Search Results (3,487)

Background: Oropharyngeal squamous cell carcinoma (OPSCC) is a major subtype of head and neck cancer, with prognosis increasingly influenced by the tumour immune microenvironment. Although immune checkpoint inhibitors have improved outcomes for some patients, reliable predictive biomarkers remain limited. Methods: This study aimed to investigate the prognostic relevance and epigenetic regulation of natural killer T (NKT)-cell-related gene signatures in OPSCC. Clinicopathological and transcriptomic data from 81 OPSCC patients were analysed using single-sample gene set enrichment analysis (ssGSEA) to evaluate immune-related gene set enrichment scores. Associations with overall survival and clinical features were assessed, and candidate prognostic genes were further explored through expression, methylation, and network analyses. Results: High NKT cell differentiation enrichment scores were significantly associated with improved survival and favourable clinical features. Gene-level analyses identified ITK, ZNF683, and ATF2 as key prognostic markers linked to T-cell signalling and epigenetic regulation. Methylation profiling revealed hypermethylation of ITK and hypomethylation of ZNF683 in tumour tissues, suggesting an epigenetic basis for altered gene expression. Conclusions: These findings highlight NKT cell differentiation as a strong prognostic indicator in OPSCC and support further exploration of epigenetic–immunologic interactions as potential therapeutic targets. Full article

While previous studies have extensively demonstrated that summer heat stress (HS) impairs oocyte quality via follicular granulosa cell (GC) mediation, the molecular mechanisms underlying HS-induced GC-mediated oocyte damage—particularly at the multi-omics level—remain poorly understood. This study integrated metabolomic and transcriptomic analyses of GCs from gilts under seasonal thermal stress (winter CON vs. summer HS) to elucidate GC-mediated regulatory networks affecting oocyte quality. Non-targeted metabolomics identified 45 differentially accumulated metabolites (DAMs, p < 0.05), with 69% being lipids/lipid-like molecules enriched in pathways such as glycerophospholipid metabolism, choline metabolism, linoleic acid metabolism, the adipocytokine signaling pathway, and the sphingolipid signaling pathway. Parallel transcriptomics revealed 9085 differentially expressed genes (DEGs, Padj < 0.05), ‌of which the predominant genes were associated with lipid metabolism, hormone synthesis, and cellular senescence pathways. Cross-omics integration highlighted significant correlations between DAMs and DEGs, particularly for lysoPC(20:4) and 1-hexadecyl-2-eicosatrienoyl-sn-glycero-3-phosphocholine, which showed co-regulation with 69 and 48 genes, respectively. Notably, candidate genes like TMEM94, SLIT3, DACT3, and CEBPD, were identified as key regulators of GCs metabolic reprogramming. This study demonstrates for the first time that in vivo HS compromises oocyte developmental competence by disrupting the GC metabolic activities, particularly through lipid metabolism and associated pathways. The identified metabolic signatures and regulatory genes offer mechanistic insights into seasonal infertility and potential biomarkers for thermo-protective strategies in swine reproduction. Full article

Type-P5 ATPases are the least characterized among the P-type ATPases and this is especially true in the case of the malaria parasite. In this study, Spf1, a subtype-P5A ATPase of yeast, and ATP13A2, a subtype-P5B ATPase of humans, were used as templates to extensively characterize the sequences and structural features of haemosporidian type-P5 ATPases. Malaria parasites have both subtype-P5A and subtype-P5B ATPase genes and the structural features of the proteins recapitulate the known structures of subtype-P5A and subtype-P5B ATPases. Detailed structural analysis detected an additional α-helix in the P-domain of subtype-P5A ATPases, which is not found in subtype-P5B ATPases. This feature may be an additional signature to distinguish subtype-P5A and subtype-P5B ATPases, in addition to the previously described differences in the membrane loops of the N-terminal domain, the arm in the P-domain of subtype-P5A, and substrate differences. A notable difference in the type-P5 ATPases from the malaria parasite, as compared to the templates, is the insertion of multiple variable and low-complexity regions that form intrinsically disorganized loops. These loops may form a shroud-like structure that protects the core ATPase structure and/or participates in low-affinity interprotein interactions. Homology modeling did not provide definitive answers about the substrate specificity of the haemosporidian type-P5 ATPases. However, the haemosporidian subtype-P5A ATPase is likely an ER transmembrane dislocase as are the other subtype-P5A ATPases. In contrast, the subtype-P5B ATPases of the malaria parasite are not likely to be polyamine transporters in lysosomes, as have been described in fungi and metazoans. This suggests that subtype-P5B ATPases have undergone lineage-specific divergence in regard to their function(s). Full article

Breast cancer remains a major global health challenge. Yet, genomic data from Middle Eastern and North African (MENA) populations are limited, restricting insights into disease drivers and therapeutic opportunities in this demographic. To address this gap, we performed whole-exome sequencing (WES) on 52 breast cancer samples, including 51 from the MENA region, to characterize somatic mutations and potential therapeutic targets. Across the cohort, 37,369 somatic variants matched entries in the COSMIC database, and driver prediction tools (BoostDM and OncodriveMUT) identified 2451 predicted driver mutations, including 648 known driver variants in genes such as TP53, PIK3CA, GATA3, PTEN, SF3B1, and KMT2C. In addition, 1803 novel predicted drivers were detected, many affecting DNA repair pathways, including homologous recombination (BRCA2, RAD51C), mismatch repair (MLH1, MSH2), and nucleotide excision repair (ERCC2, ERCC3), as well as regulators such as TP53 and ATM. Mutational signature analysis revealed a predominance of C>T substitutions and subtype-specific patterns, with SBS22 and SBS43 enriched in Luminal A tumors. Therapeutic annotation using OncoKB identified 223 actionable or likely oncogenic variants, highlighting potential targets for precision oncology. This study provides a comprehensive characterization of the breast cancer mutational landscape in MENA patients and offers a valuable resource for advancing genomic and therapeutic research in this demographic. Full article

Background: How the tumor microenvironment (TME) influences treatment response in HER2+ breast cancer following HER2-directed therapy is crucial for individualizing therapies and is currently understudied. The purpose of this exploratory analysis was to elucidate changes in the TME following treatment with trastuzumab. Methods: Fourteen HER2+ early-stage breast cancer patients underwent tissue biopsies before and after a dose of trastuzumab. Samples were evaluated for stromal tumor-infiltrating lymphocytes (TILs) and RNA-based cell and gene expression signatures. Tumor inflammation signature scores were generated to measure whether an adaptive immune response developed to trastuzumab within the tumor. Patients were also stratified as immune responders or non-responders based on changes in TILs. Results: Of the 14 enrolled patients, 13 had samples available for analysis, and 7 had an immune response as assessed by changes in TILs compared to 6 non-responders. Trastuzumab treatment decreased PD-L1 and TGF-Beta signatures and increased CTLA4 gene signatures, although results were not statistically significant, and increased DUSP1 expression. In the TIL responder group, there was increased expression of dendritic cells as well as MARCO expression. Conclusions: These findings, although exploratory in nature, highlight trastuzumab’s ability to induce an immune response and suggest that some patients may be more primed to mount an immune response following treatment than others. Patients without a robust response in TILs may benefit from additional agents to favorably modulate the TME for optimized responses to HER2-directed therapy, an area of research which warrants further study. Full article

The quality and flavor of chicken meat are fundamentally determined by muscle metabolite composition, which reflects the regulatory effects of genetic background on metabolic pathways and muscle development. In this study, we profiled the meat quality of breast muscle across 3 crossbreeding combinations (D×HD, HD×D, and D×LD) between the Yunong D line and Houdan chickens to elucidate the metabolic mechanisms underlying flavor variation. Eighteen representative breast muscle samples were analyzed using common physicochemical indexes, untargeted metabolomics based on Gas Chromatography-Time-of-Flight Mass Spectrometry (GC-TOF-MS) and Ultra-High-Performance Liquid Chromatography coupled with Quadrupole Exactive Mass Spectrometry (UHPLC-QE-MS). Differential metabolites were identified through Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Multivariate analysis revealed distinct metabolic signatures among crossbreeding combinations, with HD×D exhibiting the most favorable tenderness, color, and water-holding capacity. A total of nine differential metabolites (5 upregulated and 4 downregulated) were identified between D×HD and HD×D, and thirty-eight metabolites (18 upregulated and 27 downregulated) between D×HD and D×LD. The identified metabolites were predominantly associated with amino acid metabolism, lipid biosynthesis, nucleotide turnover, and energy metabolism. Among these, arachidonic acid, taurine, L-alanine, and citric acid exhibited marked intergroup differences. Enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated significant involvement of pathways such as amino acid biosynthesis, taurine and hypotaurine metabolism, and ABC transporters in flavor formation. Hierarchical clustering and Pearson correlation analyses further delineated synergistic or antagonistic interactions among key metabolites, suggesting the existence of intricate regulatory mechanisms. These findings reveal critical metabolites and metabolic pathways associated with flavor attributes, offering both a theoretical framework and potential molecular targets for enhancing poultry meat quality through breeding strategies. Full article

Background: Clear cell renal cell carcinoma (ccRCC) is a prevalent urological malignancy, accounting for approximately 1.6% of all cancer-related deaths in 2022. While endocrine-disrupting chemicals (EDCs) have been implicated as risk factors for ccRCC, the toxicological profiles and immune mechanisms underlying Bisphenol A (BPA) exposure in ccRCC progression remain inadequately understood. Materials and Methods: Protein–protein interaction (PPI) analysis and visualization were performed on overlapping genes between ccRCC and BPA exposure. This was followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to elucidate potential underlying mechanisms. Subsequently, 108 distinct machine learning algorithm combinations were evaluated to identify the optimal predictive model. An integrated CoxBoost and Ridge regression model was constructed to develop a prognostic signature, the performance of which was rigorously validated across two independent external datasets. Finally, molecular docking analyses were employed to investigate interactions between key genes and BPA. Results: A total of 114 overlapping targets associated with both ccRCC and BPA were identified. GO and KEGG analyses revealed enrichment in cancer-related pathways, including pathways in cancer, endocrine resistance, PD-L1 expression and PD-1 checkpoint signaling, T-cell receptor signaling, endocrine function, and immune responses. Machine learning algorithm selection identified the combined CoxBoost-Ridge approach as the optimal predictive model (achieving a training set concordance index (C-index) of 0.77). This model identified eight key genes (CHRM3, GABBR1, CCR4, KCNN4, PRKCE, CYP2C9, HPGD, FASN), which were the top-ranked by coefficient magnitude in the prognostic model. The prognostic signature demonstrated robust predictive performance in two independent external validation cohorts (C-index = 0.74 in cBioPortal; C-index = 0.81 in E-MTAB-1980). Furthermore, molecular docking analyses predicted strong binding affinities between BPA and these key targets (Vina scores all <−6.5 kcal/mol), suggesting a potential mechanism through which BPA may modulate their activity to promote renal carcinogenesis. Collectively, These findings suggested potential molecular mechanisms that may underpin BPA-induced ccRCC progression, generating hypotheses for future experimental validation. Conclusions: These findings enhance our understanding of the molecular mechanisms by which BPA induces ccRCC and highlight potential targets for therapeutic intervention, particularly in endocrine and immune-related pathways. This underscores the need for collaborative efforts to mitigate the impact of environmental toxins like BPA on public health. Full article

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with tumor recurrence and metastasis posing significant challenges despite advances in targeted therapies and immunotherapy. Cellular dormancy, a reversible, quiescent state marked by cell cycle arrest, has emerged as a key driver of therapeutic resistance and disease relapse, particularly in small-cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Multiple mechanisms, including autophagy, stress-adaptive signaling, microenvironmental cues, and epigenetic dysregulation, have been implicated in the regulation of dormancy and long-term cell survival. Among these, epigenetic modifications such as DNA methylation, histone modifications, and non-coding RNAs (ncRNAs) play pivotal roles in maintaining dormancy by repressing proliferative gene expression programs. Increasing evidence suggests that dormant tumor cells harbor distinct epigenomic signatures, which may serve as predictive biomarkers for minimal residual disease (MRD) and relapse risk. This review summarizes current advances in understanding the epigenetic regulation of cellular dormancy in lung cancer, with a particular emphasis on the interplay between epigenetic modifiers and oncogenic signaling pathways. Furthermore, emerging molecular targets and associated therapeutic agents currently under clinical evaluation are presented, emphasizing how a deeper understanding of the epigenetic landscape governing dormancy may inform the development of novel interventions to improve long-term clinical outcomes in lung cancer patients. Full article

Liposarcomas represent a heterogeneous group of malignant mesenchymal neoplasms, with diverse histological subtypes and molecular alterations. This study aimed to investigate the gene expression profiles of SOX9, GATA3, and GATA4 in liposarcoma subtypes and to assess their associations with clinicopathological parameters. Forty-two formalin-fixed, paraffin-embedded liposarcoma samples were analyzed. Total RNA was extracted, reverse-transcribed, and quantified by qRT-PCR using GAPDH as an endogenous control. Relative quantification (RQ) values were categorized, and statistical analyses included Fisher’s exact test, Kaplan–Meier survival analysis, and Cox proportional hazards modeling. SOX9 expression significantly varied among histological subtypes (p = 0.017), with ALT/WDLS cases showing a predominance of high-level expression (RQ > 50 in 12/15 cases), in contrast to myxoid subtypes clustering mainly in the 10–50 RQ range. GATA4 overexpression correlated with smaller tumor size (<100 mm) (p = 0.049), being more frequent in 15/20 small tumors compared to 10/22 larger ones. GATA3 and GATA4 demonstrated the strongest inter-gene correlation (r = 0.68, p < 0.05), suggesting possible functional interplay. Kaplan–Meier analysis revealed no statistically significant survival differences for individual gene expression, but a high combined GATA3–GATA4 signature was associated with a favorable trend. These findings indicate that SOX9, GATA3, and GATA4 are broadly upregulated in liposarcomas, with subtype- and size-dependent expression patterns. The strong association between GATA3 and GATA4 expression supports their potential synergistic role in tumor biology. Integration of these molecular markers into diagnostic and prognostic workflows may enhance subtype characterization and inform targeted therapeutic strategies. Further studies in larger cohorts are warranted to validate these biomarkers and explore their mechanistic interplay in liposarcoma pathogenesis. Full article

The increasing prevalence of the mobile tigecycline resistance gene tet(X) poses a severe global health threat, and the genus Acinetobacter is a major reservoir. This study aimed to develop a rapid and specific multiplex PCR assay for detecting the predominant tet(X)-positive Acinetobacter species. Through pan-genome analyses of 390 tet(X)-positive Acinetobacter genomes, a total of 20 tet(X) variants were identified in 24 Acinetobacter species, including 17 published lineages and seven taxonomically unresolved Taxa. Acinetobacter indicus (30.8%), Acinetobacter amyesii (17.2%), and Acinetobacter towneri (16.1%) were the top three hosts of diverse tet(X) variants. Species-specific signature genes were identified and used for primer design, yielding amplicons of 267 bp (tet(X)), 424 bp (A. indicus), 690 bp (A. amyesii), and 990 bp (A. towneri). The assay was rigorously adjusted for an optimal annealing temperature of 52.8 °C and a primer ratio of 1:1:1:1, demonstrating high sensitivity with a detection limit of 0.3 ng/μL DNA and excellent stability under −20 °C, 4 °C, 20 °C storage conditions. Validation experiments on 151 bacterial strains showed high accuracy for DNA templates (≥97.8%) and bacterial suspensions (≥93.5%) within two hours. This cost-effective and highly accurate multiplex PCR provides a powerful tool for proactive surveillance and control of the critical Acinetobacter sp. pathogens. Full article

Type I interferons (IFN-I) are pivotal effectors of innate immunity and constitute a central axis of host defense against pathogens. Sensing of exogenous or endogenous nucleic acids by pattern-recognition receptors—exemplified by Toll-like receptors—triggers transcriptional induction of IFN-I. Engagement of the heterodimeric IFN-I receptor on nucleated cells reprograms cellular states via canonical Janus kinase–signal transducer and activator of transcription (JAK–STAT) signaling as well as STAT-independent, noncanonical pathways. This axis is tempered by multilayered regulatory mechanisms, including epigenetic remodeling, and important aspects remain incompletely defined. Dysregulation of IFN-I activity underlies diverse autoimmune disorders, notably systemic lupus erythematosus, wherein IFN-responsive gene signatures stratify disease endotypes, reflect disease activity trajectories, and predict therapeutic responsiveness. In recent years, therapeutic strategies targeting this pathway are now available: anti-IFN-I receptor therapy for systemic lupus erythematosus (SLE) and JAK inhibition for rheumatoid arthritis (RA) and giant cell arteritis (GCA). Altogether, a refined understanding of the IFN-I axis furnishes a pragmatic framework for patient stratification, response prediction, and mechanism-informed therapy design across immune-mediated diseases. Full article

Cryptorchidism, characterized by undescended testes, is associated with infertility and increased cancer risk through complex, multifactorial pathophysiological mechanisms involving interconnected alterations in testicular microenvironment, including but not limited to elevated temperature, hormonal dysregulation, altered vascular perfusion, and immune responses. These factors interact synergistically to drive testicular pathology. Using a surgically induced bilateral cryptorchid mouse model established at postnatal day 21 (PND21), we investigated phase-specific pathological mechanisms through analyses at prepubertal (PND35) and sexually mature (PND70) phases. Our transcriptome analysis revealed distinct molecular signatures at different developmental phases, with prepubertal cryptorchid testes showing 2570 differentially expressed genes predominantly enriched in immunoproteasome components and inflammatory pathways, while sexually mature testes exhibited 883 differentially expressed genes primarily related to extracellular matrix (ECM) remodeling and oncogenic pathways. Prepubertal molecular changes indicated immunoproteasome activation and inflammatory responses, whereas mature-phase alterations were characterized by ECM reorganization and fibrotic remodeling. Functional analysis demonstrated prepubertal enrichment in spermatogenesis regulation and interferon responses, while mature-phase signatures were associated with apoptosis, epithelial–mesenchymal transition, and inflammatory signaling cascades. Phase-specific oncogenic pathway correlations revealed distinct mechanisms: metabolic reprogramming and epigenetic regulation in prepubertal testes versus structural remodeling and invasion-related pathways in mature testes. Molecular validation confirmed elevated PI3K-Akt and NF-κB signaling at both developmental phases, identifying these as potential therapeutic targets. This first phase-resolved characterization of cryptorchidism pathology provides insights into developmental phase-specific mechanisms and suggests timing-dependent therapeutic strategies. Although differing from human congenital cryptorchidism in developmental timing and etiology, our surgically induced model recapitulates anatomical testicular malposition with multiple inseparable pathophysiological alterations, and the identified molecular signatures reflect integrated responses to the complex cryptorchid microenvironment. Full article

Background: Hepatocellular carcinoma remains a global health challenge with high mortality rates. The tumor immune microenvironment significantly impacts disease progression and survival. However, traditional analyses predominantly focus on single immune genes, overlooking the critical interplay among multiple immune gene signatures. Our study explores the prognostic significance of chemokine (C-C motif) ligand 5 (CCL5) expression and associated immune genes through an innovative combination of Autoregressive Integrated Moving Average (ARIMA) and Convolutional Neural Network (CNN) models. Methods: A time series dataset of CCL5 expression, comprising 230 liver cancer patients, was analyzed using an ARIMA model to capture its temporal dynamics. The residuals from the ARIMA model, combined with immune gene expression data, were utilized as input features for a CNN to predict survival outcomes. Survival analyses were conducted using the Cox proportional hazards model and Kaplan–Meier curves. Furthermore, the ARIMA-CNN framework’s results were systematically compared with traditional median-based stratification methods, establishing a benchmark for evaluating model efficacy and highlighting the enhanced predictive power of the proposed integrative approach. Results: CNN-extracted features demonstrated superior prognostic capability compared to traditional median-split analyses of single-gene datasets. Features derived from CD8⁺ T cells and effector T cells achieved a hazard ratio (HR) of 0.7324 (p = 0.0008) with a statistically significant log-rank p-value (0.0131), highlighting their critical role in anti-tumor immunity. Hierarchical clustering of immune genes further identified distinct survival associations. Notably, a cluster comprising B cells, Th2 cells, T cells, and NK cells demonstrated a moderate protective effect (HR: 0.8714, p = 0.1093) with a significant log-rank p-value (0.0233). Conversely, granulocytes, Tregs, macrophages, and myeloid-derived suppressor cells showed no significant survival association, emphasizing the complex regulatory landscape within the tumor immune microenvironment. Conclusions: Our study provides the first ARIMA-CNN framework for modeling gene expression and survival analysis, marking a significant innovation in integrating temporal dynamics and machine learning for biological data interpretation. This model offers deeper insights into the tumor immune microenvironment and underscores the potential for advancing precision immunotherapy strategies and identifying novel biomarkers, contributing significantly to innovative cancer management solutions. Full article

Background/Objectives: Despite continuous pharmacological advances, the treatment of schizophrenia remains challenging, and suboptimal outcomes are still too frequent. There are currently limited new approved drugs without resistance. Methods: For this reason, drug repurposing presents a promising solution for identifying existing drugs with therapeutic effects for schizophrenia. In this study, we provide a workflow of signature-based drug repurposing methodology. We initially utilized a dataset from Gene Expression Omnibus which consists of RNA sequence data from blood-derived leukocyte samples from individuals with schizophrenia and control subjects, and conducted an analysis. Results: This analysis identified 1205 statistically significant differentially expressed genes, of which 150 upregulated and 150 downregulated genes were used in the CMap and L1000CDS2 tools. Then, each database generated a list of potential compounds that could reverse the disease’s signature and potentially have therapeutic effects for schizophrenia. Subsequently, the compounds associated with the disease, as identified in the research, were chemically clustered, and then their modes of action were predicted. In the last stage, we conducted a literature review to evaluate the relationship of these modes of action with the disease. Conclusions: This systematic analysis provided a list of potential drugs for schizophrenia treatment so that their efficacy can be evaluated in the wet-lab experiments, which is the next stage of drug repurposing. Full article

Background: Recurrent cervical cancer is one of the most defining threats to patient longevity, underscoring the need for prognostic models to identify high-risk patients. Objectives: The aim of the study is to integrate clinical data with the GSE44001 Dataset to identify key risk factors associated with the recurrence of cervical cancer. Patients are stratified into high-, moderate-, and low-risk groups using selected clinical and molecular features. Identifying a long non-coding RNA (lncRNA) gene signature associated with recurrent cervical cancer. Methods: From the total data collected, 138 recurrent cervical cancer patients were identified. GSE44001 Dataset is downloaded from the NCBI GEO Database. When using the GENCODE Annotation tool, the long non-coding RNA is filtered. The dataset is then linked with filtered long non-coding RNA. The Least Absolute Shrinkage Selection Operator (LASSO) is employed to find attributes in gene expression analysis. Risk factors of recurrent cervical cancer are identified. Risk value is assigned to each individual based on the selected lncRNAs and the corresponding overfitting coefficients. Result: The RNN Long Short-Term Memory model demonstrates a prognostic value, where high-risk patients experience a shorter duration of recurrence-free survival (p < 0.05). Individuals with a recurrence of cervical carcinoma, a progressive disease, were associated with the ATXN8OS marker, the C5orf60 indicator, and the INE1 index gene. In contrast, patients diagnosed at earlier stages are aligned with the KCNQ1DN marker, LOH12CR2 gauge, RFPL1S value, and KCNQ1OT1 indicator. Patients in moderate stages were primarily associated with the EMX2OS score. Conclusions: The research findings demonstrate that the nine-lncRNA signature, when combined with deep learning, offers a powerful approach for recurrence risk stratification in cervical cancer. Full article