Exploring the Therapeutic Potential of Native Plant Compounds: Unveiling the Therapeutic Potential of Ferula gummosa in Colorectal Cancer through Bioinformatics and Experimental Validation

Exploring the Therapeutic Potential of Native Plant Compounds: Unveiling the Therapeutic Potential of Ferula gummosa in Colorectal Cancer through Bioinformatics and Experimental Validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring the Therapeutic Potential of Native Plant Compounds: Unveiling the Therapeutic Potential of Ferula gummosa in Colorectal Cancer through Bioinformatics and Experimental Validation Abbas Alibakhshi, Shima Gharibi, Ali Shojaeian, Atefeh Asgari, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4443245/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Colorectal cancer (CRC) presents a significant global health challenge, which demands advanced molecular understanding for personalised treatments. Molecular profiling has revealed biomarkers crucial for prognosis, treatment response, and targeted therapies. This study explores the role of native plant compounds, using bioinformatics and experimental assays to identify potential CRC-specific therapeutic targets. A drug-target network analysis identified four proteins (ANG, DPP4, INR, and MAPK14) as potential targets for further investigation. Molecular docking studies identified the cauferoside from Ferula gummosa as a compound with high binding affinity to these proteins. Molecular dynamics simulations confirmed stability in the compound-protein complexes. In vitro assays demonstrated the cytotoxic effects of F. gummosa extracts on CRC cells, with leaf extract significantly downregulating the expression of the ANG, DPP4, INR, and MAPK14 genes. Root extract exhibited differential effects on gene expression. These findings suggest the potential therapeutic efficacy of F. gummosa against CRC and emphasize the importance of a dual methodology involving bioinformatics and experimental validation in drug discovery. Further in vivo and clinical studies are warranted to validate these findings and advance potential therapeutic applications. Biological sciences/Computational biology and bioinformatics Biological sciences/Systems biology Plant Compounds Colorectal Cancer Ferula gummosa System biology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Colorectal cancer (CRC) is a formidable global health challenge, accounting for a significant portion of cancer-related morbidity and mortality. CRC is a complex and heterogeneous disease, and the integration of molecular biomarkers has become instrumental in the design of treatment strategies. Molecular profiling has advanced our understanding of CRC, allowing the identification of specific biomarkers that play crucial roles in prognosis, treatment response, and the development of targeted therapies 1 . As understanding of the molecular intricacies governing this malignancy evolves, researchers are increasingly exploring novel avenues for therapeutic intervention. Studies show that mutations in the RAS and BRAF genes 2 , high EGFR expression levels of EGFR 3 , mutations associated with the activation of the PI3K-AKT-mTOR pathway 4 , and some other molecular factors are critical contributors and determinants of the progression of CRC. The incorporation of molecular biomarkers in the management of colorectal cancer signifies a fundamental alteration towards precision medicine. Understanding the genetic and molecular characteristics of specific tumours can help healthcare professionals make knowledgeable choices, enhancing therapeutic results, and reducing detrimental consequences. The progressions in this domain offer hope for an additional enhancement in the treatment of colorectal cancer by disclosing fresh objectives and even cultivating a flexible environment for personalised methodologies in CRC treatment 5 . Interfering with specific molecules that play a crucial role in cancer cell growth and survival could inhibit the growth and spread of cancer cells. Natural plant compounds have been the subject of extensive research for their potential role in cancer prevention and treatment. Many plants produce bioactive compounds with diverse chemical structures that exhibit anticancer properties. They can interact with specific molecules involved in cancer development and progression 6 . These compounds may modulate epigenetic processes, inhibit cell proliferation, induce apoptosis, and regulate signaling pathways associated with cancer development. Some plant compounds such as luteolin, resveratrol, and quercetin target specific kinases and cell cycle regulators that play crucial roles in cancer cell growth and survival. Myricetin and indole alkaloids can also target molecules involved in apoptosis. Other studies show that specific molecular targets for plant compounds can vary depending on the compound and the type of cancer studied 7 . Therefore, to identify an appropriate target and intelligent compound with significant capacity to impede the proliferation and development of cancer, it becomes essential to employ suitable and occasionally novel methodologies to effectively determine the most optimal testing approach in the laboratory. Bioinformatics assumes a pivotal function in the realm of cancer therapy through the utilisation of computational methodologies and examination of extensive biological data sets to improve our comprehension of cancer and formulate more effective treatment methodologies. Cancer bioinformatics is a rapidly evolving field that facilitates the prediction of therapeutic responses to various treatment alternatives. By analysing genomic data, bioinformatics tools can identify genetic alterations and molecular signatures that can influence treatment outcomes. Furthermore, these tools can aid in therapy selection by analysing genomic data and identifying potential therapeutic vulnerabilities in cancer cells. They can prioritise therapeutic targets and guide the selection of appropriate treatment strategies 8 . Bioinformatics techniques also enable virtual screening and docking studies, where large databases of chemical compounds are screened against target proteins to identify potential drug candidates. These methods help prioritise compounds for further experimental testing, saving time and resources. There are currently approaches that can be used to identify new therapeutic uses for existing drugs. Through the examination of gene expression profiles or protein interaction networks, these methodologies have the potential to propose alternative applications for drugs or natural compounds that have already been approved or are currently being used in clinical settings 9 , 10 . Bioinformatics analyses can encounter challenges in accurately predicting interactions between plant compounds and molecular targets, necessitating robust validation through experimental assays. Therefore, this research aims to find the most effective of native plant compounds and potential therapeutic targets specific to colorectal cancer through a meticulous fusion of bioinformatics methodologies and experimental tests. By leveraging cutting-edge bioinformatics tools, we seek to identify promising candidates within the vast array of plant compounds, subsequently subjecting them to experimental scrutiny. This dual methodology seeks to bridge the gap between computational predictions and empirical validation, offering a smart understanding of the potential benefits and challenges associated with native plant extractions in the context of colorectal cancer treatment. 2. Materials and Methods 2.1. Data Recovery of Compounds Identification of medicinal plants native to Iran was done by searching the Internet and reviewing local articles and books, and their names were written in an Excel file. By downloading all the chemical compounds available in the ChEBI database 11 , and matching their sources with the file of native plant names, the compounds that were present in the extracted indigenous medicinal plants were obtained. In addition to their names, the three-dimensional structures of the compounds were recovered as SDF (structural data file) from the PubChem database 12 . 2.2. Identification of Potential Target Candidates for Compounds The open web server PharmMapper was used to identify possible target candidates of extracted compounds from native plants. PharmMapper is a backup of the pharmacophore database extracted from all the targets in four databases related to drugs named TargetBank, DrugBank, BindingDB, and PDTD. Human protein targets were selected for the target set option, and for the option of maximum number of reserved matching targets, the number of 300 was determined. The output of each compound was saved as an Excel file, and then after selecting the top 20 targets for each compound, all possible targets were collected in one file. After sharing and removing duplicates, a final file of targets was prepared for the next steps. 2.3. Retrieval of colorectal cancer targets With the help of the NCBI database and Gene resource ( https://www.ncbi.nlm.nih.gov/gene ), all differentially expressed genes in colorectal cancer were recovered. Meanwhile, for this study, the genes that had a significant increase in expression are selected. Next, the output of this step was matched with the output obtained from the previous step to select common and identical names. In this way, possible targets for the compounds in colorectal cancer are obtained. 2.4. Construction of drug target (DT) network The drug-target network produces useful information for the analysis of relationships between drugs, targets, and diseases, as well as for the discovery of new drug targets. In this study, a drug-target network was constructed by Cytoscape v3.6.0 software. On the basis of this, an input file with possible colorectal cancer targets for each compound was prepared and given to the software. After building the network, the NetworkAnalyzer plugin 13 was used to analyze the quantitative characteristics of the indirect network. Then, the degree and centrality parameters were used to select the nodes 14 . Then, the main targets in terms of gene expression were checked by a literature review to select genes with a significant increase in expression compared to normal tissue. 2.5. Retrieval of the three-dimensional structure of proteins In this study, four proteins were finally selected as potential targets for colorectal cancer to retrieve their three-dimensional structure for further study. Using the database PDB (Protein Data Bank) ( http://www.rcsb.org/PDB ) 15 , the structures of the proteins ANG (PDB ID: 1B1I), DPP4 (PDB ID: 3SWW), INR (PDB ID: 5KQV) and MAPK14 (PDB ID: 5XYY) were recovered. The information and choosing the correct structure was chosen for each protein was done with the help of the UniProt database 16 . The structures were saved as PDB files. 2.6. Molecular Docking The SDF structures of selected compounds (47 compounds) were converted to mol2 files using Open Babel software 17 and then converted to PDBQT using AutoDockTools-1.5.7 software, Raccoon programme 18 . For the four protein structures in PDB format, the first water molecules and ligands available in the structure were removed using Chimera 1.16 software 19 and then converted to PDBQT using Autodock Tools software. Next, all the structures were given to PyRx software 20 . Using the Autodock Vina programme 21 hosted in this software, molecular docking was performed for each of the target proteins. Depending on the type of target protein, the binding site position of each protein was considered as the target box. Nine modes were selected for each compound, and the results were sorted according to the interaction energy. The docked protein-compound complexes were then analysed by PyMOL ( https://pymol.org/2/ ) and Discovery Studio software ( https://discover.3ds.com/discovery-studio-visualizer-download ) in terms of 2D and 3D structures and amino acids and molecules involved in binding. 2.7. Molecular dynamics (MD) simulations The dynamics of four docked complexes consisting of the ANG, DPP4, INR, and MAPK14 proteins and cauferoside compound was simulated by GROMACS 5.4.1 software 22 . In this method, 54A7 force field 23 was used on molecules surrounded by SPC water. The system was neutralized by Na + ions and then energy was minimized for the relaxation of internal constraints. Equilibration in the NVT and NPT ensembles was done under positional restraints for 100 ps. Finally, the MD production run was performed for 50 ns with a time step of 3 fs. The trajectory was then analyzed by Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) to characterize the dynamics of the ligand-protein. 2.8. Plant extract preparation 2.8.1. Plant Materials The leaves and roots of F. gummosa were collected in Naghan, Chahar Mahal-e Bakhtiari (31◦980 N and 50◦680 N) at an altitude of 2026 meters with a herbarium number of 13342. The samples were collected and identified by Prof. Mehdi Rahimmalek using Flora Iranica (Rechinger, 1963), and the samples were deposited in the herbarium of Isfahan University of Technology, Isfahan, Iran. The collection of the samples were permitted from research institute of forest and rangelands and complies with local and national guidelines and legislation. The collected plants were dried at 25 ° C for three days under shade conditions. 2.9. HPLC Analysis F. gummosa aerial parts and root were used for polyphenolic compound determination according to the standards (gallic acid, caffeic acid, ferulic acid, p -coumaric acid, rutin, rosmarinic aci, chlorogenic acid and the other available standards (Phytolab, Germany, 98% purity). 15 g of dried plant materials were mixed with 300 ml of methanol and shaken at 90 rpm for 24 hours. The filtered extract was concentrated and dried using a rotary evaporator under a vacuum at 40°C. The extract was dissolved in HPLC solvent A (1 mL), filtered (0.22 µm disk), and 20 µL was injected into an Agilent 1090 system with a detection range of 260 and 350 nm. In this experiment, a 250 × 4.6 mm, 5 µm, symmetry C18 column (Waters Crop., Milford, MA, USA) was applied. The mobile phase included formic acid (99.9:0.1) as a solution (A) and acetonitrile/formic acid (99.9:0.1) as a solution (B) with gradient elution at 25 ◦C and a flow rate of 0.8 mL min − 1. The gradient programme started from A: B (90:10) for 1 min, followed by 10–26% B for 40 min, 26–65% B for 30 min, and finally 65–100% B for 5 min followed by equilibration with 0–90% A for 4 min. Polyphenolic compounds were determined by comparing UV spectra and retention times with pure standards, and the amount was reported in mg per 100 g of dry sample weight. 2.10. Cell culture Cells (viz SW948 cell line, Pasteur Institute) were cultured on Roswell Park Memorial Institute (RPMI) 1640 medium, added by 10% v/v foetal bovine serum (FBS, Gibco, Parsley, UK). The medium was also enriched with streptomycin (100 µg/ml) and penicillin (100 U/ml) (Gibco, Paisley, UK). Cells were maintained in a humidified atmosphere containing 5% carbon dioxide (CO2) at a temperature of 37 ° C. They were housed in flasks with a surface area of 25 cm2. To keep the cells in their exponential growth phase, they were passaged twice a week 24 . 2.11. Cytotoxicity assay Cell viability was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay, which is based on the ability of the mitochondrial succinate dehydrogenase to convert the tetrazolium salt into insoluble violet crystals. This reaction is indicative of the number of viable cells. SW948 cells were initially placed in a 96-well plate at a density of 1 × 104 cells per well and left to incubate overnight. A serial concentration from 0 (control group) to 500 µg/ml of both leaf and root extracts was prepared and added to the wells in triplicate. The toxicity test was then performed at 24-hour intervals. Subsequently, a 5 mg/ml MTT solution was prepared and 60 µl of MTT (0.5 mg/ml) was added to each well, which was then incubated at 37 ° C for 4 hours. To dissolve the formazan deposits, 100 µl of dimethylsulfoxide (DMSO) (Merck, Germany) was added to each well. After a 30-minute wait, absorbance was measured at 570 nm using an enzyme-linked immunosorbent assay (ELISA) reader (Stat-Fax-2100, USA) 25 . The growth inhibitory effects of Ferula gummosa (leaf and root extract) were evaluated by determining the IC50 values. 2.12. Cell treatment Three flasks were considered for three groups for root extract, leaf extract, and control. After the confluence of the cells reached the desired level, 250 ug/ml of leaf extract and 125 ug/ml of root extract were added separately to two flasks. Then, after 24 hours, the cells were harvested to be used for the next step. 2.13. RNA isolation, cDNA synthesis, and RT‑qPCR Total RNA was isolated from SW948 using RNX-Plus Reagent (Sinaclon, Tehran, Iran) according to the manufacturer’s protocol and measured by NanoDrop™ 2000/2000c Spectrophotometer (Thermo Fisher Scientific, MA). The 260/280 and 260/230 values were higher than 1.9. Furthermore, 1 µg total RNA of each sample was synthesized using a cDNA Synthesis Kit (Yekta-Tajhiz-Azma [YTA], Tehran, Iran) and transferred into the qRT-PCR (quantitative real‑time reverse transcription polymerase chain reaction). The transcription levels of ANG, DPP4, INSR, MAPK14, BRAF, and VIM were also evaluated through a SYBR ® Green PCR Master Mix (Yekta-Tajhiz-Azma [YTA], Tehran, Iran). Specific cycling parameters in the qRT-PCR included an initial denaturation step at 95°C for 2 min, denaturation at 95°C for 10 s, annealing at 61°C for 20 s, followed by an extension step at 72°C for 25 s. The number of cycles was optimized at 40. The primer sequences used are represented as follows: ANG, (F) TAGCAGCTCTGGTTCCGTTT and (R) CTCCTGGGTGTGTTTCCTGT; DPP4, (F) CTGCTTGCTCCAATTTAGCC and (R) ACACTTGCTAGAGCCCAGGA; INSR, (F) GAAGCTCTGTGCCAAGAACC and (R) CCGTTGCTACAAGGGTCATT; MAPK14, (F) CCAGAGGCAGTTTTCTCCTG and (R) TGCTCACCCACATGTTTTGT; BRAF, (F) CTTCATGAAGACCTCCAGT and (R) CATCCACAAAATGGATCCAG; VIM, (F) GAAGAGAACTTTGCCGTTGAAG and (R) TGAGCAGGTCTTGGTATTCAC; GAPDH, (F) GAG TCC ACT GGC GTC TTC AC and (R) ATG ACG AAC ATG GGG GCA. In addition, the transcription level of GAPDH was used as an endogenous control. The 2 − ΔΔCt method was also utilized to determine the relative expression of each sample at the time points analyzed. The RT-qPCRs were performed using Roche LightCycler equipment (Roche LightCycler ® 480, Germany) 26 . 2.14. Data Analysis The RT-qPCR data were statistically analyzed using the GraphPad Prism software (version 8) (GraphPad Software, CA, USA). A one-way analysis of variance (ANOVA) and Tukey’s post hoc test were used to compare the differences between the experimental groups and the controls. Any p-values less than 0.05 were deemed statistically significant 27 . 3. Results 3.1. Investigation of Compound and Protein Libraries When searching the sources, 315 approved native medicinal plants were found. According to the number of native medicinal plants and also based on the information available in the ChEBI database, 53 known compounds were found in these plants. The names of medicinal plants and compounds are given in the Supplementary file. Furthermore, after sharing the final targets obtained of PharmMapper and those from colorectal cancer, 14 target proteins were obtained. The names of medicinal plants, compounds, and proteins are given in the Supplementary file. 3.2. HPLC Analysis The results of the HPLC analyses revealed the presence of some major phenolic compounds (Table 1 ). The roots and leaves showed a different pattern of phenolic accumulation. The phenolic compound was much higher in the leaves of F. gummosa compared to their leaves. The most abundant phenolic acids in leaves were gallic acid and rosmarinic acid, while in the roots gallic acid, p -coumaric acid and chlorogenic acid were the most frequent ones (Table 1 ). With regard to flavonoids, rutin revealed the highest amount. Interestingly, the rutin had a higher amount in the roots compared to the leaves (Table 1 ). Table 1 Major phenolic and flavonoid compounds of Ferula gummosa . Compound RT (Retention time, min. ) Root (mg/100g DW)) Leaf (mg/100g DW)) Gallic acid 5.09 11.18 ± 0.3 * 25.05 ± 0.5 Chlorogenic acid 13.57 3.23 ± 0.2 3.38 ± 0.3 Caffeic acid 14.49 0.02 ± 0.01 1.29 ± 0.1 p -Coumaric acid 26.85 4.2 ± 0.1 4.9 ± 0.2 Rutin 28.41 2.94 ± 0.08 1.26 ± 0.1 Ferrulic acid 29.20 0.06 ± 0.04 0.01 ± 0.02 Rosmarinic acid 39.10 0.07 ± 0.02 5.64 ± 0.23 * The results are expressed as means ± SD 3.3. DT network analysis A drug-target network with 47 compounds and 14 protein targets was created that included 65 nodes and 144 edges. Using NetworkAnalyser, the number of node values and betweenness centrality are determined. Based on this, the results of the analysis were sorted based on both nodes and betweenness centrality. Figure 1 shows the bipartite network for drug-target based on degrees, where the size of nodes is depicted according to the number of degrees. Also, a circle layout of the DT network is made based on betweenness centrality. Compounds showed degrees in the range of 6 to 1 and proteins 29 to 1. Based on the analysis of this network and the results of the literature review, four protein targets were selected for the molecular docking step. The output of the network based on degree and betweenness centrality is given in the Supplementary file. 3.4. Molecular Interaction Analysis To estimate the binding affinity of the compounds to the protein targets, molecular docking was performed and then results were obtained as a file representing the binding affinity as the interaction energy (kcal/mol). The results of the ten compound molecules that showed the highest binding affinity to each protein are shown in Table 2 . The protein-ligand docked structures were studied in terms of 3D and 2D models by Discovery Studio software so that the involved amino acids and the manner of their involvement were shown. Therefore, it can be determined whether the compound can effectively occupy the binding site of the protein target and prevent its activity. Based on the amount of interaction energy and the type of interactions, finally 10 compound molecules were selected for further study and their source (Table 3 ). The top three molecules in this list have been observed in the Ferula gummosa plant. Therefore, this plant was selected for investigation in colorectal cancer cells in the laboratory. Additionally, four protein targets including angiogenin (ANG), dipeptidyl peptidase 4 (DPP4), insulin receptor (INR), mitogen-activated protein kinase 14 (MAPK14) were selected for investigation in molecular MD and laboratory investigations. Due to the structural similarity of the first 3 compounds, only one of them (cauferoside) was selected for analysis in molecular MD. Figure 2 shows the 3D and 2D structures of the interaction of cauferoside with the 4 mentioned proteins. Table 2 The docking binding energy and calculated affinity of the highest scores for 4 target proteins and plant compounds ANG Binding Affinity DPP4 Binding Affinity INR Binding Affinity MAPK14 Binding Affinity Cauferoside -7.4 epi -maslinicacid -8.8 Rotundifolioside A -7.2 Ferilin -9.5 Paxanthonin -6.9 Daucosterol -8.8 Gnidilatimonoein -7.2 Gumosin -9.2 Miquelianin -6.9 Rotundifolioside A -8.5 epi -Naslinic acid -7 Cauferoside -9.2 Lactucopicrin -6.9 Feselol -8.5 Ferilin -6.8 Feselol -9.1 Rotundifolioside A -6.8 Cauferoside -8.4 Perovskone B -6.7 epi -Maslinic acid -9.1 Feselol -6.8 Perovskone B -8.3 Feselol -6.7 Lactucopicrin -8.8 Ferilin -6.7 Ferilin -8.3 Cauferoside -6.7 Multiorthoquinone -8.6 Hispaglabridina -6.6 Multiorthoquinone -8.2 12-demethylmulticaulin -6.6 Multicaulin -8.5 Gnidilatimonoein -6.5 Isoliquiritin -8.2 Multiorthoquinone -6.5 Hyperxanthone E -8.4 epi -Pinoresinol -6.5 Hispaglabridin A -8 Daucosterol -6.5 Liquiritigenin -8.3 Table 3 Top plant compounds according to the amount of interaction energy and their sources Compound Plant Part cauferoside Ferula gumosa Root feselol Ferula gumosa Root ferilin Ferula gumosa Root miquelianin Hypericum perforatum - rotundifoliosideA Bupleurum rotundifolium Fruit gnidilatimonoein Daphne mucronata Leaf epi-maslinic acid Prunella vulgaris Leaf and Stem multiorthoquinone Salvia multicaulis Root multicaulin Salvia multicaulis Root daucosterol Ferula gumosa Root 3.5. MD simulation analysis After 50 ns of simulation for each of the four docked compound-protein complexes, their structures were checked by RMSD and RMSF (Figur 3). As can be seen, all of the complexes show little fluctuations and changes. The INR-cauferoside complex shows a bit more changes than the others. The average RMSD values were 0.22 ± 0.03, 0.18 ± 0.009, and 0.4 ± 0.063 and 0.23 ± 0.024 for ANG-cauferoside, DPP4-cauferoside, INR-cauferoside and MAPK14-cauferoside, respectively. 3.6. Evaluation of cytotoxicity of Ferula gummosa extract via an MTT assay IC 50 was evaluated for the SW948 cell line exposed to plant extract. The IC 50 for leaf and root extracts were evaluated as 267.74 µg/ml and 127.75 µg/ml, respectively (Fig. 4 ). Comparison of SW948 survival using the MTT assay (Sigma-Aldrich Corp., UK) indicated that cell survival decreased significantly decreased compared to the control group over time as leaf and root extract rose. When evaluating the cytotoxic effects of compounds used in cancer treatment, it is crucial that the drug selectively inhibits cancer cell lines while minimally affecting normal cell lines. Therefore, the lower the effective concentration of the compound, the greater its potential for clinical application. 3.7. The gene expression analysis in mRNA The relative expression of the ANG, DPP4, INR, MAPK14, BRAF, and VIM genes was assessed using RT-qPCR in cells treated with leaf/root extract (Fig. 5 ). Treatment with leaf extract led to a significant reduction in the activity of all genes except ANG compared to the control group. In cells treated with root extract, the expression of the INR and BRAF genes decreased significantly, whereas the expression levels of other genes remained unchanged relative to the control (no significant change compared to the control). The most pronounced down-regulation was observed in INR mRNA levels after leaf extract. The plant root extract not only had no inhibitory effect on ANG, but this gene showed higher expression in the group treated with the extract. Furthermore, in this study, two genes, VIM and BRAF, were investigated because of their effect on the causing of cell cancer and because they act mostly downstream of the genes, to determine whether the decrease in the proteins expression of the studied proteins has a direct or indirect effect on cancer genes. RT-qPCR results showed that the effect of plant extracts partially inhibited the expression of these. 4. Discussion This study aimed to identify a potential native plant for the treatment of colorectal cancer using bioinformatics and in vitro methods. The study involved the investigation of compound and protein libraries, DT network analysis, molecular interaction analysis, cytotoxicity MD simulation analysis, assessment of plant extract, and gene expression analysis. We searched and identified 315 approved native medicinal plants. From these plants, 53 known compounds were found based on the information available in the ChEBI database. Furthermore, 14 target proteins were obtained using PharmMapper and colorectal cancer data. The construction of a drug-target network provided a holistic perspective, incorporating 47 compounds and 14 protein targets. Network analysis, considering node values and betweenness centrality, facilitated the prioritization of compounds and proteins for further investigation. Four protein targets (ANG, DPP4, INR and MAPK14) were selected based on network analysis. Molecular docking studies were conducted to estimate binding affinities between the selected compounds and protein targets. The top ten compounds exhibiting the highest binding affinity for each protein were identified. Notably, cauferoside, sourced from Ferula gummosa , displayed significant binding affinity to all four proteins. This observation prompted further investigation of the anti-colorectal cancer potential of Ferula gummosa . Molecular dynamics (MD) simulations were used to scrutinize the stability of compound-protein complexes over a 50 ns timeframe. RMSD and RMSF analyses revealed overall structural stability, with slight fluctuations in the INR-cauferoside complex. Average RMSD values provided a quantitative measure of stability across the complexes. The study progressed to in vitro investigations, evaluating the cytotoxicity of Ferula gummosa extracts using the MTT assay. The IC50 values for leaf and root extracts were determined, indicating a concentration-dependent decrease in cell survival. Importantly, the lower effective concentration of the compound is highlighted for its potential clinical application. Gene expression analysis by qRT-PCR further elucidated the molecular mechanisms underlying the observed cytotoxic effects. Treatment with Ferula gummosa leaf extract significantly down-regulated the expression of the ANG, DPP4, INR, and MAPK14 genes. In particular, INR mRNA levels exhibited the most pronounced reduction. The root extract showed different effects, with increased expression of ANG and minimal impact on other genes. Angiogenin (ANG) is a protein that plays a crucial role in angiogenesis, the process of forming new blood vessels. In the context of cancer, angiogenin has been extensively studied due to its involvement in tumour growth, invasion, and metastasis. Several studies have shown that angiogenin is overexpressed in various types of cancer, including breast, lung, and colorectal cancer. In addition, angiogenin has been shown to have angiogenesis-independent effects in cancer, such as promoting cancer cell survival and resistance to therapy. The findings highlight the importance of angiogenin as a potential therapeutic target and a biomarker for cancer diagnosis and treatment 28 . Dipeptidyl peptidase-4 (DPP4), also known as CD26, is an enzyme that plays a crucial role in various physiological processes, including immune regulation and glucose metabolism. In recent years, there has been a growing interest in understanding the involvement of DPP4 in cancer. Studies have revealed that DPP4 expression is dysregulated in several types of cancers, including breast, prostate, colorectal, and pancreatic cancer. DPP4 has been implicated in cancer progression and metastasis through its effects on tumor cell invasion, migration, and angiogenesis. Furthermore, DPP4 has been explored as a potential biomarker for the prognosis of cancer and as a target for therapeutic intervention 29 . The insulin receptor (INSR) is a cell surface receptor that plays a crucial role in mediating the effects of insulin. In recent years, there has been increasing evidence suggesting a link between INSR and cancer. Studies have shown that dysregulation of INSR signaling is associated with tumour growth, progression, and resistance to therapy in various types of cancer, including breast, colorectal, and lung cancer. Aberrant INSR activation can promote cancer cell survival, proliferation, and metastasis by stimulating downstream signaling pathways involved in cell growth and survival 30 . MAPK14, also known as p38 MAPK, is a member of the mitogen-activated protein kinase (MAPK) family that plays a crucial role in cellular signaling pathways involved in inflammation, stress responses, and cell proliferation. Over the years, there has been increasing evidence linking MAPK14 to cancer development and progression. Dysregulation of MAPK14 signaling has been observed in various cancer types, including breast, lung, colorectal, and pancreatic cancer. MAPK14 activation has been shown to promote tumor cell survival, proliferation, invasion, and metastasis. The different findings underscore the significance of MAPK14 in cancer and its potential as a therapeutic target 31 . The qRT-PCR results also showed partial inhibition of the the expression of VIM and BRAF genes by plant extracts. BRAF is a proto-oncogene that is usually involved in cancer cell signaling and VIM (vimentin) participates in cancer cell migration and cell adhesion structures. Vimentin is often associated with the epithelial-mesenchymal transition (EMT), a process in which cells lose their epithelial characteristics and gain mesenchymal properties. EMT is involved in cancer progression and metastasis 32 , 33 . 5. Conclusions In summary, this study utilised bioinformatics and in-vitro methods to identify a potential plant for the treatment of colorectal cancer. The results provide valuable insights into the potential of Ferula gummosa extract and its compounds for further investigation in the treatment of colorectal cancer. In conclusion, the study findings collectively suggest that Ferula gummosa , specifically its cauferoside compound, holds promise as a potential therapeutic agent against colorectal cancer. The approach, which integrates system biology, molecular docking, MD simulations, and in vitro analyses, strengthens the credibility of the results. Further studies, including in-vivo experiments and clinical trials, are warranted to validate and translate these findings into practical therapeutic applications, paving the way for the development of novel therapeutic interventions against colorectal cancer. Declarations Author Contributions: Conceptualization, A.Al., A.Sh., and A.As.; methodology and validation, A.Al., M.R., R.A., and S.G., formal analysis and investigation, A.Al. and S.A.; resources, A.Al., A.Sh., and A.As.; data curation, R.A., and A.As.; writing—original draft preparation, A.Al., S.G., and S.A.; writing—review and editing, M.R. and A.Sh.; supervision, S.A. and A.Sz.; project administration, A.Al and S.A.; All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by a research grant from the Hamadan University of Medical Sciences, Hamadan, Iran, grant number 14010206881 as well as analyses in Wrocław University of Environmental and Life Sciences. The APC is financed by Wrocław University of Environmental and Life Sciences. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Acknowledgments : We appreciate the Research Center for Molecular Medicine, Hamadan University of Medical Sciences. We also appreciate the research and ethics committee (IR.UMSHA.REC.1401.079) of Hamadan University of Medical Sciences. We appreciate the Polish National Agency for Academic Exchange (NAWA) Ulam 2021 program under grant number BPN.ULM.2021.1.00250.U.00001 for supporting analytical studies. Data Availability Statement: The data presented in this study are available upon request from the corresponding author. Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. References Alves Martins, B. A. et al. Biomarkers in Colorectal Cancer: The Role of Translational Proteomics Research. Front Oncol 9, 1284, doi: 10.3389/fonc.2019.01284 (2019). Bellio, H., Fumet, J. D. & Ghiringhelli, F. Targeting BRAF and RAS in Colorectal Cancer. Cancers (Basel) 13, doi: 10.3390/cancers13092201 (2021). Janani, B. et al. EGFR-Based Targeted Therapy for Colorectal Cancer-Promises and Challenges. Vaccines (Basel) 10, doi: 10.3390/vaccines10040499 (2022). Therkildsen, C., Bergmann, T. K., Henrichsen-Schnack, T., Ladelund, S. & Nilbert, M. The predictive value of KRAS, NRAS, BRAF, PIK3CA and PTEN for anti-EGFR treatment in metastatic colorectal cancer: A systematic review and meta-analysis. Acta Oncol 53, 852–864, doi: 10.3109/0284186x.2014.895036 (2014). Vacante, M., Borzì, A. M., Basile, F. & Biondi, A. Biomarkers in colorectal cancer: Current clinical utility and future perspectives. World J Clin Cases 6, 869–881, doi: 10.12998/wjcc.v6.i15.869 (2018). Mia, M. A. R. et al. The efficacy of natural bioactive compounds against prostate cancer: Molecular targets and synergistic activities. Phytother Res, doi: 10.1002/ptr.8017 (2023). Bhullar, K. S. et al. Kinase-targeted cancer therapies: progress, challenges and future directions. Molecular Cancer 17, 48, doi: 10.1186/s12943-018-0804-2 (2018). Wu, D., Rice, C. M. & Wang, X. Cancer bioinformatics: a new approach to systems clinical medicine. BMC Bioinformatics 13, 71, doi: 10.1186/1471-2105-13-71 (2012). Xia, X. Bioinformatics and Drug Discovery. Curr Top Med Chem 17, 1709–1726, doi: 10.2174/1568026617666161116143440 (2017). Li, K., Du, Y., Li, L. & Wei, D. Q. Bioinformatics Approaches for Anti-cancer Drug Discovery. Curr Drug Targets 21, 3–17, doi: 10.2174/1389450120666190923162203 (2020). Hastings, J. et al. ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Res 44, D1214-1219, doi: 10.1093/nar/gkv1031 (2016). Kim, S. et al. PubChem 2023 update. Nucleic Acids Research 51, D1373-D1380, doi: 10.1093/nar/gkac956 (2023). Assenov, Y., Ramírez, F., Schelhorn, S. E., Lengauer, T. & Albrecht, M. Computing topological parameters of biological networks. Bioinformatics 24, 282–284, doi: 10.1093/bioinformatics/btm554 (2008). Alibakhshi, A., Malekzadeh, R., Hosseini, S. A. & Yaghoobi, H. Investigation of the therapeutic role of native plant compounds against colorectal cancer based on system biology and virtual screening. Scientific Reports 13, 11451, doi: 10.1038/s41598-023-38134-5 (2023). Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Research 28, 235–242, doi: 10.1093/nar/28.1.235 (2000). The UniProt, C. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Research 51, D523-D531, doi: 10.1093/nar/gkac1052 (2023). O'Boyle, N. M. et al. Open Babel: An open chemical toolbox. Journal of Cheminformatics 3, 33, doi: 10.1186/1758-2946-3-33 (2011). Forli, S. et al. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nature Protocols 11, 905–919, doi: 10.1038/nprot.2016.051 (2016). Pettersen, E. F. et al. UCSF Chimera–a visualization system for exploratory research and analysis. J Comput Chem 25, 1605–1612, doi: 10.1002/jcc.20084 (2004). Dallakyan, S. & Olson, A. J. Small-molecule library screening by docking with PyRx. Methods Mol Biol 1263, 243–250, doi: 10.1007/978-1-4939-2269-7_19 (2015). Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling 61, 3891–3898, doi: 10.1021/acs.jcim.1c00203 (2021). Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25, doi: https://doi.org/10.1016/j.softx.2015.06.001 (2015). Schmid, N. et al. Definition and testing of the GROMOS force-field versions 54A7 and 54B7. European Biophysics Journal 40, 843–856, doi: 10.1007/s00249-011-0700-9 (2011). Shojaeian, A., Mehri-Ghahfarrokhi, A. & Banitalebi-Dehkordi, M. Increased in vitro migration of human umbilical cord mesenchymal stem cells toward acellular foreskin treated with bacterial derivatives of monophosphoryl lipid A or supernatant of Lactobacillus acidophilus. Hum Cell 33, 10–22, doi: 10.1007/s13577-019-00308-7 (2020). Shojaeian, A., Mehri-Ghahfarrokhi, A. & Banitalebi-Dehkordi, M. Migration gene expression of human umbilical cord mesenchymal stem cells: a comparison between monophosphoryl lipid A and supernatant of Lactobacillus acidophilus. International Journal of Molecular and Cellular Medicine 8, 154 (2019). Saffari-Chaleshtori, J., Shojaeian, A., Heidarian, E. & Shafiee, S. M. Inhibitory Effects of Bilirubin on Colonization and Migration of A431 and SK-MEL-3 Skin Cancer Cells Compared with Human Dermal Fibroblasts (HDF). Cancer Invest 39, 721–733, doi: 10.1080/07357907.2021.1943428 (2021). Shojaeian, A., Mehri-Ghahfarrokhi, A. & Banitalebi-Dehkordi, M. Monophosphoryl Lipid A and Retinoic Acid Combinations Increased Germ Cell Differentiation Markers Expression in Human Umbilical Cord-derived Mesenchymal Stromal Cells in an In vitro Ovine Acellular Testis Scaffold. Int J Mol Cell Med 9, 288–296, doi: 10.22088/ijmcm.bums.9.4.288 (2020). Wang, R. et al. Gemcitabine resistance is associated with epithelial-mesenchymal transition and induction of HIF-1α in pancreatic cancer cells. Curr Cancer Drug Targets 14, 407–417, doi: 10.2174/1568009614666140226114015 (2014). Ng, L. et al. Repurposing DPP-4 Inhibitors for Colorectal Cancer: A Retrospective and Single Center Study. Cancers (Basel) 13, doi: 10.3390/cancers13143588 (2021). Arcaro, A. Targeting the insulin-like growth factor-1 receptor in human cancer. Front Pharmacol 4, 30, doi: 10.3389/fphar.2013.00030 (2013). Cuadrado, A. & Nebreda, A. R. Mechanisms and functions of p38 MAPK signalling. Biochem J 429, 403–417, doi: 10.1042/bj20100323 (2010). Orlandi, A. et al. BRAF in metastatic colorectal cancer: the future starts now. Pharmacogenomics 16, 2069–2081, doi: 10.2217/pgs.15.140 (2015). Strouhalova, K. et al. Vimentin Intermediate Filaments as Potential Target for Cancer Treatment. Cancers 12 (2020). Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile3d1.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4443245","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":311449582,"identity":"67cd34b4-8f26-4861-8a4b-35e1a07479d6","order_by":0,"name":"Abbas Alibakhshi","email":"","orcid":"","institution":"Hamadan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abbas","middleName":"","lastName":"Alibakhshi","suffix":""},{"id":311449583,"identity":"24c8e6ae-0f00-4826-a0cd-27e418373e2e","order_by":1,"name":"Shima Gharibi","email":"","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shima","middleName":"","lastName":"Gharibi","suffix":""},{"id":311449584,"identity":"fdcb6624-8d85-4dda-a04b-a750eaeec4d4","order_by":2,"name":"Ali Shojaeian","email":"","orcid":"","institution":"Hamadan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Shojaeian","suffix":""},{"id":311449585,"identity":"b0277a9d-07f9-4c92-b4f4-33663432ca0a","order_by":3,"name":"Atefeh Asgari","email":"","orcid":"","institution":"Hamadan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Atefeh","middleName":"","lastName":"Asgari","suffix":""},{"id":311449586,"identity":"86c6154a-e1ce-412d-b772-c52a67f9271b","order_by":4,"name":"Razieh Amini","email":"","orcid":"","institution":"Hamadan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Razieh","middleName":"","lastName":"Amini","suffix":""},{"id":311449587,"identity":"14f770be-ca0d-4823-84d7-f253aeb1f772","order_by":5,"name":"Mehdi Rahimmalek","email":"","orcid":"","institution":"Isfahan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Rahimmalek","suffix":""},{"id":311449588,"identity":"c88cf4e9-6343-4225-940c-e68b959c699e","order_by":6,"name":"Shahrzad Ahangarzadeh","email":"","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shahrzad","middleName":"","lastName":"Ahangarzadeh","suffix":""},{"id":311449589,"identity":"84c6a8b3-cf1b-46f4-90cb-683283737225","order_by":7,"name":"Antoni Szumny","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYNACAxijAkrzADEfTuXMyFrOIGlhw6sFBhjbiNDCP7v/4OOCAgZ7+RnZaY955x3OM7h9gPHB2zaGPFxaJO4cZjaeYcCQ2Dgjd7sx77bDxQbnEpgN57YxFON02I1kNmkeA4YEZoncbdJALYnbzjCwSfO2MSS24dAhD9VizwbWMgeshf03Pi0GUC2MPWAtDRBbmPFpMbyRbGzMYyCROIPn7TbJOcfSi+3PMDZLzjkngdMvcjcSHz7m+WNjL9+eu03iTY11nmQP88EPb8ps8vhxeR8CJMAkEzA6EoCx0wASScCvAwoYf4C1QABxWkbBKBgFo2AkAAAmOE7IwRDFuAAAAABJRU5ErkJggg==","orcid":"","institution":"Wroclaw University of Life Sciences","correspondingAuthor":true,"prefix":"","firstName":"Antoni","middleName":"","lastName":"Szumny","suffix":""}],"badges":[],"createdAt":"2024-05-19 06:54:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4443245/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4443245/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57903421,"identity":"4176173c-b423-46d0-98be-6e7ecbcc0697","added_by":"auto","created_at":"2024-06-07 09:16:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":707611,"visible":true,"origin":"","legend":"\u003cp\u003eThe drug-target (DT) network. A. The bipartite DT network. B. The circle layout of the DT network. The purple circular nodes represent the target proteins, and the green triangular nodes represent the plant compounds. The size of the nodes is based on the number of their degrees.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4443245/v1/f5465f4bc2a39c857b0f48a9.png"},{"id":57903424,"identity":"830690f8-bd1b-48cd-8063-0abc19a00182","added_by":"auto","created_at":"2024-06-07 09:16:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":830811,"visible":true,"origin":"","legend":"\u003cp\u003e3D and 2D structures of complexes formed between 4 important protein targets and an active plant compound, cauferoside. A. ANG-cauferoside. B. DPP4-cauferoside. C. INR-cauferoside. D. MAPK14-cauferoside\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4443245/v1/b47562feaeb676bcd0be839f.png"},{"id":57904040,"identity":"f299bbca-2a9d-4a36-826f-c1f223cd9b37","added_by":"auto","created_at":"2024-06-07 09:24:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":418892,"visible":true,"origin":"","legend":"\u003cp\u003eA. Root mean square deviation of the complexes (RMSD) and B. Root-mean-square fluctuation (RMSF) plots of proteins in complex with cauferoside after MD simulation.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4443245/v1/a479f8fec6de4c40ff62ce8f.png"},{"id":57903419,"identity":"22dcd891-c081-4613-9d96-8dbe698004cc","added_by":"auto","created_at":"2024-06-07 09:16:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":867836,"visible":true,"origin":"","legend":"\u003cp\u003eMTT assay graph as absorbance of the living cells versus concentration of plant extract after 24h.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4443245/v1/8c4a793a26615e9c778e04bb.png"},{"id":57903420,"identity":"cd1f148a-f1e2-436a-8f3f-cc6894ec085e","added_by":"auto","created_at":"2024-06-07 09:16:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":178496,"visible":true,"origin":"","legend":"\u003cp\u003eQuantitative real-time PCR analysis for mRNA expression of genes after treatment. A. ANG. B. DPP4. C. INSR. D. MAPK14. E. BRAF. F. VIM.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4443245/v1/14c82955d4e7fa9b9e2f7abf.png"},{"id":69814364,"identity":"f76d50a2-5a75-435b-b44c-46724fa186d4","added_by":"auto","created_at":"2024-11-25 13:09:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3722692,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4443245/v1/6631a2fe-4f74-4d92-9126-dc448e069a59.pdf"},{"id":57903425,"identity":"6ef8cf24-3fc2-4511-80cf-a1a35d94ff4d","added_by":"auto","created_at":"2024-06-07 09:16:24","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22298,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile3d1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4443245/v1/db8f99209dbbb547c3f9d673.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Therapeutic Potential of Native Plant Compounds: Unveiling the Therapeutic Potential of Ferula gummosa in Colorectal Cancer through Bioinformatics and Experimental Validation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eColorectal cancer (CRC) is a formidable global health challenge, accounting for a significant portion of cancer-related morbidity and mortality. CRC is a complex and heterogeneous disease, and the integration of molecular biomarkers has become instrumental in the design of treatment strategies. Molecular profiling has advanced our understanding of CRC, allowing the identification of specific biomarkers that play crucial roles in prognosis, treatment response, and the development of targeted therapies\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. As understanding of the molecular intricacies governing this malignancy evolves, researchers are increasingly exploring novel avenues for therapeutic intervention. Studies show that mutations in the RAS and BRAF genes \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, high EGFR expression levels of EGFR \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, mutations associated with the activation of the PI3K-AKT-mTOR pathway \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, and some other molecular factors are critical contributors and determinants of the progression of CRC.\u003c/p\u003e \u003cp\u003eThe incorporation of molecular biomarkers in the management of colorectal cancer signifies a fundamental alteration towards precision medicine. Understanding the genetic and molecular characteristics of specific tumours can help healthcare professionals make knowledgeable choices, enhancing therapeutic results, and reducing detrimental consequences. The progressions in this domain offer hope for an additional enhancement in the treatment of colorectal cancer by disclosing fresh objectives and even cultivating a flexible environment for personalised methodologies in CRC treatment \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Interfering with specific molecules that play a crucial role in cancer cell growth and survival could inhibit the growth and spread of cancer cells. Natural plant compounds have been the subject of extensive research for their potential role in cancer prevention and treatment. Many plants produce bioactive compounds with diverse chemical structures that exhibit anticancer properties. They can interact with specific molecules involved in cancer development and progression \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These compounds may modulate epigenetic processes, inhibit cell proliferation, induce apoptosis, and regulate signaling pathways associated with cancer development. Some plant compounds such as luteolin, resveratrol, and quercetin target specific kinases and cell cycle regulators that play crucial roles in cancer cell growth and survival. Myricetin and indole alkaloids can also target molecules involved in apoptosis. Other studies show that specific molecular targets for plant compounds can vary depending on the compound and the type of cancer studied \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Therefore, to identify an appropriate target and intelligent compound with significant capacity to impede the proliferation and development of cancer, it becomes essential to employ suitable and occasionally novel methodologies to effectively determine the most optimal testing approach in the laboratory.\u003c/p\u003e \u003cp\u003eBioinformatics assumes a pivotal function in the realm of cancer therapy through the utilisation of computational methodologies and examination of extensive biological data sets to improve our comprehension of cancer and formulate more effective treatment methodologies. Cancer bioinformatics is a rapidly evolving field that facilitates the prediction of therapeutic responses to various treatment alternatives. By analysing genomic data, bioinformatics tools can identify genetic alterations and molecular signatures that can influence treatment outcomes. Furthermore, these tools can aid in therapy selection by analysing genomic data and identifying potential therapeutic vulnerabilities in cancer cells. They can prioritise therapeutic targets and guide the selection of appropriate treatment strategies \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Bioinformatics techniques also enable virtual screening and docking studies, where large databases of chemical compounds are screened against target proteins to identify potential drug candidates. These methods help prioritise compounds for further experimental testing, saving time and resources. There are currently approaches that can be used to identify new therapeutic uses for existing drugs. Through the examination of gene expression profiles or protein interaction networks, these methodologies have the potential to propose alternative applications for drugs or natural compounds that have already been approved or are currently being used in clinical settings \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBioinformatics analyses can encounter challenges in accurately predicting interactions between plant compounds and molecular targets, necessitating robust validation through experimental assays. Therefore, this research aims to find the most effective of native plant compounds and potential therapeutic targets specific to colorectal cancer through a meticulous fusion of bioinformatics methodologies and experimental tests. By leveraging cutting-edge bioinformatics tools, we seek to identify promising candidates within the vast array of plant compounds, subsequently subjecting them to experimental scrutiny. This dual methodology seeks to bridge the gap between computational predictions and empirical validation, offering a smart understanding of the potential benefits and challenges associated with native plant extractions in the context of colorectal cancer treatment.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data Recovery of Compounds\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIdentification of medicinal plants native to Iran was done by searching the Internet and reviewing local articles and books, and their names were written in an Excel file. By downloading all the chemical compounds available in the ChEBI database \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and matching their sources with the file of native plant names, the compounds that were present in the extracted indigenous medicinal plants were obtained. In addition to their names, the three-dimensional structures of the compounds were recovered as SDF (structural data file) from the PubChem database \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Identification of Potential Target Candidates for Compounds\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe open web server PharmMapper was used to identify possible target candidates of extracted compounds from native plants. PharmMapper is a backup of the pharmacophore database extracted from all the targets in four databases related to drugs named TargetBank, DrugBank, BindingDB, and PDTD. Human protein targets were selected for the target set option, and for the option of maximum number of reserved matching targets, the number of 300 was determined. The output of each compound was saved as an Excel file, and then after selecting the top 20 targets for each compound, all possible targets were collected in one file. After sharing and removing duplicates, a final file of targets was prepared for the next steps.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Retrieval of colorectal cancer targets\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWith the help of the NCBI database and Gene resource (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), all differentially expressed genes in colorectal cancer were recovered. Meanwhile, for this study, the genes that had a significant increase in expression are selected. Next, the output of this step was matched with the output obtained from the previous step to select common and identical names. In this way, possible targets for the compounds in colorectal cancer are obtained.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Construction of drug target (DT) network\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe drug-target network produces useful information for the analysis of relationships between drugs, targets, and diseases, as well as for the discovery of new drug targets. In this study, a drug-target network was constructed by Cytoscape v3.6.0 software. On the basis of this, an input file with possible colorectal cancer targets for each compound was prepared and given to the software. After building the network, the NetworkAnalyzer plugin \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e was used to analyze the quantitative characteristics of the indirect network. Then, the degree and centrality parameters were used to select the nodes \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Then, the main targets in terms of gene expression were checked by a literature review to select genes with a significant increase in expression compared to normal tissue.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Retrieval of the three-dimensional structure of proteins\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, four proteins were finally selected as potential targets for colorectal cancer to retrieve their three-dimensional structure for further study. Using the database PDB (Protein Data Bank) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org/PDB\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org/PDB\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, the structures of the proteins ANG (PDB ID: 1B1I), DPP4 (PDB ID: 3SWW), INR (PDB ID: 5KQV) and MAPK14 (PDB ID: 5XYY) were recovered. The information and choosing the correct structure was chosen for each protein was done with the help of the UniProt database \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The structures were saved as PDB files.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Molecular Docking\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe SDF structures of selected compounds (47 compounds) were converted to mol2 files using Open Babel software \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and then converted to PDBQT using AutoDockTools-1.5.7 software, Raccoon programme \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. For the four protein structures in PDB format, the first water molecules and ligands available in the structure were removed using Chimera 1.16 software \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and then converted to PDBQT using Autodock Tools software. Next, all the structures were given to PyRx software \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Using the Autodock Vina programme \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e hosted in this software, molecular docking was performed for each of the target proteins. Depending on the type of target protein, the binding site position of each protein was considered as the target box. Nine modes were selected for each compound, and the results were sorted according to the interaction energy. The docked protein-compound complexes were then analysed by PyMOL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pymol.org/2/\u003c/span\u003e\u003cspan address=\"https://pymol.org/2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Discovery Studio software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.3ds.com/discovery-studio-visualizer-download\u003c/span\u003e\u003cspan address=\"https://discover.3ds.com/discovery-studio-visualizer-download\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in terms of 2D and 3D structures and amino acids and molecules involved in binding.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Molecular dynamics (MD) simulations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe dynamics of four docked complexes consisting of the ANG, DPP4, INR, and MAPK14 proteins and cauferoside compound was simulated by GROMACS 5.4.1 software \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In this method, 54A7 force field \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e was used on molecules surrounded by SPC water. The system was neutralized by Na\u0026thinsp;+\u0026thinsp;ions and then energy was minimized for the relaxation of internal constraints. Equilibration in the NVT and NPT ensembles was done under positional restraints for 100 ps. Finally, the MD production run was performed for 50 ns with a time step of 3 fs. The trajectory was then analyzed by Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) to characterize the dynamics of the ligand-protein.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Plant extract preparation\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.8.1. Plant Materials\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe leaves and roots \u003cem\u003eof F. gummosa\u003c/em\u003e were collected in Naghan, Chahar Mahal-e Bakhtiari (31◦980 N and 50◦680 N) at an altitude of 2026 meters with a herbarium number of 13342. The samples were collected and identified by Prof. Mehdi Rahimmalek using Flora Iranica (Rechinger, 1963), and the samples were deposited in the herbarium of Isfahan University of Technology, Isfahan, Iran. The collection of the samples were permitted from research institute of forest and rangelands and complies with local and national guidelines and legislation. The collected plants were dried at 25 \u0026deg; C for three days under shade conditions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.9. HPLC Analysis\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003eF. gummosa\u003c/em\u003e aerial parts and root were used for polyphenolic compound determination according to the standards (gallic acid, caffeic acid, ferulic acid, \u003cem\u003ep\u003c/em\u003e-coumaric acid, rutin, rosmarinic aci, chlorogenic acid and the other available standards (Phytolab, Germany, 98% purity). 15 g of dried plant materials were mixed with 300 ml of methanol and shaken at 90 rpm for 24 hours. The filtered extract was concentrated and dried using a rotary evaporator under a vacuum at 40\u0026deg;C. The extract was dissolved in HPLC solvent A (1 mL), filtered (0.22 \u0026micro;m disk), and 20 \u0026micro;L was injected into an Agilent 1090 system with a detection range of 260 and 350 nm. In this experiment, a 250 \u0026times; 4.6 mm, 5 \u0026micro;m, symmetry C18 column (Waters Crop., Milford, MA, USA) was applied. The mobile phase included formic acid (99.9:0.1) as a solution (A) and acetonitrile/formic acid (99.9:0.1) as a solution (B) with gradient elution at 25 ◦C and a flow rate of 0.8 mL min\u0026thinsp;\u0026minus;\u0026thinsp;1. The gradient programme started from A: B (90:10) for 1 min, followed by 10\u0026ndash;26% B for 40 min, 26\u0026ndash;65% B for 30 min, and finally 65\u0026ndash;100% B for 5 min followed by equilibration with 0\u0026ndash;90% A for 4 min. Polyphenolic compounds were determined by comparing UV spectra and retention times with pure standards, and the amount was reported in mg per 100 g of dry sample weight.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Cell culture\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCells (viz SW948 cell line, Pasteur Institute) were cultured on Roswell Park Memorial Institute (RPMI) 1640 medium, added by 10% v/v foetal bovine serum (FBS, Gibco, Parsley, UK). The medium was also enriched with streptomycin (100 \u0026micro;g/ml) and penicillin (100 U/ml) (Gibco, Paisley, UK). Cells were maintained in a humidified atmosphere containing 5% carbon dioxide (CO2) at a temperature of 37 \u0026deg; C. They were housed in flasks with a surface area of 25 cm2. To keep the cells in their exponential growth phase, they were passaged twice a week \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.11. Cytotoxicity assay\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCell viability was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay, which is based on the ability of the mitochondrial succinate dehydrogenase to convert the tetrazolium salt into insoluble violet crystals. This reaction is indicative of the number of viable cells. SW948 cells were initially placed in a 96-well plate at a density of 1 \u0026times; 104 cells per well and left to incubate overnight. A serial concentration from 0 (control group) to 500 \u0026micro;g/ml of both leaf and root extracts was prepared and added to the wells in triplicate. The toxicity test was then performed at 24-hour intervals. Subsequently, a 5 mg/ml MTT solution was prepared and 60 \u0026micro;l of MTT (0.5 mg/ml) was added to each well, which was then incubated at 37 \u0026deg; C for 4 hours. To dissolve the formazan deposits, 100 \u0026micro;l of dimethylsulfoxide (DMSO) (Merck, Germany) was added to each well. After a 30-minute wait, absorbance was measured at 570 nm using an enzyme-linked immunosorbent assay (ELISA) reader (Stat-Fax-2100, USA)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The growth inhibitory effects of \u003cem\u003eFerula gummosa\u003c/em\u003e (leaf and root extract) were evaluated by determining the IC50 values.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Cell treatment\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThree flasks were considered for three groups for root extract, leaf extract, and control. After the confluence of the cells reached the desired level, 250 ug/ml of leaf extract and 125 ug/ml of root extract were added separately to two flasks. Then, after 24 hours, the cells were harvested to be used for the next step.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.13. RNA isolation, cDNA synthesis, and RT‑qPCR\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTotal RNA was isolated from SW948 using RNX-Plus Reagent (Sinaclon, Tehran, Iran) according to the manufacturer\u0026rsquo;s protocol and measured by NanoDrop\u0026trade; 2000/2000c Spectrophotometer (Thermo Fisher Scientific, MA). The 260/280 and 260/230 values were higher than 1.9. Furthermore, 1 \u0026micro;g total RNA of each sample was synthesized using a cDNA Synthesis Kit (Yekta-Tajhiz-Azma [YTA], Tehran, Iran) and transferred into the qRT-PCR (quantitative real‑time reverse transcription polymerase chain reaction). The transcription levels of ANG, DPP4, INSR, MAPK14, BRAF, and VIM were also evaluated through a SYBR \u0026reg; Green PCR Master Mix (Yekta-Tajhiz-Azma [YTA], Tehran, Iran). Specific cycling parameters in the qRT-PCR included an initial denaturation step at 95\u0026deg;C for 2 min, denaturation at 95\u0026deg;C for 10 s, annealing at 61\u0026deg;C for 20 s, followed by an extension step at 72\u0026deg;C for 25 s. The number of cycles was optimized at 40. The primer sequences used are represented as follows: ANG, (F) TAGCAGCTCTGGTTCCGTTT and (R) CTCCTGGGTGTGTTTCCTGT; DPP4, (F) CTGCTTGCTCCAATTTAGCC and (R) ACACTTGCTAGAGCCCAGGA; INSR, (F) GAAGCTCTGTGCCAAGAACC and (R) CCGTTGCTACAAGGGTCATT; MAPK14, (F) CCAGAGGCAGTTTTCTCCTG and (R) TGCTCACCCACATGTTTTGT; BRAF, (F) CTTCATGAAGACCTCCAGT and (R) CATCCACAAAATGGATCCAG; VIM, (F) GAAGAGAACTTTGCCGTTGAAG and (R) TGAGCAGGTCTTGGTATTCAC; GAPDH, (F) GAG TCC ACT GGC GTC TTC AC and (R) ATG ACG AAC ATG GGG GCA. In addition, the transcription level of GAPDH was used as an endogenous control. The 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCt method was also utilized to determine the relative expression of each sample at the time points analyzed. The RT-qPCRs were performed using Roche LightCycler equipment (Roche LightCycler \u0026reg; 480, Germany) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.14. Data Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe RT-qPCR data were statistically analyzed using the GraphPad Prism software (version 8) (GraphPad Software, CA, USA). A one-way analysis of variance (ANOVA) and Tukey\u0026rsquo;s post hoc test were used to compare the differences between the experimental groups and the controls. Any p-values less than 0.05 were deemed statistically significant \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Investigation of Compound and Protein Libraries\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhen searching the sources, 315 approved native medicinal plants were found. According to the number of native medicinal plants and also based on the information available in the ChEBI database, 53 known compounds were found in these plants. The names of medicinal plants and compounds are given in the Supplementary file. Furthermore, after sharing the final targets obtained of PharmMapper and those from colorectal cancer, 14 target proteins were obtained. The names of medicinal plants, compounds, and proteins are given in the Supplementary file.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2. \u003cem\u003eHPLC Analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe results of the HPLC analyses revealed the presence of some major phenolic compounds (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The roots and leaves showed a different pattern of phenolic accumulation. The phenolic compound was much higher in the leaves of \u003cem\u003eF. gummosa\u003c/em\u003e compared to their leaves. The most abundant phenolic acids in leaves were gallic acid and rosmarinic acid, while in the roots gallic acid, \u003cem\u003ep\u003c/em\u003e-coumaric acid and chlorogenic acid were the most frequent ones (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). With regard to flavonoids, rutin revealed the highest amount. Interestingly, the rutin had a higher amount in the roots compared to the leaves (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMajor phenolic and flavonoid compounds of \u003cem\u003eFerula gummosa\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRT (Retention time, min. )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoot (mg/100g DW))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeaf (mg/100g DW))\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGallic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e25.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorogenic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaffeic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Coumaric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRutin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerrulic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRosmarinic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003csup\u003e*\u003c/sup\u003eThe results are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3. DT network analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA drug-target network with 47 compounds and 14 protein targets was created that included 65 nodes and 144 edges. Using NetworkAnalyser, the number of node values and betweenness centrality are determined. Based on this, the results of the analysis were sorted based on both nodes and betweenness centrality. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the bipartite network for drug-target based on degrees, where the size of nodes is depicted according to the number of degrees. Also, a circle layout of the DT network is made based on betweenness centrality. Compounds showed degrees in the range of 6 to 1 and proteins 29 to 1. Based on the analysis of this network and the results of the literature review, four protein targets were selected for the molecular docking step. The output of the network based on degree and betweenness centrality is given in the Supplementary file.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Molecular Interaction Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo estimate the binding affinity of the compounds to the protein targets, molecular docking was performed and then results were obtained as a file representing the binding affinity as the interaction energy (kcal/mol). The results of the ten compound molecules that showed the highest binding affinity to each protein are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The protein-ligand docked structures were studied in terms of 3D and 2D models by Discovery Studio software so that the involved amino acids and the manner of their involvement were shown. Therefore, it can be determined whether the compound can effectively occupy the binding site of the protein target and prevent its activity. Based on the amount of interaction energy and the type of interactions, finally 10 compound molecules were selected for further study and their source (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The top three molecules in this list have been observed in the \u003cem\u003eFerula gummosa\u003c/em\u003e plant. Therefore, this plant was selected for investigation in colorectal cancer cells in the laboratory. Additionally, four protein targets including angiogenin (ANG), dipeptidyl peptidase 4 (DPP4), insulin receptor (INR), mitogen-activated protein kinase 14 (MAPK14) were selected for investigation in molecular MD and laboratory investigations. Due to the structural similarity of the first 3 compounds, only one of them (cauferoside) was selected for analysis in molecular MD. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the 3D and 2D structures of the interaction of cauferoside with the 4 mentioned proteins.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe docking binding energy and calculated affinity of the highest scores for 4 target proteins and plant compounds\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinding Affinity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDPP4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinding Affinity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBinding Affinity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMAPK14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBinding Affinity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCauferoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eepi\u003c/em\u003e-maslinicacid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRotundifolioside A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFerilin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaxanthonin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaucosterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGnidilatimonoein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eGumosin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiquelianin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRotundifolioside A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eepi\u003c/em\u003e-Naslinic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eCauferoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactucopicrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeselol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFerilin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eFeselol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotundifolioside A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCauferoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePerovskone B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cem\u003eepi\u003c/em\u003e-Maslinic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeselol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerovskone B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFeselol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eLactucopicrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerilin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFerilin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCauferoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMultiorthoquinone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispaglabridina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultiorthoquinone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12-demethylmulticaulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMulticaulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnidilatimonoein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsoliquiritin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultiorthoquinone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eHyperxanthone E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eepi\u003c/em\u003e-Pinoresinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHispaglabridin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDaucosterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eLiquiritigenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop plant compounds according to the amount of interaction energy and their sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePart\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecauferoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFerula gumosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efeselol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFerula gumosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eferilin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFerula gumosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiquelianin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypericum perforatum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erotundifoliosideA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBupleurum rotundifolium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFruit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egnidilatimonoein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaphne mucronata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeaf\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eepi-maslinic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrunella vulgaris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeaf and Stem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emultiorthoquinone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalvia multicaulis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emulticaulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalvia multicaulis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edaucosterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFerula gumosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5. MD simulation analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAfter 50 ns of simulation for each of the four docked compound-protein complexes, their structures were checked by RMSD and RMSF (Figur 3). As can be seen, all of the complexes show little fluctuations and changes. The INR-cauferoside complex shows a bit more changes than the others. The average RMSD values were 0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03, 0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009, and 0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063 and 0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024 for ANG-cauferoside, DPP4-cauferoside, INR-cauferoside and MAPK14-cauferoside, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Evaluation of cytotoxicity of Ferula gummosa extract via an MTT assay\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIC\u003csub\u003e50\u003c/sub\u003e was evaluated for the SW948 cell line exposed to plant extract. The IC\u003csub\u003e50\u003c/sub\u003e for leaf and root extracts were evaluated as 267.74 \u0026micro;g/ml and 127.75 \u0026micro;g/ml, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Comparison of SW948 survival using the MTT assay (Sigma-Aldrich Corp., UK) indicated that cell survival decreased significantly decreased compared to the control group over time as leaf and root extract rose. When evaluating the cytotoxic effects of compounds used in cancer treatment, it is crucial that the drug selectively inhibits cancer cell lines while minimally affecting normal cell lines. Therefore, the lower the effective concentration of the compound, the greater its potential for clinical application.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.7. The gene expression analysis in mRNA\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe relative expression of the ANG, DPP4, INR, MAPK14, BRAF, and VIM genes was assessed using RT-qPCR in cells treated with leaf/root extract (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Treatment with leaf extract led to a significant reduction in the activity of all genes except ANG compared to the control group. In cells treated with root extract, the expression of the INR and BRAF genes decreased significantly, whereas the expression levels of other genes remained unchanged relative to the control (no significant change compared to the control). The most pronounced down-regulation was observed in INR mRNA levels after leaf extract. The plant root extract not only had no inhibitory effect on ANG, but this gene showed higher expression in the group treated with the extract. Furthermore, in this study, two genes, VIM and BRAF, were investigated because of their effect on the causing of cell cancer and because they act mostly downstream of the genes, to determine whether the decrease in the proteins expression of the studied proteins has a direct or indirect effect on cancer genes. RT-qPCR results showed that the effect of plant extracts partially inhibited the expression of these.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study aimed to identify a potential native plant for the treatment of colorectal cancer using bioinformatics and in vitro methods. The study involved the investigation of compound and protein libraries, DT network analysis, molecular interaction analysis, cytotoxicity MD simulation analysis, assessment of plant extract, and gene expression analysis. We searched and identified 315 approved native medicinal plants. From these plants, 53 known compounds were found based on the information available in the ChEBI database. Furthermore, 14 target proteins were obtained using PharmMapper and colorectal cancer data.\u003c/p\u003e \u003cp\u003eThe construction of a drug-target network provided a holistic perspective, incorporating 47 compounds and 14 protein targets. Network analysis, considering node values and betweenness centrality, facilitated the prioritization of compounds and proteins for further investigation. Four protein targets (ANG, DPP4, INR and MAPK14) were selected based on network analysis. Molecular docking studies were conducted to estimate binding affinities between the selected compounds and protein targets. The top ten compounds exhibiting the highest binding affinity for each protein were identified. Notably, cauferoside, sourced from \u003cem\u003eFerula gummosa\u003c/em\u003e, displayed significant binding affinity to all four proteins. This observation prompted further investigation of the anti-colorectal cancer potential of \u003cem\u003eFerula gummosa\u003c/em\u003e. Molecular dynamics (MD) simulations were used to scrutinize the stability of compound-protein complexes over a 50 ns timeframe. RMSD and RMSF analyses revealed overall structural stability, with slight fluctuations in the INR-cauferoside complex. Average RMSD values provided a quantitative measure of stability across the complexes.\u003c/p\u003e \u003cp\u003eThe study progressed to in vitro investigations, evaluating the cytotoxicity of \u003cem\u003eFerula gummosa\u003c/em\u003e extracts using the MTT assay. The IC50 values for leaf and root extracts were determined, indicating a concentration-dependent decrease in cell survival. Importantly, the lower effective concentration of the compound is highlighted for its potential clinical application. Gene expression analysis by qRT-PCR further elucidated the molecular mechanisms underlying the observed cytotoxic effects. Treatment with \u003cem\u003eFerula gummosa\u003c/em\u003e leaf extract significantly down-regulated the expression of the ANG, DPP4, INR, and MAPK14 genes. In particular, INR mRNA levels exhibited the most pronounced reduction. The root extract showed different effects, with increased expression of ANG and minimal impact on other genes.\u003c/p\u003e \u003cp\u003eAngiogenin (ANG) is a protein that plays a crucial role in angiogenesis, the process of forming new blood vessels. In the context of cancer, angiogenin has been extensively studied due to its involvement in tumour growth, invasion, and metastasis. Several studies have shown that angiogenin is overexpressed in various types of cancer, including breast, lung, and colorectal cancer. In addition, angiogenin has been shown to have angiogenesis-independent effects in cancer, such as promoting cancer cell survival and resistance to therapy. The findings highlight the importance of angiogenin as a potential therapeutic target and a biomarker for cancer diagnosis and treatment \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Dipeptidyl peptidase-4 (DPP4), also known as CD26, is an enzyme that plays a crucial role in various physiological processes, including immune regulation and glucose metabolism. In recent years, there has been a growing interest in understanding the involvement of DPP4 in cancer. Studies have revealed that DPP4 expression is dysregulated in several types of cancers, including breast, prostate, colorectal, and pancreatic cancer. DPP4 has been implicated in cancer progression and metastasis through its effects on tumor cell invasion, migration, and angiogenesis. Furthermore, DPP4 has been explored as a potential biomarker for the prognosis of cancer and as a target for therapeutic intervention \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The insulin receptor (INSR) is a cell surface receptor that plays a crucial role in mediating the effects of insulin. In recent years, there has been increasing evidence suggesting a link between INSR and cancer. Studies have shown that dysregulation of INSR signaling is associated with tumour growth, progression, and resistance to therapy in various types of cancer, including breast, colorectal, and lung cancer. Aberrant INSR activation can promote cancer cell survival, proliferation, and metastasis by stimulating downstream signaling pathways involved in cell growth and survival \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. MAPK14, also known as p38 MAPK, is a member of the mitogen-activated protein kinase (MAPK) family that plays a crucial role in cellular signaling pathways involved in inflammation, stress responses, and cell proliferation. Over the years, there has been increasing evidence linking MAPK14 to cancer development and progression. Dysregulation of MAPK14 signaling has been observed in various cancer types, including breast, lung, colorectal, and pancreatic cancer. MAPK14 activation has been shown to promote tumor cell survival, proliferation, invasion, and metastasis. The different findings underscore the significance of MAPK14 in cancer and its potential as a therapeutic target \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe qRT-PCR results also showed partial inhibition of the the expression of VIM and BRAF genes by plant extracts. BRAF is a proto-oncogene that is usually involved in cancer cell signaling and VIM (vimentin) participates in cancer cell migration and cell adhesion structures. Vimentin is often associated with the epithelial-mesenchymal transition (EMT), a process in which cells lose their epithelial characteristics and gain mesenchymal properties. EMT is involved in cancer progression and metastasis \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn summary, this study utilised bioinformatics and in-vitro methods to identify a potential plant for the treatment of colorectal cancer. The results provide valuable insights into the potential of \u003cem\u003eFerula gummosa\u003c/em\u003e extract and its compounds for further investigation in the treatment of colorectal cancer. In conclusion, the study findings collectively suggest that \u003cem\u003eFerula gummosa\u003c/em\u003e, specifically its cauferoside compound, holds promise as a potential therapeutic agent against colorectal cancer. The approach, which integrates system biology, molecular docking, MD simulations, and in vitro analyses, strengthens the credibility of the results. Further studies, including in-vivo experiments and clinical trials, are warranted to validate and translate these findings into practical therapeutic applications, paving the way for the development of novel therapeutic interventions against colorectal cancer.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, A.Al., A.Sh., and A.As.; methodology and validation, A.Al., M.R., R.A., and S.G., formal analysis and investigation, A.Al. and S.A.; resources, A.Al., A.Sh., and A.As.; data curation, R.A., and A.As.; writing\u0026mdash;original draft preparation, A.Al., S.G., and S.A.; writing\u0026mdash;review and editing, M.R. and A.Sh.; supervision, S.A. and A.Sz.; project administration, A.Al and S.A.; All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by a research grant from the Hamadan University of Medical Sciences, Hamadan, Iran, grant number 14010206881 as well as analyses in Wrocław University of Environmental and Life Sciences. The APC is financed by Wrocław University of Environmental and Life Sciences. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e We appreciate the Research Center for Molecular Medicine, Hamadan University of Medical Sciences. We also appreciate the research and ethics committee (IR.UMSHA.REC.1401.079) of Hamadan University of Medical Sciences. We appreciate the Polish National Agency for Academic Exchange (NAWA) Ulam 2021 program under grant number BPN.ULM.2021.1.00250.U.00001 for supporting analytical studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe data presented in this study are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlves Martins, B. A. \u003cem\u003eet al.\u003c/em\u003e Biomarkers in Colorectal Cancer: The Role of Translational Proteomics Research. 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Molecular profiling has revealed biomarkers crucial for prognosis, treatment response, and targeted therapies. This study explores the role of native plant compounds, using bioinformatics and experimental assays to identify potential CRC-specific therapeutic targets. A drug-target network analysis identified four proteins (ANG, DPP4, INR, and MAPK14) as potential targets for further investigation. Molecular docking studies identified the cauferoside from \u003cem\u003eFerula gummosa\u003c/em\u003e as a compound with high binding affinity to these proteins. Molecular dynamics simulations confirmed stability in the compound-protein complexes. In vitro assays demonstrated the cytotoxic effects of \u003cem\u003eF. gummosa\u003c/em\u003e extracts on CRC cells, with leaf extract significantly downregulating the expression of the ANG, DPP4, INR, and MAPK14 genes. Root extract exhibited differential effects on gene expression. These findings suggest the potential therapeutic efficacy of \u003cem\u003eF. gummosa\u003c/em\u003e against CRC and emphasize the importance of a dual methodology involving bioinformatics and experimental validation in drug discovery. Further \u003cem\u003ein vivo\u003c/em\u003e and clinical studies are warranted to validate these findings and advance potential therapeutic applications.\u003c/p\u003e","manuscriptTitle":"Exploring the Therapeutic Potential of Native Plant Compounds: Unveiling the Therapeutic Potential of Ferula gummosa in Colorectal Cancer through Bioinformatics and Experimental Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 09:16:19","doi":"10.21203/rs.3.rs-4443245/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e90b857d-f9cd-40d8-bf09-9244b4cd1ac1","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32919011,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":32919012,"name":"Biological sciences/Systems biology"}],"tags":[],"updatedAt":"2024-11-25T13:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 09:16:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4443245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4443245","identity":"rs-4443245","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}