Computers in biology and medicine最新文献

{"title":"MLFusion: Multilevel Data Fusion using CNNS for atrial fibrillation detection","authors":"Arlene John ,&nbsp;Keshab K. Parhi ,&nbsp;Barry Cardiff ,&nbsp;Deepu John","doi":"10.1016/j.compbiomed.2025.109901","DOIUrl":"10.1016/j.compbiomed.2025.109901","url":null,"abstract":"<div><div>Data fusion, involving the simultaneous integration of signals from multiple sensors, is an emerging field that facilitates more accurate inferences in instrumentation applications. This paper presents a novel fusion methodology for multi-sensor multimodal data using convolutional neural networks (CNNs), which introduces a data-driven approach to automatically select the optimal level of information abstraction for fusion. Unlike traditional methods, the proposed model allows for a self-learning fusion process, eliminating the need for manual selection of the fusion level by the designer. The model uniquely integrates feature extraction and fusion into a unified framework, enhancing efficiency and performance. Additionally, the fusion network incorporates signal quality indicators (SQIs) as input streams, enabling the model to account for the quality of each input signal during the fusion process. The methodology is evaluated on the task of atrial fibrillation detection through the fusion of two multimodal signal inputs taken from a portion of the MIMIC III database. The fusion network, using electrocardiogram (ECG) and photoplethsymogram (PPG) signals as inputs, and trained with an average loss function, achieved an accuracy of 99.33% and a sensitivity of 99.74%. The model’s robustness in the presence of noisy inputs is also analyzed, demonstrating the effectiveness of the SQI-based multi-level fusion approach. This novel methodology represents a significant advancement in data fusion by offering a fully automated, quality-aware approach to multi-sensor multimodal signal fusion.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109901"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A semi-automated tool for digital and mechanical articulators comparative analysis of condylar path elements.","authors":"Mattia Maltauro, Elisa Vargiu, Federico Tozzi, Leonardo Ciocca, Roberto Meneghello","doi":"10.1016/j.compbiomed.2025.109724","DOIUrl":"10.1016/j.compbiomed.2025.109724","url":null,"abstract":"<p><p>Digital workflows have revolutionized dentistry, especially when it comes to fabrication of complete dentures through Computer-Aided Design and Computer-Aided Manufacturing (CAD-CAM) procedures. Digital articulators manage to simulate mandibular movements and are emerging as alternatives to mechanical articulators like the Gerber semi-adjustable model. Despite being a promising tool, digital articulators require refinement in order to grant consistent functionality and effective occlusal balance. The aim of this research is to present a semi-automated MATLAB tool designed to compare trajectories from different articulator types-digital versus analog-used in dental practice. Validation of the MATLAB tool compared to existing data demonstrates its reliability and effectiveness. Sensitivity analyses assess the tool's robustness under various settings. Results suggest optimal input parameters and settings ensuring precision. Future developments may include integrating anatomically-based reference systems and advanced metrics for rotational analysis of condylar path elements (CPEs), thereby enhancing digital dentistry potentialities. Ultimately, the semi-automated MATLAB tool represents a significant step towards improving dental occlusal analysis, bridging the gap between analog and digital methodologies and enabling comparison among these tools.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"186 ","pages":"109724"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cell adhesion on substrates with variable curvature: Effects on genetic transcription processes","authors":"Óscar L. Rodríguez-Montaño,&nbsp;Lorenzo Santoro,&nbsp;Lorenzo Vaiani,&nbsp;Luciano Lamberti,&nbsp;Antonio E. Uva,&nbsp;Antonio Boccaccio","doi":"10.1016/j.compbiomed.2025.109917","DOIUrl":"10.1016/j.compbiomed.2025.109917","url":null,"abstract":"<div><div>Several studies suggest that changes in nuclear morphology due to forces and deformations as result of cell adhesion on biological substrates can induce molecular streaming through nuclear pore openings and alter chromatin structure. The condensed state of chromatin hinders transcription and replication, while its decompaction, induced by adhesion, plays a key role in differentiation. However, assessing nuclear stress/strain <em>in vivo</em> remains challenging, and the impact of substrate curvature on nuclear mechanics and chromatin structures is still unclear.</div><div>In this study, we developed an axisymmetric finite element model of a mesenchymal stem cell adhering to substrates with different curvatures to analyze nuclear stress distribution and identify locations where adhesion-induced gene expression may occur. Results reveal a nuclear stress field with principal stresses in radial and circumferential directions, leading to chromatin decondensation and nuclear pore opening. The predicted forces acting on chromatin fibers, estimated and compared with experimental data, remain slightly below 5 pN—the threshold at which internucleosomal attraction is disrupted, triggering chromatin condensation-decondensation transition—. During early spreading, nuclear forces achieved through adhesion on convex substrates approach this threshold more closely than in concave or flat cases.</div><div>These findings provide insights for tissue engineering and regenerative medicine, where early control of stem cell fate through substrate design is crucial. Understanding how mesenchymal stem cells respond to substrate curvature could lead to improved biomaterial surface topographies for guiding cell behavior. Tailoring curvature and mechanical properties may enhance early lineage commitment, optimizing regenerative strategies for tissue repair and organ regeneration.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109917"},"PeriodicalIF":7.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain imaging-to-graph generation using adversarial hierarchical diffusion models for MCI causality analysis","authors":"Qiankun Zuo ,&nbsp;Hao Tian ,&nbsp;Yudong Zhang ,&nbsp;Jin Hong","doi":"10.1016/j.compbiomed.2025.109898","DOIUrl":"10.1016/j.compbiomed.2025.109898","url":null,"abstract":"<div><div>Effective connectivity can describe the causal patterns among brain regions. These patterns have the potential to reveal the pathological mechanism and promote early diagnosis and effective drug development for cognitive disease. However, the current methods utilize software toolkits to extract empirical features from brain imaging to estimate effective connectivity. These methods heavily rely on manual parameter settings and may result in large errors during effective connectivity estimation. In this paper, a novel brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment (MCI) analysis. The proposed BIGG framework is based on the diffusion denoising probabilistic models (DDPM), where each denoising step is modeled as a generative adversarial network (GAN) to progressively translate the noise and conditional fMRI to effective connectivity. By introducing the diffusive factor, the denoising inference with a large sampling step size is more efficient and can maintain high-quality results. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model. The proposed model not only achieves superior prediction performance compared with other competing methods but also predicts MCI-related causal connections that are consistent with clinical studies.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109898"},"PeriodicalIF":7.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-objective optimization framework through genetic algorithm for hyperthermia-mediated drug delivery","authors":"Adabbo G ,&nbsp;Andreozzi A ,&nbsp;Iasiello M ,&nbsp;Napoli G ,&nbsp;Vanoli G.P","doi":"10.1016/j.compbiomed.2025.109895","DOIUrl":"10.1016/j.compbiomed.2025.109895","url":null,"abstract":"<div><div>This study presents an approach to the multi-objective optimization of hyperthermia-mediated drug delivery using thermo-sensitive liposomes (TSLs) for the treatment of hepatocellular carcinoma. The research focuses on addressing the non-optimal coupling methods that combine thermal treatments and chemotherapy by employing a Multi-Objective Genetic Algorithm (MOGA) optimization process, in order to identify the right combination of design variables to achieve better treatment outcomes. The proposed model integrates Computational Fluid Dynamics (CFD) analysis using the Pennes’ Bioheat equation for tissue heating and a convection-diffusion model for drug delivery. The goal is to maximize the fraction of killed cancer cells through the pharmaceutical treatment while minimizing thermal damage to the tissue, aiming to not hinder the drug feeding from the vascular system. The optimization considers several design variables, including heating power, timing, and the number of antenna slots for the microwave heating. Simulations results suggest that a two-slots antenna configuration with a specific heating schedule yields optimal therapeutic outcomes by maximizing drug concentration in the tumor while limiting damage to healthy tissue. The results of the CFD analysis also show a significant improvement in the treatment outcomes compared to non-optimized results proposed previously in the literature, leading to an increase from the 10 % up to the 33 % for the fraction of killed cells function. The proposed optimization through Genetic Algorithm framework could significantly improve patient-specific treatment planning for hyperthermia-mediated drug delivery.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109895"},"PeriodicalIF":7.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomic alterations and clinical correlates of hypothalamic nuclei in ALS","authors":"Benedetta Tafuri ,&nbsp;Alessia Giugno ,&nbsp;Salvatore Nigro ,&nbsp;Stefano Zoccolella ,&nbsp;Roberta Barone ,&nbsp;Ludovica Tamburrino ,&nbsp;Valentina Gnoni ,&nbsp;Daniele Urso ,&nbsp;Eleonora Rollo ,&nbsp;Roberto De Blasi ,&nbsp;Giancarlo Logroscino","doi":"10.1016/j.compbiomed.2025.109906","DOIUrl":"10.1016/j.compbiomed.2025.109906","url":null,"abstract":"<div><h3>Objective</h3><div>The aim of this study is to analyze hypothalamic changes and clinical/metabolic correlates with a radiomic approach in Amyotrophic Lateral Sclerosis (ALS).</div></div><div><h3>Methods</h3><div>We retrospectively identified 54 sporadic ALS patients and 53 matched controls. We compared radiomics features over hypothalamic subunits in T1-weighted. Semi-partial correlation (Spearman's correlation) assessed the relationship between Body Mass Index (BMI) and clinical scores with radiomics features. We considered only moderate correlations (rho&gt;|0.4|).</div></div><div><h3>Results</h3><div>Compared to HC, individuals with ALS showed significantly higher values of radiomic measures in the left Anterior-Inferior, Posterior and Inferior-Tubular hypothalamic subunits. Similarly, right hypothalamic nuclei reported significant differences in Anterior-Superior, Posterior and Inferior-Tubular nuclei. Two radiomics measures of randomness of the intensities in left Anterior-Inferior subunit showed highly significant correlation with greater BMI values. Higher local homogeneity of the right Inferior-Tubular subunit corresponded to higher ALS Functional Rating Scale-Revised (ALSFRS-r), while finer textures of the left Anterior-Superior subunit were negatively related with disease progression rate.</div></div><div><h3>Conclusions</h3><div>These results support the hypothesis that a degenerative process affecting hypothalamus in ALS extends beyond the atrophy process. Intriguingly, the close relationship between the entropy of left Anterior-Inferior nucleus and the higher BMI may further demonstrate the critical role of hypothalamus in eating abnormalities. Furthermore, the inhomogeneity of the right Inferior-Tubular subunit reflects a more severe clinical condition by ALSFRS-R.</div><div>This work represents a significant advancement in the study of ALS and its association with hypothalamic changes through a novel radiological approach, uncovering new associations between sub-hypothalamic radiomic changes, anthropometric measures, and disease outcomes.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109906"},"PeriodicalIF":7.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-driven prediction in healthcare systems: Applying advanced CNNs for enhanced breast cancer detection","authors":"Marouene Chaieb ,&nbsp;Malek Azzouz ,&nbsp;Mokhles Ben Refifa ,&nbsp;Mouadh Fraj","doi":"10.1016/j.compbiomed.2025.109858","DOIUrl":"10.1016/j.compbiomed.2025.109858","url":null,"abstract":"<div><div>The mortality risk associated with breast cancer is experiencing an exponential rise, underscoring the critical importance of early detection. It is the primary cause of mortality among women under 50 and ranks as the second deadliest disease globally. Timely identification is crucial, as heightened public awareness and accurate diagnosis can significantly reduce mortality rates. Patients with a positive prognosis and timely diagnosis have a far greater chance of full recovery. A comprehensive study was conducted to develop a robust breast cancer detection system using Convolutional Neural Networks (CNNs). This study details the processes of data collection, preprocessing, model building, and performance evaluation. The Mini-DDSM dataset was utilized, which includes 1952 scanned film mammograms from a diverse population. Data preprocessing involved normalization, denoising, illumination correction, and augmentation techniques to enhance data quality and diversity. During the model-building stage, several CNN architectures were explored, including Basic CNN, FT-VGG19, FT-ResNet152, and FT-ResNet50. The FT-ResNet50 model, fine-tuned with transfer learning, emerged as the top performer, achieving an accuracy of 97.54%. The integrated system leverages the strengths of each model to deliver accurate and reliable results, significantly advancing early detection and treatment methods for breast cancer. The comparative analysis demonstrated that the developed models outperformed existing state-of-the-art models. By leveraging the capabilities of deep learning and meticulous design, the objective is to significantly advance early detection and treatment methods for breast cancer, leading to better patient outcomes and ultimately, saving lives.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109858"},"PeriodicalIF":7.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated classification of oral cancer lesions: Vision transformers vs radiomics","authors":"Eva Chilet-Martos ,&nbsp;Joan Vila-Francés ,&nbsp;José V. Bagan ,&nbsp;Yolanda Vives-Gilabert","doi":"10.1016/j.compbiomed.2025.109913","DOIUrl":"10.1016/j.compbiomed.2025.109913","url":null,"abstract":"<div><h3>Background and objective</h3><div>Early diagnosis is paramount in the effective management of oral cancer, offering numerous benefits including improved treatment outcomes, reduced morbidity and mortality, preservation of function and appearance, cost-effectiveness, and enhanced quality of life for patients. Transformer-based models, increasingly used in medical image analysis, are the focus of our study. We aim to compare a vision transformer (ViT) classification method with a fully automated radiomics approach. This involves using object detection and segmentation algorithms to effectively classify oral lesions in both cancer and control cases. A combined approach is also presented.</div></div><div><h3>Methods</h3><div>The analysis included 322 patients with oral lesions, comprising 120 cancer cases and 202 controls, with standard JPG images. Pretrained transformer-based algorithms like DEtection TRansformer (DETR), Segment Anything (SAM), and Vision Transformers (ViT) were used to explore different pipelines for lesion classification. For the ViT approach, images were inputted in three configurations: the entire image, a bounding box around the lesion, and a lesion delineation. The radiomics approach involved pipelines with bounding boxes and lesion segmentations. Additionally, a ViT-Radiomics combined approach was proposed, using ViT attention maps as radiomics masks. To validate the models, a five-fold cross validation was used.</div></div><div><h3>Results</h3><div>The combined ViT-radiomics model demonstrated superior performance for small training sets, achieving specificity = 0.97 ± 0.04, sensitivity = 0.96 ± 0.05, and accuracy = 0.97 ± 0.02 for the 100 % of the training set. When analyzed independently, the ViT approach using the entire image achieved the best results with specificity = 0.99 ± 0.02, sensitivity = 0.96 ± 0.05, and accuracy = 0.96 ± 0.02. Following closely was the pipeline using automatically obtained segmentations, while the one with bounding boxes had the least favourable outcomes. In the radiomics approach, the most effective classifier used the attention masks from the ViT classifier (derived from the entire image), achieving specificity = 0.97 ± 0.05, sensitivity = 0.95 ± 0.08, and accuracy = 0.94 ± 0.03. Manual segmentations yielded the best results for both approaches, indicating potential for performance enhancement through improved lesion segmentation.</div></div><div><h3>Conclusions</h3><div>The ViT classification surpassed the radiomics-based approach yet combining ViT with radiomics yielded similar results. However, the attention maps generated by ViT tend to associate oral lesions in cancer patients with regions distant from the lesions in control patients. For tasks requiring the examination and comparison of features within cancer and control oral lesions, utilizing the radiomics approach with an automatic lesion segmentation algorithm is recommended.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109913"},"PeriodicalIF":7.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of solid abutment diameter and implant placement depth on stress distribution in the posterior mandible: A finite element analysis study","authors":"Gabriela Ayres ,&nbsp;Ana Paula Macedo ,&nbsp;Beatriz Roque Kubata,&nbsp;Valdir Antonio Muglia","doi":"10.1016/j.compbiomed.2025.109911","DOIUrl":"10.1016/j.compbiomed.2025.109911","url":null,"abstract":"<div><h3>Statement of problem</h3><div>Narrow diameter implants have been considered effective for implant placement in anterior region of maxilla and mandible. However, in regions with heavy masticatory loads, narrow implants may be excessively stressed.</div></div><div><h3>Purpose</h3><div>The purpose of this finite element analysis study was to evaluate the stress generated by narrow implants placed level with and below the bone margin in the posterior mandible and the biomechanical effects of different solid abutment diameters.</div></div><div><h3>Material and methods</h3><div>Four 3-dimensional models of an implant-supported prosthesis were simulated in the mandibular bone section of the first molar region. The implants were placed level with and below the bone margin, differing in the gingival height of the prosthetic abutments and testing different abutment diameters. The occlusal force of 365 N was simulated both axially and obliquely to represent medium-intensity physiological loads. Equivalent von Mises stresses were evaluated in the implant-to-abutment connection, and maximum and minimum principal stresses were evaluated in the surrounding bone.</div></div><div><h3>Results</h3><div>For the implant-abutment interface, under axial loading, stress values decreased by approximately 19 % with increasing abutment diameter. For the surrounding bone under axial loads, tensile stress values increased with subcrestal implant placement, averaging 32.8 MPa for cortical bone and 18.5 MPa for trabecular bone. Conversely, compressive stress in cortical bone decreased by an average of 76.2 MPa with subcrestal implant placement. Regarding the change in abutment diameter, there were no major variations in the stress values of the surrounding bone. With oblique loading, all stresses were considerably higher than with axial loading.</div></div><div><h3>Conclusions</h3><div>Although subcrestal implants showed higher stress values, stresses in the bone crest area decreased. Larger diameter abutments tended to generate better stress distribution for posterior prostheses.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"188 ","pages":"Article 109911"},"PeriodicalIF":7.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overexpression of miR-23b–3p+miR-218-5p+miR-124-3p differentially modifies the transcriptome of C-33A and CaSki cells and the regulation of cellular processes involved in the progression of cervical cancer","authors":"Manuel Joaquín Romero-López ,&nbsp;Hilda Jiménez-Wences ,&nbsp;Hober Nelson Nuñez-Martínez ,&nbsp;Merlin Itsel Cruz-De la Rosa ,&nbsp;Judit Alarcón-Millán ,&nbsp;Gloria Fernández-Tilapa","doi":"10.1016/j.compbiomed.2025.109886","DOIUrl":"10.1016/j.compbiomed.2025.109886","url":null,"abstract":"<div><h3>Summary</h3><div>Dysregulation of tumor suppressor miRNAs (tsmiRs) is associated with tumor progression in cancer. miR-23b-3p, miR-218–5p and miR-124–3p are tsmiRs in cervical cancer (CC) and regulate the translation of genes involved in metastasis-related biological processes.</div></div><div><h3>Objective</h3><div>To analyze transcriptome changes in cervical cancer cell lines (C-33A HPV-negative and CaSki HPV-positive) overexpressing miR-23b–3p + miR-218–5p + miR-124–3p, to identify specific target transcripts common to all three miRNAs, as well as signaling pathways and cellular processes related to tumor progression.</div></div><div><h3>Methods</h3><div>The transcriptome of C-33A and CaSki cells transfected with miR-23b–3p + miR-218–5p + miR-124–3p was analyzed by RNA-seq. Differentially expressed genes (DEGs) were subjected to Gene Ontology analysis on the DAVID platform. The function of under-regulated genes was analyzed on the GEPIA 2.0, Kaplan-Meier plotter and STRING platforms. On the TargetScanHuman platform it was determined which transcripts have MREs for miR-23b-3p, miR-218–5p and/or miR-124–3p in their 3′UTR region.</div></div><div><h3>Results</h3><div>Simultaneous overexpression of miR-218–5p, miR-124–3p and miR-23b-3p induced changes in global gene expression in C-33A and CaSki cells. In C-33A cells, DEGs included 45 over- and 172 under-regulated transcripts; in CaSki, 125 transcripts were over- and 84 under-regulated. The under-regulated transcripts enrich proliferation, migration, apoptosis and angiogenesis; 20 of these genes are associated with overall survival (OS) in women with CC, and 18 of the 20 mRNAs have MREs for one, two or all three miRNAs.</div></div><div><h3>Conclusions</h3><div>miR-23b–3p + miR-218–5p + miR-124–3p, differentially modify global gene expression in C-33A and CaSki cells. The results indicate that these miRNAs act synergistically and modulate CC progression through individual and shared targets by two or all three miRNAs.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"188 ","pages":"Article 109886"},"PeriodicalIF":7.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}