Current gene therapy最新文献

Background: Using predicted drug sensitivity scores as survival biomarkers may improve precision medicine and overcome the limitations of genomically guided approaches in clinical trials.

Methods: Pan-Cancer Drug Sensitivity Score Survival Analysis (DrugSurvPlot) is an interactive, login-free web analyzer built with R (v4.3.1), leveraging the Shiny package for interface/server logic, the DT package for data table queries/downloads, and the survival package for survival analysis. Data preprocessing was performed using OncoPredict, enabling users to export processed tables and results.

Results: DrugSurvPlot integrates 189 GEO datasets (including 10 immune checkpoint inhibitor treatment datasets) and 33 TCGA datasets, totaling 85,531 records across 52 cancer types and 13 survival status data types, while incorporating 198 anticancer drugs from GDSC2. This tool supports two cutoff strategies for drug sensitivity scores, offers advanced survival analysis methods, and enables customizable high-definition visualization of results.

Discussion: DrugSurvPlot represents a significant advancement in computational oncology by establishing predicted drug sensitivity scores as novel prognostic biomarkers for tumor survival analysis. This interactive platform integrates comprehensive datasets spanning 198 anticancer drugs and 52 cancer types, while providing researchers with intuitive tools for generating publication-ready Kaplan-Meier analyses. Current limitations in drug repertoire coverage and dataset diversity will be addressed through ongoing expansion of pharmacological databases and incorporation of emerging data modalities, including single-cell transcriptomics.

Conclusions: In summary, DrugSurvPlot offers a no-code platform with comprehensive datasets, diverse cancer coverage, and customizable survival analysis, addressing critical research gaps. Continuous enhancements will improve predictive accuracy and clinical utility, establishing it as an evolving powerhouse in drug-survival investigations.

Genomic medicine is revolutionizing genetic disease diagnosis and therapy; has a major impact on clinical practice, particularly in diagnosis and treatment. In addition, next-generation sequencing (NGS) has transformed diagnostics. These advances have made genome profiling costeffective and fast, helping us find pathogenic variations that cause a variety of genetic illnesses. Given its influence on diagnostic methods, NGS mutation detection accuracy and reliability must be assessed. In therapeutics, genomic medicine has introduced precision methods. CRISPR-Cas9 gene editing, and new RNA-based therapies are being evaluated for the treatment of genetic mutations Pharmacogenomics' capacity to customize medication regimens to genetic profiles, optimizing therapeutic results while minimizing side effects, is also evaluated. Although genetic medicine has transformational promise, its widespread acceptance is difficult. Obtaining widespread acceptance of genetic medicine is difficult because of worries around ethical implications, privacy problems, and the possibility for genetic information to be misused. Ethics and privacy issues surrounding genetic information usage require considerable thought. Genomic data integration into clinical practice requires robust regulatory frameworks. The influence of NGS technology and precision treatments on genetic disease diagnosis and therapy is significant. This review emphasizes the importance of assessing diagnostic tools, comprehending novel therapy modalities, and addressing ethical and regulatory issues to enable responsible and successful clinical integration.

Human mitochondrial DNA (mtDNA) stands at the nexus of scientific intrigue and controversy, owing to its distinctive genetic features and indispensable role in cellular energy dynamics. This narrative review explores the complexities, controversies, and key issues in current research on human mtDNA. A comprehensive search on literature spanning from January 2000 to January 2025 was conducted across electronic databases including PubMed, Scopus, Web of Science, and Google Scholar. Keywords such as "mitochondrial DNA," "mtDNA mutations," "mtDNA inheritance," "mitochondrial genetics," "mitochondrial diseases," and "future perspectives of mtDNA" were used to identify relevant studies published in peer-reviewed journals, books, and reputable conference proceedings. Articles selected for inclusion were limited to those written in English and focused on human mtDNA research. Review articles, original research papers, metaanalyses, and authoritative texts were prioritized. Information extracted from selected studies was synthesized to provide a comprehensive overview. The synthesized data were critically analyzed to highlight emerging trends, unresolved controversies, and future research directions in the field of mtDNA research. Decoding the complexities of human mtDNA offers profound insights into fundamental biological processes and evolutionary history. This review emphasizes the ongoing significance of mtDNA research in shaping the future of biomedical sciences and highlights the importance of continued exploration into its intricate molecular code.

Background: Gut bacteria critically influence digestion, facilitate the breakdown of complex food substances, aid in essential nutrient synthesis, and contribute to immune system balance. However, current knowledge regarding intestinal bacteria remains insufficient.

Objective: This study aims to discover essential differences for different intestinal bacteria.

Methods: This study was conducted by investigating a total of 1478 gut bacterial samples comprising 235 Actinobacteria, 447 Bacteroidetes, and 796 Firmicutes, by utilizing sophisticated machine learning algorithms. By building on the dataset provided by Chen et al., we engaged sophisticated machine learning techniques to further investigate and analyze the gut bacterial samples. Each sample in the dataset was described by 993 unique features associated with gut bacteria, including 342 features annotated by the Antibiotic Resistance Genes Database, Comprehensive Antibiotic Research Database, Kyoto Encyclopedia of Genes and Genomes, and Virulence Factors of Pathogenic Bacteria. We employed incremental feature selection methods within a computational framework to identify the optimal features for classification.

Results: Eleven feature ranking algorithms selected several key features as pivotal to the characteristics and functions of gut bacteria. These features appear to facilitate the identification of specific gut bacterial species. Additionally, we established quantitative rules for identifying Actinobacteria, Bacteroidetes, and Firmicutes.

Conclusion: This research underscores the significant potential of machine learning in studying gut microbes and enhances our understanding of the multifaceted roles of gut bacteria.

Introduction: Enterovirus D68 (EV-D68) is a non-enveloped, positive-sense, singlestranded RNA virus known for causing severe respiratory illnesses and its association with acute flaccid myelitis (AFM) in children. Despite its increasing public health significance, no vaccines or antiviral drugs are currently available for EV-D68. This study aimed to design an immune-boosting multi-epitope subunit vaccine against EV-D68 using advanced immunoinformatic and machine learning approaches.

Methods: Capsid proteins VP1, VP2, and VP3 of EV-D68 were screened for immunogenic HTL, CTL, and B-cell epitopes to develop a non-allergenic, highly immunogenic multi-epitope vaccine. Predicted epitopes were subjected to 3D structural modeling and molecular dynamics simulations to validate folding and structural stability. Molecular docking and immune simulation techniques were employed to evaluate vaccine-TLR3 interactions and predict immune responses, respectively.

Results: Molecular docking analysis revealed strong binding affinities between the vaccine constructs and the TLR3 receptor, with scores of -299 kcal/mol, -361 kcal/mol, -258 kcal/mol, and -312 kcal/mol for VP1, VP2, VP3, and combined vaccine-TLR3 complexes. Molecular dynamic simulation and dissociation constant analyses confirmed the strength of these interactions, with binding free energies ranging from -57.75 kcal/mol to -101.35 kcal/mol. Codon adaptation index (CAI) values of 0.96 and GC content of ~69% supported the high expression potential of the vaccine constructs. Immune simulations demonstrated robust immune responses characterized by elevated IgG, IgM, cytokines, and interleukins, along with effective antigen clearance.

Discussion: The strong molecular interactions with TLR3 and simulated immune responses suggest that the designed vaccines can activate both innate and adaptive immunity. The high CAI and GC values support their expression feasibility in E. coli, enhancing prospects for production.

Conclusion: This study provides a strong foundation for the development of a safe and effective EV-D68 vaccine, showcasing the potential of computational vaccine design.

Flavonoids exhibit anti-tumor properties against many human cancer cells, indicating their potential as effective anticancer medicines. Oroxylin A (OrA) is a monoflavonoid molecule that shows significant promise against several types of cancer and possesses a substantial anticancer impact while causing minimal harm to normal tissue. Limited studies have provided a systematic review deciphering the role of oroxylin A in combating breast carcinoma. Hence, we thoroughly analyzed existing research to report various mechanism by which OrA impedes tumor advancement in breast carcinoma, including autophagy, cell cycle arrest, angiogenesis suppression, apoptosis, and glycolysis inhibition. We collected several significant research related to the anticancer potential of oroxylin A and demonstrated anticancerous potential of OrA and its specific mode of action in several human carcinomas. Additionally, we have also incorporated several studies to decipher the structure, bioavailability, and anti-breast cancer potential of Oroxylin A in breast cancer. Overall, this review supports the potential of oroxylin A for developing better anti breast cancer therapeutic approach.

Introduction: Aggressive Pituitary Neuroendocrine Tumors (PitNETs) pose significant therapeutic challenges due to their invasive behavior and resistance to conventional therapies. Current prognostic markers lack the ability to capture molecular heterogeneity, necessitating novel biomarkers. Dysregulated Programmed Cell Death (PCD) pathways are implicated in tumorigenesis, but their prognostic relevance in invasive PitNETs remains unexplored.

Method: GEO datasets (GSE51618, GSE169498, GSE260487) were analyzed to identify differential gene expression between noninvasive and invasive PitNETs. A curated panel of 1,548 PCDrelated genes was integrated. Machine learning (LASSO regression and SVM-RFE) was employed to construct a PCD-associated Index (PCDI). For validation, ROC analysis, immune infiltration assessment (CIBERSORT, TIMER, ssGSEA), and experimental validation via RT-qPCR were performed.

Results: The PCDI, comprising 11 genes (e.g., FGFR3, MAPK11, SLC7A11), distinguished invasive from noninvasive PitNETs with high accuracy. High-PCDI tumors exhibited enriched metabolic pathways and immune activation. Consensus clustering stratified PitNETs into two molecular subtypes (C1/C2), with C2 (high-PCDI) showing elevated immune scores and pathway activity. Experimental validation confirmed the differential expression of key genes in invasive tumors (*p<0.05).

Discussion: The PCDI outperforms traditional prognostic models by capturing PCD-immunemetabolic crosstalk. High-PCDI tumors demonstrate adaptive immune evasion despite an elevated checkpoint molecule expression, suggesting therapeutic potential for combined MAPK inhibitors and immunotherapy. Limitations include retrospective data and small validation cohorts.

Conclusion: The PCDI provides a robust molecular framework for risk stratification and personalized therapy in invasive PitNETs. Future studies should validate its clinical utility and explore pancancer relevance.

Breast cancer remains a prevalent and diverse disease, significantly contributing to cancer- related deaths among women worldwide. Recent advancements in molecular biology have paved the way for targeted therapies and pharmacogenomics, which are crucial for developing personalized treatment strategies. This literature review synthesizes findings from recent studies on these approaches, emphasizing clinical trials, genomic profiling, and personalized medicine. It aims to focus on studies examining targeted treatments, such as human epidermal growth factor receptor- 2 (HER2) inhibitors and CDK4/6 inhibitors, alongside pharmacogenomic data that influence drug metabolism, efficacy, and toxicity. Additionally, it examines the role of gene SNPs (Single Nucleotide Polymorphisms) correlated with treatment resistance, which have emerged as key biomarkers affecting therapeutic outcomes in breast cancer. These SNPs, found in genes involved in drug metabolism and tumor progression, contribute to variability in treatment responses and resistance in specific subtypes. They encompass various breast cancer subtypes, including hormone receptorpositive (HR+), HER2-positive, and triple-negative breast cancer (TNBC). The targeted therapies, particularly HER2 inhibitors, have markedly improved outcomes for specific subtypes. Furthermore, pharmacogenomics personalizes treatment by identifying genetic variations that affect drug response, optimizing therapy selection, and minimizing adverse effects. Despite these advancements, drug resistance remains a significant challenge, highlighting the necessity for ongoing research in molecular diagnostics and innovative therapeutic combinations. The literature suggests that precision medicine, driven by genomic profiling, pharmacogenomic data, and single nucleotide polymorphisms (SNPs) analysis, is enhancing treatment efficacy for breast cancer patients. HER2- positive and HR+ patients have especially benefitted from these targeted therapies while emerging treatments are addressing the complexities of TNBC. Additionally, genetic testing, such as BRCA1/2 mutation screening, is vital for guiding treatment decisions. Targeted therapies and pharmacogenomics have revolutionized breast cancer treatment, providing more personalized and effective care. Nevertheless, overcoming drug resistance and expanding access to genomic testing are essential for future advancements in this field.

Depression is a complex psychiatric disorder that arises from various underlying biological mechanisms. In this review, the role of microRNAs (miRNAs) in modulating gut microbiotacytokine communication and their potential to unravel the pathophysiology of depression and develop novel therapeutic strategies are discussed. MiRNAs are small non-coding RNA molecules that have emerged as key regulators in the bidirectional signaling of the gut-brain axis by modulating gene expression and fine-tuning an intricate dialogue between the microbiota, immune system, and central nervous system. Results show how gut microbiota can shape miRNA expression in brain regions involved in mood regulation; conversely, evidence is accumulating, elucidating how miRNA perturbations can shape microbial ecology. Gut bacteria-derived short-chain fatty acids (SCFAs) fuel this nexus by exerting effects on neurogenesis, neurotransmitter synthesis, neuroinflammation, affective behavior alterations, and depressive-like phenotypes. Pro-inflammatory cytokines such as IL-6, TNF-α, and IL-1β are also known to be associated with depressive symptoms related to altered expression patterns of specific miRNAs across these disorders. This review exposes the novel potential biomarkers and therapeutic targets/strategies to develop innovative methods in the diagnosis and treatment of depression by exploring bidirectional relations among miRNAs, gut microbiota, and cytokines. The knowledge of these molecular networks and pathways has provided the opportunity for designing new-generation therapeutics such as phytobiotics, probiotics, psychobiotics, diet therapies, and nanomedicine based on miRNAs from a future perspective, which will revolutionize the management of mental disorders.

Gallbladder Cancer (GBC) is a highly concerning malignancy, particularly prevalent in the Asian continent, attributed to irregularities in the bile tract. As of 2022, GLOBOCAN data ranks GBC as the 22nd most common cause of cancer-related mortality globally and the 6th among gastrointestinal cancers. According to recent World Cancer Research statistics, approximately 122,491 new cases of gallbladder cancer were reported by the end of 2022, ranking it 23rd among cancers in men and 20th in women worldwide. Towards the therapy of GBC, genetic studies have provided valuable insights into the molecular mechanisms driving GBC. Mutations in TP53, KRAS, ERBB2 (HER2), CDKN2A, and PIK3CA play crucial roles in tumor initiation and progression. Additionally, epigenetic modifications and aberrant signaling pathways, including Wnt/β-catenin, Notch, and PI3K/AKT/mTOR, have been implicated in GBC pathogenesis. Exploring these genetic alterations has led to targeted therapies, such as HER2 inhibitors (trastuzumab, pertuzumab) and immune checkpoint inhibitors, offering new treatment prospects. Further, current treatment approaches, including surgical resection, chemotherapy (gemcitabine-cisplatin), and radiation therapy, offer suboptimal outcomes in advanced stages of GBC. Despite its prevalence, effective therapeutic approaches and early-stage diagnostic methods remain elusive. This review provides a comprehensive overview of GBC, including its genetic mutations, epidemiology, risk factors, prevention, diagnosis, treatment options, and challenges. This work aims to offer valuable insights into the various factors directly or indirectly associated with GBC, which may assist in preparing an effective strategy against this growing malignancy.