Computational Biology and Chemistry最新文献

{"title":"GAN-based novel feature selection approach with hybrid deep learning for heartbeat classification from ECG signal","authors":"S. Haseena Beegum ,&nbsp;R. Manju","doi":"10.1016/j.compbiolchem.2025.108704","DOIUrl":"10.1016/j.compbiolchem.2025.108704","url":null,"abstract":"<div><div>Heart arrhythmias are one of the most important categories of cardiovascular illness. A heartbeat that is abnormal like too early, too slow, too fast, or uneven is indicated as an arrhythmia. Though some cardiac arrhythmias are benign, others can be dangerous and fatal if they are thought to be abnormal or the outcome of a damaged heart. The arrhythmias can be recognized by looking at and classifying the electrocardiogram (ECG) heartbeats. The automatic explanation of ECG data has witnessed a prominent development with the emergence of machine learning techniques. This paper develops an optimal deep learning technique to classify heartbeats. At first, pre-processing is done using median filter, resolution wavelet-based technique is exploited to recognize wave components. Subsequently, the features, like Discrete Wavelet Transform (DWT), autoregressive, Fractional Fourier-Transform (FrFT), and morphological features, are extracted. As the next step, feature fusion is performed by employing Kendall Tau, wrapper, and kraskov entropy together with Generative Adversarial Network (GAN). Lastly, heartbeat classification is done by employing proposed SExpHGS based DBN-VGG, where DBN-VGG is adopted by integration of Deep Belief Network and VGG, trained by employing Serial Exponential Hunger Games Search Algorithm (SExpHGS). Experimental outcomes illustrate that the SExpHGS based DBN-VGG approach performed superior when compared to conventional models with 95.7 % accuracy, 97.2 % sensitivity, and 94.9 % specificity rate.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108704"},"PeriodicalIF":3.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234586","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 systematic review on deep learning based brain tumor segmentation and detection using MRI: Past insights, present techniques and future trends","authors":"Krupa Chary Pasunoori,&nbsp;Ch. Rajendra Prasad,&nbsp;K. Raj Kumar","doi":"10.1016/j.compbiolchem.2025.108696","DOIUrl":"10.1016/j.compbiolchem.2025.108696","url":null,"abstract":"<div><div>The abnormal growth of cells leads to brain malignancy in humans, which is among the most prevalent causes of fatalities in adults worldwide. Patients' likelihood of survival increases, and therapeutic opportunities improve when brain tumors are identified early. Compared to other imaging techniques, Magnetic Resonance Imaging (MRI) scans provide more comprehensive information. A brain tumor can be diagnosed and differentiated from MRI images using a variety of brain tumor recognition and segmentation approaches. The utilization of deep learning-based models has proven effective in analyzing the vast volume of MRI data. The main purpose of this review is to provide an overview of brain tumor segmentation and detection techniques. To efficiently process the large volume of images, this review presents a detailed analysis of deep learning models. Furthermore, a chronological analysis is carried out to validate the robustness of the techniques. Following that, to better understand the performance of the models, the strengths and limitations of standard deep learning methods are discussed. In addition, the dataset details, performance evaluations, and simulation tools are discussed in this review. Finally, the challenges and research gaps in brain tumor segmentation and detection models are highlighted.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108696"},"PeriodicalIF":3.1,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234578","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":"Ahnak and Nckap1l as potential diagnostic biomarkers and therapeutic targets in Landiolol-mediated sepsis treatment","authors":"Weiyu Pan ,&nbsp;Junli Cui ,&nbsp;Su Tu ,&nbsp;Bin Qian ,&nbsp;Xiaoxia Liu ,&nbsp;Xingping Zhu","doi":"10.1016/j.compbiolchem.2025.108700","DOIUrl":"10.1016/j.compbiolchem.2025.108700","url":null,"abstract":"<div><h3>Purpose</h3><div>Landiolol is a beta-blocker used in the treatment of Sepsis. However, how this drug influences key genes and pathways involved in disease remains unknown. This study aimed to explore potential biomarkers involved in the mechanism of Landiolol’s action in sepsis.</div></div><div><h3>Methods</h3><div>Two microarray datasets from the Gene Expression Omnibus database were downloaded. Differentially expressed genes (DEGs) were identified. Then, Landiolol-associated genes (lnd-DEGs) were screened using weighted gene co-expression network analysis (WGCNA), followed by enrichment analysis and protein-protein interaction (PPI) network investigation. Biomarkers were explored using three machine learning methods (LASSO, SVM-RFE, and RF), followed by diagnostic and prognostic analyses of these biomarkers.</div></div><div><h3>Results</h3><div>After landiolol treatment, a total of 45 DEGs were identified when compared to normal samples. These genes were primarily associated with 357 biological functions, including the inositol phosphate metabolic process, and six key pathways, including the phosphatidylinositol signaling system. Using three different machine learning methods, 4 signature genes related to landiolol’s action on sepsis were identified. Receiver operating characteristic (ROC) analysis demonstrated high predictive accuracy for Ahnak and Nckap1l in sepsis. Clinical correlation analysis revealed that Nckap1l and Ahnak were significantly associated with endotype and overall survival (OS) of sepsis, respectively. Finally, the prognostic value of Ahnak was validated through Kaplan-Meier analysis.</div></div><div><h3>Conclusions</h3><div>Ahnak and Nckap1l are potential diagnostic biomarkers and targets for therapeutic intervention in landiolol-induced sepsis following administration of Landiolol. Nckap1l can be used for endotype analysis of sepsis.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108700"},"PeriodicalIF":3.1,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262631","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":"Uncovering the therapeutic potential of Si-Miao-Yong-An decoction in abdominal aortic aneurysm: An integrative study combining network pharmacology, machine learning, molecular docking and dynamics simulation","authors":"Ming Xie ,&nbsp;Yufeng Zhang ,&nbsp;Ming Zhao ,&nbsp;Xiandeng Li ,&nbsp;Yong Xue ,&nbsp;Guobao Chen ,&nbsp;Jia Liu ,&nbsp;Haibing Hua","doi":"10.1016/j.compbiolchem.2025.108701","DOIUrl":"10.1016/j.compbiolchem.2025.108701","url":null,"abstract":"<div><div>Abdominal aortic aneurysm (AAA) is a progressive and life-threatening vascular disorder characterized by abnormal dilation of the abdominal aorta and a high risk of rupture. Current pharmacological interventions remain limited in efficacy, highlighting the need for alternative therapeutic strategies. Si-Miao-Yong-An Decoction (SMYAD), a classical formula in traditional Chinese medicine, has demonstrated anti-inflammatory and vascular-protective effects, yet its underlying mechanisms in AAA treatment remain unclear. This study employed an integrative approach combining network pharmacology, machine learning, and molecular modeling to elucidate the pharmacological basis of SMYAD against AAA. A total of 106 bioactive compounds and 235 putative targets were identified from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database. These were cross-referenced with disease-associated and differentially expressed genes from GEO datasets, identifying 15 targets potentially involved in AAA pathogenesis. Functional enrichment analyses revealed their involvement in the interleukin-17 and tumor necrosis factor signaling pathways. Integrated PPI network analysis and 3 machine learning algorithms jointly identified 6 hub genes (IL6, PTGS2, IL1B, FOS, MAOA, and COL1A1) as central to AAA pathology. Gene expression profiling and ROC curve analysis further supported the diagnostic relevance of these targets. Five key compounds—quercetin, luteolin, kaempferol, isorhamnetin, and stigmasterol—exhibited strong binding affinities with the identified hub targets. Molecular docking and dynamics simulations confirmed stable interactions between the selected compounds and their targets. Overall, this study provides mechanistic insights into the multi-target actions of SMYAD in AAA and offers theoretical support for its potential clinical application.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108701"},"PeriodicalIF":3.1,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216559","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":"Systems analysis of Berberis vulgaris alkaloids unveils their functional synergy and drug-like potential","authors":"Chinenyenwa Fortune Chukwuneme ,&nbsp;Samantha Gildenhuys","doi":"10.1016/j.compbiolchem.2025.108698","DOIUrl":"10.1016/j.compbiolchem.2025.108698","url":null,"abstract":"<div><div><em>Berberis</em> species are rich in isoquinoline alkaloids with promising therapeutic properties for various diseases, including SARS-CoV-2. Despite their known medicinal attributes, the potential for combining them at suitable doses remains underexplored. This study investigated the compound–target interactions, functional enrichment, and pharmacokinetic profiles of seven <em>B. vulgaris</em> alkaloids (berberine, palmatine, berberrubine, lambertine, obamegine, berbidine, and berbamine) using an in-silico approach. Compound–target interactions were identified using SwissTargetPrediction. Protein-protein interaction (PPI) networks were constructed using STRING in Cytoscape, and an UpSet plot was generated in Python to visualize overlapping targets and potential synergy. Functional enrichment analysis was performed in DAVID using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, followed by compound-gene-pathway network construction in Cytoscape. Pharmacokinetic profiles of compounds were assessed using ADMET-AI. A total of 42 genes were shared by at least two compounds. Genes associated with neurotransmission (DRD, ADRA, and ADRB) were identified as hubs mediating key functional interactions. Berbamine and obamegine shared the highest number of targets (10). Functional enrichment by KEGG and GO identified 16 and 20 significantly enriched pathways and biological processes, respectively, and formed networks consisting of distinct nodes (pathway = 67, GOBP = 69) and edges (pathway = 228, GOBP = 229). Favorable drug-likeness was identified for all alkaloids, excluding berbamine and obamegine (0.3), and low clinical toxicity (0.0–0.3). The results highlight the therapeutic potential of B. vulgaris alkaloids to provide complementary and synergistic effects across different disease pathways and support their development in botanical medicine.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108698"},"PeriodicalIF":3.1,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208845","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":"Methyl 2-(7-hydroxy-3-methyloctyl)-1,3-dimethyl-4-oxocyclohex-2-enecarboxylate as a natural and potent antitubercular lead: An in silico study integrating molecular docking, molecular dynamics, FMO, and DFT analyses","authors":"Fathima Asra ,&nbsp;Kannan Vadivel ,&nbsp;Srikanth Jeyabalan ,&nbsp;Srilekha Chintala ,&nbsp;Naresh Dumala","doi":"10.1016/j.compbiolchem.2025.108691","DOIUrl":"10.1016/j.compbiolchem.2025.108691","url":null,"abstract":"<div><div>The currently marketed antitubercular drugs have limited efficacy with the potential to cause organ toxicity. Thus, there is a need for new drug therapies to combat tuberculosis. Methyl 2-(7-hydroxy-3-methyloctyl)-1,3-dimethyl-4-oxocyclohex-2-enecarboxylate (PE14) and (<em>E</em>)-3,7,11,15-tetramethylhexadec-2-en-1-ol (EA8) are the natural antitubercular lead-like molecules isolated from petroleum ether and ethyl acetate leaf extracts of <em>Ipomea sepiaria,</em> respectively. Extensive research has demonstrated the wide range of health benefits associated with this plant. However, the antitubercular effects of phytocompounds isolated from this species have not been systematically investigated. To evaluate the antitubercular effect of the natural compound, <em>in silico</em> prediction of binding affinity against selected antitubercular target proteins was conducted, and this was compared with co-crystallized ligands as a standard. Additionally, the physicochemical properties, pharmacokinetics, and various toxicity-related parameters were also predicted. Two ligand docking complexes were selected for molecular dynamics simulations to calculate the binding free energy over 250 ns. Moreover, FMO and DFT were also investigated. PE14 complies with RO5 and exhibits suitable ADMET profiles. The molecular docking scores in kcal/mol showed comparatively more potency against antitubercular drug targets compared to the co-crystalized ligand of the target protein as well as EA8. Overall, the strength of interaction between the ligands with their selected target proteins from the molecular docking study, heat change that occurs during the ligand-target interactions from a molecular dynamic simulation study, the electronic reactivity trend was established as STD &gt; PE14 &gt; CIP &gt; EA8 from FMO analysis and other multi-parametric druggability profiles of target proteins suggests that PE14 can be considered as a suitable antitubercular lead-like for the treatment of <em>M. tuberculosis</em>. The results of the current study were closely correlated with those of our previous study on <em>Ipomea sepiaria</em> in the LRP assay. However, necessary <em>in vitro</em> and <em>in vivo</em> studies on the synthesized pure compound must be carried out to participate in a clinical trial, where the <em>in silico</em> results would help expedite the process of drug development.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108691"},"PeriodicalIF":3.1,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154794","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":"Prognosis models for nasopharyngeal carcinoma recurrences by using tabu search algorithm","authors":"Yara Raslan ,&nbsp;Mushabab Asiri ,&nbsp;Ahmed M. Maklad ,&nbsp;Alaa Fahim","doi":"10.1016/j.compbiolchem.2025.108687","DOIUrl":"10.1016/j.compbiolchem.2025.108687","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Cancer is a significant public health issue that has a global impact. Significant mortality rates have already been observed due to this disease, and more mortalities are expected in the future. In recent times, there has been a growing interest among otolaryngologists and oncologists in the development of appropriate treatment regimens for patients with recurrent nasopharyngeal carcinoma (NPC). The primary objective of these treatment modalities is to extend the lifespan of patients following recurrence and enhance their overall survival and quality of life. For instance, metaheuristic algorithms (MH), a form of soft computing technology, are commonly utilized in healthcare data due to their effectiveness. Furthermore, metaheuristics rely on the evolutionary search principle. They direct the search process to effectively explore the search space in order to find near-optimal solutions for solving global optimization problems. Tabu search (TS) is a method used in optimization problems and falls under metaheuristic techniques. An essential element of TS is its utilization of adaptive memory, which enhances search efficiency by avoiding local optimality and promoting flexibility. Another example is data mining, which is a subset of artificial intelligence that utilizes data to extract meaningful information from previously unknown patterns. It has been increasingly used in healthcare to aid clinical diagnostics and disease prediction. The proposed technique treated data mining problems as combinatorial optimization problems and used metaheuristics to address data mining challenges, such as classification for unknown data and finding association rules for significant patterns. The Tabu Search Classifier Method (TSCM) outlined in this paper primarily utilizes the Tabu Search (TS) algorithm, enhanced with the incorporation of Dynamic Neighborhood Structure (DNHS), which contributes to better discovery of the search space. The TSCM algorithm identifies three rules based on the patients’ data and generates three precise artificial predictive models to determine and categorize individuals who are at risk of recurrent NPC. With each stage of the treatment, additional features become accessible. The first model relies on a primary data set that includes descriptive data. The second model incorporates more features than the first model but does not include the response feature. The third model utilizes all existing features and includes the response feature, which is observed three months after the treatment phase concludes, the third model is considered a post-treatment monitoring. This paper introduces an Artificial Advisory Healthcare System (AAHS) that utilizes these models to accurately predict the occurrence of recurrence during each stage of treatment and after the treatment as a post-treatment monitoring. This prediction enables the adjustment of the treatment plan and the implementation of additional measures in accordance with the system","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108687"},"PeriodicalIF":3.1,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208796","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":"Non-invasive breast cancer detection: Leveraging the potential of BN-doped C60 heterofullerene for formaldehyde sensing using DFT theory","authors":"Bharath Kumar Chagaleti ,&nbsp;Arafat Toghan ,&nbsp;Magdi E.A. Zaki ,&nbsp;Ali Oubella ,&nbsp;Reda A. Haggam","doi":"10.1016/j.compbiolchem.2025.108692","DOIUrl":"10.1016/j.compbiolchem.2025.108692","url":null,"abstract":"<div><div>Breast cancer remains a leading cause of mortality among women, necessitating the development of non-invasive diagnostic methods. Formaldehyde (FA) has emerged as a potential biomarker for early detection of breast cancer in urine. This study explores the efficacy of boron-nitrogen-doped C60 heterofullerenes (BN_(5,6)C₅₈ and BN(6,6)C58) as highly sensitive and selective biosensors for FA detection using density functional theory (DFT). A comprehensive set of electronic, thermodynamic, and quantum chemical descriptors was employed to evaluate the sensing potential. Key computed parameters (including a significantly reduced energy gap (HLG = 0.49 eV), a high adsorption energy (Eads = −12.55 kcal/mol), a favorable Gibbs free energy change (ΔG = −12.73 kcal/mol), an enhanced dipole moment (μ = 7.425 D), increased polarizability (α = 525.640), and non-covalent interaction (NCI) analysis) collectively confirmed that BN doping significantly enhances the interaction strength with FA, with BN(6,6)C58 exhibiting the highest sensitivity (1.9 ×10¹⁷). Electronic property analyses demonstrated a reduced energy gap and enhanced charge transfer in BN(6,6)C58@FA, corroborated by molecular electrostatic potential and NCI analyses. The sensor's rapid recovery time (1.65 ×10⁻³ s) and high electrical conductivity (16 A.m⁻²) further underscore its potential for real-time breath analysis. These findings highlight BN(6,6)C58 as a promising candidate for non-invasive breast cancer diagnostics, paving the way for developing advanced electrochemical biosensors.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108692"},"PeriodicalIF":3.1,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118467","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":"Unveiling cancer stem cell marker networks: A hypergraph approach","authors":"David H. Margarit ,&nbsp;Gustavo Paccosi ,&nbsp;Marcela V. Reale ,&nbsp;Lilia M. Romanelli","doi":"10.1016/j.compbiolchem.2025.108684","DOIUrl":"10.1016/j.compbiolchem.2025.108684","url":null,"abstract":"<div><div>We propose a novel computational framework leveraging hypergraph theory to analyse cancer stem cell markers (CSCMs) across multiple organs. Hypergraphs provide a robust representation of CSCM co-expression patterns, capturing their complex multi-organ relationships more comprehensively than traditional graph-based methods. By integrating mutual information analysis and Markov models, we identify key markers driving tumour heterogeneity and metastasis, offering detailed insights into their interdependencies. This approach establishes hypergraphs as a computationally powerful tool to model cancer progression and metastatic dynamics, contributing to the understanding of complex biological systems and supporting the development of targeted therapeutic strategies.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108684"},"PeriodicalIF":3.1,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118468","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":"Development and validation of a breast cancer survival prediction model based on perioperative anesthesia-related drug target genes and analysis of immune microenvironment and drug sensitivity","authors":"Dongmei Yu ,&nbsp;Jiajia Li ,&nbsp;Wenjing Ma ,&nbsp;Yue Pei ,&nbsp;Yingchao Qi ,&nbsp;Tong Yu ,&nbsp;Wenkai Li ,&nbsp;Xiaohan Sun ,&nbsp;Jingyan Zhang ,&nbsp;Xuantonghe Li ,&nbsp;Longyan Liang ,&nbsp;Yunen Liu ,&nbsp;Yichen Wang","doi":"10.1016/j.compbiolchem.2025.108681","DOIUrl":"10.1016/j.compbiolchem.2025.108681","url":null,"abstract":"<div><h3>Introduction</h3><div>This study aimed to create a survival prediction model for breast cancer(BC) using perioperative anesthesia - related drug target genes(PARDTGs). It explored their immune microenvironment and drug sensitivity for personalized therapy.</div></div><div><h3>Methods</h3><div>Transcriptomic sequencing data of BC were downloaded from The Cancer Genome Atlas (TCGA) database. Common PARDTGs were retrieved from the DrugBank and ChemBL databases. Transcriptomic data were analyzed to identify differentially expressed PARDTGs (DE-PARDTGs) using rigorous statistical thresholds. A total of 101 machine learning algorithms were applied to construct PARDTG-based survival prediction models. Patients were stratified into high- and low-risk groups based on model-derived risk scores. Model performance was validated using an independent dataset from the Gene Expression Omnibus (GEO). Clinical-pathological correlations, immune profiling, and mutational landscapes were compared between risk groups in the TCGA-BRCA cohort. Drug sensitivity to commonly used therapies was predicted via transcriptomic correlations.</div></div><div><h3>Results</h3><div>We identified five DE - PARDTGs (PTGS2, TACR1, ADRB1, ABCB1, ACKR3) for a BC prognostic model. Receiver Operating Characteristic - Area Under the Curves（ROC - AUCs) for 1 -, 3 -, 5 - year overall survival(OS) were 0.722, 0.730, 0.691. TACR1 and ADRB1 high - expression meant better prognosis. Risk groups differed in immunity, with TACR1 correlating with immune checkpoints and drug sensitivity. Conclusions: The PARDTG - based model predicts BC survival independently. TACR1, key to immune response and drug sensitivity, could be a new therapeutic target. These results stress the importance of focusing on perioperative anesthesia - related drug targets in BC research.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108681"},"PeriodicalIF":3.1,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152197","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}