Search Results (1,139)

Background: Nasopharyngeal carcinoma (NPC) is an epithelial malignancy arising from the nasopharyngeal mucosa. Despite treatment advances such as the use of intensity-modulated radiotherapy and immune checkpoint inhibitors, resistance remains a significant clinical challenge. Many tumours are also diagnosed at an advanced stage associated with poor prognosis. Objective: This review aims to explore the biological roles of autophagy in NPC, primarily highlighting its involvement in disease pathogenesis and treatment resistance. Methods: We performed a review of the recent literature examining the role of autophagy-related pathways in NPC pathogenesis, biomarker discovery, and therapeutic targeting. Results: Autophagy plays a dual role in NPC as it contributes to both tumour suppression and progression. It is involved in tumour initiation, metastasis, immune modulation, and treatment resistance. Autophagy-related genes such as SQSTM1, Beclin-1, and AURKA may serve as prognostic and therapeutic biomarkers. Various strategies are being investigated for their role to modulate autophagy using pharmacologic inhibitors, RNA interventions, and natural compounds. Conclusions: Further research into autophagy’s context-dependent roles in NPC may inform the development of personalised therapies and allow progress in translational and precision oncology. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by amyloid-β (Aβ) plaques, neurofibrillary tangles, blood–brain barrier dysfunction, oxidative stress (OS), and neuroinflammation. Current treatments provide symptomatic relief, but do not halt the disease’s progression. OS plays a crucial role in AD pathogenesis by promoting Aβ accumulation. Nuclear factor erythroid 2-related factor 2 (NRF2) is a key regulator of the antioxidant response, influencing genes involved in OS mitigation, mitochondrial function, and inflammation. Dysregulation of NRF2 is implicated in AD, making it a promising therapeutic target. Emerging evidence suggests that adherence to a Mediterranean diet (MD), which is particularly rich in polyphenols from extra virgin olive oil (EVOO), is associated with improved cognitive function and a reduced risk of mild cognitive impairment. Polyphenols can activate NRF2, enhancing endogenous antioxidant defenses. This study employs a computational approach to explore the potential of bioactive compounds in EVOO to modulate NRF2-related pathways in AD. We analyzed transcriptomic data from AD and EVOO-treated samples to identify NRF2-associated genes, and used chemical structure-based analysis to compare EVOO’s bioactive compounds with known NRF2 activators. Enrichment analysis was performed to identify common biological functions between NRF2-, EVOO-, and AD-related pathways. Our findings highlight important factors and biological functions that provide new insight into the molecular mechanisms through which EVOO consumption might influence cellular pathways associated with AD via modulation of the NRF2 pathway. The presented approach provides a different perspective in the discovery of compounds that may contribute to neuroprotective mechanisms in the context of AD. Full article

Drosophila melanogaster is one of the most known and used organisms worldwide, not just to study general biology problems but above all for modeling complex human diseases. During the decades, it has become a central tool to understand the genetics of human disease, how mutations alter the behavior and health of cells, tissues, and organs, and more recently to test new compounds with a potential therapeutic use. But how did this small insect become so crucial in genetics? And how is it currently used in the study of human conditions affecting millions of people? In this review, we retrace the historical origins of its adoption in genetics laboratories and list all the advantages it provides to scientific research, both for its daily usage and for the fine tuning of gene regulation through genetic engineering approaches. We also provide some examples of how it is used to study human diseases such as cancer, neurological and infectious diseases, and its importance in drug discovery and testing. Full article

Background: Rheumatoid arthritis (RA) is an autoimmune disease that remains incurable. An increasing number of proteomic genome-wide association studies (GWASs) are emerging, offering immense potential for identifying novel therapeutic targets for diseases. This study aims to identify potential therapeutic targets for RA based on human plasma proteome. Methods: Protein quantitative trait loci were extracted and integrated from eight large-scale proteomic GWASs. Proteome-wide Mendelian randomization (Pro-MR) was performed to prioritize proteins causally associated with RA. Further validation of the reliability and stratification of prioritized proteins was performed using MR meta-analysis, colocalization, and transcriptome-wide summary-data-based MR. Subsequently, prioritized proteins were characterized through protein–protein interaction and enrichment analyses, pleiotropy assessment, genetically engineered mouse models, cell-type-specific expression analysis, and druggability evaluation. Phenotypic expansion analyses were also conducted to explore the effects of the prioritized proteins on phenotypes such as endocrine disorders, cardiovascular diseases, and other immune-related diseases. Results: Pro-MR prioritized 32 unique proteins associated with RA risk. After validation, prioritized proteins were stratified into four reliability tiers. Prioritized proteins showed interactions with established RA drug targets and were enriched in an immune-related functional profile. Four trans-associated proteins exhibited vertical or horizontal pleiotropy with specific genes or proteins. Genetically engineered mouse models for 18 prioritized protein-coding genes displayed abnormal immune phenotypes. Single-cell RNA sequencing data were used to validate the enriched expression of several prioritized proteins in specific synovial cell types. Nine prioritized proteins were identified as targets of existing drugs in clinical trials or were already approved. Further phenome-wide MR and mediation analyses revealed the effects and potential mediating roles of some prioritized proteins on other phenotypes. Conclusions: This study identified 32 plasma proteins as potential therapeutic targets for RA, expanding the prospects for drug discovery and deepening insights into RA pathogenesis. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article

Amyotrophic lateral sclerosis (ALS) remains a progressive neurodegenerative disease, lacking effective causal therapies. The Wobbler mouse model harboring a spontaneous autosomal recessive mutation in the vacuolar protein sorting associated protein (Vps54), has emerged as a valuable model for investigating ALS pathophysiology and potential treatments. This model exhibits cellular and phenotypic parallels to human ALS, including protein aggregation, microglia and astrocyte activation, as well as characteristic disease progression at distinct stages. Exploring the underlying pathomechanisms and identifying therapeutic targets requires a comprehensive analysis of gene and protein expression. In this study, we examined the expression of three well-established housekeeping genes and proteins—calnexin, ß-actin, and ßIII-tubulin—in the cervical spinal cord of the Wobbler model. These candidates were selected based on their demonstrated stability across various systems like animal models or cell culture. Calnexin, an integral protein of the endoplasmic reticulum, ß-actin, a structural component of the cytoskeleton, and ß-tubulin III, a component of microtubules, were quantitatively assessed using quantitative reverse transcription-polymerase chain reaction (RT-PCR) for gene expression and Western blotting for protein expression. Our results revealed no significant differences in the expression of CANX, ACTB, and TUBB3 between spinal cords of wild-type and Wobbler mice at the symptomatic stage (p40) at both the gene and protein levels. These findings suggest that the pathophysiological alterations induced by the Wobbler mutation do not significantly affect the expression of these crucial housekeeping genes and proteins at p40. Overall, this study provides a basis for further investigations using the Wobbler mouse model, while highlighting the potential use of calnexin, ß-actin, and ßIII-tubulin as reliable reference genes and proteins in future research to aid in the discovery for effective therapeutic interventions. Full article

The growing threat of infectious diseases requires novel therapeutics with different mechanisms of action. Antimicrobial peptides (AMPs), which are crucial for innate immunity, are a promising research area. The medicinal leech (Hirudo medicinalis) is a potential source of bioactive AMPs that are vital while interacting with microorganisms. This study aims to investigate the antimicrobial properties of peptides found in the H. medicinalis genome using a novel high-throughput screening method based on the expression of recombinant AMP genes in Escherichia coli. This approach enables the direct detection of AMP activity within cells, skipping the synthesis and purification steps, while allowing the simultaneous analysis of multiple peptides. The application of this method to the first identified candidate AMPs from H. medicinalis resulted in the discovery of three novel peptides: LBrHM1, NrlHM1 and NrlHM2. These peptides, which belong to the lumbricin and macin families, exhibit significant activity against E. coli. Two fragments of the new LBrHM1 homologue were synthesised and studied: a unique N-terminal fragment (residues 1–23) and a fragment (residues 27–55) coinciding with the active site of lumbricin I. Both fragments exhibited antimicrobial activity in a liquid medium against Bacillus subtilis. Notably, the N-terminal fragment lacks homologues among previously described AMPs. Full article

Circular RNAs (circRNAs) are covalently closed RNA molecules generated through a non-canonical splicing event known as back-splicing. This particular class of non-coding RNAs has attracted growing interest due to its evolutionary conservation across eukaryotes, high expression in the central nervous system, and frequent dysregulation in various pathological conditions, including cancer. Traditionally, circRNAs have been characterised by their ability to function as microRNA (miRNA) and protein sponges. However, recent discoveries from multiple research groups have uncovered a novel and potentially transformative mechanism of action: the direct interaction of circRNAs with messenger RNAs (mRNAs) to regulate their fate. These interactions can influence mRNA stability and translation, revealing a new layer of post-transcriptional gene regulation. In this review, we present and analyse the latest evidence supporting the emerging role of circRNAs in diverse biological contexts. We highlight the growing body of research demonstrating circRNA-mRNA interactions as a functional regulatory mechanism and explore their involvement in key physiological and pathophysiological processes. Understanding this novel mechanism expands our knowledge of RNA-based regulation and opens new opportunities for therapeutic strategies targeting circRNA-mRNA networks in human disease. Full article

Background/Objectives: Obesity is associated with cardiovascular disease worldwide. An increased lipoprotein A (LpA) level is an independent risk factor for cardiovascular disease in children. Genetic polymorphisms of the LPA gene may play an important role in susceptibility to obesity. The aim of this study was to investigate the association of LPA rs10455872 polymorphism with the risk and clinical phenotypes of childhood obesity. Methods: This study included 103 children with obesity and 77 healthy controls. Genotyping of the LPA rs10455872 polymorphism was performed using real-time PCR. Results: The genotype distributions of the LPA rs10455872 polymorphism did not differ significantly between children with obesity and healthy children (p = 0.563). A marked difference in insulin levels was observed between children with obesity carrying the AG (16.90 IU/mL) and AA (25.57 IU/mL) genotypes. A marked difference was also observed in CRP levels between children with obesity with the AG (2.31 mg/L) and AA (4.25 mg/L) genotypes. After correcting for multiple comparisons using the false discovery rate (FDR), significant differences were found between AG and AA genotypes in vitamin B12 (adjusted p = 0.024). Serum iron showed a borderline association (adjusted p = 0.072). A statistically significant correlation was found between the metabolic syndrome index and body fat ratio among children with obesity with the AA genotype (p = 0.028). Conclusions: Although limited by the small number of children with obesity with the AG genotype, some differences were noted between the AG and AA genotypes. These exploratory findings require further investigation in adequately powered studies. In children with obesity with the AA genotype, the metabolic syndrome index increases as the body fat ratio increases. Full article

According to the guidelines of the American Society of Clinical Oncology (ASCO) and the European Society of Medical Oncology (ESMO), the most significant genetic factor in the diagnosis and treatment of breast cancer is the mutation status of the BRCA1 and BRCA2 genes. Additional genes with a significant influence on cancer risk were selected for genetic panel screening. For these genes, the disease risk score was predicted to be greater than 20%. In clinical practice, it is observed that rare genetic variants have a significant impact in young patients, characterized by increased pathogenesis and a poorer overall prognosis. The ability to predict the potential effects of these rare variants may reveal important information regarding possible phenotypes and may also provide new insights leading to more efficacious treatments and overall improved clinical management. This paper presents the case of a 38-year-old woman with bilateral breast cancer who is likely a carrier of a pathogenic point mutation in the LZTR1 gene (LZTR1: c.1260+1del variant). With this clinical case report herein described, we intend to display the usefulness of performing detailed molecular tests in the field of genetic diagnostics for patients with breast cancer. Understanding the pathogenesis of hereditary cancer development, which is more predictable and reliable than that of sporadic tumors, will allow for the discovery of hitherto hidden intrinsic signaling pathways, facilitating replicable experimentation and thereby expediting the discovery of novel therapeutic treatments. Full article

Recently, a number of new genes (NFE2L2, HFE, HMOX, HIF-1α, ALOX5, GPX4, PTGS2, and IL-6) have been recognized as playing a role in ferroptosis and genetic predisposition to cardiovascular diseases (CVDs). Identifying these novel genes may facilitate the discovery of therapeutic agents and improve the clinical evaluation of phenotypes and prognoses in CVD patients. In the future, it will be crucial to develop genetic markers that correlate with clinical outcomes for individuals with CVDs. This review highlights recent developments in ferroptosis research while interpreting how genetic factors may contribute to the pathogenesis of CVDs. Understanding this relationship could be invaluable for predicting disease progression in individual patients, informing suitable medical interventions, and facilitating early diagnosis and treatment. Furthermore, we examine the possible uses of these disorders in diagnosis and the various treatment strategies, along with the associated challenges and existing limitations. Full article

Tonsils are secondary lymphoid organs that play a crucial role in the immunological response, with B cells being a major component involved in both innate and adaptive immunity. Periodic fever, aphthous stomatitis, pharyngitis, and adenitis (PFAPA) syndrome and obstructive sleep apnea syndrome (OSAS) are both common pediatric conditions involving tonsillar pathology. In both syndromes, the molecular pathways dysregulated in tonsillar B cells are still to be understood. The study aimed to unravel and compare the proteomic profiles of tonsillar CD19+ B cells isolated from pediatric patients with PFAPA (n = 6) and OSAS (n = 6) to identify disease-specific molecular signatures. B cells were isolated from the tonsillar tissue using magnetic microbeads (with a purity of 93.50%). Proteomic analysis was performed by nanoLC-MS/MS with both data-dependent (DDA) and data-independent acquisition (DIA) methods, followed by comprehensive bioinformatic analysis. By merging DDA and DIA datasets, a total of 18.078 unique proteins were identified. Differential expression analysis revealed 83 proteins increased and 49 proteins decreased in OSAS B cells compared to PFAPA B cells (fold change ≥ 1.5 or ≤0.6, p < 0.05). Distinct pathway enrichments were highlighted, including alterations in the regulation of PTEN gene transcription, circadian gene expression, inflammasome pathways (IPAF and AIM2), and the metabolism of angiotensinogen to angiotensin. Specific proteins such as p53, Hdac3, RPTOR, MED1, Caspase-1, Cathepsin D, Chymase, and TLR2 (validated by WB) were shown to be differentially expressed. These findings reveal distinct proteomic signatures in tonsillar B cells from patients with PFAPA and OSAS, offering novel insights into the pathophysiology and potential avenues for biomarker discovery. Full article

The selection of appropriate biomarkers in clinical practice aids in the early detection, treatment, and prevention of disease while also assisting in the development of targeted therapeutics. Recently, multi-omics data generated from advanced technology platforms has become available for disease studies. Therefore, the integration of this data with associated clinical data provides a unique opportunity to gain a deeper understanding of disease. However, the effective integration of large-scale multi-omics data remains a major challenge. To address this, we propose a novel deep learning model—the Multi-Omics Graph Attention biomarker Discovery network (MOGAD). MOGAD aims to efficiently classify diseases and discover biomarkers by integrating various omics data such as DNA methylation, gene expression, and miRNA expression. The model consists of three main modules: Multi-head GAT network (MGAT), Multi-Graph Attention Fusion (MGAF), and Attention Fusion (AF), which work together to dynamically model the complex relationships among different omics layers. We incorporate clinical data (e.g., APOE genotype) which enables a systematic investigation of the influence of non-omics factors on disease classification. The experimental results demonstrate that MOGAD achieves a superior performance compared to existing single-omics and multi-omics integration methods in classification tasks for Alzheimer’s disease (AD). In the comparative experiment on the ROSMAP dataset, our model achieved the highest ACC (0.773), F1-score (0.787), and MCC (0.551). The biomarkers identified by MOGAD show strong associations with the underlying pathogenesis of AD. We also apply a Hi-C dataset to validate the biological rationality of the identified biomarkers. Furthermore, the incorporation of clinical data enhances the model’s robustness and uncovers synergistic interactions between omics and non-omics features. Thus, our deep learning model is able to successfully integrate multi-omics data to efficiently classify disease and discover novel biomarkers. Full article

Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care. Full article

COVID-19 continues to spread six years after its discovery. Cancer patients are at an increased risk of severe outcomes, likely due to immunosuppression and tumor-related dysregulation. Programmed cell death protein 1 (PD-1), encoded by the PDCD1 gene, is a critical immune checkpoint involved in T-cell regulation. Since genetic polymorphisms can influence immune responses and individual susceptibility to SARS-CoV-2 infection, this case–control study aimed to investigate the association between the PDCD1 rs41386349 polymorphism and COVID-19 severity in individuals with and without cancer. This study included 279 COVID-19-positive and 160 negative individuals, genotyped by qPCR. COVID-19- positive cancer patients were significantly more likely to develop moderate (OR = 13.6) and severe (OR > 200) disease compared to cancer-negative individuals. No association was observed between the PDCD1 polymorphism and SARS-CoV-2 infection or disease severity, even after adjusting for cancer status, age and sex. However, age and sex were independently associated with severe outcomes: each additional year of age increased the odds of severe disease by 5.3%, and male patients had a three times higher risk of severe COVID-19. These findings confirm that cancer, male sex and older age are major predictors of worse prognosis in COVID-19, while the rs41386349 polymorphism alone does not appear to influence susceptibility or disease progression. Full article