Frontiers in bioinformatics最新文献

One area of bioinformatics that is currently attracting particular interest is the classification of polymicrobial diseases using machine learning (ML), with data obtained from high-throughput amplicon sequencing of the 16S rRNA gene in human microbiome samples. The microbial dysbiosis underlying these types of diseases is particularly challenging to classify, as the data is highly dimensional, with potentially hundreds or even thousands of predictive features. In addition, the imbalance in the composition of the microbial community is highly heterogeneous across samples. In this paper, we propose a curated pipeline for binary phenotype classification based on a count table of 16S rRNA gene amplicons, which can be applied to any microbiome. To evaluate our proposal, raw 16S rRNA gene sequences from samples of healthy and periodontally affected oral microbiomes that met certain quality criteria were downloaded from public repositories. In the end, a total of 2,581 samples were analysed. In our approach, we first reduced the dimensionality of the data using feature selection methods. After tuning and evaluating different machine learning (ML) models and ensembles created using Dynamic Ensemble Selection (DES) techniques, we found that all DES models performed similarly and were more robust than individual models. Although the margin over other methods was minimal, DES-P achieved the highest AUC and was therefore selected as the representative technique in our analysis. When diagnosing periodontal disease with saliva samples, it achieved with only 13 features an F1 score of 0.913, a precision of 0.881, a recall (sensitivity) of 0.947, an accuracy of 0.929, and an AUC of 0.973. In addition, we used EPheClass to diagnose inflammatory bowel disease (IBD) and obtained better results than other works in the literature using the same dataset. We also evaluated its effectiveness in detecting antibiotic exposure, where it again demonstrated competitive results. This highlights the importance and generalisation aspect of our classification approach, which is applicable to different phenotypes, study niches, and sample types. The code is available at https://gitlab.citius.usc.es/lara.vazquez/epheclass.

Background: Gastric cancer (GC) remains a major global health burden despite advances in diagnosis and treatment. In recent years, natural products have gained increasing attention as promising sources of anticancer agents, including GC.

Methods: In this study, we applied an in silico ensemble-based modeling strategy to predict compounds with potential inhibitory effects against four GC-related cell lines: AGS, NCI-N87, BGC-823, and SNU-16. Individual predictive models were developed using several algorithms and further integrated into two consensus ensemble multi-objective models. A comprehensive database of over 100,000 natural compounds from 21,665 plant species, was screened for validation and to identify potential molecular candidates.

Results: The ensemble models demonstrated a 12-15-fold improvement in identifying active molecules compared to random selection. A total of 340 molecules were prioritized, many belonging to bioactive classes such as taxane diterpenoids, flavonoids, isoflavonoids, phloroglucinols, and tryptophan alkaloids. Known anticancer compounds, including paclitaxel, orsaponin (OSW-1), glycybenzofuran, and glyurallin A, were successfully retrieved, reinforcing the validity of the approach. Species from the genera Taxus, Glycyrrhiza, Elaphoglossum, and Seseli emerged as particularly relevant sources of bioactive candidates.

Conclusion: While some genera, such as Taxus and Glycyrrhiza, have well-documented anticancer properties, others, including Elaphoglossum and Seseli, require further experimental validation. These findings highlight the potential of combining multi-objectives ensemble modeling with natural product databases to discover novel phytochemicals relevant to GC treatment.

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

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 in vivo 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 (ΔG -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.

Introduction: 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.

Methods: 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.

Results: 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.

Discussion: 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.

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.

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 via 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, MAPK10 isoforms showed distinct phenotypic correlations, while COL1A1 and UMOD 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 via CancerRCDShiny (https://cancerrcdshiny.shinyapps.io/cancerrcdshiny/), supporting future efforts in biomarker discovery and therapeutic target development across diverse cancer types.

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Introduction: 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.

Methods: 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.

Results: 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.

Discussion: 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.

Background: 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.

Results: 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.

Conclusion: 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.