Integrated Network Pharmacology and Molecular Docking Reveal Therapeutic Potential of Moringa oleifera Glycosides, Targeting Key Regulatory Genes in Colorectal Cancer

Integrated Network Pharmacology and Molecular Docking Reveal Therapeutic Potential of Moringa oleifera Glycosides, Targeting Key Regulatory Genes in Colorectal Cancer | 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 Research Article Integrated Network Pharmacology and Molecular Docking Reveal Therapeutic Potential of Moringa oleifera Glycosides, Targeting Key Regulatory Genes in Colorectal Cancer Anupam Sharma, Abhinav Sharma, Devinder Kumar Maheshwari, Sunil Kumar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6830750/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2025 Read the published version in In Silico Pharmacology → Version 1 posted 16 You are reading this latest preprint version Abstract BACKGROUND Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality globally. This study investigates the therapeutic potential of glycoside compounds derived from Moringa oleifera leaves: Niazirinin (NZR), Niazimicin A (NZA), 4-(Rhamnosyloxy)phenylacetonitrile (RPA), and Moringyne (MRG) in the context of CRC. METHODS An integrative network pharmacology strategy was used including compound-target prediction, protein–protein interaction (PPI) analysis, and Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Eleven hub genes (NFKB1, PIK3R1, PIK3CD, PIK3CB, CHUK, GRB2, NOS2, SLC2A1, ABL1, PDGFRA, and STAT1) were identified. Additional validation involved mRNA expression profiling, overall survival analysis, and tumor stage-specific expression analysis. Lastly, molecular docking was conducted to assess the binding affinity of the glycosides with major CRC regulatory proteins. RESULTS Five major pathways, PI3K-Akt, cAMP, Ras, HIF-1 signaling, and MicroRNAs in cancer were highly enriched. Of the eleven hub genes, six (PIK3R1, NOS2, SLC2A1, ABL1, PDGFRA, and STAT1) were significantly dysregulated in colon (COAD) and rectal (READ) cancer tissues. In particular, NOS2, SLC2A1, and STAT1 were significantly upregulated, and PIK3R1, ABL1, and PDGFRA were significantly down regulated, indicating possible oncogenic and tumor-suppressive functions, respectively. Stage-specific analysis identified that expression of SLC2A1 differed significantly among pathological stages (F = 4.31, p = 0.00531), which warrants its consideration as a stage-specific prognostic biomarker. Molecular docking showed NOS2 and SLC2A1 have high-affinity interaction of NZR and MRG (–8.6 kcal/mol), positing potent inhibitory activity against CRC metabolic and inflammatory targets. CONCLUSION This integrated analysis presents the therapeutic potential of Moringa oleifera glycosides, particularly NZR and MRG, as novel multi-target therapeutic drugs for the treatment of CRC. SLC2A1 and NOS2 genes as hub genes represent therapeutic targets worth considering in further preclinical and clinical studies. Graphical Abstract Moringa oleifera glycosides Colorectal cancer (CRC) Network pharmacology Molecular docking Hub genes SLC2A1 NOS2 PI3K-Akt signaling pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Colorectal cancer(CRC) is the third most commonly diagnosed cancer, and the second most common cause of cancer-related deaths globally (Duan et al. 2022 ). However, with the significant rise in the number of reported cases in older people, the incidence of CRC globally is estimated to increase more than twice by 2035, with the largest growth being reported in less developed countries (ACS 2022 ). Most cases are caused by environmental factors. A minority result from inherited genetic conditions. Much of CRC, perhaps half of all cases, is due to poor diet (for example, a diet with low whole grain and high processed meat content), smoking, high body mass index and physical inactivity (Nassa 2023 ). Conventional therapy modalities for CRC such as surgery, chemotherapy, and radiotherapy may be applied individually or in combination. These treatments, however, often cause severe side effects because of their cytotoxicity on all cells and their non-specific nature. In addition, a large number of patients suffer from relapses after these treatments. Therefore, it is crucial to seek more effective treatment interventions for CRC patients (Li et al. 2025b ). The treatment options for primary and metastatic cancers (mtCRC) have increased significantly in the recent past. Some of these include laparoscopic surgery for early-stage CRC, aggressive removal of metastatic CRC (e.g., lung and liver metastasis), radiotherapy for RC, and neoadjuvant and palliative chemotherapy (Afshar et al. 2020 ). Chemotherapy and radiotherapy are the primary forms of treatment for CRC patients in the present time. Yet, they often result in adverse side effects among patients, such as liver impairment, gastrointestinal issues, and bone marrow suppression that complicate the completion of the full treatment by the patient (Baidoun et al. 2021 ). Despite significant advancement in the domains of screening, diagnosis, and treatment for CRC, the prognosis is still unfavorable and significant hurdles remain. Biomarkers have also been considered recently for their potential in individualizing therapy, and they were found to be beneficial in improving patient survival. But still there is need for more sensitive and specific markers that are applicable to CRC diagnosis and are cheaper and less invasive compared to present methods (Zajkowska and Mroczko 2023 ). In spite of progress in surgery, screening, and chemotherapy, CRC remains a therapeutic challenge through late-stage diagnosis, drug resistance, and heterogeneity of the tumor. Therefore, there is a pressing need for safer, more potent, and multi-targeted drugs that can act at multiple stages of cancer progression (Dekker et al. 2019 ). Natural phytochemicals have come up as exciting candidates in cancer prevention and therapy because of their broad-spectrum bioactivity, low toxicity, and capacity to modulate several molecular targets at the same time (Tuorkey 2015 ). Phytochemicals have a great effect on molecular processes implicated in cancer growth and advancement. These are to increase antioxidant capacity, inactivate carcinogens, stop cell growth, induce cell cycle arrest and death, and inhibit immune function (Choudhari et al. 2020 ). In drug discovery, the approach of designing multi-target drugs towards complicated diseases like cancer is accelerating at a fast pace. Network pharmacology is an integrated paradigm in drug research that explores traditional medications by investigating complicated network models and using high-throughput data analysis and calculation strategies (Bhatia et al. 2022 ). A network is an illustration of a set of data that emphasizes the relationships between nodes. These nodes, which represent genes, proteins, small molecules, and other entities that may interact within the system being modeled, are linked by edges, which represent the properties of the interactions (Berger and Iyengar 2009 ). Network pharmacology is increasingly being used in order to understand how herbal medicines work. Through the identification of hub genes, researchers can derive key drug-affected targets. When a hub gene is modulated by the drug, several downstream effects can be triggered that lead to therapeutic manifestations (Janani et al. 2022 ). Plant-derived glycosides have emerged as promising topical candidates for anticancer agents given their plethora of biological activities. They exert their anticancer effects through a variety of mechanisms, which include induction of apoptotic mediated cell death, adopting cell cycle arrest, inhibiting angiogenesis, inhibiting metastasis, and modulating immune response (T 2024). Here, we report a thorough in silico evaluation of four Moringa oleifera (M. oleifera) glycosides, Niazirinin(NZR), Niazimicin A (NZA),4-(Rhamnosyloxy) phenylacetonitrile (RPA) and Moringyne (MRG ) in relation to CRC. The miracle tree, Moringa oleifera (M. oleifera), exists throughout the world in nearly all tropical and subtropical areas, although it is thought to be native to Afghanistan, Bangladesh, India, and Pakistan. Moringa oleifera or the "tree of life" or "miracle tree" is an important herb plant because it has enormous medicinal uses. The plant is used traditionally to treat wounds, pain, ulcers, liver disease, heart disease, cancer, and inflammation (Pareek et al. 2023 ). Our pipeline combines network pharmacology to find common targets of these four phytochemicals and CRC-associated genes, Protein-Protein Interaction ( PPI) network analysis to find central hub genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis, mRNA expression analysis to confirm hub gene expression patterns in CRC vs. normal tissue and molecular docking to compare binding affinities of the compounds with essential targets. This integrative strategy will seek to elucidate the multi-target activities of these phytochemicals and evaluate their prospect as complement agents to provide a novel therapeutic intervention therapy perspective in CRC treatment. This research does not involve human subjects, human materials, and will not require approval by an institutional ethics committee. 2. Materials and Methods 2.1 Potential Target Identification for Network Pharmacology The active glycoside phytochemicals of M. oleifera were screened using an IMPPAT: Indian Medicinal Plants, Phytochemistry and Therapeutics, curated database, where oral bioavailability (OB ≥ 50%) and drug-like properties (DL ≥ 0.50) and Molecular weight (g/mol) ≤ 500 were primary criteria for evaluating drug development (Pareek et al. 2023 ). SMILES were retrieved from “IMPPAT: Indian Medicinal Plants, Phytochemistry And Therapeutics” (Mohanraj et al. 2018 ; Vivek-Ananth et al. 2023 ). which are further used for extracting Uniprot IDs from super-PRED ( https://prediction.charite.de/subpages/target_prediction.php ) on 27 May 2025 in order to identify the potential targets of selected glycosides (Nickel et al. 2014 ). These uniprot IDs were then imported to another online server STRING ( https://string-db.org/ Version: 12.0 ) to identify the target genes for the titled glycosides by specifying species as “Homo sapiens”, high confidence score cut off 0.700 and a medium false discovery rate (FDR) stringency of 5% (Szklarczyk et al. 2023 ). The number of extracted genes for titled glycosides were Niazirinin-78, Niazimicin A-78, 4-(Rhamnosyloxy) phenylacetonitrile-88 and Moringyne − 82. We searched for CRC-related target genes by using the keyword “colorectal cancer in GeneCards ( https://www.genecards.org/ ) accessed on 27 May 2025 (GeneCards 2025 ). Total 50,000 target genes were downloaded, which were then shortlisted by Gifts filter ≥ 60 and 1232 gene targets of CRC were used for further investigation. 2.2 Protein-Protein Interaction (PPI) Network Construction Further the overlapping target gene of the NZR, NZA, RPA and MRG and CRC are identified by using Venny 2.1 ( https://bioinfogp.cnb.csic.es/tools/venny/ ) (Oliveros 2025 ). There were 179 overlapping predicted gene targets across the compound-CRC pairs, 43 for NZR, 41 for NZA, 48 for RPA and 47 for MRG, as shown in Fig. 1 (a). These overlapping gene targets were imported into STRING ( https://string-db.org/ Version: 12.0) on 27 May 2025 for PPI network construction by selecting species as “Homo sapiens”, medium confidence score cut off 0.400 and a medium false discovery rate (FDR) stringency of 5% ,the constructed PPI network is shown in Fig. 1 (b) (Szklarczyk et al. 2023 ). Furthermore three different clusters were identified as shown in Fig. 1 (c, d & e). 2.3 GO and KEGG enrichment Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis of overlapping genes was extracted from STRING ( https://string-db.org/ ) Version: 12.0, to identify the possible mechanisms of the titled glycosides in CRC. Analyses produced a total of 712 GO-terms, including 566 biological processes (BP), 81 molecular functions (MF), 65 cellular component (CC) terms and the top 10 GO terms (BP,MF,CC) are represented in the bubble plot as presented in Figure.2 (a,b,c). For extracting BP,MF,CC, Go terms are grouped by similarity > = 0.8, Maximum FDR shown ≤ 0.05, Minimum signal shown ≥ 0.01, Minimum strength shown ≥ 0.01 Minimum count in network was set at 2. Additionally a total of 148 KEGG pathways were created, out of which we selected 11 pathways that are applicable to CRC based on FDR = 0.01, Minimum strength > = 0.01 and Minimum count in network 2 are presented in Table 1 and Fig. 2 (d). Genes associated with these top 10 pathways were considered as CRC targets for network construction. 2 .4 Network Pharmacology Analysis and Hub Gene Identification Following the GO and KEGG analysis, the target network of 179 overlapping genes was constructed, as shown in Figure. 3 (a). The network of CRC- top 10 target pathways and related genes is depicted in Figure. 3 (b), and the merged network is presented in Fig. 3 (c). These networks were constructed to investigate the interactions of active genes within the complex biological system by using Cytoscape V3.10.3 on 28th May, 2025 (Shannon et al. 2003 ). Cytoscape software was used to visualize and analyze the network by calculating centrality and other key topological parameters. Then the plugin CytoHubba was employed to identify the top twenty nodes based on Maximal Clique Centrality as shown in Table 2 (Chin et al. 2014 ). Figure 3 (d) illustrates the top eleven hub genes with colour gradient. 2.5 Gene expression and Survival analysis In the current research, Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia2.cancer-pku.cn/ ) was used to check the diverse expression of the hub genes in Colon adenocarcinoma (COAD) and Rectum adenocarcinoma (READ) and normal tissues (Tang et al. 2017 ). GEPIA is a web server that provides interactive and flexible functionalities using data from ‘The Cancer Genome Atlas’ (TCGA) and Genotype-Tissue Expression (GTEx) database. For boxplots analysis |Log2FC| Cutoff was set to 1 and p-value Cutoff was set at 0.01 and used log2 (TPM + 1) for log-scale. Matched data with TCGA normal and GTEx data for comparison purpose is used. 2.6 Molecular docking analysis Following gene expression analysis, the molecular docking analysis of statistically significant genes (PIK3R1, NOS2, SLC2A1, ABL1, PDGFRA and STAT1) was conducted. The ligand molecules and target protein structures were prepared for molecular docking by using the Discovery Studio, 4.5 software Dassault Systèmes BIOVIA (BIOVIA 2017). The molecular docking (auto blind docking) was carried out using CB-Dock2 ( https://cadd.labshare.cn/cb-dock2/index.php ) an online tool which automatically identifies active sites within a given protein, Cavity volume (Å3),Center,(x, y, z),Docking size(x, y, z) and Contact residues by using autodock vina (Liu et al. 2022b ). 3. Results and Discussion 3.1 PPI network The PPI network (Figure. 1b) was built by using 179 shared gene targets, with confidence level of 0.400 and 5% FDR, contains 78 nodes, 284 edges (interactions), 7.28 average node degree (i.e., each protein interacts with about 7.28 other proteins), avg. local clustering coefficient of 0.482 (i.e., about 48.2% of the neighbours of a protein are themselves connected to each other), and PPI enrichment p-value was < 1.0e-16. The very low p-value (< 0.05) suggests that the network connectivity in the study observed is statistically significant and have not occurred due to random fluctuations. After constructing PPI network ,MCL clustering was employed and 3 different clusters were identified.Cluster 1 has gene count 15(number of nodes), 48 number of edges, average node degree 6.4,avg. local clustering coefficient 0.729, expected number of edges 13 and PPI enrichment p-value < 1.0e-16. Cluster 1 is correlated with immune modulation, positive regulation of interferon-alpha production, and Chagas disease, describing possible overlapping inflammatory or infection-related signaling pathways that may be of relevance to tumor immunology in CRC. Cluster 2 has gene count 13 and PPI enrichment p-value of < 1.0e-16. Genes in this group are associated with the neurotrophin signaling pathway and intracellular signaling downstream, implying function in cell survival, differentiation, and stress adaptation processes frequently hijacked in cancer development and drug resistance. Cluster 3 has gene count of eight and it was found to be associated with genes for mitotic drive and chromosome movement, including functions like prometaphase chromosome condensation, spindle elongation, and outer kinetochore activity, and shows a very high association with cell cycle control and genomic integrity, both of which are essential in CRC development. From the PPI network it is evident that the titled glycosides of M. oleifera could be exerting their pharmacological action on CRC because of clusters of proteins with high connectivity and important biological significance. The identified proteins which are using this network could be used as potential biomarkers or therapeutic targets for experimental verification in the future. 3.2 GO and KEGG Analysis Following the construction of PPI networks for overlapping targets of M. oleifera glycosides—NZR, NZA, RPA, and MRG and CRC, Gene Ontology (GO) enrichment analysis was carried out to find out the important biological processes of the 179 targets. The resulting dot plots of BP,MF and CC are shown in Figure. 2 (a, b, c) which displays the top 10 enriched GO terms across three ontologies. The dots with lightest shade represent FDR < 1e-15, confirming stronger evidence for the enrichment. The color gradient represents the False Discovery Rate (FDR), the significance of enrichment (Li et al. 2023 ). Darker shades (e.g., dark blue or purple) refer to higher FDR values (less significant), and lighter shades such as green and cyan represent lower FDR values (more significant). Enrichment with light green color (e.g., lowest FDR values) are of higher significance. The size of the bubbles is related to the number of genes associated with GO terms larger size indicates more number of genes are associated with that particular process. Among the enriched BP terms, the most prominent is response to organic substance. This terminology suggests that the candidate compounds are capable of affecting those genes involved in the recognition and response towards a wide range of endogenous and exogenous organic molecules, which is particularly relevant in view of the involvement of tumor microenvironment and metabolite signaling in CRC development. Other supplemented BP were response to cellular organic substance, response to chemical, and response to oxygen-containing compound also provide support for the hypothesis that these glycosides control important stress and chemical response pathways. Together, these findings point towards the bioactivity of M. oleifera, glycosides to control chemical response pathways, which may contribute to their anti-CRC activity. The most enriched molecular processes is catalytic activity, which means that the target proteins plays a key role in catalyzing biochemical reactions. This is related to the role of enzymes in cancer processes including metabolism, proliferation, and apoptosis. Other enrichment terms such as small molecule binding, nucleotide binding, and anion/ion binding indicate that most of the targets interact with signaling molecules and metabolic intermediates, also imply their role in CRC pathophysiological regulation. Notably, kinase binding and protein kinase binding enrichment suggest potential modulation of kinase signaling pathways PI3K/AKT and MAPK cascades extensively involved in cancer cell survival and drug resistance. The protein binding enrichment further suggests that these compounds may modulate diverse PPI. Furthermore, in case of Cellular component enrichment analysis, integral component of plasma membrane, is the most enriched term, which indicates that many target proteins are linked with the cell membrane, suggesting a role in signal transduction, transport, and interaction with extracellular stimuli that are critical processes often dysregulated in CRC. Moreover, enrichment in plasma membrane, cell periphery, and cell junction further supports that these glycosides may be modulating intercellular communication which is vital in tumor progression and metastasis. Additionally, the presence of targets in the cytoplasm, cytoplasmic vesicles, and protein-containing complexes gives indication that the target proteins may be involved in intracellular trafficking, protein complex assembly, and signal relay mechanisms. Integration of these findings highlights the multi-targeted therapeutic potential of M. oleifera glycosides against CRC through the modulation of enzymatic activity and key signaling nodes. The Top 10 pathways enrichment is depicted in Fig. 2 (d) and respective gene count, FDR and matching proteins in the network is depicted in Table 1 . For shortlisting the KEGG pathways we have considered genes count ≥ 4 and FDR < 0.01. Table 1 Top 10 Enriched Biological Pathways Associated with overlapping target gene of the NZR, NZA, RPA and MRG and CRC #term ID Pathway Genes FDR Matching proteins in our network (labels) hsa05200 Pathways in cancer 18 6.77E-10 NFKB1,PTGER2,PDGFRA,NOS2,STAT1,CHUK,HSP90AB1,ABL1,AR,PIK3CD, GRB2,KEAP1,ITGB1,NFE2L2,GSTP1,SLC2A1,PIK3R1,PIK3CB hsa04066 HIF-1 signaling pathway 10 2.30E-09 SERPINE1,NFKB1,FLT1,NOS3,NOS2,TLR4,PIK3CD,SLC2A1,PIK3R1,PIK3CB hsa04024 cAMP signaling pathway 12 4.82E-09 CFTR,NFKB1,PTGER2,SLC9A1,GRIA2,PDE3A,ADORA1,GRIN1,PIK3CD,CHRM2, PIK3R1,PIK3CB hsa04014 Ras signaling pathway 12 9.49E-09 NFKB1,PDGFRA,FLT1,CHUK,GRIN1,ABL1,PIK3CD,GRB2,PLA2G2A,PIK3R1 ,PTPN11,PIK3CB hsa05235 PD-L1 expression and PD-1 checkpoint pathway in cancer 9 9.49E-09 NFKB1,STAT1,CHUK,TLR4,CSNK2B,PIK3CD,PIK3R1,PTPN11,PIK3CB hsa05206 MicroRNAs in cancer 10 4.25E-08 HDAC5,NFKB1,PDGFRA,CDC25C,ABL1,PIK3CD,GRB2,ABCC1,PIK3R1,PIK3CB hsa04151 PI3K-Akt signaling pathway 13 4.41E-08 NFKB1,PDGFRA,FLT1,NOS3,CHUK,HSP90AB1,TLR4,PIK3CD,GRB2,ITGB1, CHRM2,PIK3R1,PIK3CB hsa04620 Toll-like receptor signaling pathway 8 2.33E-07 NFKB1,TLR8,STAT1,CHUK,TLR4,PIK3CD,PIK3R1,PIK3CB hsa04062 Chemokine signaling pathway 9 1.13E-06 NFKB1,PRKCD,STAT1,CHUK,PIK3CD,GRB2,ITK,PIK3R1,PIK3CB hsa05210 Colorectal cancer 4 0.0012 PIK3CD,GRB2,PIK3R1,PIK3CB 3.3 Network Pharmacology and Hub Genes Identification In order to identify major molecular targets influenced by M. oleifera glycosides in the context of CRC, we constructed an integrated network. M. oleifera Glycoside-Target Gene Network comprised the four Moringa glycosides (Niazirinin (NZR), Niazimicin A (NZA), 4-(Rhamnosyloxy) phenylacetonitrile (RPA), and Moringyne (MRG)) as nodes, linked to their respective target genes involved in CRC. This network comprised 82 nodes and 179 edges, as illustrated in Fig. 3 (a).CRC Pathway-Gene Network depicted important CRC-related pathways and their respective genes as nodes, and their known interactions as edges. It had 48 nodes and 105 edges, as illustrated in Fig. 3 (b). These two isolated networks were then combined in Cytoscape (version 3.10.3) to build an overall integrated network. This last network had 92 nodes and 284 edges, and is illustrated in Fig. 3 (c). Furthermore, to find the hub genes, a plugin CytoHubba, in Cytoscape, was used to compute centrality values within the integrated network. The top 25 nodes, ranked by various topological scores including Maximal Clique Centrality and Edge Percolated Component (EPC), are presented in Table 2 and visualized in Fig. 3 (d).The output of cytohubba has different color gradient (red to yellow) which signifies node importance within the network, where darker nodes represent higher centrality and thus greater importance. Based on an MCC score threshold of ≥ 5, eleven prominent hub genes were identified: NFKB1, PIK3R1, PIK3CD, PIK3CB, CHUK, GRB2, NOS2, SLC2A1, ABL1, PDGFRA and STAT1. The top five pathways, most probable to be modulated by the bioactive M. oleifera glycosides are the Pathways in cancer, PI3K-Akt signaling pathway, cAMP signaling pathway, Ras signaling pathway, HIF-1 signaling pathway, and MicroRNAs in cancer. These pathways demonstrated an MCC score of ≥ 10, proposing a multi-component regulatory mechanism underlying the prospective anti-CRC actions of the glycosides. Table 2 Topological Centrality Analysis of Key 25 Nodes in the Integrated Moringa oleifera-CRC network showing top eleven hub genes. node_name MCC EPC Closeness Radiality Betweenness Stress RPA 48 32.09 64.5 3.25275 2253.343 48122 MRG 47 32.187 63.83333 3.23077 2533.405 57692 NZR 43 31.861 61.16667 3.14286 1412.394 35606 NZA 41 31.284 59.83333 3.0989 1734.489 42288 Pathways in cancer 18 25.532 44.5 2.59341 269.1354 8832 PI3K-Akt signaling pathway 13 21.94 41.16667 2.48352 124.5075 4518 cAMP signaling pathway 12 20.492 40.5 2.46154 125.6221 4192 Ras signaling pathway 12 22.564 40.5 2.46154 94.92664 3738 HIF-1 signaling pathway 10 20.853 39.16667 2.41758 65.0883 2550 MicroRNAs in cancer 10 20.842 39.16667 2.41758 53.71243 2146 PD-L1 expression and PD-1 checkpoint pathway in cancer 9 19.005 38.5 2.3956 63.67957 2536 Chemokine signaling pathway 9 18.955 38.5 2.3956 68.92142 2740 Toll-like receptor signaling pathway 8 17.261 37.83333 2.37363 44.98398 1892 Colorectal cancer 4 12.651 35.16667 2.28571 3.79199 296 NFKB1 13 25.646 51.83333 3.18681 321.1431 10418 PIK3R1 13 25.223 50.58333 3.07692 271.2631 7300 PIK3CD 11 22.074 45.25 2.68132 121.7121 2588 PIK3CB 11 22.251 45.25 2.68132 121.7121 2588 CHUK 10 24.195 49.83333 3.12088 223.2264 7746 GRB2 10 23.29 49.83333 3.12088 240.1239 7320 NOS2 6 20.057 47.16667 3.03297 88.04065 4264 SLC2A1 6 20.203 47.16667 3.03297 88.04065 4264 ABL1 6 19.273 45.66667 2.9011 63.63297 2810 PDGFRA 6 18.423 43.91667 2.74725 45.71507 1792 STAT1 5 13.761 40.25 2.46154 35.5437 906 3.4 mRNA expression levels, Stage plots and Kaplan–Meier analysis of hub genes To assess the transcriptional significance of the hub genes, mRNA expression analysis was conducted by using GEPIA 2. Box plot comparisons of colon adenocarcinoma (COAD; tumor tissue samples = 275, normal tissue samples = 349) and rectum adenocarcinoma (READ; tumor = 92, normal = 318) tissues showed differential expression of the hub genes as shown in Fig. 4 (a), along with stage plots. Asterisks on PIK3R1, NOS2, SLC2A1, ABL1, PDGFRA and STAT1 indicate statistically significant expression differences between tumor and normal tissues. Among these NOS2, SLC2A1 and STAT1 genes were significantly up regulated, while PIK3R1, ABL1, and PDGFRA were significantly down regulated in COAD and READ tumor samples. These findings suggest that PIK3R1, ABL1, and PDGFRA my act as potential tumor suppressors or involved in pathways that suppress tumor growth, whereas NOS2, SLC2A1 and STAT1 may act putative oncogenes in CRC. For stage plots there is statistically significant variation in the level of expression of SLC2A1 gene in different pathological stages as F value = 4.31 and Pr (> F) = 0.00531 which is much smaller than required standard significance level (α) of 0.05. That implies the expression of SLC2A1 is not always identical in all stages of CRC. The important difference in SLC2A1 expression between various stages of CRC (p = 0.00531), as well as its established function in tumor growth and metabolism, supports that SLC2A1 is a possible stage-specific prognostic biomarker for colorectal cancer (Liu et al. 2022a ). This makes SLC2A1 an especially compelling hub gene to explore further investigation, given its differential expression between stages and implication of a disease progression role. (b) Kaplan–Meier survival curves showing the association between gene expression and overall survival (OS) in colorectal cancer (CRC) patients. The blue line represents low expression group, while the red line represents the high expression group. Furthermore, we generated Kaplan–Meier plots for Overall Survival (OS) as shown in Figure. 4(b). The blue line represents the low expression group, and the red line represents the high expression group. Each plot compares ~ 181 patients with high gene expression and low gene expression. Genes with hazard ratio (HR) > 1 and p < 0.05 are considered as risk genes, since their increased expression is linked to higher disease progression or mortality risk. On the other hand, genes with HR < 1 and p < 0.05 are referred to as protective genes ,as their expression is associated with a reduced risk of disease progression (Lin et al. 2020 ). In our study, the genes NFKB1, PIK3CB, CHUK, NOS2, ABL1, PDGFRA and STAT1 exhibited HR 1, indicating a tendency towards higher risk. However, these values were not statistically significant, as their p-values exceeded the 0.05 threshold. Interestingly, NOS2 demonstrated a log-rank p-values is 0.02,which is less than 0.05 cutoff, indicating statistically significant improved survival among patients with high expression levels. In our analysis ,NOS2 was found to be significantly up regulated in CRC ,as shown in boxplot ,despite its elevated expression in tumor tissues, it exhibited HR < 1 with a statistically significant p-value of 0.02 in OS analysis ,indicating that higher expression is associated with better patient survival. In some cases, NOS2-derived nitric oxide (NO) can induce cell death and inhibit tumor growth, while in others, it can promote cancer cell proliferation, metastasis, and angiogenesis (Mintz et al. 2021 ; Vannini et al. 2015 ). These findings are in consistent with our results. These results confirm the multifaceted function of NOS2 within the CRC tumor microenvironment and propose its potential as a protective prognostic biomarker, warranting further functional assessment and study in large, stratified cohorts (Thomas and Wink 2017 ). These results suggest that while these hub genes may influence prognosis, validation in larger cohorts or through integrated multi-omics analyses is required to confirm their value as prognostic biomarkers in CRC. 3.5 Molecular Docking Further in our study, a molecular docking approach was employed to examine the interactions between the genes NOS2, SLC2A1, and STAT1 (significantly upregulated), and PIK3R1, ABL1, and PDGFRA (significantly downregulated) and Moringa oleifera glycosides: Niazirinin (NZR), Niazimicin A (NZA), 4-(Rhamnosyloxy)phenylacetonitrile (RPA), and Moringyne (MRG). Binding affinity and interaction profiling of NZR, NZA, RPA, and MRG against key signaling proteins in CRC is described in Table 3 .Two- and three-dimensional interaction diagrams along with binding affinity in kcal/mol is shown in Fig. 5 (a, b). The Favorable receptor–ligand conformations, indicated by lower (more negative) binding energy values, correspond to greater binding affinity and higher likelihood of biological activity. A binding energy value below 0 kJ/mol indicates that there is spontaneous binding, while values below − 5 kJ/mol indicate strong ligand–receptor interactions (Hu et al. 2023 ). The results of docking are presented in Table 3 . All target proteins have shown good binding energies (≤ − 6.4 kcal/mol) with all Moringa oleifera glycosides: NZR, NZA, RPA, and MRG. Titled Glycosides could inhibit pro-inflammatory and pro-tumorigenic NOS2 activity. Earlier studies have shown that inhibition of NO production in tumors can improve antitumor immune response in animal models (Hegardt et al. 2001 ; Ito et al. 2015 ; Ridnour et al. 2015 ). H. et al has demonstrated that knockdown of SLC2A1 led to low cell viability, reduced migration, and enhanced apoptosis in Caco-2 and SW480 cells (Li et al. 2025a ). Knockout of STAT1 in CRC cells suppressed symptoms of colitis and tumor growth in the AOM/DSS-induced CRC mouse model (Chou et al. 2022 ). From our docking results and suitable binding affinity we can draw analysis that downregulated genes (PIK3R1, ABL1, and PDGFRA) may get Glycosides -induced stabilization, while upregulated genes (NOS2, SLC2A1, and STAT1) might be inhibited through high-affinity binding, which could have anticancer effects. Table 3 Binding Affinity and Interaction Profiling of Niazirinin (NZR), Niazimicin A (NZA), 4-(Rhamnosyloxy) phenylacetonitrile (RPA), and Moringyne (MRG) Against Key Signaling Proteins in CRC. Values are represented as (Binding Energy in kcal/mol) / Conventional Hydrogen Bond Residues / Other Non-Covalent favorable Interactions. Protein (PDB ID) Niazirinin (NZR), Niazimicin A (NZA) 4-(Rhamnosyloxy)phenylacetonitrile (RPA) Moringyne (MRG). NOS2 (2NSI) (–8.6) / TYR 489 / Vander Waals, Pi-Alkyl, Pi-Pi stacked (–7.2) / TYR 491 / Vander Waals, Pi-Sigma, Pi-Sulfur, Pi-Pi stacked, Pi-Pi T-shaped, Pi-Alkyl (–8.5) / TYR 489,TRP 372 / Vander Waals, Pi-Pi stacked, Alkyl, Pi-Alkyl (–8.6) / GLY 371/ Vander Waals, Pi-Alkyl, Pi-Pi stacked,Pi-sigma SLC2A1 (6THA) (–8.6) / ASN 288,GLN 283,TRP 412,asn 415 / Vander Waals, C-H bond, Pi-Pi T-shaped (–7.8) / GLU380,THR 137 / Vander Waals, Alkyl, Pi-Pi T-shaped (–7.9) / GLN 283,ASN 288 / Vander Waals, C-H bond ,Pi-anion (-8) / GLN 282, TRP 388,HIS 160 / Vander Waals, C-H bond, Pi-anion, Pi-Pi T-shaped STAT1 (8D3F) (–6.5) / SER 315/ Vander Waals, C-H bond, Pi-sigma (–6.5) / GLN 243,GLU 449 / Vander Waals, C-H bond (–6.8) / GLN 314,THR 450 / Vander Waals, C-H bond, Pi-sigma (–7.2) / GLN 322,GLN 243,THR 451,THR 450,VAL 319 / Vander Waals, Pi-sigma, Pi-Alkyl PIK3R1 (8TS7) (–7.3) / LYS346,LYS 575,GLN 579/ Vander Waals, C-H bond, Pi-donor hydrogen bond, Pi-sigma (–6.5) / GLN 433 / Vander Waals, Pi-sigma, Pi-Alkyl, Pi-Sulfur, Pi-Pi stacked (–6.4) / LYS 575 / Vander Waals, C-H bond, Pi-Alkyl, Pi-Pi stacked, Pi-Alkyl (–6.9) / LYS 575 / Vander Waals, C-H bond Pi-Sigma, Pi-Alkyl, Pi-Pi stacked ABL1 (3QRK) (–7.5) / - / Vander Waals, Pi-Alkyl, Pi-anion (–7.3) / GLY 249 ,THR 315 / Vander Waals, C-H bond, Pi-sigma, Pi-Alkyl, Alkyl (–7.2) / ASP 325 ,ASN 322,MET 318,GLY 249 / Vander Waals, Pi-sigma, Pi-Pi T-shaped (–7.1) / ASN 322,GLY 249 / Vander Waals Alkyl, Pi-Alkyl, C-H bond, Pi-sigma PDGFRA (5GRN) (–7.7) / THR 674,GLU 675 / Vander Waals, Pi-sigma, Pi-Alkyl (–8.2) / TYR 676,LYS 627,GLU 644 / Vander Waals, Pi-Alkyl, Pi-sigma (–8.1) / ASP 836 / Vander Waals, Pi-sigma, Pi-Alkyl (–7.3) / LYS 627,ASP 836,THR 674 / Vander Waals, Pi-sigma, Pi-Alkyl 4. Conclusion In this study we investigated the therapeutic value of Moringa oleifera glycosides — Niazirinin (NZR), Niazimicin A (NZA), 4-(Rhamnosyloxy) phenylacetonitrile (RPA), and Moringyne (MRG) for CRC. By network pharmacology analysis, such as target prediction, protein-protein interaction (PPI) mapping, and GO and KEGG pathway enrichment, eleven hub genes (NFKB1, PIK3R1, PIK3CD, PIK3CB, CHUK, GRB2, NOS2, SLC2A1, ABL1, PDGFRA and STAT1) were identified as central regulators potentially regulated by these glycoside compounds. We identified five key pathways (PI3K-Akt signaling pathway, cAMP signaling pathway,Ras signaling pathway,HIF-1 signaling pathway and MicroRNAs in cancer) involved in the treatment of CRC by active phytochemicals of Moringa oleifera. Further, mRNA gene expression and overall survival analysis between tumor and normal tissues indicated that PIK3R1, NOS2, SLC2A1, ABL1, PDGFRA and STAT1 are statistically significant. Among these NOS2, SLC2A1 and STAT1 genes were significantly up regulated, while PIK3R1, ABL1, and PDGFRA were significantly down regulated in COAD and READ tumor samples. These findings suggest that PIK3R1, ABL1, and PDGFRA my act as potential tumor suppressors or involved in pathways that suppress tumor growth, whereas NOS2, SLC2A1 and STAT1 may act putative oncogenes in CRC. From the stage plots it was found that there is statistically significant variation in the level of expression of SLC2A1 gene in different pathological stages as F value = 4.31 and Pr (> F) = 0.00531 which supports that SLC2A1 is a possible stage-specific prognostic biomarker for colorectal cancer .This makes SLC2A1 an especially compelling hub gene to explore further investigation, given its differential expression between stages and implication of a disease progression role. Further, molecular docking study showed high binding (–8.6 kcal/mol) with Niazirinin (NZR) and Moringyne (MRG) indicates strong inhibitory potential, particularly against metabolic (SLC2A1) and inflammatory (NOS2) targets In general, this integrative analysis underscores the therapeutic potential of M. oleifera glycosides to modulate central regulatory genes of CRC, and offers a platform for additional experimental confirmation and drug development directed towards hub genes like SLC2A1, and NOS2. 5. Future prospects It is to note that additional research is required to ascertain the possible toxicity and therapeutic effectiveness of combining these four Glycosides. It is advised to do animal studies to assess the safety and effectiveness before beginning clinical trials. To determine the ideal dosage for treatment, dose-response studies are also required. Mechanistic studies are required to gain a deep understanding of how individual Glycoside affect PI3K-Akt signaling pathway, cAMP signaling pathway, Ras signaling pathway,HIF-1 signaling pathway and MicroRNAs in cancer pathways. Confirmation of gene expression and survival patterns by using greater and more heterogeneous CRC patient cohorts would enhance the prognostic significance of the discovered hub genes. Declarations Ethics approval and consent to participate Not Applicable Consent for publication All the authors hereby affirm for the publication of the manuscript in the Journal. Availability of data and material Data will be made available upon reasonable request. Competing interests Authors declare that they have no competing interests. Funding No funding was received for the said work. Author Contributions AS conceptualized and planned the study and written the original draft. Abhinav S. and DKM analysed, visualized and reviewed the manuscript along with preparation of figures; SK: helped in analysis, visualization and arrangement of references; AKS: conceptualized and supervised the study and contributed to writing the sections and editing the manuscript critically. All authors have critically edited and reviewed the manuscript. Acknowledgements Authors express gratitude to the Chancellor, Guru Kashi University, Talwandi Sabo, Bathinda for providing the requisite platform to carry out the said work. References ACS (2022) Cancer Facts & Figures. American Cancer Society; Atlanta, GA, USA [Accessed on 26 May 2023]: Available online: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2022.html Afshar S, Sedighi Pashaki A, Najafi R et al. (2020) Cross-Resistance of Acquired Radioresistant Colorectal Cancer Cell Line to gefitinib and regorafenib. Iran J Med Sci 45:50-58 doi:10.30476/ijms.2019.44972 Baidoun F, Elshiwy K, Elkeraie Y et al. (2021) Colorectal Cancer Epidemiology: Recent Trends and Impact on Outcomes. Curr Drug Targets 22:998-1009 doi:10.2174/1389450121999201117115717 Berger SI, Iyengar R (2009) Network analyses in systems pharmacology. Bioinformatics 25:2466-2472 doi:10.1093/bioinformatics/btp465 Bhatia N, Mokashi A, Nathore N et al. (2022) Network Pharmacology: An Emphasis on Traditional Chinese Medicines and Its Adaptability for Ayurveda Medicines in India. International J of Med Sci and Clin Res Studies 2:1608-1620 doi:10.47191/ijmscrs/v2-i12-40. BIOVIA DS (2017) Dassault Systèmes BIOVIA. Discovery Studio Modeling Environment. Dassault Systèmes Release 4.5, San Diego. Chin CH, Chen SH, Wu HH et al. (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8 Suppl 4:S11 doi:10.1186/1752-0509-8-S4-S11 Chou PH, Luo CK, Wali N et al. (2022) A chemical probe inhibitor targeting STAT1 restricts cancer stem cell traits and angiogenesis in colorectal cancer. J Biomed Sci 29:20 doi:10.1186/s12929-022-00803-4. Choudhari AS, Mandave PC, Deshpande M et al. (2020) Corrigendum: Phytochemicals in Cancer Treatment: From Preclinical Studies to Clinical Practice. Front Pharmacol 11:175 doi:10.3389/fphar.2020.00175 Dekker E, Tanis PJ, Vleugels JLA et al. (2019) Colorectal cancer. Lancet 394:1467-1480 doi:10.1016/S0140-6736(19)32319-0 Duan B, Zhao Y, Bai J et al. (2022) Colorectal Cancer: An Overview doi:NBK586003 [bookaccession] 10.36255/exon-publications-gastrointestinal-cancers-colorectal-cancer GeneCards (2025) The Human Gene Database. Weizmann Institute of Science. Available Online: https://www.genecards.org/. Hegardt P, Widegren B, Li L et al. (2001) Nitric oxide synthase inhibitor and IL-18 enhance the anti-tumor immune response of rats carrying an intrahepatic colon carcinoma. Cancer Immunol Immunother 50:491-501 doi:10.1007/s002620100230 Hu M, Yan H, Li H et al. (2023) Use of network pharmacology and molecular docking to explore the mechanism of action of curcuma in the treatment of osteosarcoma. Sci Rep 13:9569 doi:10.1038/s41598-023-36687-z. Ito H, Ando T, Ogiso H et al. (2015) Inhibition of induced nitric oxide synthase enhances the anti-tumor effects on cancer immunotherapy using TLR7 agonist in mice. Cancer Immunol Immunother 64:429-436 doi:10.1007/s00262-014-1644-6. Janani B, Vijayakumar M, Priya K et al. (2022) A network-based pharmacological investigation to identify the mechanistic regulatory pathway of andrographolide against colorectal cancer. Front Pharmacol 13:967262 doi:10.3389/fphar.2022.967262. Li K, Dai Y-J, Zhang H et al. (2025a) YAP1 activates SLC2A1 transcription and augments the malignant behavior of colorectal cancer cells by activating the Wnt/beta-catenin signaling pathway. Cell Div 20:8 doi:10.1186/s13008-025-00148-y. Li S, Li Q, Zhou Q et al. (2023) Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis. BMC Med Genomics 16:102 doi:10.1186/s12920-023-01533-8 Li Y, Cheng Z, Li S et al. (2025b) Immunotherapy in colorectal cancer: Statuses and strategies. Heliyon 11:e41354 doi:10.1016/j.heliyon.2024.e41354. Lin J, Yang J, Xu X et al. (2020) A robust 11-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts. Cancer Cell Int 20:402 doi:10.1186/s12935-020-01491-6 Liu XS, Yang JW, Zeng J et al. (2022a) SLC2A1 is a Diagnostic Biomarker Involved in Immune Infiltration of Colorectal Cancer and Associated With m6A Modification and ceRNA. Front Cell Dev Biol 10:853596 doi:10.3389/fcell.2022.853596 Liu Y, Yang X, Gan J et al. (2022b) CB-Dock2: improved protein-ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Res 50:W159-W164 doi:10.1093/nar/gkac394 Mintz J, Vedenko A, Rosete O et al. (2021) Current Advances of Nitric Oxide in Cancer and Anticancer Therapeutics. Vaccines (Basel) 9 doi:10.3390/vaccines9020094 Mohanraj K, Karthikeyan BS, Vivek-Ananth RP et al. (2018) IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics. Sci Rep 8:4329 doi:10.1038/s41598-018-22631-z Nassa G (2023) Colorectal Cancers Also Known As Bowel Cancer or Rectal Cancer. Chemotherapy. 11. Nickel J, Gohlke BO, Erehman J et al. (2014) SuperPred: update on drug classification and target prediction. Nucleic Acids Res 42:W26-31 doi:10.1093/nar/gku477 Oliveros JC (2025) Venny: An interactive tool for comparing lists with Venn's diagrams. Available from: https://bioinfogpcnbcsices/tools/venny/indexhtml Pareek A, Pant M, Gupta MM et al. (2023) Moringa oleifera: An Updated Comprehensive Review of Its Pharmacological Activities, Ethnomedicinal, Phytopharmaceutical Formulation, Clinical, Phytochemical, and Toxicological Aspects. Int J Mol Sci 24 doi:10.3390/ijms24032098 Ridnour LA, Cheng RY, Weiss JM et al. (2015) NOS Inhibition Modulates Immune Polarization and Improves Radiation-Induced Tumor Growth Delay. Cancer Res 75:2788-2799 doi:10.1158/0008-5472.CAN-14-3011 Shannon P, Markiel A, Ozier O et al. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498-2504 doi:10.1101/gr.1239303 Szklarczyk D, Kirsch R, Koutrouli M et al. (2023) The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51:D638-D646 doi:10.1093/nar/gkac1000 T P (2024) Plant-derived glycosides in cancer treatment: diverse strategies for tumor suppression Nat Prod Res:1-3 doi:10.1080/14786419.2024.2409974 Tang Z, Li C, Kang B et al. (2017) GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 45:W98-W102 doi:10.1093/nar/gkx247 Thomas DD, Wink DA (2017) NOS2 as an Emergent Player in Progression of Cancer. Antioxid Redox Signal 26:963-965 doi:10.1089/ars.2016.6835 Tuorkey MJ (2015) Cancer Therapy with Phytochemicals: Present and Future Perspectives. Biomed Environ Sci 28:808-819 doi:10.3967/bes2015.112 Vannini F, Kashfi K, Nath N (2015) The dual role of iNOS in cancer. Redox Biol 6:334-343 doi:10.1016/j.redox.2015.08.009 Vivek-Ananth RP, Mohanraj K, Sahoo AK et al. (2023) IMPPAT 2.0: An Enhanced and Expanded Phytochemical Atlas of Indian Medicinal Plants. ACS Omega 8:8827-8845 doi:10.1021/acsomega.3c00156 Zajkowska M, Mroczko B (2023) A Novel Approach to Staging and Detection of Colorectal Cancer in Early Stages. J Clin Med 12 doi:10.3390/jcm12103530 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Nov, 2025 Read the published version in In Silico Pharmacology → Version 1 posted Editorial decision: Revision requested 27 Jul, 2025 Reviews received at journal 26 Jul, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviews received at journal 26 Jul, 2025 Reviews received at journal 23 Jul, 2025 Reviewers agreed at journal 23 Jul, 2025 Reviewers agreed at journal 22 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers invited by journal 16 Jul, 2025 Editor assigned by journal 06 Jun, 2025 Submission checks completed at journal 06 Jun, 2025 First submitted to journal 05 Jun, 2025 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-6830750","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486438580,"identity":"4de3d1c3-0b4e-4036-bf47-22994a6e428c","order_by":0,"name":"Anupam Sharma","email":"","orcid":"","institution":"Guru Kashi University","correspondingAuthor":false,"prefix":"","firstName":"Anupam","middleName":"","lastName":"Sharma","suffix":""},{"id":486438583,"identity":"2c9f471d-538c-4698-a636-ca8fb464c7d7","order_by":1,"name":"Abhinav Sharma","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abhinav","middleName":"","lastName":"Sharma","suffix":""},{"id":486438586,"identity":"fd820585-5e88-42a8-bbb9-492d85d76f73","order_by":2,"name":"Devinder Kumar Maheshwari","email":"","orcid":"","institution":"Guru Kashi University","correspondingAuthor":false,"prefix":"","firstName":"Devinder","middleName":"Kumar","lastName":"Maheshwari","suffix":""},{"id":486438588,"identity":"49303c67-6b6f-45ca-8b84-34cd526534ca","order_by":3,"name":"Sunil Kumar","email":"","orcid":"","institution":"Graphic Era University","correspondingAuthor":false,"prefix":"","firstName":"Sunil","middleName":"","lastName":"Kumar","suffix":""},{"id":486438589,"identity":"3ead60aa-a980-465e-bd57-134f128bf6c9","order_by":4,"name":"Anil Kumar Sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYFACxjaGBBAtwdj44AOQZmMnTosBUAtzs+EMkBZmwtawATFIC3ubNA+IT0gL/+zmtgcPKv7I8c9ubDa2+bVNno+ZgfHDxxzcWiTuHGw3SDhjYAxkND7O7btt2MbMwCw5cxsea24ktkkkthkkbpBIbDbO7bnNCNTCxsyLR4s8VEs9UEubtGXPbXuCWgygWhIMQFoYftxOJKjF8EYiyC/GhjNuJDYb9jbcTm5jZmzG6xe5G+nPHv6okJPnn5H+8MGPP7dt57c3H/zwEZ/3UQAwWkFkA7HqQeAPKYpHwSgYBaNgpAAAMINTJA7DnYkAAAAASUVORK5CYII=","orcid":"","institution":"Amity University Punjab","correspondingAuthor":true,"prefix":"","firstName":"Anil","middleName":"Kumar","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2025-06-05 15:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6830750/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6830750/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40203-025-00461-y","type":"published","date":"2025-11-06T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87063745,"identity":"3a7cfdf4-35fe-4db5-bb29-0268eebe86c6","added_by":"auto","created_at":"2025-07-18 17:48:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":389822,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Venn diagram of overlapping gene targets across the \u003cem\u003eM. oleifera \u0026nbsp;\u003c/em\u003eglycosides –CRC pairs from top to bottom, NZR –CRC(43), NZA –CRC(41), RPA –CRC(48) and MRG –CRC(47), (b) PPI map of 179 overlapping genes with 78 nodes and PPI enrichment p-value:\u0026lt; 1.0e-16 (c) Cluster 1: 15 genes (nodes) and their association with each other (48 edges), (PPI enrichment p-value: 1.05e-13). Cluster 2: 13 genes [nodes] and their association with each other (47 edges), (PPI enrichment p-value: \u0026lt; 1.0e-16). Cluster 3: 8 genes (nodes) and their association with each other (15 edges), (PPI enrichment p-value: 3.45e-08).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6830750/v1/467181348bbaed763a9baafd.png"},{"id":87063746,"identity":"c799a8a4-5914-42f1-8bcb-8bdf751508f0","added_by":"auto","created_at":"2025-07-18 17:48:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":228967,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology (GO) enrichment results across three main categories: (a) Biological Process (BP)\u003cstrong\u003e, \u003c/strong\u003e(b) Molecular Function (MF), (c)\u003cstrong\u003e \u003c/strong\u003eCellular Component (CC) and (d) KEGG Pathways enrichment,highlighting the functional relevance of the intersecting genes between\u003cstrong\u003e \u003c/strong\u003eNZR, NZA, RPA and MRG and CRC.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6830750/v1/b1677858fdf82fdaa35ef1af.png"},{"id":87063748,"identity":"699ecf0b-9b25-431d-b982-2c1a649b6b02","added_by":"auto","created_at":"2025-07-18 17:48:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":716466,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Network of \u003cem\u003eM.\u003c/em\u003eoleifera glycosides, Niazirinin(NZR) , Niazimicin A (NZA),4-(Rhamnosyloxy) phenylacetonitrile(RPA) and Moringyne (MRG )and their associated target genes involved in CRC, (b) Network of CRC target pathways and associated genes , (c) Merged Network of \u003cem\u003eM.oleifera\u003c/em\u003eGlycoside Target Genes and CRC Pathway Genes, illustrating potential therapeutic interactions, (d) Top eleven hub genes identified based on centrality measures from network pharmacology analysis which includes NFKB1, PIK3R1, PIK3CD, PIK3CB, CHUK, GRB2,NOS2,SLC2A1,ABL1,PDGFRA and STAT1.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6830750/v1/5f1f6902e324b707ab59b531.png"},{"id":87063765,"identity":"3a4ba8a5-1c6c-4b4c-8e01-ebf95177ee2a","added_by":"auto","created_at":"2025-07-18 17:48:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":495365,"visible":true,"origin":"","legend":"\u003cp\u003e(a)\u003cstrong\u003e B\u003c/strong\u003eox plots and stage plots showing mRNA expression levels\u003cstrong\u003e of \u003c/strong\u003eNFKB1, PIK3R1, PIK3CD, PIK3CB, CHUK, GRB2, NOS2, SLC2A1, ABL1, PDGFRA and STAT1\u003cstrong\u003e \u003c/strong\u003ein\u003cstrong\u003e \u003c/strong\u003ecolon adenocarcinoma (COAD; tumor tissue samples = 275, normal tissue samples = 349) and rectum adenocarcinoma (READ; tumor = 92, normal = 318) tissues ,based on TCGA normal and GTEx data. Differential gene expression analysis (tumor vs. normal) identified significant upregulation of NOS2, SLC2A1, and STAT1, and significant downregulation of PIK3R1, ABL1, and PDGFRA. Asterisks indicate statistical significance (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6830750/v1/97fbb6c7b7ed276edf8260dd.png"},{"id":87063751,"identity":"cd96b336-936b-49c7-9c2b-2bc4326acdb5","added_by":"auto","created_at":"2025-07-18 17:48:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":645735,"visible":true,"origin":"","legend":"\u003cp\u003eTwo- and three-dimensional interaction diagrams along with binding affinity in kcal/mol of the genes NOS2, SLC2A1, and STAT1, which were significantly upregulated in CRC (a), and PIK3R1, ABL1, and PDGFRA, which were significantly downregulated in CRC (b), with Moringa oleifera glycosides: Niazirinin (NZR), Niazimicin A (NZA), 4-(Rhamnosyloxy)phenylacetonitrile (RPA), and Moringyne (MRG).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6830750/v1/6b11e1f270af1c19307a7e2c.png"},{"id":95564147,"identity":"2b77a66c-de11-4122-8b65-4b7548515914","added_by":"auto","created_at":"2025-11-10 16:08:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3082532,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6830750/v1/964b108b-3ef7-49db-bd92-447425be5446.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Network Pharmacology and Molecular Docking Reveal Therapeutic Potential of Moringa oleifera Glycosides, Targeting Key Regulatory Genes in Colorectal Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eColorectal cancer(CRC) is the third most commonly diagnosed cancer, and the second most common cause of cancer-related deaths globally (Duan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, with the significant rise in the number of reported cases in older people, the incidence of CRC globally is estimated to increase more than twice by 2035, with the largest growth being reported in less developed countries (ACS \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Most cases are caused by environmental factors. A minority result from inherited genetic conditions. Much of CRC, perhaps half of all cases, is due to poor diet (for example, a diet with low whole grain and high processed meat content), smoking, high body mass index and physical inactivity (Nassa \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conventional therapy modalities for CRC such as surgery, chemotherapy, and radiotherapy may be applied individually or in combination. These treatments, however, often cause severe side effects because of their cytotoxicity on all cells and their non-specific nature. In addition, a large number of patients suffer from relapses after these treatments. Therefore, it is crucial to seek more effective treatment interventions for CRC patients (Li et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). The treatment options for primary and metastatic cancers (mtCRC) have increased significantly in the recent past. Some of these include laparoscopic surgery for early-stage CRC, aggressive removal of metastatic CRC (e.g., lung and liver metastasis), radiotherapy for RC, and neoadjuvant and palliative chemotherapy (Afshar et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Chemotherapy and radiotherapy are the primary forms of treatment for CRC patients in the present time. Yet, they often result in adverse side effects among patients, such as liver impairment, gastrointestinal issues, and bone marrow suppression that complicate the completion of the full treatment by the patient (Baidoun et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite significant advancement in the domains of screening, diagnosis, and treatment for CRC, the prognosis is still unfavorable and significant hurdles remain. Biomarkers have also been considered recently for their potential in individualizing therapy, and they were found to be beneficial in improving patient survival. But still there is need for more sensitive and specific markers that are applicable to CRC diagnosis and are cheaper and less invasive compared to present methods (Zajkowska and Mroczko \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In spite of progress in surgery, screening, and chemotherapy, CRC remains a therapeutic challenge through late-stage diagnosis, drug resistance, and heterogeneity of the tumor. Therefore, there is a pressing need for safer, more potent, and multi-targeted drugs that can act at multiple stages of cancer progression (Dekker et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNatural phytochemicals have come up as exciting candidates in cancer prevention and therapy because of their broad-spectrum bioactivity, low toxicity, and capacity to modulate several molecular targets at the same time (Tuorkey \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Phytochemicals have a great effect on molecular processes implicated in cancer growth and advancement. These are to increase antioxidant capacity, inactivate carcinogens, stop cell growth, induce cell cycle arrest and death, and inhibit immune function (Choudhari et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn drug discovery, the approach of designing multi-target drugs towards complicated diseases like cancer is accelerating at a fast pace. Network pharmacology is an integrated paradigm in drug research that explores traditional medications by investigating complicated network models and using high-throughput data analysis and calculation strategies (Bhatia et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A network is an illustration of a set of data that emphasizes the relationships between nodes. These nodes, which represent genes, proteins, small molecules, and other entities that may interact within the system being modeled, are linked by edges, which represent the properties of the interactions (Berger and Iyengar \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Network pharmacology is increasingly being used in order to understand how herbal medicines work. Through the identification of hub genes, researchers can derive key drug-affected targets. When a hub gene is modulated by the drug, several downstream effects can be triggered that lead to therapeutic manifestations (Janani et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Plant-derived glycosides have emerged as promising topical candidates for anticancer agents given their plethora of biological activities. They exert their anticancer effects through a variety of mechanisms, which include induction of apoptotic mediated cell death, adopting cell cycle arrest, inhibiting angiogenesis, inhibiting metastasis, and modulating immune response (T 2024).\u003c/p\u003e\u003cp\u003eHere, we report a thorough in silico evaluation of four Moringa oleifera (M. oleifera) glycosides, Niazirinin(NZR), Niazimicin A (NZA),4-(Rhamnosyloxy) phenylacetonitrile (RPA) and Moringyne (MRG ) in relation to CRC. The miracle tree, Moringa oleifera (M. oleifera), exists throughout the world in nearly all tropical and subtropical areas, although it is thought to be native to Afghanistan, Bangladesh, India, and Pakistan. Moringa oleifera or the \"tree of life\" or \"miracle tree\" is an important herb plant because it has enormous medicinal uses. The plant is used traditionally to treat wounds, pain, ulcers, liver disease, heart disease, cancer, and inflammation (Pareek et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur pipeline combines network pharmacology to find common targets of these four phytochemicals and CRC-associated genes, \u003cb\u003eProtein-Protein Interaction (\u003c/b\u003ePPI) network analysis to find central hub genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis, mRNA expression analysis to confirm hub gene expression patterns in CRC vs. normal tissue and molecular docking to compare binding affinities of the compounds with essential targets. This integrative strategy will seek to elucidate the multi-target activities of these phytochemicals and evaluate their prospect as complement agents to provide a novel therapeutic intervention therapy perspective in CRC treatment. This research does not involve human subjects, human materials, and will not require approval by an institutional ethics committee.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Potential Target Identification for Network Pharmacology\u003c/h2\u003e\u003cp\u003eThe active glycoside phytochemicals of \u003cem\u003eM. oleifera\u003c/em\u003e were screened using an IMPPAT: Indian Medicinal Plants, Phytochemistry and Therapeutics, curated database, where oral bioavailability (OB\u0026thinsp;\u0026ge;\u0026thinsp;50%) and drug-like properties (DL\u0026thinsp;\u0026ge;\u0026thinsp;0.50) and Molecular weight (g/mol)\u0026thinsp;\u0026le;\u0026thinsp;500 were primary criteria for evaluating drug development (Pareek et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). SMILES were retrieved from \u0026ldquo;IMPPAT: Indian Medicinal Plants, Phytochemistry And Therapeutics\u0026rdquo; (Mohanraj et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vivek-Ananth et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). which are further used for extracting Uniprot IDs from super-PRED \u003cb\u003e(\u003c/b\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prediction.charite.de/subpages/target_prediction.php\u003c/span\u003e\u003cspan address=\"https://prediction.charite.de/subpages/target_prediction.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on 27 May 2025 in order to identify the potential targets of selected glycosides (Nickel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These uniprot IDs were then imported to another online server STRING \u003cb\u003e(\u003c/b\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Version: 12.0\u003cb\u003e)\u003c/b\u003e to identify the target genes for the titled glycosides by specifying species as \u0026ldquo;Homo sapiens\u0026rdquo;, high confidence score cut off 0.700 and a medium false discovery rate (FDR) stringency of 5% (Szklarczyk et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The number of extracted genes for titled glycosides were Niazirinin-78, Niazimicin A-78, 4-(Rhamnosyloxy) phenylacetonitrile-88 and Moringyne \u0026minus;\u0026thinsp;82. We searched for CRC-related target genes by using the keyword \u0026ldquo;colorectal cancer in GeneCards \u003cb\u003e(\u003c/b\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) accessed on 27 May 2025 (GeneCards \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Total 50,000 target genes were downloaded, which were then shortlisted by Gifts filter\u0026thinsp;\u0026ge;\u0026thinsp;60 and 1232 gene targets of CRC were used for further investigation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Protein-Protein Interaction (PPI) Network Construction\u003c/h2\u003e\u003cp\u003eFurther the overlapping target gene of the NZR, NZA, RPA and MRG and CRC are identified by using Venny 2.1 \u003cb\u003e(\u003c/b\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Oliveros \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). There were 179 overlapping predicted gene targets across the compound-CRC pairs, 43 for NZR, 41 for NZA, 48 for RPA and 47 for MRG, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (a). These overlapping gene targets were imported into STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Version: 12.0) on 27 May 2025 for PPI network construction by selecting species as \u0026ldquo;Homo sapiens\u0026rdquo;, medium confidence score cut off 0.400 and a medium false discovery rate (FDR) stringency of 5% ,the constructed PPI network is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (b) (Szklarczyk et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore three different clusters were identified as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (c, d \u0026amp; e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 GO and KEGG enrichment\u003c/h2\u003e\u003cp\u003eAdditionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis of overlapping genes was extracted from STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) Version: 12.0, to identify the possible mechanisms of the titled glycosides in CRC. Analyses produced a total of 712 GO-terms, including 566 biological processes (BP), 81 molecular functions (MF), 65 cellular component (CC) terms and the top 10 GO terms (BP,MF,CC) are represented in the bubble plot as presented in Figure.2 (a,b,c). For extracting BP,MF,CC, Go terms are grouped by similarity\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.8, Maximum FDR shown\u0026thinsp;\u0026le;\u0026thinsp;0.05, Minimum signal shown\u0026thinsp;\u0026ge;\u0026thinsp;0.01, Minimum strength shown\u0026thinsp;\u0026ge;\u0026thinsp;0.01 Minimum count in network was set at 2. Additionally a total of 148 KEGG pathways were created, out of which we selected 11 pathways that are applicable to CRC based on FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Minimum signal\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.01, Minimum strength\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.01 and Minimum count in network 2 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (d). Genes associated with these top 10 pathways were considered as CRC targets for network construction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2\u003cb\u003e.4 Network Pharmacology Analysis and Hub Gene Identification\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eFollowing the GO and KEGG analysis, the target network of 179 overlapping genes was constructed, as shown in Figure. 3 (a). The network of CRC- top 10 target pathways and related genes is depicted in Figure. 3 (b), and the merged network is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (c). These networks were constructed to investigate the interactions of active genes within the complex biological system by using Cytoscape V3.10.3 on 28th May, 2025 (Shannon et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Cytoscape software was used to visualize and analyze the network by calculating centrality and other key topological parameters. Then the plugin CytoHubba was employed to identify the top twenty nodes based on Maximal Clique Centrality as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Chin et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (d) illustrates the top eleven hub genes with colour gradient.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Gene expression and Survival analysis\u003c/h2\u003e\u003cp\u003eIn the current research, Gene Expression Profiling Interactive Analysis (GEPIA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to check the diverse expression of the hub genes in Colon adenocarcinoma (COAD) and Rectum adenocarcinoma (READ) and normal tissues (Tang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). GEPIA is a web server that provides interactive and flexible functionalities using data from \u0026lsquo;The Cancer Genome Atlas\u0026rsquo; (TCGA) and Genotype-Tissue Expression (GTEx) database. For boxplots analysis |Log2FC| Cutoff was set to 1 and p-value Cutoff was set at 0.01 and used log2 (TPM\u0026thinsp;+\u0026thinsp;1) for log-scale. Matched data with TCGA normal and GTEx data for comparison purpose is used.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Molecular docking analysis\u003c/h2\u003e\u003cp\u003eFollowing gene expression analysis, the molecular docking analysis of statistically significant genes (PIK3R1, NOS2, SLC2A1, ABL1, PDGFRA and STAT1) was conducted. The ligand molecules and target protein structures were prepared for molecular docking by using the Discovery Studio, 4.5 software Dassault Syst\u0026egrave;mes BIOVIA (BIOVIA 2017). The molecular docking (auto blind docking) was carried out using CB-Dock2 ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cadd.labshare.cn/cb-dock2/index.php\u003c/span\u003e\u003cspan address=\"https://cadd.labshare.cn/cb-dock2/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) an online tool which automatically identifies active sites within a given protein, Cavity volume (\u0026Aring;3),Center,(x, y, z),Docking size(x, y, z) and Contact residues by using autodock vina (Liu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 PPI network\u003c/h2\u003e\u003cp\u003eThe PPI network (Figure. 1b) was built by using 179 shared gene targets, with confidence level of 0.400 and 5% FDR, contains 78 nodes, 284 edges (interactions), 7.28 average node degree (i.e., each protein interacts with about 7.28 other proteins), avg. local clustering coefficient of 0.482 (i.e., about 48.2% of the neighbours of a protein are themselves connected to each other), and PPI enrichment p-value was \u0026lt;\u0026thinsp;1.0e-16. The very low p-value (\u0026lt;\u0026thinsp;0.05) suggests that the network connectivity in the study observed is statistically significant and have not occurred due to random fluctuations. After constructing PPI network ,MCL clustering was employed and 3 different clusters were identified.Cluster 1 has gene count 15(number of nodes), 48 number of edges, average node degree 6.4,avg. local clustering coefficient 0.729, expected number of edges 13 and PPI enrichment p-value\u0026thinsp;\u0026lt;\u0026thinsp;1.0e-16.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCluster 1 is correlated with immune modulation, positive regulation of interferon-alpha production, and Chagas disease, describing possible overlapping inflammatory or infection-related signaling pathways that may be of relevance to tumor immunology in CRC. Cluster 2 has gene count 13 and PPI enrichment p-value of \u0026lt;\u0026thinsp;1.0e-16. Genes in this group are associated with the neurotrophin signaling pathway and intracellular signaling downstream, implying function in cell survival, differentiation, and stress adaptation processes frequently hijacked in cancer development and drug resistance. Cluster 3 has gene count of eight and it was found to be associated with genes for mitotic drive and chromosome movement, including functions like prometaphase chromosome condensation, spindle elongation, and outer kinetochore activity, and shows a very high association with cell cycle control and genomic integrity, both of which are essential in CRC development. From the PPI network it is evident that the titled glycosides of M. oleifera could be exerting their pharmacological action on CRC because of clusters of proteins with high connectivity and important biological significance. The identified proteins which are using this network could be used as potential biomarkers or therapeutic targets for experimental verification in the future.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 GO and KEGG Analysis\u003c/h2\u003e\u003cp\u003eFollowing the construction of PPI networks for overlapping targets of \u003cem\u003eM. oleifera\u003c/em\u003e glycosides\u0026mdash;NZR, NZA, RPA, and MRG and CRC, Gene Ontology (GO) enrichment analysis was carried out to find out the important biological processes of the 179 targets. The resulting dot plots of BP,MF and CC are shown in Figure. 2 (a, b, c) which displays the top 10 enriched GO terms across three ontologies. The dots with lightest shade represent FDR\u0026thinsp;\u0026lt;\u0026thinsp;1e-15, confirming stronger evidence for the enrichment. The color gradient represents the False Discovery Rate (FDR), the significance of enrichment (Li et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Darker shades (e.g., dark blue or purple) refer to higher FDR values (less significant), and lighter shades such as green and cyan represent lower FDR values (more significant). Enrichment with light green color (e.g., lowest FDR values) are of higher significance. The size of the bubbles is related to the number of genes associated with GO terms larger size indicates more number of genes are associated with that particular process. Among the enriched BP terms, the most prominent is response to organic substance. This terminology suggests that the candidate compounds are capable of affecting those genes involved in the recognition and response towards a wide range of endogenous and exogenous organic molecules, which is particularly relevant in view of the involvement of tumor microenvironment and metabolite signaling in CRC development. Other supplemented BP were response to cellular organic substance, response to chemical, and response to oxygen-containing compound also provide support for the hypothesis that these glycosides control important stress and chemical response pathways. Together, these findings point towards the bioactivity of M. oleifera, glycosides to control chemical response pathways, which may contribute to their anti-CRC activity. The most enriched molecular processes is catalytic activity, which means that the target proteins plays a key role in catalyzing biochemical reactions. This is related to the role of enzymes in cancer processes including metabolism, proliferation, and apoptosis. Other enrichment terms such as small molecule binding, nucleotide binding, and anion/ion binding indicate that most of the targets interact with signaling molecules and metabolic intermediates, also imply their role in CRC pathophysiological regulation.\u003c/p\u003e\u003cp\u003eNotably, kinase binding and protein kinase binding enrichment suggest potential modulation of kinase signaling pathways PI3K/AKT and MAPK cascades extensively involved in cancer cell survival and drug resistance. The protein binding enrichment further suggests that these compounds may modulate diverse PPI.\u003c/p\u003e\u003cp\u003eFurthermore, in case of Cellular component enrichment analysis, integral component of plasma membrane, is the most enriched term, which indicates that many target proteins are linked with the cell membrane, suggesting a role in signal transduction, transport, and interaction with extracellular stimuli that are critical processes often dysregulated in CRC. Moreover, enrichment in plasma membrane, cell periphery, and cell junction further supports that these glycosides may be modulating intercellular communication which is vital in tumor progression and metastasis. Additionally, the presence of targets in the cytoplasm, cytoplasmic vesicles, and protein-containing complexes gives indication that the target proteins may be involved in intracellular trafficking, protein complex assembly, and signal relay mechanisms. Integration of these findings highlights the multi-targeted therapeutic potential of M. oleifera glycosides against CRC through the modulation of enzymatic activity and key signaling nodes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Top 10 pathways enrichment is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(d) and respective gene count, FDR and matching proteins in the network is depicted in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For shortlisting the KEGG pathways we have considered genes count\u0026thinsp;\u0026ge;\u0026thinsp;4 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\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\u003eTop 10 Enriched Biological Pathways Associated with overlapping target gene of the NZR, NZA, RPA and MRG and CRC\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e#term ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePathway\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGenes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFDR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMatching proteins in our network (labels)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePathways in cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.77E-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNFKB1,PTGER2,PDGFRA,NOS2,STAT1,CHUK,HSP90AB1,ABL1,AR,PIK3CD,\u003c/p\u003e\u003cp\u003eGRB2,KEAP1,ITGB1,NFE2L2,GSTP1,SLC2A1,PIK3R1,PIK3CB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa04066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHIF-1 signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.30E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSERPINE1,NFKB1,FLT1,NOS3,NOS2,TLR4,PIK3CD,SLC2A1,PIK3R1,PIK3CB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa04024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecAMP signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.82E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCFTR,NFKB1,PTGER2,SLC9A1,GRIA2,PDE3A,ADORA1,GRIN1,PIK3CD,CHRM2,\u003c/p\u003e\u003cp\u003ePIK3R1,PIK3CB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa04014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRas signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.49E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNFKB1,PDGFRA,FLT1,CHUK,GRIN1,ABL1,PIK3CD,GRB2,PLA2G2A,PIK3R1\u003c/p\u003e\u003cp\u003e,PTPN11,PIK3CB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePD-L1 expression and PD-1 checkpoint pathway in cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.49E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNFKB1,STAT1,CHUK,TLR4,CSNK2B,PIK3CD,PIK3R1,PTPN11,PIK3CB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMicroRNAs in cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.25E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHDAC5,NFKB1,PDGFRA,CDC25C,ABL1,PIK3CD,GRB2,ABCC1,PIK3R1,PIK3CB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa04151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePI3K-Akt signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.41E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNFKB1,PDGFRA,FLT1,NOS3,CHUK,HSP90AB1,TLR4,PIK3CD,GRB2,ITGB1,\u003c/p\u003e\u003cp\u003eCHRM2,PIK3R1,PIK3CB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa04620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eToll-like receptor signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.33E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNFKB1,TLR8,STAT1,CHUK,TLR4,PIK3CD,PIK3R1,PIK3CB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa04062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChemokine signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNFKB1,PRKCD,STAT1,CHUK,PIK3CD,GRB2,ITK,PIK3R1,PIK3CB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa05210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eColorectal cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePIK3CD,GRB2,PIK3R1,PIK3CB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Network Pharmacology and Hub Genes Identification\u003c/h2\u003e\u003cp\u003eIn order to identify major molecular targets influenced by M. oleifera glycosides in the context of CRC, we constructed an integrated network. \u003cem\u003eM. oleifera\u003c/em\u003e Glycoside-Target Gene Network comprised the four Moringa glycosides (Niazirinin (NZR), Niazimicin A (NZA), 4-(Rhamnosyloxy) phenylacetonitrile (RPA), and Moringyne (MRG)) as nodes, linked to their respective target genes involved in CRC.\u003c/p\u003e\u003cp\u003eThis network comprised 82 nodes and 179 edges, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a).CRC Pathway-Gene Network depicted important CRC-related pathways and their respective genes as nodes, and their known interactions as edges. It had 48 nodes and 105 edges, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b).\u003c/p\u003e\u003cp\u003eThese two isolated networks were then combined in Cytoscape (version 3.10.3) to build an overall integrated network. This last network had 92 nodes and 284 edges, and is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(c).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurthermore, to find the hub genes, a plugin CytoHubba, in Cytoscape, was used to compute centrality values within the integrated network. The top 25 nodes, ranked by various topological scores including Maximal Clique Centrality and Edge Percolated Component (EPC), are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(d).The output of cytohubba has different color gradient (red to yellow) which signifies node importance within the network, where darker nodes represent higher centrality and thus greater importance. Based on an MCC score threshold of \u0026ge;\u0026thinsp;5, eleven prominent hub genes were identified: NFKB1, PIK3R1, PIK3CD, PIK3CB, CHUK, GRB2, NOS2, SLC2A1, ABL1, PDGFRA and STAT1. The top five pathways, most probable to be modulated by the bioactive M. oleifera glycosides are the Pathways in cancer, PI3K-Akt signaling pathway, cAMP signaling pathway, Ras signaling pathway, HIF-1 signaling pathway, and MicroRNAs in cancer. These pathways demonstrated an MCC score of \u0026ge;\u0026thinsp;10, proposing a multi-component regulatory mechanism underlying the prospective anti-CRC actions of the glycosides.\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\u003eTopological Centrality Analysis of Key 25 Nodes in the Integrated Moringa oleifera-CRC network showing top eleven hub genes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003enode_name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMCC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEPC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCloseness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRadiality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBetweenness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.25275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2253.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e48122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63.83333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.23077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2533.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e57692\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNZR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.16667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.14286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1412.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNZA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.83333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.0989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1734.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42288\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathways in cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.59341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e269.1354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8832\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePI3K-Akt signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41.16667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.48352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e124.5075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4518\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecAMP signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.46154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125.6221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRas signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.46154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e94.92664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIF-1 signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.16667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.41758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e65.0883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicroRNAs in cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.16667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.41758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e53.71243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD-L1 expression and PD-1 checkpoint pathway in cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.3956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63.67957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2536\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemokine signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.3956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e68.92142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2740\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eToll-like receptor signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.83333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.37363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e44.98398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColorectal cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.16667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.28571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.79199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e296\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNFKB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.83333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.18681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e321.1431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIK3R1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.58333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.07692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e271.2631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIK3CD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.68132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e121.7121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIK3CB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.68132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e121.7121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHUK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.83333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.12088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e223.2264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7746\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.83333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.12088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e240.1239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7320\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNOS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.16667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.03297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.04065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4264\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSLC2A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.16667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.03297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.04065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4264\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eABL1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.66667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.9011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63.63297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2810\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDGFRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.91667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.74725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45.71507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1792\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTAT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.46154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35.5437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e906\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 mRNA expression levels, Stage plots and Kaplan\u0026ndash;Meier analysis of hub genes\u003c/h2\u003e\u003cp\u003eTo assess the transcriptional significance of the hub genes, mRNA expression analysis was conducted by using GEPIA 2. Box plot comparisons of colon adenocarcinoma (COAD; tumor tissue samples\u0026thinsp;=\u0026thinsp;275, normal tissue samples\u0026thinsp;=\u0026thinsp;349) and rectum adenocarcinoma (READ; tumor\u0026thinsp;=\u0026thinsp;92, normal\u0026thinsp;=\u0026thinsp;318) tissues showed differential expression of the hub genes as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a), along with stage plots. Asterisks on PIK3R1, NOS2, SLC2A1, ABL1, PDGFRA and STAT1 indicate statistically significant expression differences between tumor and normal tissues. Among these NOS2, SLC2A1 and STAT1 genes were significantly up regulated, while PIK3R1, ABL1, and PDGFRA were significantly down regulated in COAD and READ tumor samples. These findings suggest that PIK3R1, ABL1, and PDGFRA my act as potential tumor suppressors or involved in pathways that suppress tumor growth, whereas NOS2, SLC2A1 and STAT1 may act putative oncogenes in CRC. For stage plots there is statistically significant variation in the level of expression of SLC2A1 gene in different pathological stages as F value\u0026thinsp;=\u0026thinsp;4.31 and Pr (\u0026gt;\u0026thinsp;F)\u0026thinsp;=\u0026thinsp;0.00531 which is much smaller than required standard significance level (α) of 0.05. That implies the expression of SLC2A1 is not always identical in all stages of CRC. The important difference in SLC2A1 expression between various stages of CRC (p\u0026thinsp;=\u0026thinsp;0.00531), as well as its established function in tumor growth and metabolism, supports that SLC2A1 is a possible stage-specific prognostic biomarker for colorectal cancer (Liu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). This makes SLC2A1 an especially compelling hub gene to explore further investigation, given its differential expression between stages and implication of a disease progression role.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(b)\u003c/b\u003e Kaplan\u0026ndash;Meier survival curves showing the association between gene expression and overall survival (OS) in colorectal cancer (CRC) patients. The blue line represents low expression group, while the red line represents the high expression group.\u003c/p\u003e\u003cp\u003eFurthermore, we generated Kaplan\u0026ndash;Meier plots for Overall Survival (OS) as shown in Figure. 4(b). The blue line represents the low expression group, and the red line represents the high expression group. Each plot compares\u0026thinsp;~\u0026thinsp;181 patients with high gene expression and low gene expression. Genes with hazard ratio (HR)\u0026thinsp;\u0026gt;\u0026thinsp;1 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are considered as risk genes, since their increased expression is linked to higher disease progression or mortality risk. On the other hand, genes with HR\u0026thinsp;\u0026lt;\u0026thinsp;1 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are referred to as protective genes ,as their expression is associated with a reduced risk of disease progression (Lin et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In our study, the genes NFKB1, PIK3CB, CHUK, NOS2, ABL1, PDGFRA and STAT1 exhibited HR\u0026thinsp;\u0026lt;\u0026thinsp;1, suggesting a possibly protective role, while PIK3CD had HR value of 1, indicating a possibly neutral role. Conversely, PIK3R1, GRB2 and SLC2A1 had HR\u0026thinsp;\u0026gt;\u0026thinsp;1, indicating a tendency towards higher risk. However, these values were not statistically significant, as their p-values exceeded the 0.05 threshold. Interestingly, NOS2 demonstrated a log-rank p-values is 0.02,which is less than 0.05 cutoff, indicating statistically significant improved survival among patients with high expression levels. In our analysis ,NOS2 was found to be significantly up regulated in CRC ,as shown in boxplot ,despite its elevated expression in tumor tissues, it exhibited HR\u0026thinsp;\u0026lt;\u0026thinsp;1 with a statistically significant p-value of 0.02 in OS analysis ,indicating that higher expression is associated with better patient survival. In some cases, NOS2-derived nitric oxide (NO) can induce cell death and inhibit tumor growth, while in others, it can promote cancer cell proliferation, metastasis, and angiogenesis (Mintz et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vannini et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These findings are in consistent with our results. These results confirm the multifaceted function of NOS2 within the CRC tumor microenvironment and propose its potential as a protective prognostic biomarker, warranting further functional assessment and study in large, stratified cohorts (Thomas and Wink \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These results suggest that while these hub genes may influence prognosis, validation in larger cohorts or through integrated multi-omics analyses is required to confirm their value as prognostic biomarkers in CRC.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Molecular Docking\u003c/h2\u003e\u003cp\u003eFurther in our study, a molecular docking approach was employed to examine the interactions between the genes NOS2, SLC2A1, and STAT1 (significantly upregulated), and PIK3R1, ABL1, and PDGFRA (significantly downregulated) and Moringa oleifera glycosides: Niazirinin (NZR), Niazimicin A (NZA), 4-(Rhamnosyloxy)phenylacetonitrile (RPA), and Moringyne (MRG). Binding affinity and interaction profiling of NZR, NZA, RPA, and MRG against key signaling proteins in CRC is described in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.Two- and three-dimensional interaction diagrams along with binding affinity in kcal/mol is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (a, b). The Favorable receptor\u0026ndash;ligand conformations, indicated by lower (more negative) binding energy values, correspond to greater binding affinity and higher likelihood of biological activity. A binding energy value below 0 kJ/mol indicates that there is spontaneous binding, while values below \u0026minus;\u0026thinsp;5 kJ/mol indicate strong ligand\u0026ndash;receptor interactions (Hu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The results of docking are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All target proteins have shown good binding energies (\u0026le; \u0026minus;\u0026thinsp;6.4 kcal/mol) with all Moringa oleifera glycosides: NZR, NZA, RPA, and MRG. Titled Glycosides could inhibit pro-inflammatory and pro-tumorigenic NOS2 activity. Earlier studies have shown that inhibition of NO production in tumors can improve antitumor immune response in animal models (Hegardt et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ito et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ridnour et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). H. \u003cem\u003eet al\u003c/em\u003e has demonstrated that knockdown of SLC2A1 led to low cell viability, reduced migration, and enhanced apoptosis in Caco-2 and SW480 cells (Li et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Knockout of STAT1 in CRC cells suppressed symptoms of colitis and tumor growth in the AOM/DSS-induced CRC mouse model (Chou et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From our docking results and suitable binding affinity we can draw analysis that downregulated genes (PIK3R1, ABL1, and PDGFRA) may get Glycosides -induced stabilization, while upregulated genes (NOS2, SLC2A1, and STAT1) might be inhibited through high-affinity binding, which could have anticancer effects.\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\u003eBinding Affinity and Interaction Profiling of Niazirinin (NZR), Niazimicin A (NZA), 4-(Rhamnosyloxy) phenylacetonitrile (RPA), and Moringyne (MRG) Against Key Signaling Proteins in CRC. \u003cem\u003eValues are represented as (Binding Energy in kcal/mol) / Conventional Hydrogen Bond Residues / Other Non-Covalent favorable Interactions.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein (PDB ID)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNiazirinin (NZR),\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNiazimicin A (NZA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4-(Rhamnosyloxy)phenylacetonitrile (RPA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMoringyne (MRG).\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNOS2\u003c/p\u003e\u003cp\u003e(2NSI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u0026ndash;8.6) / TYR 489 / Vander Waals, Pi-Alkyl, Pi-Pi stacked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u0026ndash;7.2) / TYR 491 / Vander Waals, Pi-Sigma, Pi-Sulfur, Pi-Pi stacked, Pi-Pi T-shaped, Pi-Alkyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u0026ndash;8.5) / TYR 489,TRP 372 / Vander Waals, Pi-Pi stacked, Alkyl, Pi-Alkyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(\u0026ndash;8.6) / GLY 371/ Vander Waals, Pi-Alkyl, Pi-Pi stacked,Pi-sigma\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSLC2A1\u003c/p\u003e\u003cp\u003e(6THA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u0026ndash;8.6) / ASN 288,GLN 283,TRP 412,asn 415 / Vander Waals, C-H bond, Pi-Pi T-shaped\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u0026ndash;7.8) / GLU380,THR 137 / Vander Waals, Alkyl, Pi-Pi T-shaped\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u0026ndash;7.9) / GLN 283,ASN 288 / Vander Waals, C-H bond ,Pi-anion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-8) / GLN 282, TRP 388,HIS 160 / Vander Waals, C-H bond, Pi-anion, Pi-Pi T-shaped\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTAT1\u003c/p\u003e\u003cp\u003e(8D3F)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u0026ndash;6.5) / SER 315/ Vander Waals, C-H bond, Pi-sigma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u0026ndash;6.5) / GLN 243,GLU 449 / Vander Waals, C-H bond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u0026ndash;6.8) / GLN 314,THR 450 / Vander Waals, C-H bond, Pi-sigma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(\u0026ndash;7.2) / GLN 322,GLN 243,THR 451,THR 450,VAL 319 / Vander Waals, Pi-sigma, Pi-Alkyl\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIK3R1\u003c/p\u003e\u003cp\u003e(8TS7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u0026ndash;7.3) / LYS346,LYS 575,GLN 579/ Vander Waals, C-H bond, Pi-donor hydrogen bond, Pi-sigma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u0026ndash;6.5) / GLN 433 / Vander Waals, Pi-sigma, Pi-Alkyl, Pi-Sulfur, Pi-Pi stacked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u0026ndash;6.4) / LYS 575 / Vander Waals, C-H bond, Pi-Alkyl, Pi-Pi stacked, Pi-Alkyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(\u0026ndash;6.9) / LYS 575 / Vander Waals, C-H bond Pi-Sigma, Pi-Alkyl, Pi-Pi stacked\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eABL1\u003c/p\u003e\u003cp\u003e(3QRK)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u0026ndash;7.5) / - / Vander Waals, Pi-Alkyl, Pi-anion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u0026ndash;7.3) / GLY 249 ,THR 315 / Vander Waals, C-H bond, Pi-sigma, Pi-Alkyl, Alkyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u0026ndash;7.2) / ASP 325 ,ASN 322,MET 318,GLY 249 / Vander Waals, Pi-sigma, Pi-Pi T-shaped\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(\u0026ndash;7.1) / ASN 322,GLY 249 / Vander Waals Alkyl, Pi-Alkyl, C-H bond, Pi-sigma\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDGFRA\u003c/p\u003e\u003cp\u003e(5GRN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u0026ndash;7.7) / THR 674,GLU 675 / Vander Waals, Pi-sigma, Pi-Alkyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u0026ndash;8.2) / TYR 676,LYS 627,GLU 644 / Vander Waals, Pi-Alkyl, Pi-sigma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u0026ndash;8.1) / ASP 836 / Vander Waals, Pi-sigma, Pi-Alkyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(\u0026ndash;7.3) / LYS 627,ASP 836,THR 674 / Vander Waals, Pi-sigma, Pi-Alkyl\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"},{"header":"4. Conclusion","content":"\u003cp\u003eIn this study we investigated the therapeutic value of Moringa oleifera glycosides \u0026mdash; Niazirinin (NZR), Niazimicin A (NZA), 4-(Rhamnosyloxy) phenylacetonitrile (RPA), and Moringyne (MRG) for CRC. By network pharmacology analysis, such as target prediction, protein-protein interaction (PPI) mapping, and GO and KEGG pathway enrichment, eleven hub genes (NFKB1, PIK3R1, PIK3CD, PIK3CB, CHUK, GRB2, NOS2, SLC2A1, ABL1, PDGFRA and\u003c/p\u003e\u003cp\u003eSTAT1) were identified as central regulators potentially regulated by these glycoside compounds. We identified five key pathways (PI3K-Akt signaling pathway, cAMP signaling pathway,Ras signaling pathway,HIF-1 signaling pathway and MicroRNAs in cancer) involved in the treatment of CRC by active phytochemicals of Moringa oleifera. Further, mRNA gene expression and overall survival analysis between tumor and normal tissues indicated that PIK3R1, NOS2, SLC2A1, ABL1, PDGFRA and STAT1 are statistically significant. Among these NOS2, SLC2A1 and STAT1 genes were significantly up regulated, while PIK3R1, ABL1, and PDGFRA were significantly down regulated in COAD and READ tumor samples. These findings suggest that PIK3R1, ABL1, and PDGFRA my act as potential tumor suppressors or involved in pathways that suppress tumor growth, whereas NOS2, SLC2A1 and STAT1 may act putative oncogenes in CRC. From the stage plots it was found that there is statistically significant variation in the level of expression of SLC2A1 gene in different pathological stages as F value\u0026thinsp;=\u0026thinsp;4.31 and Pr (\u0026gt;\u0026thinsp;F)\u0026thinsp;=\u0026thinsp;0.00531 which supports that SLC2A1 is a possible stage-specific prognostic biomarker for colorectal cancer .This makes SLC2A1 an especially compelling hub gene to explore further investigation, given its differential expression between stages and implication of a disease progression role. Further, molecular docking study showed high binding (\u0026ndash;8.6 kcal/mol) with Niazirinin (NZR) and Moringyne (MRG) indicates strong inhibitory potential, particularly against metabolic (SLC2A1) and inflammatory (NOS2) targets\u003c/p\u003e\u003cp\u003eIn general, this integrative analysis underscores the therapeutic potential of \u003cem\u003eM. oleifera\u003c/em\u003e glycosides to modulate central regulatory genes of CRC, and offers a platform for additional experimental confirmation and drug development directed towards hub genes like SLC2A1, and NOS2.\u003c/p\u003e"},{"header":"5. Future prospects","content":"\u003cp\u003eIt is to note that additional research is required to ascertain the possible toxicity and therapeutic effectiveness of combining these four Glycosides. It is advised to do animal studies to assess the safety and effectiveness before beginning clinical trials. To determine the ideal dosage for treatment, dose-response studies are also required. Mechanistic studies are required to gain a deep understanding of how individual Glycoside affect PI3K-Akt signaling pathway, cAMP signaling pathway, Ras signaling pathway,HIF-1 signaling pathway and MicroRNAs in cancer pathways. Confirmation of gene expression and survival patterns by using greater and more heterogeneous CRC patient cohorts would enhance the prognostic significance of the discovered hub genes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors hereby affirm for the publication of the manuscript in the Journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for the said work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAS conceptualized and planned the study and written the original draft. Abhinav S. and DKM analysed, visualized and reviewed the manuscript along with preparation of figures; SK: helped in analysis, visualization and arrangement of references; AKS: conceptualized and supervised the study and contributed to writing the sections and editing the manuscript critically.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors have critically edited and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors express gratitude to the Chancellor, Guru Kashi University, Talwandi Sabo, Bathinda for providing the requisite platform to carry out the said work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eACS (2022) Cancer Facts \u0026amp; Figures. American Cancer Society; Atlanta, GA, USA [Accessed on 26 May 2023]: Available online: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2022.html\u003c/li\u003e\n\u003cli\u003eAfshar S, Sedighi Pashaki A, Najafi R et al. (2020) Cross-Resistance of Acquired Radioresistant Colorectal Cancer Cell Line to gefitinib and regorafenib. Iran J Med Sci 45:50-58 doi:10.30476/ijms.2019.44972\u003c/li\u003e\n\u003cli\u003eBaidoun F, Elshiwy K, Elkeraie Y et al. (2021) Colorectal Cancer Epidemiology: Recent Trends and Impact on Outcomes. Curr Drug Targets 22:998-1009 doi:10.2174/1389450121999201117115717\u003c/li\u003e\n\u003cli\u003eBerger SI, Iyengar R (2009) Network analyses in systems pharmacology. Bioinformatics 25:2466-2472 doi:10.1093/bioinformatics/btp465\u003c/li\u003e\n\u003cli\u003eBhatia N, Mokashi A, Nathore N et al. (2022) Network Pharmacology: An Emphasis on Traditional Chinese Medicines and Its Adaptability for Ayurveda Medicines in India. International J of Med Sci and Clin Res Studies 2:1608-1620 doi:10.47191/ijmscrs/v2-i12-40.\u003c/li\u003e\n\u003cli\u003eBIOVIA DS (2017) Dassault Syst\u0026egrave;mes BIOVIA. Discovery Studio Modeling Environment. Dassault Syst\u0026egrave;mes Release 4.5, San Diego.\u003c/li\u003e\n\u003cli\u003eChin CH, Chen SH, Wu HH et al. (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8 Suppl 4:S11 doi:10.1186/1752-0509-8-S4-S11\u003c/li\u003e\n\u003cli\u003eChou PH, Luo CK, Wali N et al. (2022) A chemical probe inhibitor targeting STAT1 restricts cancer stem cell traits and angiogenesis in colorectal cancer. J Biomed Sci 29:20 doi:10.1186/s12929-022-00803-4.\u003c/li\u003e\n\u003cli\u003eChoudhari AS, Mandave PC, Deshpande M et al. (2020) Corrigendum: Phytochemicals in Cancer Treatment: From Preclinical Studies to Clinical Practice. Front Pharmacol 11:175 doi:10.3389/fphar.2020.00175\u003c/li\u003e\n\u003cli\u003eDekker E, Tanis PJ, Vleugels JLA et al. (2019) Colorectal cancer. Lancet 394:1467-1480 doi:10.1016/S0140-6736(19)32319-0\u003c/li\u003e\n\u003cli\u003eDuan B, Zhao Y, Bai J et al. (2022) Colorectal Cancer: An Overview doi:NBK586003 [bookaccession] 10.36255/exon-publications-gastrointestinal-cancers-colorectal-cancer\u003c/li\u003e\n\u003cli\u003eGeneCards (2025) The Human Gene Database. Weizmann Institute of Science. Available Online: https://www.genecards.org/.\u003c/li\u003e\n\u003cli\u003eHegardt P, Widegren B, Li L et al. (2001) Nitric oxide synthase inhibitor and IL-18 enhance the anti-tumor immune response of rats carrying an intrahepatic colon carcinoma. Cancer Immunol Immunother 50:491-501 doi:10.1007/s002620100230\u003c/li\u003e\n\u003cli\u003eHu M, Yan H, Li H et al. (2023) Use of network pharmacology and molecular docking to explore the mechanism of action of curcuma in the treatment of osteosarcoma. Sci Rep 13:9569 doi:10.1038/s41598-023-36687-z.\u003c/li\u003e\n\u003cli\u003eIto H, Ando T, Ogiso H et al. (2015) Inhibition of induced nitric oxide synthase enhances the anti-tumor effects on cancer immunotherapy using TLR7 agonist in mice. Cancer Immunol Immunother 64:429-436 doi:10.1007/s00262-014-1644-6.\u003c/li\u003e\n\u003cli\u003eJanani B, Vijayakumar M, Priya K et al. (2022) A network-based pharmacological investigation to identify the mechanistic regulatory pathway of andrographolide against colorectal cancer. Front Pharmacol 13:967262 doi:10.3389/fphar.2022.967262.\u003c/li\u003e\n\u003cli\u003eLi K, Dai Y-J, Zhang H et al. (2025a) YAP1 activates SLC2A1 transcription and augments the malignant behavior of colorectal cancer cells by activating the Wnt/beta-catenin signaling pathway. Cell Div 20:8 doi:10.1186/s13008-025-00148-y.\u003c/li\u003e\n\u003cli\u003eLi S, Li Q, Zhou Q et al. (2023) Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis. BMC Med Genomics 16:102 doi:10.1186/s12920-023-01533-8\u003c/li\u003e\n\u003cli\u003eLi Y, Cheng Z, Li S et al. (2025b) Immunotherapy in colorectal cancer: Statuses and strategies. Heliyon 11:e41354 doi:10.1016/j.heliyon.2024.e41354.\u003c/li\u003e\n\u003cli\u003eLin J, Yang J, Xu X et al. (2020) A robust 11-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts. Cancer Cell Int 20:402 doi:10.1186/s12935-020-01491-6\u003c/li\u003e\n\u003cli\u003eLiu XS, Yang JW, Zeng J et al. (2022a) SLC2A1 is a Diagnostic Biomarker Involved in Immune Infiltration of Colorectal Cancer and Associated With m6A Modification and ceRNA. Front Cell Dev Biol 10:853596 doi:10.3389/fcell.2022.853596\u003c/li\u003e\n\u003cli\u003eLiu Y, Yang X, Gan J et al. (2022b) CB-Dock2: improved protein-ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Res 50:W159-W164 doi:10.1093/nar/gkac394\u003c/li\u003e\n\u003cli\u003eMintz J, Vedenko A, Rosete O et al. (2021) Current Advances of Nitric Oxide in Cancer and Anticancer Therapeutics. Vaccines (Basel) 9 doi:10.3390/vaccines9020094\u003c/li\u003e\n\u003cli\u003eMohanraj K, Karthikeyan BS, Vivek-Ananth RP et al. (2018) IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics. Sci Rep 8:4329 doi:10.1038/s41598-018-22631-z\u003c/li\u003e\n\u003cli\u003eNassa G (2023) Colorectal Cancers Also Known As Bowel Cancer or Rectal Cancer. Chemotherapy. 11.\u003c/li\u003e\n\u003cli\u003eNickel J, Gohlke BO, Erehman J et al. (2014) SuperPred: update on drug classification and target prediction. Nucleic Acids Res 42:W26-31 doi:10.1093/nar/gku477\u003c/li\u003e\n\u003cli\u003eOliveros JC (2025) Venny: An interactive tool for comparing lists with Venn\u0026apos;s diagrams. Available from: https://bioinfogpcnbcsices/tools/venny/indexhtml\u003c/li\u003e\n\u003cli\u003ePareek A, Pant M, Gupta MM et al. (2023) Moringa oleifera: An Updated Comprehensive Review of Its Pharmacological Activities, Ethnomedicinal, Phytopharmaceutical Formulation, Clinical, Phytochemical, and Toxicological Aspects. Int J Mol Sci 24 doi:10.3390/ijms24032098\u003c/li\u003e\n\u003cli\u003eRidnour LA, Cheng RY, Weiss JM et al. (2015) NOS Inhibition Modulates Immune Polarization and Improves Radiation-Induced Tumor Growth Delay. Cancer Res 75:2788-2799 doi:10.1158/0008-5472.CAN-14-3011\u003c/li\u003e\n\u003cli\u003eShannon P, Markiel A, Ozier O et al. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498-2504 doi:10.1101/gr.1239303\u003c/li\u003e\n\u003cli\u003eSzklarczyk D, Kirsch R, Koutrouli M et al. (2023) The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51:D638-D646 doi:10.1093/nar/gkac1000\u003c/li\u003e\n\u003cli\u003eT P (2024) Plant-derived glycosides in cancer treatment: diverse strategies for tumor suppression Nat Prod Res:1-3 doi:10.1080/14786419.2024.2409974\u003c/li\u003e\n\u003cli\u003eTang Z, Li C, Kang B et al. (2017) GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 45:W98-W102 doi:10.1093/nar/gkx247\u003c/li\u003e\n\u003cli\u003eThomas DD, Wink DA (2017) NOS2 as an Emergent Player in Progression of Cancer. Antioxid Redox Signal 26:963-965 doi:10.1089/ars.2016.6835\u003c/li\u003e\n\u003cli\u003eTuorkey MJ (2015) Cancer Therapy with Phytochemicals: Present and Future Perspectives. Biomed Environ Sci 28:808-819 doi:10.3967/bes2015.112\u003c/li\u003e\n\u003cli\u003eVannini F, Kashfi K, Nath N (2015) The dual role of iNOS in cancer. Redox Biol 6:334-343 doi:10.1016/j.redox.2015.08.009\u003c/li\u003e\n\u003cli\u003eVivek-Ananth RP, Mohanraj K, Sahoo AK et al. (2023) IMPPAT 2.0: An Enhanced and Expanded Phytochemical Atlas of Indian Medicinal Plants. ACS Omega 8:8827-8845 doi:10.1021/acsomega.3c00156\u003c/li\u003e\n\u003cli\u003eZajkowska M, Mroczko B (2023) A Novel Approach to Staging and Detection of Colorectal Cancer in Early Stages. J Clin Med 12 doi:10.3390/jcm12103530\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"in-silico-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"insp","sideBox":"Learn more about [In Silico Pharmacology](https://link.springer.com/journal/40203)","snPcode":"40203","submissionUrl":"https://submission.nature.com/new-submission/40203/3","title":"In Silico Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Moringa oleifera, glycosides, Colorectal cancer (CRC), Network pharmacology, Molecular docking, Hub genes, SLC2A1, NOS2, PI3K-Akt signaling pathway","lastPublishedDoi":"10.21203/rs.3.rs-6830750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6830750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e\u003cp\u003eColorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality globally. This study investigates the therapeutic potential of glycoside compounds derived from Moringa oleifera leaves: Niazirinin (NZR), Niazimicin A (NZA), 4-(Rhamnosyloxy)phenylacetonitrile (RPA), and Moringyne (MRG) in the context of CRC.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e\u003cp\u003eAn integrative network pharmacology strategy was used including compound-target prediction, protein\u0026ndash;protein interaction (PPI) analysis, and Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Eleven hub genes (NFKB1, PIK3R1, PIK3CD, PIK3CB, CHUK, GRB2, NOS2, SLC2A1, ABL1, PDGFRA, and STAT1) were identified. Additional validation involved mRNA expression profiling, overall survival analysis, and tumor stage-specific expression analysis. Lastly, molecular docking was conducted to assess the binding affinity of the glycosides with major CRC regulatory proteins.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e\u003cp\u003eFive major pathways, PI3K-Akt, cAMP, Ras, HIF-1 signaling, and MicroRNAs in cancer were highly enriched. Of the eleven hub genes, six (PIK3R1, NOS2, SLC2A1, ABL1, PDGFRA, and STAT1) were significantly dysregulated in colon (COAD) and rectal (READ) cancer tissues. In particular, NOS2, SLC2A1, and STAT1 were significantly upregulated, and PIK3R1, ABL1, and PDGFRA were significantly down regulated, indicating possible oncogenic and tumor-suppressive functions, respectively. Stage-specific analysis identified that expression of SLC2A1 differed significantly among pathological stages (F\u0026thinsp;=\u0026thinsp;4.31, p\u0026thinsp;=\u0026thinsp;0.00531), which warrants its consideration as a stage-specific prognostic biomarker. Molecular docking showed NOS2 and SLC2A1 have high-affinity interaction of NZR and MRG (\u0026ndash;8.6 kcal/mol), positing potent inhibitory activity against CRC metabolic and inflammatory targets.\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e\u003cp\u003eThis integrated analysis presents the therapeutic potential of Moringa oleifera glycosides, particularly NZR and MRG, as novel multi-target therapeutic drugs for the treatment of CRC. SLC2A1 and NOS2 genes as hub genes represent therapeutic targets worth considering in further preclinical and clinical studies.\u003c/p\u003e\u003ch2\u003eGraphical Abstract\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Integrated Network Pharmacology and Molecular Docking Reveal Therapeutic Potential of Moringa oleifera Glycosides, Targeting Key Regulatory Genes in Colorectal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 17:48:32","doi":"10.21203/rs.3.rs-6830750/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-27T10:49:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T02:22:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166221373505604359237344493618527461063","date":"2025-07-26T17:02:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-26T06:41:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-23T17:46:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219117081668598389905377455974262224085","date":"2025-07-23T07:20:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133967358349440001598286597770201754205","date":"2025-07-22T12:00:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16172443645963356247283341744191621920","date":"2025-07-22T01:13:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254143546702941695553061096167965046023","date":"2025-07-21T17:01:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274101816795426989736938703657976970487","date":"2025-07-21T15:17:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184620260982608899604637262387745562589","date":"2025-07-21T13:16:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159224242479944355344724366763110761506","date":"2025-07-16T15:35:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T15:11:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-06T04:20:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-06T04:18:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"In Silico Pharmacology","date":"2025-06-05T15:44:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"in-silico-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"insp","sideBox":"Learn more about [In Silico Pharmacology](https://link.springer.com/journal/40203)","snPcode":"40203","submissionUrl":"https://submission.nature.com/new-submission/40203/3","title":"In Silico Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cdae21b0-ef7d-4054-b495-43c171c23690","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:04:18+00:00","versionOfRecord":{"articleIdentity":"rs-6830750","link":"https://doi.org/10.1007/s40203-025-00461-y","journal":{"identity":"in-silico-pharmacology","isVorOnly":false,"title":"In Silico Pharmacology"},"publishedOn":"2025-11-06 15:57:30","publishedOnDateReadable":"November 6th, 2025"},"versionCreatedAt":"2025-07-18 17:48:32","video":"","vorDoi":"10.1007/s40203-025-00461-y","vorDoiUrl":"https://doi.org/10.1007/s40203-025-00461-y","workflowStages":[]},"version":"v1","identity":"rs-6830750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6830750","identity":"rs-6830750","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}