in prostate cancer progression, the coactivators must be targeted. The binding of 3,3ʹ,5-triiodothyroacetic acid to AR activation function (AF-2) weakens the cofactor’s binding effectiveness. Recent studies have revealed the presence of binding function 3 (BF3) on the AR. This is related to AR’s transcriptional activity. Combating prostate cancer with BF 3 is a better approach. Ligand-dependent nuclear transport is crucial for the successful function of AR.

The un-ligated AR interacts with 5-α dihydrotestosterone and changes gene transcription upon translocation into the nucleus. Importin-alpha, a nuclear import factor, aids AR’s nuclear import. Importin alpha functions as a receptor, binding to cargo protein nuclear localization signal motifs. Nuclear import is being held up in the event of a decrease in binding affinity to importin alpha (Table 2). Anti-androgen drugs target androgen receptor binding sites in prostate cancer treatment. Because of their ongoing use, these binding sites are prone to resistance and one of the novel tactics is to employ virtual screening to target AF2 of AR inhibitors. We attempted to dock bicalutamide and quercetin both of which interacted with AR transcription activation. This will help to predict future hits (Table 3). According to in silico analysis, the vina score for both drugs is nearly the same, indicating that natural quercetin affects both targets equally.

Ligand activated Peroxisome Proliferation-Activated Receptors (PPARs) are transcription factors belonging to the nuclear hormone receptor superfamily. One of the functions of PPARs is to regulate tumorigenesis (Tyagi et al. 2011). As a result, targeting PPARs is a viable therapeutic strategy. Nuclear and androgen receptors play important roles in cancer progression and resistance. Thyroid hormones, sterols, and bile acids regulate PPARs as well as nuclear and androgen receptors. Thyroid hormone therapy (Kotolloshi et al. 2020) reduces cell growth in PCa cells. Androgen deprivation therapy inhibits prostate cancer cell proliferation by targeting androgens and androgen receptors. PPARs are functional when they bind to PPAR response elements of DNA (PPREs) which serve to regulate gene expression. Clinical investigations show that Peroxisome Proliferator-Activated Receptor gamma (PPARγ) levels are elevated in advanced prostate and metastatic conditions. Targeting PPARγ is an excellent strategy since it promotes PCa cell proliferation. Transcription factors (TFs) control gene expression. They contribute to gene expression both in the presence and in the absence of signals. TFs’ activities are merged with cell division, differentiation and apoptosis. Cancer can be caused by abnormalities in either expression or function. It has been proven in silico that both drugs targeted the TFs almost equally.

Twelve hub genes were chosen from the published literature and interactions were studied. These play important roles in activities such as transcription, ligase, cytokine, signal, oxidoreductase and cell cycle. The pattern was consistent across all genes studied (Tables 1, 2, and 3). The Enhancer/E-box hexanucleotide (5ʹ-CACGTG-3ʹ) is recognized by structurally identical Myc-Max and Mad-Max transcription factors. This pairing determines whether the cell divides and proliferates or differentiates and deregulation results in cancer. Fatty acids serve as building blocks of membrane synthesis as well as energy sources for cell proliferation. Acetyl-CoA carboxylase alpha, encoded by Acetyl-CoA carboxylase 1, is abundant in PCa cells. Obesity increases Adipose Stromal Cells (ASC), and turnover recruitment is connected to cancer aggressiveness. CXCL12, a cytokine expressed in tumor cells, has been linked to ASC recruitment (Su et al. 2021). Chemokine receptors are involved in the regulation of cell migration, development, and inflammation. CXCR4, a G-protein-coupled chemokine receptor, expresses on endothelial, epithelial, and mesenchymal cells and has a role in cancer cell motility, invasion, and metastasis. The aberrant expression of adhesion proteins, cadherins is linked to cancer growth and proliferation. E-cadherins sustain cell–cell adhesion and the loss of expression leads to PCa cell metastatic progression (Anuradha and James 2013). Stearoyl-coenzyme A desaturase-1, (SCD), is an enzyme involved in lipid metabolism. It is highly expressed in PCa cells, SCD boosts AR-positive cancer cells and increases DHT-induced AR transcriptional activity. SCD Knockdown inhibits AR transcription activation and decreases cell division implying that SDC modulates AR transcription activation. In PCa activity, PDZ binding kinase regulates full-length AR and AR variants. In in vitro, inhibiting PBK results in a reduced growth pattern of PCa. Drugs that inhibit calcium influx prevent cancer cell apoptosis caused by androgen deprivation. The continued increase of intracellular Ca+2 and calmodulin (a Ca+2 binding protein) affect cancer cell proliferation and viability (Cifuentes et al. 2004).

Centromere protein (Cenp-I) is a kinetochore protein that regulates the centromeres and contributes to incorrect segregation, which leads to carcinogenesis. Overexpression of centromere genes is associated with cancer (Chen et al. 2022). CDK-2, Cyclin-dependent kinase is essential for cell cycle progression and many malignancies, including PCa. CDK-2 is engaged in several stages of the cycle, including synthesis, G1-S transition, and G2 advancement. Arora B, a kinase component, is the catalytic part of the chromosomal passenger complex (CPC). The CPC regulates genome segregation and controls kinetochore-microtubule attachment and cytokinesis (Carmena et al. 2012). Peroxisomal metabolism is an important factor in PCa. PCa benefits from peroxisomal lipid metabolism, increased peroxisomal-based fatty acid transport, ROS metabolism, import of membrane and matrix peroxisomal proteins, and ensures higher peroxisomal metabolic activity (Valença et al. 2020).

MYC encodes a nuclear phosphoprotein that participates in cell cycle progression, cellular transformation, and apoptosis. MYC activation is observed in the early stage of PCa. CDH1 encoding Cadherin is a member of the cadherin superfamily. It contributes to invasive tumors when its expression is low or absent. CXCR4, the cell surface receptor for the chemokine CXCL12 has been shown to be over-expressed in PCa. The CXCL12/CXCR4 axis is linked to PCa The progression and metastasis of PCa. CXCL12 signaling via CXCR4 increases the cancer cell adhesion to bone marrow endothelial cells and aids in progression and metastasis to bone marrow.

The long-chain fatty acids are catabolized by ACOX1. Saturated long-chain fatty acids activate inflammatory and immunological cell reactivity. These responses are crucial in metastasis. ACSL1 promotes the synthesis and metabolism of complex lipids and fatty acids, both of which contribute to tumor growth. When ACACA was knocked down, PCa cell growth was suppressed. SCD plays a role in PCa cell survival, initiation, and advancement (Zhang et al. 2023). The proteinaceous kinetochore is required for normal chromosomal segregation. Defects in CENP cause malfunction of the inner kinetochore resulting in incorrect chromosomal segregation and cell death (37 Hu et al. 2019). Cadherins’ (transmembrane cell adhesion proteins) abnormal expression has been linked to cancer growth and cell proliferation. CALD1, an actin-myosin binding protein, is found in smooth muscle’s thin filaments and non-muscular cells. Actin α-2 (ACTA2) when bound by phosphorylated CALD1, promotes smooth muscle contraction actin and myosin binding to regulate smooth muscle. CALD1 down-regulation promotes irregular vasoconstriction and PCa development (Zhu et al. 2023). KIF20A is a kinesin that is more abundant in castration-resistant PCa (CRPC) than androgen-dependent PCa. KIF20A is essential for mitotic processes. KIF20A treatment with a KIF20A selective inhibitor or its depletion reduces CRPC (Copello and Burnstein 2022). Reduced CRPC is the result of CDKN3 (cyclin-dependent kinase inhibitor) which is associated with cancer progression. CDKN3 regulates prostate cell lines. CDKN3 knockdown accelerates G1 phase arrest, increases apoptosis rate, and inhibits cell invasion in PCa cells (Yu et al. 2017). The above characteristics of these hub genes are thought to contribute to tumor progression in PCa patients. The up and down-regulation of hub genes are related to the overall survival rate of PCa patients.

Overexpression of c-Myc inhibits the transcriptional activity of the androgen receptor (AR), which is a driving force in PCa and the primary therapeutic target in advanced cases. Despite being a high-value therapeutic target, no clinically approved anti-Myc drugs have been developed. Myc must form a heterodimer with its partner Max to bind DNA and initiate transcription of a spectrum of target genes that stimulate cell growth, proliferation, metabolism, and death while preventing differentiation (Carabet et al. 2018).

Aside from toxicity and efficacy, the failure in drug development is poor pharmacokinetics followed by bioavailability. The two behaviors in various stages of drug discovery are BBB and GI absorption. The Brain Or Intestinal EstimateD (BOILED-Egg) predicts the two physicochemical qualities, lipophilicity and polarity; and also BBB and passive GI absorption (Fig. 7). Oral administration is the most often used mode of delivery. The white zone represents passive GI absorption, the yellow region represents BBB, and the gray region represents intestinal absorption space. The blind docking research of these two drugs demonstrated that they have similar features. Quercetin’s improvement is mediated by numerous pathways and a combination of drugs is a better alternative in limiting cell multiplication (Sharma et al. 2023).

Chemotherapy is commonly used to treat all cancers, including prostate cancer, either alone or in conjugation with other drugs. Cancer cell resistance, on the other hand, leads to poor therapeutic response and disease return. As a result, new therapeutic choices or agents are required to manage the patient’s ailment while minimizing the adverse effects. According to several epidemiological researches, dietary factors influence the risk of human cancers. Consumption of a range of fruits and vegetables containing flavonoids provides convincing evidence for cancer prevention (Key et al. 2020). A previous study reports that the abundant bioflavonoid quercetin decreased the capacity of prostate cancer cells to form colonies (Ward et al. 2018). As a result, we targeted the undisclosed quercetin-rich edible plant derived quercetin and its antiproliferative effect on prostate cancer cell lines and less or no effect on non-cancerous mouse fibroblast cells when coupled with bicalutamide.

Bicalutamide has failed in androgen deprivation therapy for prostate cancer treatment due to resistance. Advanced prostate cancers are susceptible to androgen deprivation therapy at first, but progress to CRPC later. As a result, adjustments to the therapeutic application of drugs in chemotherapy are required. Because of these constraints, many people lack treatment alternatives and a significant percentage of patients die as a result of recurrence and metastasis. The combination with adjuvant therapies is a viable treatment option for the malignancy. Quercetin has been shown to have a therapeutic effect against prostate cancers, modulating several mechanisms involved in cancer progression and metastasis (Penson et al. 2016; Hashemzaei et al. 2017). Polyphenolic substances have been shown in studies to work as an alternative to androgen receptors by targeting androgen-regulated genes (Boam 2015).

In androgen-free settings, quercetin not only encourages intrinsic and extrinsic pathway-mediated apoptosis but also regulates insulin-like growth factor signaling processes (Ward et al. 2018). Because of the inhibitory effect of quercetin on androgen receptor signaling, as well as other features such as pro-oxidant action, cell cycle arrest, and apoptotic effect (Choudhari et al. 2020; Zhang et al. 2018), quercetin a chemopreventive agent or as an adjuvant to existing therapy for prostate cancer.

We found that combining quercetin with bicalutamide, an androgen receptor antagonist, inhibited prostate cancer cell proliferation, implicating a signaling pathway. Other studies have demonstrated that bicalutamide inhibits androgen-independent prostate cancer cell proliferation via AR-independent mechanisms (Penson et al. 2016). According to Kawabata et al., (2011) bicalutamide when combined with 5-fluorouracil, inhibited CRPC growth by upregulating insulin-like growth factor-binding protein 3. These encouraging findings help bicalutamide’s therapeutic effects. We reasoned that quercetin would be a promising drug to suppress androgen-independent cancer cell proliferation. Prostate cancer inhibition by quercetin combination sensitized the therapeutic impact of bicalutamide. According to a cell-based morphology experiment, quercetin analogs or quercetin anti-androgen plus bicalutamide were more powerful than quercetin alone.

Bicalutamide is widely available as a low-cost generic drug in most industrialized and developing nations and its patent protection has expired. This makes the combination treatment (quercetin plus bicalutamide) not only broadly available but also more cost-effective when compared to conventional treatment strategies. The possible dangers of using these agents in prostate cancer metastasis necessitate greater research and thorough review to determine the most effective combination for prostate cancer.

Although there are numerous therapeutic options for cancer in recent years, a successful therapy with an anticancer drug remains difficult due to the drug’s non-selective cytotoxicity. Despite being cytotoxic and cytostatic, chemotherapeutic drugs are the most commonly used therapy agents. Furthermore, today’s chemotherapeutic drugs are highly cytotoxic to normal cells, resulting in a cascade of side effects that might lead to the death of cancer patients (Senapati et al. 2018). A promising new anticancer agent is predicted based not just on its ability to accelerate tumor cell death, but also on its selectivity or ability to harm normal cells as little as possible. The findings suggest that when paired with a synthetic drug, plant quercetin is very selective in cancer treatment, causing minor injury to normal cell lines while causing significant harm to cancer cell lines. As a result, this combination can be used to design a new therapeutic mixture for use in clinical trials in the future when evaluated on different models.

Green leafy vegetables, ginger, turmeric, onion, and soybeans have chemo-preventive effects, and the most identified and investigated phytochemicals are curcumin, quercetin, luteolin, and genistein (Gibellini et al. 2011). Among them, quercetin possesses some therapeutic actions such as antioxidant, anti-inflammatory, and anticancer effects. It occur in the form of glycoside or aglycone when consumed. The safe amount of quercetin is considered as 1 gm/day with no adverse effects (Rather and Bhagat 2020). As a result, quercetin derived from A. graveolens and R. sativus may be an excellent choice for a healthy lifestyle.

Conclusion

The current study findings show that quercetin has powerful anti-proliferative properties when combined with bicalutamide and can be further investigated as a chemotherapeutic agent. Cancer bioinformatics tracks and forecasts the effectiveness of precision treatment in patients with gene and protein level variations. Giving the right treatment to the right patient at the right time is heavily reliant on the molecular characteristics of a person’s cancer. Through therapeutic design and cancer bioinformatics, it is feasible to identify, treat, and prevent cancer. The target and the drug structures constitute the foundation of computational cancer research. Bioinformatics was used to examine the effective binding properties of the two drugs by analyzing a few genes such as Androgen receptors, Peroxisome-proliferator-activated receptors, human retinoic acid receptor gamma, Thyroid Hormone Receptor, and HNF4alpha Nuclear Receptor as well as hub genes such as, adhesion associated, ECM enrich, and cell migration.

Lacuna of the study: Bicalutamide is gradually being replaced by new drugs and combinations. The trial must be conducted using these newer drugs in conjugation with extracted natural quercetin. Precision medicine is expected to be the most risk-free therapeutic strategy. Despite the fact that these bioinformatics findings have a theoretically expected impact on prostate cancer cells, this study did not demonstrate any experimental validity.

Author contributions

Dr. Kiranmayee P.: The concept, result analysis, manuscript writing, editing. Dr. Mary Shobharani I.: Performed the experiments, written and edited the manuscript.

Data availability

The authors confirm that the data supporting the findings of this study are available within the article.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

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References

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