Frontiers in bioinformatics最新文献

{"title":"Integrative machine learning and transcriptomic analysis identifies key molecular targets in MNPN-associated oral squamous cell carcinoma pathogenesis.","authors":"Xiangjun Wang, Panpan Jin, Juan Xu, Junyi Li, Mengzhen Ji","doi":"10.3389/fbinf.2025.1664576","DOIUrl":"https://doi.org/10.3389/fbinf.2025.1664576","url":null,"abstract":"<p><strong>Background: </strong>Oral squamous cell carcinoma (OSCC) represents a significant global health challenge, with betel nut consumption being a major risk factor. 3-(methylnitrosamino)propionitrile (MNPN), a betel nut-derived nitrosamine, has been identified as a potential carcinogen, but its molecular targets in OSCC pathogenesis remain poorly understood.</p><p><strong>Methods: </strong>We employed a comprehensive computational framework integrating target prediction, transcriptomic analysis, weighted gene co-expression network analysis (WGCNA), and machine learning approaches. Four OSCC datasets from Gene Expression Omnibus (GEO) were analyzed, and MNPN targets were predicted using ChEMBL, PharmMapper, and SwissTargetPrediction databases. Machine learning algorithms (n = 127 combinations) were evaluated for optimal biomarker identification, with model interpretability assessed using SHAP (SHapley Additive exPlanations) analysis.</p><p><strong>Results: </strong>Target prediction identified 881 potential MNPN targets across three databases. WGCNA revealed 534 OSCC-associated differentially expressed genes, with 38 overlapping MNPN targets. Machine learning optimization identified 13 hub genes, with PLAU demonstrating the highest predictive performance (AUC = 0.944). SHAP analysis confirmed PLAU and PLOD3 as the most influential contributors to disease prediction. Functional enrichment analysis revealed MNPN targets' involvement in xenobiotic response, hypoxic conditions, and aberrant tissue remodeling.</p><p><strong>Conclusion: </strong>This study provides the first comprehensive molecular characterization of MNPN-associated OSCC pathogenesis, identifying PLAU as a critical therapeutic target with exceptional diagnostic potential. Our findings establish a foundation for developing targeted interventions for betel nut nitrosamine-associated oral cancers and demonstrate the power of integrative computational approaches in environmental carcinogen research.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1664576"},"PeriodicalIF":3.9,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12508658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145282010","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":"Computational drug repurposing reveals Alectinib as a potential lead targeting Cathepsin S for therapeutic developments against cancer and chronic pain.","authors":"Mohammed Alrouji, Mohammed S Alshammari, Sharif Alhajlah, Syed Tasqeeruddin, Khuzin Dinislam, Anas Shamsi, Saleha Anwar","doi":"10.3389/fbinf.2025.1666573","DOIUrl":"10.3389/fbinf.2025.1666573","url":null,"abstract":"<p><p>Cathepsin S (CathS) is a cysteine protease known to play a role in extracellular matrix (ECM) re-modelling, antigen presentation, immune cells polarisation, and cancer progression and chronic pain pathophysiology. CathS also causes an immunosuppressive environment in solid tumors and is involved in nociceptive signaling. Although several small-molecule inhibitors with favorable <i>in vivo</i> properties have been developed, their clinical utility is limited due to resistance, off-target effects, and suboptimal efficacy. Therefore, alternative therapeutic strategies are urgently needed. In the present study, we utilized an integrated virtual screening protocol to screen 3,500 commercially available FDA-approved drug molecules from DrugBank against the CathS crystal structure, based on which drug-likeness profile and interaction studies were performed to filter putative candidates. Alectinib was found to be a top hit and had significant interactions with the important active-site residues His278 and Cys139. PASS predictions suggested relevant anticancer and anti-pain activities for Alectinib in reference to the control inhibitor Q1N. Later, 500-ns molecular dynamics simulations under the CHARMM36 condition revealed that the CathS-Alectinib complex maintained its structural stability, as indicated by conformational parameters, hydrogen-bond persistence, and essential dynamics analyses. Further MM-PBSA calculations also confirmed a favorable binding free energy (Δ<i>G</i> -20.16 ± 2.59 kcal/mol) dominated by the van der Waals and electrostatic contributions. These computational findings suggest that Alectinib may have potential as a repurposed CathS inhibitor, warranting further experimental testing in relevant cancer and chronic pain models. Notably, these results are based solely on computational analysis and require empirical validation.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1666573"},"PeriodicalIF":3.9,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12504298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260089","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":"Extracting a COVID-19 signature from a multi-omic dataset.","authors":"Baptiste Bauvin, Thibaud Godon, Guillaume Bachelot, Claudia Carpentier, Riikka Huusaari, Maxime Deraspe, Juho Rousu, Caroline Quach, Jacques Corbeil","doi":"10.3389/fbinf.2025.1645785","DOIUrl":"10.3389/fbinf.2025.1645785","url":null,"abstract":"<p><strong>Introduction: </strong>The complexity of COVID-19 requires approaches that extend beyond symptom-based descriptors. Multi-omic data, combining clinical, proteomic, and metabolomic information, offer a more detailed view of disease mechanisms and biomarker discovery.</p><p><strong>Methods: </strong>As part of a large-scale Quebec initiative, we collected extensive datasets from COVID-19 positive and negative patient samples. Using a multi-view machine learning framework with ensemble methods, we integrated thousands of features across clinical, proteomic, and metabolomic domains to classify COVID-19 status. We further applied a novel feature relevance methodology to identify condensed signatures.</p><p><strong>Results: </strong>Our models achieved a balanced accuracy of 89% ± 5% despite the high-dimensional nature of the data. Feature selection yielded 12- and 50-feature signatures that improved classification accuracy by at least 3% compared to the full feature set. These signatures were both accurate and interpretable.</p><p><strong>Discussion: </strong>This work demonstrates that multi-omic integration, combined with advanced machine learning, enables the extraction of robust COVID-19 signatures from complex datasets. The condensed biomarker sets provide a practical path toward improved diagnosis and precision medicine, representing a significant advancement in COVID-19 biomarker discovery.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1645785"},"PeriodicalIF":3.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12497780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245939","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":"Association between dysregulated expression of Ca²⁺ and ROS-related genes and breast cancer patient survival.","authors":"Sofia Ramos, João Gregório, Ana Sofia Fernandes, Nuno Saraiva","doi":"10.3389/fbinf.2025.1633494","DOIUrl":"10.3389/fbinf.2025.1633494","url":null,"abstract":"<p><p>The intricate interplay between Ca²⁺ and reactive oxygen species (ROS) signalling systems influences numerous cellular pathways. Dysregulated expression of genes associated with Ca²⁺ and ROS homeostasis can significantly impact cancer progression. Despite extensive research, various underlying mechanisms remain elusive, lacking a comprehensive unified perspective. Breast cancer (BC) remains the leading cause of cancer-related deaths among women, highlighting the pressing need to discover novel regulatory mechanisms, therapeutic targets, and potential biomarkers. In this study, we employed a bioinformatic approach based on data from The Cancer Genome Atlas to assess the association between combined dysregulation of specific pairs of genes involved in redox- or Ca²⁺-related cellular homeostases and patient outcome. These genes were selected by differences in their expression between normal and tumour tissues and in their individual association with patient survival rates. Cumulative proportion survival at the 5-year post-diagnosis was calculated for each quartile of expression within the population exhibiting either high or low expression of a second gene. Additional genes with expression positively or negatively correlated with the set of relevant gene pairs were identified, and a gene enrichment analysis was performed. Our results show that the simultaneous dysregulation of a selected number of gene pairs is substantially associated with BC patient survival. Notably, the expression dysregulation of these gene pairs is associated with altered expression of genes linked to cell cycle regulation, cell adhesion, and cell projection processes. This approach exhibits a significant potential to identify new prognostic biomarkers or drug targets for BC.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1633494"},"PeriodicalIF":3.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12498015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245896","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 multi-optosis model for pan-cancer candidate biomarker and therapy target discovery.","authors":"Emanuell Rodrigues de Souza, Higor Almeida Cordeiro Nogueira, Ronaldo da Silva Francisco Junior, Ana Beatriz Garcia, Enrique Medina-Acosta","doi":"10.3389/fbinf.2025.1630518","DOIUrl":"10.3389/fbinf.2025.1630518","url":null,"abstract":"<p><p>Regulated cell death (RCD) is fundamental to tissue homeostasis and cancer progression, influencing therapeutic responses across tumor types. Although individual RCD forms have been extensively studied, a comprehensive framework integrating multiple RCD processes has been lacking, limiting systematic biomarker discovery. To address this gap, we developed a multi-optosis model that incorporates 25 distinct RCD forms and integrates multi-omic and phenotypic data across 33 cancer types. This model enables the identification of candidate biomarkers with translational relevance through genome-wide significant associations. We analyzed 9,385 tumor samples from The Cancer Genome Atlas (TCGA) and 7,429 non-tumor samples from the Genotype-Tissue Expression (GTEx) database, accessed <i>via</i> UCSCXena. Our analysis involved 5,913 RCD-associated genes, spanning 62,090 transcript isoforms, 882 mature miRNAs, and 239 cancer-associated proteins. Seven omic features-protein expression, mutation, copy number variation, miRNA expression, transcript isoform expression, mRNA expression, and CpG methylation-were correlated with seven clinical phenotypic features: tumor mutation burden, microsatellite instability, tumor stemness metrics, hazard ratio contexture, prognostic survival metrics, tumor microenvironment contexture, and tumor immune infiltration contexture. We performed over 27 million pairwise correlations, resulting in 44,641 multi-omic RCD signatures. These signatures capture both unique and overlapping associations between omic and phenotypic features. Apoptosis-related genes were recurrent across most signatures, reaffirming apoptosis as a central node in cancer-related RCD. Notably, isoform-specific signatures were prevalent, indicating critical roles for alternative splicing and promoter usage in cancer biology. For example, <i>MAPK10</i> isoforms showed distinct phenotypic correlations, while <i>COL1A1</i> and <i>UMOD</i> displayed gene-level coordination in regulating tumor stemness. Notably, 879 multi-omic signatures include chimeric antigen targets currently under clinical evaluation, underscoring the translational relevance of our findings for precision oncology and immunotherapy. This integrative resource is publicly available <i>via CancerRCDShiny</i> (https://cancerrcdshiny.shinyapps.io/cancerrcdshiny/), supporting future efforts in biomarker discovery and therapeutic target development across diverse cancer types.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1630518"},"PeriodicalIF":3.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234239","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 and therapeutic investigation of biomarker genes underpinning hepatocellular carcinoma: an <i>in silico</i> study utilising molecular docking and dynamics simulation.","authors":"Jishnu Ghosh, Abdullah M Alshahrani, Aritra Palodhi, Debarghya Bhattacharyya, Subhadip Das, Sunil Kanti Mondal, Abul Kalam, S Rehan Ahmad, Chittabrata Mal","doi":"10.3389/fbinf.2025.1567748","DOIUrl":"10.3389/fbinf.2025.1567748","url":null,"abstract":"<p><strong>Background: </strong>Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality globally, and ranks fifth in terms of incidence. It primarily affects males and has a high prevalence in Asia. Risk factors include hepatitis B and C, liver cirrhosis, nonalcoholic fatty liver disease (NAFLD), and alcohol consumption. Late-stage diagnosis results in a poor survival rate of approximately 20%, underscoring the need for early detection methods to improve the survival rates. This study aimed to identify prognostic biomarkers for HCC through bioinformatic analysis of microarray datasets, providing insights into potential therapeutic targets.</p><p><strong>Methods: </strong>We analyzed five microarray datasets, comprising 402 HCC samples and 121 control samples. To identify relevant biological pathways, we conducted differential gene expression, Gene Ontology (GO), and KEGG pathway enrichment analyses. We identified hub genes and quantitatively assessed transcription factors and microRNAs targeting these genes. Additionally, molecular docking and dynamic simulations (100 ns) were used to identify potential drug candidates capable of inhibiting the activity of differentially expressed hub genes.</p><p><strong>Results: </strong>Our bioinformatic approach identified several promising HCC biomarkers. Among these, CDK1/CKS2 was identified as a key therapeutic target, with a regulatory role in HCC pathogenesis, suggesting its potential for further investigation. Digoxin (DB00390) has been highlighted as a potential repurposed drug candidate because of its favorable drug-likeness and stability, as confirmed by virtual screening, ADMET analysis, molecular docking study and dynamic simulations.</p><p><strong>Conclusion: </strong>This study enhances our understanding of HCC biology and offers new insights into drug interactions. It presents several promising biomarkers for the early diagnosis, prognosis, and therapy. Further investigation into CDK1/CKS2 as a therapeutic target and the role of the identified biomarkers could contribute to improved diagnostic and therapeutic strategies for HCC.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1567748"},"PeriodicalIF":3.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491263/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234264","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":"Interpretable artificial intelligence based on immunoregulation-related genes predicts prognosis and immunotherapy response in lung adenocarcinoma.","authors":"Minghao Wang, Yu Wang, Yitong Li, Chengyi Zhang, Canjun Li, Nan Bi","doi":"10.3389/fbinf.2025.1613761","DOIUrl":"10.3389/fbinf.2025.1613761","url":null,"abstract":"<p><strong>Introduction: </strong>Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer, and its benefit from immune checkpoint inhibitors (ICIs) is controversial, especially for patients without driver gene mutations. The potential of immunoregulation-related genes (IRGs) in predicting the prognosis of LUAD and the efficacy of immunotherapy becomes emerging. There is an urgent need to establish a reliable IRGs-based predictive model of ICI response.</p><p><strong>Methods: </strong>Extract and merge LUAD RNA sequencing data and clinical data from GEO database. The differences in genomic and tumor microenvironment (TME) cell infiltration landscape between normal lung tissue and tumor tissue were comprehensively analyzed. Unsupervised consistent cluster analysis based on genes related to immune regulation was performed on the samples. ESTIMATE and TIMER algorithms were used to analyze the infiltration of immune cells in different groups, and TIDE score was used to evaluate the effectiveness of immunotherapy. Then, lasso regression was used to establish a prognostic model based on identified key IRGs. XGBoost machine learning algorithm was further developed, with SHapley Additive exPlanations (SHAP) to interpret the model.</p><p><strong>Results: </strong>The GEO LUAD cohort was divided into two clusters based on IRG expression, with significantly better survival outcomes and immune cell infiltration in the IRG-high group compared to the IRG-low group. TIDE scores indicated that the group with high IRG pattern showed a better response to ICI treatment. Then, we developed an IRG index (IRGI) model based on identified 2 key IRGs, GREM1 and PLAU, and IRGI effectively divided patients into high-risk and low-risk groups, revealing significant differences in prognosis, mutational profiles, and immune cell infiltration in the TME between two groups. Subsequently, the interpretable XBGoost machine learning model established based on IRGs could further improve the predictive performance (AUC = 0.975), and SHAP analysis demonstrated that GREM1 had the greatest impact on the overall prediction.</p><p><strong>Discussion: </strong>IRGI can be used as a valuable biomarker to predict LUAD patient prognosis and response to ICIs. IRGs play a crucial role in shaping the diversity and complexity of TME cell infiltration, which may provide valuable guidance for ICI treatment decisions for LUAD patients.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1613761"},"PeriodicalIF":3.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234287","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":"Quantitative measures to assess the quality of cellular indexing of transcriptomes and epitopes by sequencing data.","authors":"Jie Sun, Robert Morrison, Soyeon Kim, Kairuo Yan, Hyun Jung Park","doi":"10.3389/fbinf.2025.1630161","DOIUrl":"10.3389/fbinf.2025.1630161","url":null,"abstract":"<p><strong>Background: </strong>Cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq) is a powerful technique to simultaneously measure gene expression and cell surface protein abundances in individual cells. To obtain accurate and reliable biological findings from CITE-Seq data, it is critical to ensure rigorous quality control (QC). However, no public method has yet been developed for CITE-Seq QC.</p><p><strong>Results: </strong>In this study, we propose the first software package for multi-layered, systemic, and quantitative quality control (CITESeQC). Recognizing the multi-layered nature of CITE-Seq data, CITESeQC performs QC across gene expressions, surface proteins, and their interactions. It systemically evaluates all genes and protein markers assayed in the data and filters out some of them based on individual quality measures. Furthermore, for quantitative QC that enables objective and standardized analyses, CITESeQC quantifies cell type-specific expression of genes and surface proteins using Shannon entropy and correlation-based measures. Finally, to ensure broad applicability, CITESeQC guides users through a simple process that generates a complete markdown report with supporting figures and explanations, requiring minimal user intervention.</p><p><strong>Conclusion: </strong>By quantifying the quality of CITE-Seq data, CITESeQC enables precise characterization of gene expression within cell types and reliable classification of cell types using surface protein markers, thereby enhancing its value for clinical applications.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1630161"},"PeriodicalIF":3.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12488637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234282","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":"BC-predict: mining of signal biomarkers and production of models for early-stage breast cancer subtyping and prognosis.","authors":"Sangeetha Muthamilselvan, Natarajan Vaithilingam, Ashok Palaniappan","doi":"10.3389/fbinf.2025.1644695","DOIUrl":"10.3389/fbinf.2025.1644695","url":null,"abstract":"<p><strong>Introduction: </strong>Disease heterogeneity is the hallmark of breast cancer, which is the most common female malignancy. With a disturbing increase in mortality and disease burden, there remains a need for effective early-stage theragnostic and prognostic biomarkers. In this work, we improved on BrcaDx (https://apalania.shinyapps.io/brcadx/) for cancer vs control screening and examined a cluster of adjoining learning problems in breast cancer heterogeneity: (i) identification of metastatic cancers; (ii) molecular subtyping (TNBC, HER2, or luminal); and (iii) histological subtyping (invasive ductal or invasive lobular).</p><p><strong>Methods: </strong>We analyzed the transcriptomic profiles of breast cancer patients from public-domain databases such as the TCGA using stage-encoded problem-specific statistical models of gene expression and unveiled stage-salient and progression-significant genes. Using a consensus approach, we identified potential machine learning features, and considered six model classes for each learning problem, with hyperparameter optimization on a training dataset and evaluation on a holdout test dataset. A nested approach enabled us to identify the best model class for each learning problem.</p><p><strong>Results: </strong>External validation of the best models yielded balanced accuracies of 97.42% for cancer vs normal; 88.22% for metastatic v/s non metastatic; 88.79% for ternary molecular subtyping; and ensemble accuracy of 94.23% for histological subtyping. The model for molecular subtyping was validated on a 26-sample TNBC-only out-of-distribution cohort, yielding 25 correct predictions. We performed a late integration of multi-omics datasets by validating the feature space used in each problem with miRNA profiles, methylation profiles, and commercial breast cancer panels.</p><p><strong>Discussion: </strong>Pending prospective studies, we have translated the models into BC-Predict that forks the best models developed for each problem in a unified interface and provides a complete readout for input instances of expression data, including uncertainty estimates. BC-Predict is freely available for non-commercial purposes at: https://apalania.shinyapps.io/BC-Predict.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1644695"},"PeriodicalIF":3.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12488574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234134","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":"Key genes associated with brain metastasis in non-small cell lung cancer: novel insights from bioinformatics analysis.","authors":"Shuang Zhao, He Zhang","doi":"10.3389/fbinf.2025.1625664","DOIUrl":"10.3389/fbinf.2025.1625664","url":null,"abstract":"<p><strong>Background: </strong>This study aims to investigate potential biomarkers associated with NSCLC-BM and elucidate their regulatory roles in critical pathways involved in cerebral metastatic dissemination.</p><p><strong>Methods: </strong>The identified DEGs were subjected to functional enrichment analysis. PPI networks were predicted using the STRING database and visualized with Cytoscape. Hub genes were subsequently screened from the PPI network to construct a transcription TF-miRNA regulatory network. Subsequent analyses included: survival analysis, immune infiltration assessment and comprehensive mutational profiling.</p><p><strong>Results: </strong>Among the 56 identified DEGs, 19 were upregulated while 37 were downregulated. GOntology enrichment analysis revealed significant enrichment in immune response, signaling receptor binding, and extracellular region. KEGG pathway analysis demonstrated predominant involvement in cytokine-cytokine receptor interaction and chemokine signaling pathway. Through Cytoscape-based screening, we identified 10 hub genes: CD19, CD27, IL7R, SELL, CCL5, CCR5, PRF1, GZMK, GZMA, and TIGIT. The TF-miRNA regulatory network analysis uncovered 6 transcription factors (STAT5A/B, NFKB1, EGR1, RELA, and CTCF) and 4 miRNAs(hsa-miR-204, hsa-miR-148b, hsa-miR-618, and hsa-miR-103) as critical transcriptional and post-transcriptional regulators of DEGs.Integrated analyses including Kaplan-Meier survival curves, immune infiltration profiling, and comprehensive mutational analysis demonstrated significant associations with brain metastatic progression in the studied cohort.</p><p><strong>Conclusion: </strong>This study provides novel biomarkers from a unique perspective for the diagnosis, prognosis, and development of molecular-targeted therapies or immunotherapies for brain metastasis in NSCLC.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1625664"},"PeriodicalIF":3.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12488587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234273","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}