Bioinformatics and Biology Insights最新文献

{"title":"A Novel Bioinformatics Pipeline and a Machine-Learning Approach for Antimicrobial Resistance Phenotypic Prediction.","authors":"Owen Visser, Victor Agboli, Somnath Datta","doi":"10.1177/11779322261453756","DOIUrl":"10.1177/11779322261453756","url":null,"abstract":"<p><p>Overuse of antimicrobial drugs is known to cause an increase in bacterial resistance among surviving pathogens, reducing the effectiveness of future treatments. Publicly available sequencing collections, such as the National Center for Biotechnology Information Sequence Read Archive (SRA), allow for global investigation of antimicrobial resistance across pathogens. In this study, we developed a pipeline to process 10 803 globally sourced unique bacterial isolates from publicly available SRA datasets (9 pathogens, 4 antibiotics), representing sequencing data generated by multiple laboratories using diverse sequencing platforms and protocols. The pipeline extracted SRA metadata to determine read layout and length, applied quality control, trimming, and decontamination. Preprocessed isolates were mapped reads to an antimicrobial resistance gene class and to a strain-level genome reference library constructed from complete genomes on the SRA submitted between 2009 and 2020. Three classifiers-L1-penalized logistic regression, random forest, and extreme gradient boosting-were trained on the resulting feature matrices, and their outputs were combined in a majority-vote ensemble. Internal training resulted in 83.8% balanced accuracy on average, and external testing yielded 80.2%. Variable importance analyses identified known resistance gene classes and strain markers such as <i>Acinetobacter baumannii</i> LAC-4 and <i>Campylobacter jejuni</i> 81-176 and NCTC13255, confirming biological relevance. This work demonstrates a scalable approach for antimicrobial resistance prediction using heterogeneous sequencing data.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261453756"},"PeriodicalIF":2.0,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13219803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148136703","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 Computational Profiling Links Position-specific O-Methylation to Spontaneous Complexation and Improved Binding in Flavonoid-HER2 Systems.","authors":"Precious A Akinnusi, Samuel O Olubode, David N A Okai, Oluwatobi I Akinbami, David O Oyerinde, Ayomide J Akinnusi","doi":"10.1177/11779322261455979","DOIUrl":"10.1177/11779322261455979","url":null,"abstract":"<p><p>Human epidermal growth factor receptor 2-positive (HER2+) breast cancer is characterized by amplification and overexpression of HER2. This study used a computational workflow to identify O-methylated flavonoids with favorable binding to the HER2 kinase domain and to clarify their binding modes. Among the screened compounds, Eupatolitin, Rhamnetin, Annulatin, Laricitrin, and Sterubin showed the best predicted affinities, with docking scores ranging from -10.092 to -9.260 kcal/mol. Molecular dynamics simulations were then used to assess complex stability, and density functional theory was applied to examine the reactivity of the lead compounds. These analyses were intended for computational prioritization, and experimental validation is required to confirm HER2 inhibition and biological activity. Eupatolitin, which bears methoxy groups at C-6 and C-7, showed the strongest predicted binding, suggesting that 6,7-O-methylation may favor HER2 recognition. This observation prompted a comparison of four parent flavonoids with their 6,7-O-methylated analogues. Across quercetin, myricetin, luteolin, and eriodictyol, 6,7-O-methylation consistently strengthened predicted HER2 binding: docking scores improved by 0.27-0.57 kcal/mol and MM/GBSA ΔGbind improved by 6-10 kcal/mol relative to the parent compounds. In contrast, natural O-methylation at other positions did not enhance binding. Overall, these findings identify Eupatolitin, Rhamnetin, Annulatin, Laricitrin, and Sterubin as promising computational HER2 hits and support position-specific O-methylation at C-6 and C-7 as a useful strategy for optimizing flavonoid scaffolds for HER2 targeting, but experimental validation is required.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261455979"},"PeriodicalIF":2.0,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13201941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148041163","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 Characterization of Nabilone-Induced Disruption of the CB2-HER2 Receptor Complex in HER2+ Breast Cancer.","authors":"Abbou Hanane, Zegrari Razana, Gaouzi Zainab, Festali Rihab, Chemao-Elfihri Mohammed Walid, Hakmi Mohammed, El Jaoudi Rachid","doi":"10.1177/11779322261449332","DOIUrl":"10.1177/11779322261449332","url":null,"abstract":"<p><p>Human epidermal growth factor receptor 2-positive (HER2+) breast cancer, accounting for 15% to 20% of cases, is often resistant to treatment. Delta-9-tetrahydrocannabinol (THC) disrupts HER2-cannabinoid receptor (2CB2) receptor complexes and inhibits HER2 activation. This study evaluates whether Nabilone, a synthetic cannabinoid, can similarly disrupt HER2-CB2 interactions. A CB2-HER2 complex model was generated via protein-protein docking. Three 1-µs molecular dynamics simulations (CB2-HER2, CB2-HER2-THC, CB2-HER2-Nabilone) were performed using the Schrodinger Desmond with membrane embedding and solvent. Structural stability (root mean square deviation [RMSD] and root mean square fluctuation [RMSF]), binding free energy (molecular mechanics/generalized born surface area [MM/GBSA]), and intracellular/extracellular distances between receptors were analyzed. Intermolecular interactions were assessed using the MAPIYA server. Nabilone induced comparable structural instability to THC, with increased RMSD and RMSF. The MM/GBSA analysis showed Nabilone increased the binding free energy between CB2 and HER2, indicating stronger disruption. Intracellular and extracellular distances between CB2 and HER2 increased, especially intracellularly, with Nabilone. Intermolecular interaction analysis revealed that Nabilone decreased the number of contacts, particularly hydrophobic interactions, between CB2 and HER2. Our in silico model predicts that Nabilone may disrupt the HER2-CB2 complex, suggesting a hypothesis that it could serve as a potential therapeutic agent. These computational findings warrant urgent experimental validation.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261449332"},"PeriodicalIF":2.0,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13184371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147980104","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 Analysis of lncRNA-miRNA-mRNA ceRNA Network and Identification of Hub lncRNAs in Molecular Subtypes of Gastric Cancer as Potential Prognostic Indicators.","authors":"Seyed Navid Goftari, Ahmad Reza Bahrami, Maryam M Matin","doi":"10.1177/11779322261433659","DOIUrl":"10.1177/11779322261433659","url":null,"abstract":"<p><strong>Background: </strong>Gastric cancer (GC) is the fourth most common cause of death among cancers in the world, and the most prevalent type of GC is adenocarcinoma. The Cancer Genome Atlas (TCGA) project introduced 4 molecular subtypes of GC adenocarcinomas: Epstein-Barr virus (EBV), microsatellite instability (MSI), genomically stable (GS), and chromosomal instability (CIN). However, the function of long noncoding RNAs (lncRNAs) in these subtypes still remains unknown. Here, we aimed to construct a competing endogenous RNA (ceRNA) network to clarify the role of lncRNAs in each GC subtype and predict patients' overall relapse-free survival (RFS) time.</p><p><strong>Methods: </strong>The RNA-seq data of miRNAs, lncRNAs, and mRNAs related to different GC subtypes and their corresponding normal samples from TCGA were analyzed, and a combination of signed and unsigned weighted gene co-expression network analysis (WGCNA) (csuWGCNA) was recruited to determine co-expressing gene modules. Then, lncRNA-miRNA and miRNA-mRNA interactions were predicted, and the ceRNA regulatory network was established, followed by the identification of hub lncRNAs for any GC subtype. Gene set enrichment analyses were applied to protein-coding genes of each module to investigate their functions. Finally, survival analysis was performed for identified hub lncRNAs.</p><p><strong>Results: </strong>Differentially expressed lncRNAs, miRNAs, and mRNAs related to each GC subtype were identified, ceRNA networks were constructed, and in each subtype, the top 5 lncRNAs with the most MCC scores were chosen as hub lncRNAs. Survival analysis revealed that AC138356.1, LINC01270, and AL118506.1 lncRNAs were associated with the RFS of CIN subtype, LINC02099, AL157871.2, AC068580.3, and AC004264.1 lncRNAs with EBV subtype, AL117335.1 and AC011416.3 lncRNAs with MSI subtype, and MIR210HG and LINC00565 lncRNAs with GS subtype patients.</p><p><strong>Conclusion: </strong>The identified hub lncRNAs provide insights for understanding molecular mechanisms underlying GC subtype transformation and may be useful to predict the RFS of corresponding patients; more research is required to confirm the results.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261433659"},"PeriodicalIF":2.0,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13176561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962182","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":"PathoAnalyzer-I: An Integrative Bioinformatics Platform for Chronic Disease Analysis.","authors":"Ali Aguerd, Faiza Bennis, Fatima Chegdani","doi":"10.1177/11779322261433663","DOIUrl":"10.1177/11779322261433663","url":null,"abstract":"<p><p>Chronic diseases impose a global health burden, contributing to high mortality and economic costs. Even with the recent surge in molecular data, these conditions remain largely incurable due to their biological complexity, data fragmentation, and analysis challenges, hindering early diagnosis, mechanistic understanding, and therapy. To address this, we developed PathoAnalyzer-I, an <i>in silico</i> platform that combines bioinformatics and machine learning to decipher chronic diseases. The tool requires no programming skills and provides a user-friendly interface for pathological analysis within a single framework. PathoAnalyzer-I uses a dataset of molecular data for 531 chronic diseases, from databases including the GWAS Catalog, PubChem, and STRING-db, enriched with machine learning-based predictions. Its dual-prediction system enhances molecular insights: one model imputes missing risk alleles with 77.6% accuracy, while a second predicts novel SNP-disease associations with 89.3% accuracy, providing avenues for future research. Applied to Alzheimer's disease, the platform identified diagnostic biomarkers (e.g. rs6733839T), core genes in disease mechanisms (e.g. <i>BIN1</i>, <i>APOE</i>, <i>SLC24A4</i>, <i>ABCA7</i>, <i>PTK2B</i>), major pathological mechanisms like amyloid processing, synaptic dysfunction, and cellular vulnerability, as well as therapeutic molecules including Beta-Lapachone and preventive compounds such as curcumin. With its features, PathoAnalyzer-I enables scientists, regardless of their resources, to conduct in-depth <i>in silico</i> studies of chronic diseases.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261433663"},"PeriodicalIF":2.0,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13167329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147939949","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":"Metagenomic Profiling of Taxonomic and Functional Diversity in Soil Microbial Communities at Buffelsdraai Landfill, South Africa: Implications for Bioremediation.","authors":"Xolani Mazibuko, Sbongile Mtimka, Lethukuthula M Ngobese, Thendo Mafuna, Mthokozisi Bc Simelane, Sinethemba H Yakobi, Xolani Gumede, Ofentse J Pooe","doi":"10.1177/11779322251413418","DOIUrl":"10.1177/11779322251413418","url":null,"abstract":"<p><p>Soil microbial communities in landfills play a crucial in waste degradation and pollution mitigation, yet their diversity and functionality in many regions remain underexplored. This study used shotgun metagenomic sequencing to characterise microbial communities in soil samples from the Buffelsdraai landfill waste site (samples: XM-AA, XM-BB, XM-CC, XM-DD). We identified dominant taxa, namely, <i>Actinobacteria</i>, <i>Acidobacteria</i>, and <i>Bacteroidetes</i>, and evaluated their taxonomic diversity and metabolic potential. Diversity indices revealed high richness in XM-AA (Shannon: 4.188), suggesting the potential of a strong waste-processing capacity, while XM-BB showed reduced diversity (Shannon: 1.453), likely due to contaminant stress (eg, nickel, cobalt). XM-CC and XM-DD exhibited moderate diversity (Shannon: 2.671-2.942) with <i>Actinobacteria</i> dominance (99%), suggesting adaptation to landfill conditions. Functional profiling via Kyoto Encyclopaedia of Genes and Genomes pathways highlighted carbohydrate and lipid metabolism, alongside xenobiotic biodegradation, pointing to potential for organic waste and pollutant breakdown. Physicochemical analyses detected elevated sodium (22 640 mg/kg in cell 1) and trace metals (eg, Ni: 0.1469 mg/kg), influencing microbial composition. These results emphasise microbial diversity's role in landfill soil functionality and position <i>Actinobacteria</i> as a bioremediation target for degrading leachate organics and immobilising metals. This study provides a baseline profile of microbial taxonomic and functional responses to landfill-associated environmental stressors in South Africa. The findings highlight the ecological roles of landfill microbial communities and their potential relevance for future bioremediation research.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322251413418"},"PeriodicalIF":2.0,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13161635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147926188","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":"Systems Vaccinology-Integrated Proteomics to Develop a Novel Prophylactic and Therapeutic Vaccine Against Human Papillomavirus 16.","authors":"Shirin Fathi, Aida Solhjoo, Ashkan Bagheri, Amirhossein Sakhteman, Younes Ghasemi, Manica Negahdaripour","doi":"10.1177/11779322261437247","DOIUrl":"10.1177/11779322261437247","url":null,"abstract":"<p><p>Cervical cancer remains a major global health burden, with high-risk human papillomavirus (HPV), particularly HPV-16, responsible for most cases. While licensed prophylactic HPV vaccines are effective, their type-restricted coverage and lack of therapeutic activity highlight the need for next-generation vaccine strategies. In this study, a systems vaccinology-based approach was used to design a multi-epitope vaccine with both prophylactic and therapeutic potential against HPV-16. Immunodominant epitopes eliciting immune responses from HPV-16 E5, E6, E7, and L2 proteins were identified using immunoinformatics methods. Transcriptomic and pathway enrichment analysis of cervical cancer data sets identified interleukin-17A (IL-17A) signaling as a favorable immune axis, supporting its inclusion as a cytokine adjuvant. Population coverage evaluation demonstrated broad global applicability, with a predicted coverage of 99.98% across MHC class I and II alleles. Among 7 designed constructs, 1 candidate (model D) exhibited favorable physicochemical and structural properties, including high predicted solubility (73%), strong model quality, and antigenic potential. Docking analyses indicated strong correlations between native complexes and the corresponding vaccine-IL-17 receptor A and C complexes (Pearson <i>r</i> = .98 and .88, respectively). Molecular dynamics simulations over 500 ns confirmed structural stability, with favorable MM/PBSA binding free energies (ΔG_total ≈ -31 kJ/mol). Immune response simulations demonstrated activation of CD4⁺ and CD8⁺ T-cells, memory B-cell formation, and a predominantly IFN-γ immune response. These findings demonstrate that the proposed construct is a promising HPV-16 vaccine candidate; nevertheless, targeted in vitro and in vivo studies are essential to confirm its immunogenicity, efficacy, and safety prior to further clinical developments.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261437247"},"PeriodicalIF":2.0,"publicationDate":"2026-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13103492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147761956","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":"Pangenomics-Driven Reverse Vaccinology for the Discovery of New Vaccine Candidates Against <i>Bordetella pertussis</i>.","authors":"Majid Abdelouahab, Diawara Idrissa","doi":"10.1177/11779322261442522","DOIUrl":"10.1177/11779322261442522","url":null,"abstract":"<p><p>Despite widespread vaccination, pertussis resurgence persists due to waning immunity and emerging resistant strains in <i>Bordetella pertussis</i>. This study employs a pangenomics-driven reverse vaccinology approach to identify novel vaccine candidates. Analyzing 160 genomes revealed a closed pan-genome, with approximately 60% conserved genes, including 3389 core genes of which 1312 participate in pathogen-specific pathways. Non-homologous proteins were identified by comparison against human and microbiome proteomes, yielding 205 candidates. Essentiality assessment via the Database of Essential Genes (DEG) refined this to 63 non-homologous essential proteins. A multi-criteria selection process evaluated purifiability based on physicochemical properties and transmembrane helices, accessibility (extracellular or secreted localization), and immunogenicity through antigenicity prediction and B-cell epitope mapping. This pipeline culminated in 11 high-potential vaccine targets. The in silico methodology offers rapidity, cost-effectiveness, and reduced side effects compared with conventional vaccinology, though experimental validation is essential for confirmation.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261442522"},"PeriodicalIF":2.0,"publicationDate":"2026-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13103498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147761953","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":"Multi-Epitope mRNA Vaccine Targeting <i>Dabie Bandavirus</i> Glycoprotein: An Immunoinformatics-Based Study.","authors":"Sk Faisal Ahmed, Md Shah Paran, Md Masudur Rahman Munna, Enam Ahmed, Upom Majumder, Monisha Rahmatullah, Md Zulfekar Ali","doi":"10.1177/11779322261438994","DOIUrl":"10.1177/11779322261438994","url":null,"abstract":"<p><p><i>Dabie bandavirus</i> (DBV), also called severe fever with thrombocytopenia syndrome virus (SFTSV), is a tick-borne virus that leads to a serious illness with high fever, low platelets, bleeding risks, and organ damage. Death rates can reach 5% to 30% in affected areas like China, South Korea, and Japan, where cases continue to rise. No approved vaccines or specific treatments are available, prompting the World Health Organization to mark it as a priority emerging infectious disease. This in silico study designs a multi-epitope mRNA vaccine candidate targeting the virus's membrane glycoprotein. After reviewing 1042 virus sequences, 9 cytotoxic T-cell (cytotoxic T lymphocyte [CTL]), 6 helper T-cell (helper T lymphocyte [HTL]), and 5 B-cell epitopes were chosen for their high conservation, predicted antigenicity, safety (non-toxic, low allergenicity), and lack of similarity to human proteins, achieving 94.77% global population coverage. The construct included beta-defensin-3 as an adjuvant. Computational predictions indicated favorable features: structural stability, potential binding to immune receptors (Toll-like receptor 3 [TLR3] and Toll-like receptor 4 [TLR4]), consistent behavior in molecular dynamics simulations (100 ns), encouraging patterns in immune response modeling, and good codon optimization for possible expression. These results are hypothesis-generating and based solely on in silico tools and require experimental validation, including in vitro studies, immunogenicity tests, and animal challenge models to assess any real-world potential.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261438994"},"PeriodicalIF":2.0,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13080201/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697416","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":"Comprehensive ceRNA Profiling Uncovers Clinically Relevant Hub lncRNAs in Glioblastoma.","authors":"Zohaib Rana, Joke Grans","doi":"10.1177/11779322251411169","DOIUrl":"10.1177/11779322251411169","url":null,"abstract":"<p><p>Long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) that rewire post-transcriptional regulation in glioblastoma (GBM). Previous GBM studies have focused on either single lncRNA ceRNA axis in isolation or used <i>in silico</i> predictions with small patient cohorts (< 200). In this study, we integrated RNA-seq data from 372 TCGA-GBM tumors, 5 matched adjacent TCGA-normal brain and 2 931 GTEx-normal brain (n = 3 308) samples to build an experimentally informed ceRNA atlas. Limma-voom differential analysis, intersection with 2 experimentally supported interaction databases (ENCORI and miRTarBase) distilled 517 high-confidence lncRNA-miRNA-mRNA triplets. Twelve hub lncRNAs coordinated 3 downregulated miRNAs and 262 target mRNAs enriched for cell-cycle, p53 signaling and homologous recombination pathways. Two co-expressed hubs, <i>CYTOR</i> and <i>MIR4435-2HG</i>, were significantly over-expressed in GBM tumors in comparison with normal brain tissue and independently predicted poor overall survival (log-rank <i>P</i> < .01). Their shared 25 targets include oncogenic <i>YBX1</i>, <i>MDM4</i> and <i>TGFBR1</i> mRNAs, underscoring the redundant regulation of oncogenic pathways, suggesting the need to explore combination lncRNA inhibition strategies. This population-scale analysis prioritizes <i>CYTOR</i> and <i>MIR4435-2HG</i> for functional interrogation and offers a framework for exploring biomarkers and RNA-targeted strategies in GBM.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322251411169"},"PeriodicalIF":2.0,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13070187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670306","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}