Frontiers in bioinformatics最新文献

{"title":"NfκBin: a machine learning based method for screening TNF-α induced NF-κB inhibitors.","authors":"Shipra Jain, Ritu Tomer, Sumeet Patiyal, Gajendra P S Raghava","doi":"10.3389/fbinf.2025.1573744","DOIUrl":"https://doi.org/10.3389/fbinf.2025.1573744","url":null,"abstract":"<p><strong>Introduction: </strong>Nuclear Factor kappa B (NF-κB) is a transcription factor whose upregulation is associated in chronic inflammatory diseases, including rheumatoid arthritis, inflammatory bowel disease, and asthma. In order to develop therapeutic strategies targeting NF-κB-related diseases, we developed a computational approach to predict drugs capable of inhibiting TNF-α induced NF-κB signaling pathways.</p><p><strong>Method: </strong>We utilized a dataset comprising 1,149 inhibitors and 1,332 non-inhibitors retrieved from PubChem. Chemical descriptors were computed using the PaDEL software, and relevant features were selected using advanced feature selection techniques.</p><p><strong>Result: </strong>Initially, machine learning models were constructed using 2D descriptors, 3D descriptors, and molecular fingerprints, achieving maximum AUC values of 0.66, 0.56, and 0.66, respectively. To improve feature selection, we applied univariate analysis and SVC-L1 regularization to identify features that can effectively differentiate inhibitors from non-inhibitors. Using these selected features, we developed machine learning models, our support vector classifier achieved a highest AUC of 0.75 on the validation dataset.</p><p><strong>Discussion: </strong>Finally, this best-performing model was employed to screen FDA-approved drugs for potential NF-κB inhibitors. Notably, most of the predicted inhibitors corresponded to drugs previously identified as inhibitors in experimental studies, underscoring the model's predictive reliability. Our best-performing models have been integrated into a standalone software and web server, NfκBin. (https://webs.iiitd.edu.in/raghava/nfkbin/).</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1573744"},"PeriodicalIF":3.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the selectivity <i>in silico</i> of colistin and daptomycin toward gram-negative and gram-positive bacteria, respectively, from the interaction with membrane phospholipids.","authors":"Yesid Aristizabal, Yamil Liscano, José Oñate-Garzón","doi":"10.3389/fbinf.2025.1569480","DOIUrl":"https://doi.org/10.3389/fbinf.2025.1569480","url":null,"abstract":"<p><p>Antimicrobial resistance is a significant public health concern worldwide. Currently, infections by antibiotic-resistant Gram-negative and Gram-positive bacteria are managed using the lipopeptide antibiotics colistin and daptomycin, which target the microbial membrane. Despite the fact that both are short, cyclic, and have a common acylated group, they display remarkable antimicrobial selectivity. Colistin exhibits activity only against gram-negative bacteria, while daptomycin only against gram-positive bacteria. However, the mechanism behind this selectivity is unclear. Here, we performed molecular dynamics simulations to study the interactions between <i>Escherichia coli</i> membrane models composed of 1-Palmitoyl-2-Oleoyl-sn-Glycero-3-Phosphoethanolamine (POPE)/1-Palmitoyl-2-Oleoyl-sn-Glycero-3-Phosphoglycerol (POPG) with daptomycin and colistin, independently. Similarly, we simulated the interaction between the <i>Staphyloccocus aureus</i> model membrane composed of POPG and cardiolipin (PMCL1) with both antibiotics. We observed that colistin interacted via hydrogen bonds and electrostatic interactions with the polar head of POPE in <i>E. coli</i> membrane models, mediated by 2,4-diaminobutyric acid (DAB) residues, which facilitated the insertion of its acyl tail into the hydrophobic core of the bilayer. In <i>S. aureus</i> membrane models, weaker interactions were observed with the polar head, particularly POPG, which was insufficient for the insertion of the lipid tail into the membrane. However, daptomycin displayed strong interactions with several POPG functional groups of the <i>S. aureus</i> membrane model, which favored the insertion of the fatty acid tail into the bilayer. Contrastingly, daptomycin showed negligible interactions with the <i>E. coli</i> membrane, except for the amino group of the POPE polar head, which might repel the calcium ions conjugated with the lipopeptide. Based on these results, we identified key amino acid-phospholipid interactions that likely contribute to this antibacterial selectivity, which might contribute to designing and developing future antimicrobial peptides.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1569480"},"PeriodicalIF":3.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using deep neural networks and LASSO regression to predict miRNA expression changes based on mRNA data.","authors":"Franz Leonard Böge, Helena U Zacharias, Stefanie C Becker, Klaus Jung","doi":"10.3389/fbinf.2025.1566162","DOIUrl":"10.3389/fbinf.2025.1566162","url":null,"abstract":"<p><strong>Introduction: </strong>Since the rise of molecular high-throughput technologies, many diseases are now studied on multiple omics layers in parallel. Understanding the interplay between microRNAs (miRNA) and their target mRNAs is important to understand the molecular level of diseases. While much public data from mRNA experiments are available for many diseases, few paired datasets with both miRNA and mRNA expression profiles are available. This study aimed to assess the possibility of predicting miRNA expression data based on mRNA expression data, serving as a proof of principle that such cross-omics predictions are feasible. Furthermore, current research relies on target databases where information about miRNA-target relationships is provided based on experimental and computational studies.</p><p><strong>Methods: </strong>To make use of publicly available mRNA profiles, we investigate the ability of artificial deep neural networks and linear least absolute shrinkage and selection operator (LASSO) regression to predict unknown miRNA expression profiles. We evaluate the approach using seven paired miRNA/mRNA expression datasets, four from studies on West Nile virus infection in mouse tissues and three from human immunodeficiency virus (HIV) infection in human tissues. We assessed the performance of each model first by within-data evaluations and second by cross-study evaluations. Furthermore, we investigated whether data augmentation or separate models for data from diseased and non-diseased samples can improve the prediction performance.</p><p><strong>Results: </strong>In general, most settings achieved strong correlations at the Level of individual samples. In some datasets and settings, correlations of log-fold changes and p-values from differential expression analysis (DEA) between true and predicted miRNA profiles can be observed. Correlation between log fold changes could also be seen in a cross-study evaluation for the HIV datasets. Data augmentation consistently improved performance in neural networks, while its impact on LASSO models was not significant.</p><p><strong>Discussion: </strong>Overall, cross-omics prediction of expression profiles appears possible, even with some correlations on the Level of the differential expression analysis.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1566162"},"PeriodicalIF":2.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12279838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative transcriptome analysis of different tissues of <i>Hylomecon japonica</i> provides new insights into the biosynthesis pathway of triterpenoid saponins.","authors":"Bing He, Teng Xu, Shaowei Xu, Huqiang Fang, Qingshan Yang","doi":"10.3389/fbinf.2025.1625145","DOIUrl":"10.3389/fbinf.2025.1625145","url":null,"abstract":"<p><p>Triterpenoid saponins are one of the main activities of roots and rhizomes of <i>Hylomecon japonica</i>, with various pharmacological activities such as antibacterial, anticancer, and anti-inflammatory. To elucidate the biosynthesis pathway of triterpenoid saponins in <i>H. japonica</i>, DNA nanoball sequencing technology was used to analyze the transcriptome of leaves, roots, and stems of <i>H. japonica</i>. Out of a total of 99,404 unigenes, 78,989 unigenes were annotated by seven major databases; 49 unigenes encoded 11 key enzymes in the biosynthesis pathway of triterpenoid saponins. Nine transcription factors were found to be involved in the metabolism of terpenoids and polyketides in <i>H</i>. <i>japonica</i> and a spatial structure model of squalene synthase in triterpenoid saponin biosynthesis was established. This study greatly enriched the transcriptome data of <i>H. japonica</i>, which is helpful for further analysis of the functions and regulatory mechanisms of key enzymes in the biosynthesis pathway of triterpenoid saponins.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1625145"},"PeriodicalIF":2.8,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277290/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GenoPath: a pipeline to infer tumor clone composition, mutational history, and metastatic cell migration events from tumor DNA sequencing data.","authors":"Ryan M Tobin, Shikha Singh, Sudhir Kumar, Sayaka Miura","doi":"10.3389/fbinf.2025.1615834","DOIUrl":"10.3389/fbinf.2025.1615834","url":null,"abstract":"<p><p>DNA sequencing technologies are widely used to study tumor evolution within a cancer patient. However, analyses require various computational methods, including those to infer clone sequences (genotypes of cancer cell populations), clone frequencies within each tumor sample, clone phylogeny, mutational tree, dynamics of mutational signatures, and metastatic cell migration events. Therefore, we developed GenoPath, a streamlined pipeline of existing tools to perform tumor evolution analysis. We also developed and added tools to visualize results to assist interpretation and derive biological insights. We have illustrated GenoPath's utility through a case study of tumor evolution using metastatic prostate cancer data. By reducing computational barriers, GenoPath broadens access to tumor evolution analysis. The software is available at https://github.com/SayakaMiura/GP.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1615834"},"PeriodicalIF":2.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12263698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-read microbial genome assembly, gene prediction and functional annotation: a service of the MIRRI ERIC Italian node.","authors":"Sandro Gepiro Contaldo, Antonio d'Acierno, Lorenzo Bosio, Francesco Venice, Elisa Li Perottino, Janneth Estefania Hoyos Rea, Giovanna Cristina Varese, Francesca Cordero, Marco Beccuti","doi":"10.3389/fbinf.2025.1632189","DOIUrl":"10.3389/fbinf.2025.1632189","url":null,"abstract":"<p><strong>Background: </strong>Understanding the structure and function of microbial genomes is crucial for uncovering their ecological roles, evolutionary trajectories, and potential applications in health, biotechnology, agriculture, food production, and environmental science. However, genome reconstruction and annotation remain computationally demanding and technically complex.</p><p><strong>Results: </strong>We introduce a bioinformatics platform designed explicitly for long-read microbial sequencing data to address these challenges. Developed as a service of the Italian MIRRI ERIC node, the platform provides a comprehensive solution for analyzing both prokaryotic and eukaryotic genomes, from assembly to functional protein annotation. It integrates state-of-the-art tools (e.g., Canu, Flye, BRAKER3, Prokka, InterProScan) within a reproducible, scalable workflow built on the Common Workflow Language and accelerated through high-performance computing infrastructure. A user-friendly web interface ensures accessibility, even for non-specialists.</p><p><strong>Conclusion: </strong>Through case studies involving three environmentally and clinically significant microorganisms, we demonstrate the ability of the platform to produce reliable, biologically meaningful insights, positioning it as a valuable tool for routine genome analysis and advanced microbial research.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1632189"},"PeriodicalIF":2.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12256462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of immune and major depressive disorder-related diagnostic markers for early nonalcoholic fatty liver disease by WGCNA and machine learning.","authors":"Yuyun Jia, Yanping Cao, Qin Yin, Xueqian Li, Xiu Wen","doi":"10.3389/fbinf.2025.1594971","DOIUrl":"10.3389/fbinf.2025.1594971","url":null,"abstract":"<p><strong>Background: </strong>Major depressive disorder (MDD) and nonalcoholic fatty liver disease (NAFLD) are highly prevalent conditions that exhibit significant pathophysiological overlap, particularly in metabolic and immune pathways.</p><p><strong>Objective: </strong>This study aims to bridge this gap by integrating transcriptomic data from publicly available repositories and advanced machine learning algorithms to identify novel biomarkers and construct a predictive model facilitates the provision of clinical psychological nursing interventions for early-stage NAFLD in MDD patients.</p><p><strong>Method: </strong>We systematically analyzed transcriptomic data of simple steatosis (SS), nonalcoholic steatohepatitis (NASH), and major depressive disorder (MDD) from GEO databases to construct and validate a diagnostic model. After removing batch effects, we identified differentially expressed genes (DEGs) that distinguished disease and control groups. We further applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify immune-related genes in SS/NASH patients versus controls. The intersection of shared DEGs across both conditions and WGCNA-identified genes was determined and subjected to functional enrichment analysis. Immune cell infiltration levels were quantified using single-sample gene set enrichment analysis (ssGSEA). A predictive model for SS/NASH was developed by evaluating nine machine-learning algorithms with 10-fold cross-validation on the datasets.</p><p><strong>Results: </strong>Fourteen genes strongly linked to both the immune system and the two conditions were identified. Immune cell infiltration profiling revealed distinct immune landscapes in patients versus healthy controls. Moreover, an eight-gene signature was developed, demonstrating superior diagnostic accuracy in both testing and training cohorts. Notably, these eight genes were found to correlate with the severity of early-stage NAFLD.</p><p><strong>Conclusion: </strong>This study established a predictive model for early-stage NAFLD through the integration of bioinformatics and machine learning approaches, with a focus on immune- and MDD-related genes. The eight-gene signature identified in this study represents a novel diagnostic tool for precision medicine, enabling targeted psychological nursing intervention in comorbid populations.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1594971"},"PeriodicalIF":2.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12271764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ngx-mol-viewers: Angular components for interactive molecular visualization in bioinformatics.","authors":"Damiano Clementel, Alessio Del Conte, Alexander Miguel Monzon, Silvio C E Tosatto","doi":"10.3389/fbinf.2025.1586744","DOIUrl":"10.3389/fbinf.2025.1586744","url":null,"abstract":"<p><p>Advancements in bioinformatics have been propelled by technologies like machine learning and have resulted in substantial increases in data generated from both empirical observations and computational models. Hence, well-known biological databases are growing in size and centrality by integrating data from different sources. While the primary goal of these databases is to collect and distribute data through application programming interfaces (APIs), providing visualization and analysis tools directly on the browser interface is crucial for users to understand the data, which increases the usefulness and overall impact of the databases. Currently, some front-end frameworks are available for the sustained development of the user interface (UI) and user experience (UX) of these resources. Angular is one of the most popular frameworks to be broadly adopted within the BioCompUP laboratory. This work describes a library of reusable and customizable components that can be easily integrated into the Angular framework to provide visualizations of various aspects of protein molecules, such as their sequences, structures, and annotations. Currently, the library includes three main independent components. The first is the ngx-structure-viewer, which allows visualization of molecules through the MolStar three-dimensional viewer. The second is the ngx-sequence-viewer, which provides visualization and annotation capabilities for a single sequence or multiple sequence alignments. The third the ngx-features-viewer, enables the mapping and visualization of various biological annotations onto the same molecule. All these tools are available for download through the Node Package Manager (NPM), and more information is available at https://biocomputingup.github.io/ngx-mol-viewers/ (under development).</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1586744"},"PeriodicalIF":2.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12243869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144610386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>In-silico</i> molecular analysis and blocking of the viral G protein of Nipah virus interacting with ephrin B2 and B3 receptor by using peptide mass fingerprinting.","authors":"Ayesha Sajjad, Ihteshamul Haq, Rabia Syed, Faheem Anwar, Muhammad Hamza, Muhammad Musharaf, Tehmina Kiani, Faisal Nouroz","doi":"10.3389/fbinf.2025.1526566","DOIUrl":"10.3389/fbinf.2025.1526566","url":null,"abstract":"<p><strong>Introduction: </strong>The Nipah virus (NiV), a zoonotic paramyxovirus closely related to the Hendra virus, poses a significant global health threat due to its high mortality rate, zoonotic nature, and recurring outbreaks primarily in Malaysia, Bangladesh, and India. Infection with NiV leads to severe encephalitis and carries a case fatality rate ranging from 40% to 75%. The lack of a vaccine and limited understanding of NiV pathogenesis underscore the urgent need for effective therapeutics. This study focuses on identifying viral peptides of the Nipah virus using the peptide mass fingerprinting technique. This approach identified antiviral peptides acting as potent inhibitors, targeting the viral G-protein's interaction with cellular ephrin-B2 and B3 receptors. These receptors are crucial for viral entry into host cells and subsequent pathogenesis.</p><p><strong>Methods: </strong>Identifying NiV viral peptides not only enhances our understanding of the virus's structural and functional properties but also opens avenues for developing novel therapeutic strategies. By blocking the interaction between the viral G-protein and host receptors, these antiviral peptides offer promising prospects for drug development against NiV.</p><p><strong>Results and discussion: </strong>Twenty-one peptides were identified using peptide mass fingerprinting. These peptides were then subjected to docking analysis with two antiviral peptides of the ephrin B2 receptor and a monoclonal antibody, demonstrating robust stability and binding affinity. These predicted peptides contribute to the broader field of virology by elucidating key aspects of NiV biology and paving the way for the development of targeted antiviral therapies. Future studies may further explore the therapeutic potential of these peptides and their application in combating other viral infections.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1526566"},"PeriodicalIF":2.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12238059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia.","authors":"Mengyao Sha, Jun Chen, Haifeng Hou, Huaihui Dou, Yan Zhang","doi":"10.3389/fbinf.2025.1606284","DOIUrl":"10.3389/fbinf.2025.1606284","url":null,"abstract":"<p><strong>Background: </strong>T-cell suppression in patients with Acute myeloid leukemia (AML) limits tumor cell clearance. This study aimed to explore the role of T-cell senescence-related genes in AML progression using single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (RNA-seq), and survival data of patients with AML in the TCGA database.</p><p><strong>Methods: </strong>The Uniform Manifold Approximation and Projection (UMAP) algorithm was used to identify different cell clusters in the GSE116256, and differentially expressed genes (DEGs) in T-cells were identified using the FindAllMarkers analysis. GSE114868 was used to identify DEGs in AML and control samples. Both were crossed with the CellAge database to identify aging-related genes. Univariate and multivariate regression analyses were performed to screen prognostic genes using the AML Cohort in The Cancer Genome Atlas (TCGA) Database (TCGA-LAML), and risk models were constructed to identify high-risk and low-risk patients. Line graphs showing the survival of patients with AML were created based on the independent prognostic factors, and Receiver Operating Characteristic Curve (ROC) curves were used to calculate the predictive accuracy of the line graph. GSE71014 was used to validate the prognostic ability of the risk score model. Tumor immune infiltration analysis was used to compare differences in tumor immune microenvironments between high- and low-risk AML groups. Finally, the expression levels of prognostic genes were verified using polymerase chain reaction (RT-qPCR).</p><p><strong>Results: </strong>31 AMLDEGs associated with aging identified 4 prognostic genes (CALR, CDK6, HOXA9, and PARP1) by univariate, multivariate, and stepwise regression analyses with risk modeling The ROC curves suggested that the line graph based on the independent prognostic factors accurately predicted the 1-, 3-, and 5-year survival of patients with AML. Tumor immune infiltration analyses suggested significant differences in the tumor immune microenvironment between low- and high-risk groups. Prognostic genes showed strong binding activity to target drugs (IGF1R and ABT737). RT-qPCR verified that prognostic gene expression was consistent with the data prediction results.</p><p><strong>Conclusion: </strong>CALR, CDK6, HOXA9, and PARP1 predicted disease progression and prognosis in patients with AML. Based on these, we developed and validated a new AML risk model with great potential for predicting patients' prognosis and survival.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1606284"},"PeriodicalIF":2.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12238043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}