Computational Biology and Chemistry最新文献

{"title":"Molecular docking and DFT study of antiproliferative ribofuranose nucleoside derivatives targeting EGFR and VEGFR2in cancer cells","authors":"Shamsa Bibi ,&nbsp;Shafiq Urrehaman ,&nbsp;Memoona Akram ,&nbsp;Rabia Amin ,&nbsp;Hafsa Majeed ,&nbsp;Shanza Rauf Khan ,&nbsp;Saima Younis ,&nbsp;Fu-Quan Bai","doi":"10.1016/j.compbiolchem.2024.108187","DOIUrl":"10.1016/j.compbiolchem.2024.108187","url":null,"abstract":"<div><p>Antimetabolites are the most effective chemotherapeutics for treating cancer. They have exerted their anticancer effects by interfering with DNA synthesis. Recently, interest in modified nucleoside analogues has grown due to their superior efficiency. Nucleoside analogue derivatives have emerged as crucial candidates for cancer treatment due to their ability to target the cells responsible for cancer within the body specifically. The ability of nucleoside analogues derivatives to target specific molecular pathways has reduced toxicity and increased efficiency compared to traditional chemotherapy drugs. Nucleoside analogues have interfered with physiological nucleosides and induced cytotoxicity in cancerous cells. In this investigation, derivatives of ribofuranose nucleoside analogues have been designed. Density functional theory (DFT) calculations have been performed at the B3LYP/6–311 G(d,p) level. The designed molecules have been characterized by UV/Vis spectroscopy using the CPCM model in DMSO solvent, and molecular structural parameters, such as HOMO/LUMO and MEPS, have been determined. Derivative d1m has exhibited a high energy gap and low absorption energy compared to the other derivatives. Molecular docking of the designed molecules (d1o-d2m) has been performed with the EGFR and VEGFR2 proteins. d2o has shown good binding energy with the EGFR protein, while d1o has shown good results with VEGFR2. Global chemical parameters and NBO analysis have been conducted to investigate the derivatives charge transfer properties and chemical reactivity. NBO analysis has provided information about the donor and acceptor parts within a molecule, while global chemical parameters have given insights into the reactivity, stability, and solubility of the molecules. It has been found that the derivatives are more chemically reactive, thermodynamically stable, and have better binding affinity than the parent molecule. Based on the analysis, the drug interaction with the cancer-causing protein makes it more effective for cancer treatment.</p></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108187"},"PeriodicalIF":2.6,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanistic insights into antidiabetic potential of Ficus viren against multi organ specific diabetic targets: molecular docking, MDS, MM-GBSA analysis","authors":"Sachin Sharma ,&nbsp;Manjusha Choudhary ,&nbsp;Onkar Sharma ,&nbsp;Elisha Injeti ,&nbsp;Ashwani Mittal","doi":"10.1016/j.compbiolchem.2024.108185","DOIUrl":"10.1016/j.compbiolchem.2024.108185","url":null,"abstract":"<div><p><em>Ficus viren</em> has been traditionally used to treat diabetes, and its extract inhibits carbohydrate/lipid metabolism and possesses anti-hyperglycemic potential. However, there is conflicting investigation related to <em>F. viren</em> extract effect on carbohydrate metabolism. Thus, bioactive and mechanism behind its antidiabetic potential is still scanty. This study explored <em>F. viren’s</em> anti-diabetic property by identifying potential phytoconstituents and mechanism. A sequential <em>in-silico</em> approach was used <em>i.e.,</em> druglikeness, molecular docking, post-docking MM-GBSA, ADMET studies, molecular dynamic simulation (MDS), and post-MDS MM-GBSA. We screened ∼32 phytoconstituents and twelve potential organ-specific diabetic targets (O.S.D.Ts <em>i.e.,</em> IR, DPP-4, <em>ppar-γ, ppar-α, ppar-δ</em>, GLP-1R, SIRT-1, AMPK, GSK-3β, RAGE, and AR). Drug likeness study identified 18 druggable candidates among 32 phytoconstituents. K3A, quercetin, scutellarein, sorbifolin, and vogeline J identified as potential ligands from druggable ligands, using IR as the standard target. Subsequently, potential ligands docked with remaining O.S.D.Ts. and data showed that K3A binds strongly with AMPK, <em>ppar-δ</em>, DPP-4, and GSK-3β, while scutellarein binds with AR and <em>ppar-α</em>. Sorbifolin, quercetin, and vogeline J binds with <em>ppar-α</em>, <em>ppar-γ</em>, and RAGE, respectively. Post-docking MM-GBSA data (∆G_Bind) also depicted potential ligand’s strong binding affinities with their corresponding targets. Thereafter, simulation data revealed that only scutellarein and sorbifolin showed dynamic stability with their respective targets, <em>i.e.,</em> AR/<em>ppar-α</em> and <em>ppar-α,</em> respectively. Interestingly, post-MDS MM-GBSA revealed that only scutellarein exhibited strong ∆G_Bind of −55.08 kcal/mol and −75.48 kcal/mol with AR and <em>ppar-α,</em> respectively. Though, collective computational analysis supports antidiabetic potential of <em>F. viren</em> through AR and <em>ppar-α</em> modulation by scutellarein.</p></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108185"},"PeriodicalIF":2.6,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Theoretical Investigations of a point mutation affecting H5 Hemagglutinin’s receptor binding preference","authors":"Quoc Bao Ngo ,&nbsp;André H. Juffer","doi":"10.1016/j.compbiolchem.2024.108189","DOIUrl":"10.1016/j.compbiolchem.2024.108189","url":null,"abstract":"<div><p>The avian influenza A H5N1 virus is a subtype of influenza A virus (IAV) that causes a highly infectious and severe respiratory illness in birds and poses significant economic losses in poultry farming. To infect host cell, the virus uses its surface glycoprotein named Hemagglutinin (HA) to recognize and to interact with the host cell receptor containing either α2,6- (SAα2,6 Gal) or α2,3-linked Sialic Acid (SAα2,3 Gal). The H5N1 virus has not yet acquired the capability for efficient human-to-human transmission. However, studies have demonstrated that even a single amino acid substitution in the HA can switch its glycan receptor preference from the avian-type SAα2,3 Gal to the human-type SAα2,6 Gal. The present study aims to explain the underlying mechanism of a mutation (D94N) on the H5 HA that causes the protein to change its glycan receptor-binding preference using molecular dynamics (MD) simulations. Our results reveal that the mutation alters the electrostatic interactions pattern near the HA receptor binding pocket, leading to a reduced stability for the HA-avian-type SAα2,3 Gal complex. On the other hand, the detrimental effect of the mutation D94N is not observed in the HA-human-type SAα2,6 Gal complex due to the glycan's capability to switch its topology.</p></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108189"},"PeriodicalIF":2.6,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic regulation of B cell receptor signaling pathway: Insights from expression quantitative trait locus analysis using a mixed model","authors":"Hojin Bae,&nbsp;Hyowon Jeon,&nbsp;Chaeyoung Lee","doi":"10.1016/j.compbiolchem.2024.108188","DOIUrl":"10.1016/j.compbiolchem.2024.108188","url":null,"abstract":"<div><p>The B cell receptor (BCR) signaling pathway regulates non-immune cellular response through various pathways like MAPK, NF-kB, and PI3K-Akt. This study aimed to identify expression quantitative trait loci (eQTL) and their regulatory functions on BCR signaling pathway genes. A mixed model was employed to analyze eQTL using RNA expression levels in lymphoblastoid from 376 Europeans in the GEUVADIS dataset. In total, 266 SNPs, including 115 cis-acting SNPs, were found for association with transcription of 13 genes (P &lt; 5 × 10⁻⁸), revealing 19 independent signals for five genes through linkage disequilibrium analysis. Functional analysis, aligning them with DNase sensitive sites, transcription factor binding sites, histone modification, promoters/enhancers, CpG islands, and ChIA-PET, identified regulatory variants targeting SYK, VAV2, and PLCG2. Notably, rs2562397 was validated as a SYK promoter variant, and rs694505, rs636667, and rs4889409 were confirmed as enhancer variants for VAV2 and PLCG2. Their allelic differences in gene expression were also confirmed using ENCODE ChIP-seq and Sei neural network prediction. Persistent differential expression of these genes by alleles might impact the adaptive immune system, vascular development, and/or relevant diseases that have been previously associated with other variants of the genes. Comprehensive genetic architecture studies of the BCR signaling pathway, along with experiments demonstrating related mechanisms, will greatly contribute to understanding the underlying mechanisms of relevant disease development and implementing precision medicine.</p></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108188"},"PeriodicalIF":2.6,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142135956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Res-GCN: Identification of protein phosphorylation sites using graph convolutional network and residual network","authors":"Minghui Wang ,&nbsp;Jihua Jia ,&nbsp;Fei Xu ,&nbsp;Hongyan Zhou ,&nbsp;Yushuang Liu ,&nbsp;Bin Yu","doi":"10.1016/j.compbiolchem.2024.108183","DOIUrl":"10.1016/j.compbiolchem.2024.108183","url":null,"abstract":"<div><p>An essential post-translational modification, phosphorylation is intimately related with a wide range of biological activities. The advancement of effective computational methods for correctly recognizing phosphorylation sites is important for in-depth understanding of various physiological phenomena. However, the traditional method of identifying phosphorylation sites experimentally is time-consuming and laborious, which makes it difficult to meet the processing demands of today's big data. This research proposes the use of a novel model, Res-GCN, to recognize the phosphorylation sites of SARS-CoV-2. Firstly, eight feature extraction strategies are utilized to digitize the protein sequence from multiple viewpoints, including amino acid property encodings (AAindex), pseudo-amino acid composition (PseAAC), adapted normal distribution bi-profile Bayes (ANBPB), dipeptide composition (DC), binary encoding (BE), enhanced amino acid composition (EAAC), Word2Vec, and BLOSUM62 matrices. Secondly, elastic net is utilized to eliminate redundant data in the fused matrix. Finally, a combination of graph convolutional network (GCN) and residual network (ResNet) is used to classify the phosphorylated sites and output predictions using a fully connected layer (FC). The performance of Res-GCN is tested by 5-fold cross-validation and independent testing, and excellent results are obtained on S/T and Y datasets. This demonstrates that the Res-GCN model exhibits exceptional predictive performance and generalizability.</p></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"112 ","pages":"Article 108183"},"PeriodicalIF":2.6,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel parallel feature rank aggregation algorithm for gene selection applied to microarray data classification","authors":"Imtisenla Longkumer,&nbsp;Dilwar Hussain Mazumder","doi":"10.1016/j.compbiolchem.2024.108182","DOIUrl":"10.1016/j.compbiolchem.2024.108182","url":null,"abstract":"<div><p>Microarray data often comprises numerous genes, yet not all genes are relevant for predicting cancer. Feature selection becomes a crucial step to reduce the high dimensionality in these kinds of data. While no single feature selection method consistently outperforms others across diverse domains, the combination of multiple feature selectors or rankers tends to produce more effective results compared to relying on a single ranker alone. However, this approach can be computationally expensive, particularly when handling a large quantity of features. Hence, this paper presents a parallel feature rank aggregation that utilizes borda count as the rank aggregator. The concept of vertically partitioning the data along feature space was adapted to ease the parallel execution of the aggregation task. Features were selected based on the final aggregated rank list, and their classification performances were evaluated. The model’s execution time was also observed across multiple worker nodes of the cluster. The experiment was conducted on six benchmark microarray datasets. The results show the capability of the proposed distributed framework compared to the sequential version in all the cases. It also illustrated the improved accuracy performance of the proposed method and its ability to select a minimal number of genes.</p></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"112 ","pages":"Article 108182"},"PeriodicalIF":2.6,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated deep learning model for automatic detection and classification of stenosis in coronary angiography","authors":"Tao Wang ,&nbsp;Xiaojun Su ,&nbsp;Yuchao Liang ,&nbsp;Xu Luo ,&nbsp;Xiao Hu ,&nbsp;Ting Xia ,&nbsp;Xuebin Ma ,&nbsp;Yongchun Zuo ,&nbsp;Huilin Xia ,&nbsp;Lei Yang","doi":"10.1016/j.compbiolchem.2024.108184","DOIUrl":"10.1016/j.compbiolchem.2024.108184","url":null,"abstract":"<div><p>Coronary artery disease poses a significant threat to human health. In clinical settings, coronary angiography remains the gold standard for diagnosing coronary heart disease. A crucial aspect of this diagnosis involves detecting arterial narrowings. Categorizing these narrowings can provide insight into whether patients should receive vascular revascularization treatment. The majority of current deep learning methods for analyzing coronary angiography are mostly confined to the theoretical research domain, with limited studies offering direct practical support to clinical practitioners. This paper proposes an integrated deep-learning model for the localization and classification of narrowings in coronary angiography images. The experimentation employed 1606 coronary angiography images obtained from 132 patients, resulting in an accuracy of 88.9 %, a recall rate of 85.4 %, an F1 score of 0.871, and a MAP value of 0.875 for vascular stenosis detection. Furthermore, we developed the \"Hemadostenosis\" web platform (<span><span>http://bioinfor.imu.edu.cn/hemadostenosis</span><svg><path></path></svg></span>) using Django, a highly mature HTTP framework. Users are able to submit coronary angiography image data for assessment via a visual interface. Subsequently, the system sends the images to a trained convolutional neural network model to localize and categorize the narrowings. Finally, the visualized outcomes are displayed to users and are downloadable. Our proposed approach pioneers the recognition and categorization of arterial narrowings in vascular angiography, offering practical support to clinical practitioners in their learning and diagnostic processes.</p></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"112 ","pages":"Article 108184"},"PeriodicalIF":2.6,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142076932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel biomarkers associated with oxidative stress and immune infiltration in intervertebral disc degeneration based on bioinformatics approaches","authors":"Min Xiang ,&nbsp;Yue Lai ,&nbsp;Jianlin Shen ,&nbsp;Bo Wei ,&nbsp;Huan Liu ,&nbsp;Wenhua Huang","doi":"10.1016/j.compbiolchem.2024.108181","DOIUrl":"10.1016/j.compbiolchem.2024.108181","url":null,"abstract":"<div><h3>Background</h3><p>The etiology of intervertebral disc degeneration (IVDD), a prevalent degenerative disease in the elderly, remains to be fully elucidated. The objective of this study was to identify immune infiltration and oxidative stress (OS) biomarkers in IVDD, aiming to provide further insights into the intricate pathogenesis of IVDD.</p></div><div><h3>Methods</h3><p>The Gene Expression microarrays were obtained from the Gene Expression Omnibus (GEO) database. We conducted enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. Subsequently, the R language packages CIBERSORT, MCPcounter, and WGCNA were employed to compare immune infiltration levels between IVDD samples and control samples. A protein-protein interaction (PPI) network was constructed using the Search Tools for the Retrieval of Interacting Genes (STRING) database to identify significant gene clusters. To identify hub genes, we employed Cytoscape's Molecular Complex Detection (MCODE) plug-in. The mRNA levels of hub genes in the cell model were validated by qPCR, while Western blotting was used to validate their protein levels.</p></div><div><h3>Results</h3><p>The GSE70362 dataset from the GEO database identified a total of 1799 genes that were differentially expressed. Among these, 43 genes were found to be differentially expressed and also associated with OS. The differentially expressed genes associated with OS and the immune-related module genes identified through WGCNA were further intersected, resulting in the identification of 10 key genes that were differentially expressed and played crucial roles in both immune response and OS. Subsequently, we validated four diagnostic markers (PPIA, MAP3K5, PXN, and JAK2) using the GSE122429 external dataset. In a cellular model of OS in NP cells, we have identified the upregulation of PPIA and PXN genes, which could serve as novel markers for IVDD.</p></div><div><h3>Conclusion</h3><p>The study successfully identified and validated differentially expressed genes associated with oxidative stress and immune infiltration in IVDD samples compared to normal ones. Notably, the newly discovered biomarkers PPIA and PXN have not been previously reported in IVDD-related research.</p></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"112 ","pages":"Article 108181"},"PeriodicalIF":2.6,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid similarity based feature selection and cascade deep maxout fuzzy network for Autism Spectrum Disorder detection using EEG signal","authors":"Joy Karan Singh ,&nbsp;Deepti Kakkar","doi":"10.1016/j.compbiolchem.2024.108177","DOIUrl":"10.1016/j.compbiolchem.2024.108177","url":null,"abstract":"<div><p>Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person’s comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification and continuous therapies have a huge effect on autism patients. The existing models took a long time to confirm the diagnosis process and also, it is highly complex to differentiate autism from various developmental disorders. To facilitate early diagnosis by providing timely intervention, saving healthcare costs and reducing stress for the family in the long run, this research introduces an affordable and straightforward diagnostic model to detect ASD using EEG and deep learning models. Here, a hybrid deep learning model called Cascade deep maxout fuzzy network (Cascade DMFN) is proposed to identify ASD and it is achieved by the integration of Deep Maxout Network (DMN) and hybrid cascade neuro-fuzzy. Moreover, hybrid similarity measures like Canberra distance and Kumar-hassebrook is employed to conduct the feature selection technique. Also, the EEG dataset and BCIAUT_P300 dataset are used for analyzing the designed Cascade DMFN for detecting Autism Spectrum Disorder. The designed Cascade DMFN has outperformed other classical models by yielding a high accuracy of 0.930, Negative Predictive Value (NPV) of 0.919, Positive Predictive Value (PPV) of 0.923, True Negative Rate (TNR) of 0.926, and True Positive Rate (TPR) of 0.934.</p></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108177"},"PeriodicalIF":2.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying key genes against rutin on human colorectal cancer cells via ROS pathway by integrated bioinformatic analysis and experimental validation","authors":"Mansour K. Gatasheh","doi":"10.1016/j.compbiolchem.2024.108178","DOIUrl":"10.1016/j.compbiolchem.2024.108178","url":null,"abstract":"<div><p>Colorectal cancer (CRC) poses a significant global health challenge, characterized by substantial prevalence variations across regions. This study delves into the therapeutic potential of rutin, a polyphenol abundant in fruits, for treating CRC. The primary objectives encompass identifying molecular targets and pathways influenced by rutin through an integrated approach combining bioinformatic analysis and experimental validation. Employing Gene Set Enrichment Analysis (GSEA), the study focused on identifying potential differentially expressed genes (DEGs) associated with CRC, specifically those involved in regulating reactive oxygen species, metabolic reprogramming, cell cycle regulation, and apoptosis. Utilizing diverse databases such as GEO2R, CTD, and Gene Cards, the investigation revealed a set of 16 targets. A pharmacological network analysis was subsequently conducted using STITCH and Cytoscape, pinpointing six highly upregulated genes within the rutin network, including TP53, PCNA, CDK4, CCNEB1, CDKN1A, and LDHA. Gene Ontology (GO) analysis predicted functional categories, shedding light on rutin's potential impact on antioxidant properties. KEGG pathway analysis enriched crucial pathways like metabolic and ROS signaling pathways, HIF1a, and mTOR signaling. Diagnostic assessments were performed using UALCAN and GEPIA databases, evaluating mRNA expression levels and overall survival for the identified targets. Molecular docking studies confirmed robust binding associations between rutin and biomolecules such as TP53, PCNA, CDK4, CCNEB1, CDKN1A, and LDHA. Experimental validation included inhibiting colorectal cell HT-29 growth and promoting cell growth with NAC through MTT assay. Flow cytometric analysis also observed rutin-induced G1 phase arrest and cell death in HT-29 cells. RT-PCR demonstrated reduced expression levels of target biomolecules in HT-29 cells treated with rutin. This comprehensive study underscores rutin's potential as a promising therapeutic avenue for CRC, combining computational insights with robust experimental evidence to provide a holistic understanding of its efficacy.</p></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"112 ","pages":"Article 108178"},"PeriodicalIF":2.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142076931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}