Search Results (209)

Prostate adenocarcinoma is characterized by substantial inter-patient heterogeneity, limiting the clinical reliability of conventional diagnostic tools, including prostate-specific antigen testing. This limitation underscores the need for robust molecular biomarkers that may complement conventional diagnostic tools, highlighting the urgent need for biomarkers capable of enhancing diagnostic accuracy and enabling more precise risk stratification. In the present study, transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed using an integrative bioinformatics and machine learning pipeline., The proposed workflow was designed as a stepwise and reproducible biomarker prioritization framework in which differential expression analysis, functional enrichment, protein–protein interaction (PPI) based network interpretation, graph-convolutional feature selection, and hybrid ensemble machine learning were sequentially integrated. Differential gene expression analysis was combined with pathway enrichment (Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome), protein–protein interaction network construction, and graph-convolutional feature selection. Multiple machine learning algorithms, including Random Forest, Gradient Boosting Machine, Support Vector Classifier, Artificial Neural Network, and AdaBoost, were systematically evaluated. A hybrid ensemble model integrating Gradient Boosting Machine and Random Forest (GBM+RF) was subsequently developed. Model performance was assessed using accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic (ROC) and externally validated using the independent GSE14206 dataset. The analysis revealed a coordinated molecular pattern characterized by dysregulated cell cycle activity and enhanced interferon-mediated immune signaling. Protein–protein interaction analysis identified STAT1 and PLK1 as highly connected network hub genes within immune-related and cell-cycle-associated modules. Among the evaluated models, the hybrid GBM+RF framework achieved the highest predictive performance on the TCGA dataset, with AUC: 0.9526; Accuracy: 97.49%. External validation using the GSE14206 dataset confirmed the robustness of this model (AUC: 0.9156; Accuracy: 91.53%). These findings support a broader multi-gene candidate signature in prostate adenocarcinoma, in which machine learning prioritized genes such as XAF1, APP, RPA3, IFIH1, UBE2D2, RSAD2, KIF2C, and PLK1, while STAT1 and PLK1 provided complementary network-level biological relevance. The proposed framework provides a robust and transferable strategy for biomarker discovery and precision oncology. Full article

Background: Keratoconus is a progressive corneal ectasia characterised by extracellular matrix (ECM) loss and an emerging inflammatory component, for which no disease-modifying molecular therapy exists. Exosomes derived from limbal and mesenchymal stem cells are an attractive cell-free therapeutic modality, but the cargo that should be delivered is undefined, and no curated limbal stem-cell (LSC) exosome cargo dataset currently exists. Methods: We reanalysed a public keratoconus corneal RNA-sequencing dataset (GEO: GSE77938; discovery and replication cohorts) with DESeq2, defined a replicated differentially expressed gene (DEG) set, and performed Gene Ontology, KEGG and Reactome enrichment. A high-confidence protein–protein interaction (PPI) network (STRING) identified hub genes. We integrated keratoconus disease-gene evidence (Open Targets Platform) and documented extracellular-vesicle cargo (ExoCarta, Vesiclepedia) and computed a transparent Cargo Prioritization Score (CPS) to nominate candidate LSC-exosome therapeutic cargo. Results: A total of 1677 DEGs were detected in discovery (152 up, 1525 down) and 1380 were replicated. Enrichment was dominated by extracellular matrix organisation; adaptive immune response; and mononuclear cell differentiation. Network analysis nominated ECM and immune hub genes. The CPS prioritised COL1A1, FN1, COL4A1, COL3A1, COL5A1, MMP1 as leading restoration-cargo candidates, all documented as EV cargo and present in the mesenchymal stem-cell EV reference proteome. Conclusions: This fully reproducible, real-data framework provides a ranked, evidence-traceable shortlist of candidate LSC-exosome cargo for keratoconus and an explicit account of current data gaps to guide experimental validation. Full article

This systematic review assessed transcriptomic evidence on the molecular mechanisms underlying spontaneous preterm birth (sPTB). Major electronic databases were searched from inception to October 2025. Eligible studies examined RNA transcriptomic profiles from maternal pregnancy-related tissues or biofluids in spontaneous preterm labor (sPTL) or preterm prelabor rupture of membranes (PPROM), while indicated or iatrogenic preterm births were excluded. Two reviewers independently screened studies, extracted differentially expressed genes (DEGs), and assessed study quality. DEGs were summarized by tissue type, and recurrent concordant genes were analyzed using Gene Ontology, Reactome, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, with false discovery rate < 0.05 considered significant. Twenty studies were included. Transcriptomic data were derived from placental villi, maternal peripheral blood, decidua, fetal membranes, myometrium, amniotic fluid, and vaginal secretions. Placental villi findings suggested proliferative-metabolic reprogramming and impaired maternal–fetal immune–structural homeostasis, whereas maternal blood profiles reflected systemic immune–inflammatory activation and dysregulated lipid-metabolic pathways. sPTL and PPROM showed potentially distinct signatures involving extracellular matrix disruption, collagen remodeling, matrix degradation, and myeloid/neutrophil-associated inflammation. Transcriptomic profiling may support non-invasive sPTB risk assessment, but standardized, phenotype-specific longitudinal studies are needed to confirm predictive value and clinical utility. Full article

Breast and prostate cancers remain among the most prevalent epithelial malignancies worldwide, and conventional treatments often lack tumor selectivity. Aloin, an anthraquinone glycoside derived from Aloe vera, has demonstrated promising anticancer properties. This study investigated the differential cytotoxic and apoptotic effects of Aloin under in vitro conditions in MCF-7 (breast cancer) and PC-3 (prostate cancer) cell lines compared with normal prostate epithelial cells (PNT-A1). Cells were treated with Aloin (1000–1500 µg/mL); cytotoxicity was assessed by CCK-8 assay, apoptotic morphology by DIC microscopy, protein expression by immunofluorescence with quantitative CTCF analysis (BAX, Caspase-3, Caspase-8, Caspase-9), and gene expression by qRT-PCR (2^−ΔΔCt method). An integrated log₂ fold change heatmap, pathway enrichment analysis across three independent databases (KEGG 2026, Reactome 2024, WikiPathways 2024), and STRING v12.0-based protein–protein interaction (PPI) network were constructed. Aloin exerted significant dose-dependent cytotoxicity in both cancer cell lines, while PNT-A1 viability exceeded 50% across all concentrations (Selectivity Index > 1.30 for MCF-7 at 48 h). Immunofluorescence and qRT-PCR confirmed significant upregulation of BAX (up to 6.14×), CASP8 (up to 15.51×), CASP9 (up to 9.27×), and CASP3 (3.03× in PC-3), indicating concurrent activation of intrinsic and extrinsic apoptotic pathways, while all genes remained unchanged in PNT-A1 cells. Pathway enrichment analysis confirmed that these genes are statistically central nodes in conserved apoptotic signaling networks (adj. p < 10⁻⁹). To the best of our knowledge, this is the first in vitro characterization of Aloin-induced pro-apoptotic activity in prostate cancer cells, establishing a mechanistic foundation for further investigation of this phytochemical in epithelial-derived cancer models. Full article

Metastasis remains a major cause of cancer mortality, making reliable primary–metastatic state prediction from somatic genomic alterations clinically important yet technically difficult. We present TF-GateNet, a biologically constrained neural network that combines TF-aware feature integration based on TRRUST and DoRothEA TF–gene regulatory priors with sample-specific dynamic gating on a Reactome-defined hierarchical sparse backbone. The model was evaluated on multi-center prostate and breast-cancer cohorts using mutation and copy-number features across 10 repeated runs on a fixed 80/10/10 split, together with independent prostate external validation, and was compared with biologically informed neural-network baselines (P-NET, BKGNet-Pathway, and BKGNet-Protein), a dense feed-forward neural network (FNN), and conventional machine-learning baselines (LR, SVM, RF, DT, and XGBoost). On prostate, TF-GateNet achieved the best internal performance (AUROC 0.954 ± 0.005; AUPRC 0.925 ± 0.007) and the best combined external performance (AUROC 0.952 ± 0.009; AUPRC 0.898 ± 0.018). On breast, TF-GateNet achieved the strongest internal ranking performance, reaching AUROC 0.893 ± 0.004 and AUPRC 0.835 ± 0.006. Ablation analysis indicated that TF-aware integration accounted for the larger prostate gain, whereas within the TF-GateNet family on breast, both TF-aware integration and dynamic gating contributed positively. Interpretability analysis further supported a cross-level route from TF-related genomic perturbation cues to genes, pathways, and phenotype-associated predictions. These results position TF-GateNet as a biologically grounded and interpretable framework for primary–metastatic state prediction, with the strongest overall evidence in prostate cancer and favorable internal evidence in breast cancer. Full article

Background: Exercise adaptation is increasingly recognized as an immunometabolic process driven by coordinated interactions among inflammatory signaling, mitochondrial regulation, metabolic homeostasis, and recovery-associated physiology. Within this framework, NLRP3 inflammasome activation and PPARD-mediated metabolic signaling have emerged as biologically relevant pathways potentially involved in exercise-induced physiological adaptation. However, the contribution of regulatory genetic variations linking these pathways remains poorly characterized. Objective: To synthesize current evidence regarding the integration of NLRP3- and PPARD-related pathways in exercise immunometabolism and adaptive physiological responses to exercise, with particular emphasis on the regulatory variants NLRP3 rs10754558 and PPARD rs2267668 as potential contributors to interindividual variability in exercise adaptation. Methods: A structured narrative review complemented by exploratory systems-level in silico analyses was conducted using the PubMed, Scopus, and Web of Science databases until March 2026. Evidence related to exercise physiology, inflammatory regulation, metabolic adaptation, and exercise-associated phenotypes involving the NLRP3 and PPARD pathways was evaluated. Complementary analyses included functional annotation, protein–protein interaction network analysis, and pathway enrichment using STRING, Reactome, KEGG, Gene Ontology, and other publicly available genomic databases. Particular attention was given to the functional and regulatory context of rs10754558 and rs2267668 within the interconnected inflammatory and metabolic pathways relevant to exercise adaptation. Results: The reviewed evidence identified recurrent interactions among the inflammatory and metabolic pathways involved in exercise adaptation and recovery. NLRP3 rs10754558 and PPARD rs2267668 were identified as candidate regulatory variants potentially positioned at the interface between inflammatory responsiveness and metabolic flexibility, providing a biologically plausible framework for understanding the interindividual variability in exercise adaptation. Exploratory system-level analyses identified recurrent associations among inflammatory signaling, mitochondrial function, energy-sensing pathways, and metabolic regulation. These findings primarily reflect the functional annotations and system-level pathway associations identified through exploratory analyses. Conclusions: Current evidence supports a systems-level physiological framework in which inflammatory and metabolic pathways interact dynamically during exercise adaptation and recovery. NLRP3- and PPARD-related pathways, including the candidate regulatory variants rs10754558 and rs2267668, may contribute to interindividual variability in exercise-associated physiological responses and represent promising targets for future hypothesis-driven investigations in exercise immunometabolism, exercise genomics and precision exercise medicine. Full article

Shuanghuanglian oral liquid (SHL) is widely used in companion animals and poultry, but its molecular mechanism in pneumonia–myocarditis comorbidity and heart–lung inflammatory crosstalk remains largely unclear. This computational study investigated the conserved AKT1/HIF1A-mediated immunoregulatory mechanism of SHL and its cross-species translational potential in veterinary medicine. Network pharmacology was integrated with GO, KEGG, and Reactome enrichment analyses, protein–protein interaction network construction, ADMET evaluation, cross-species sequence homology analysis (human, dog, cattle, and pig), molecular docking, and molecular dynamics simulation. A total of 61 active compounds, 251 putative targets, and 52 common targets associated with pneumonia and myocarditis were identified. These targets were mainly enriched in inflammation- and immune-related pathways, including TNF, IL-17, AGE–RAGE, and PPAR signaling. AKT1 and HIF1A showed high sequence conservation across species (85–98%). Key compounds exhibited favorable binding affinity to AKT1, and molecular dynamics simulation suggested the stability of the Baicalein–AKT1 complex. ADMET analysis suggested favorable pharmacokinetic properties and low predicted toxicity. These findings suggest that SHL may potentially alleviate pneumonia and myocarditis through modulation of the conserved AKT1/HIF1A axis and support its potential as a complementary therapeutic approach for managing heart–lung inflammatory diseases in multiple livestock species. This entirely computational study highlights promising mechanisms that should be further validated in vivo. Full article

Recent studies have identified microRNAs (miRNAs) in honey, opening a new and promising area of nutrition research. In this view, pasteurized and unpasteurized samples of Eucalyptus, Orange Blossom, Chestnut, and Sulla honeys were analyzed using manual and semi-automated RNA extraction methods. Semi-automated extraction yielded significantly higher RNA amounts than manual methods, while pasteurization selectively affected miRNA presence, depending on the type of honey. The panel of conserved miRNAs monitored was let-7a-5p, miR-1-3p, miR-7-5p, miR-10a-5p, miR-33a-5p, miR-34a-5p, miR-92a-3p, miR-125b-5p and miR-133a-3p, across honey varieties and in their extracellular vesicles with structures approximately 200 nm in diameter that retain four miRNAs in all honey types, miR-1-3p, miR-34a-5p, miR-92a-3p, and miR-133a-3p. Bioinformatic analyses of validated miRNA targets revealed enrichment in pathways related to cytoskeletal organization, transcriptional regulation, protein stability, and immune system processes, with Reactome categories clustering around signal transduction, protein metabolism, and immune interactions. Cell–type–specific enrichment suggested that gastric isthmus progenitor cells, stromal cells, and immune subsets could be potential targets, implying roles in epithelial renewal, immune modulation, and wound healing. Overall, these findings enhance our understanding of honey as a source of conserved miRNAs in extracellular vesicles, highlighting its potential as a natural carrier that protects miRNAs from degradation. This study offers new insights into the health-promoting properties of honey, warranting further preclinical studies. Full article

Background: Several studies have investigated the clinical and immunological aspects of medication-related osteonecrosis of the jaw (MRONJ). However, the underlying immunological mechanisms and signaling pathways involved in its pathophysiology remain incompletely understood. This systematic review and meta-analysis, complemented by bioinformatics analyses, aimed to identify proteomic biomarkers associated with MRONJ. Methods: Six databases (PubMed, Embase, Scopus, Web of Science, Cochrane Library, and VHL) were searched, along with gray literature and manual searches. Observational studies in English comparing proteomic profiles of individuals with and without MRONJ were included. Study selection and data management were conducted using EndNote™ X8 and Rayyan.ai, and risk of bias was assessed using the QUADOMICS tool. Functional enrichment analysis was performed using g:Profiler and Reactome, and interaction networks were constructed using GeneMANIA, STRING, and MetaboAnalyst (Cytoscape program; version 3.10.1). Meta-analysis was performed in RStudio (R-4.5, Rstudio extension 2025.05.1+513) (α = 0.05). Results: Three studies were included in the review, and two in the meta-analysis. The meta-analysis showed higher salivary levels of Apolipoprotein B-100 (APOB), Apolipoprotein A-II (APOA2), and Heparin Cofactor 2 (SERPIND1) in MRONJ patients, while the protein Keratin (KRT16) showed reduced levels without statistical significance. Bioinformatics analyses indicated involvement in lipid metabolism, impaired tissue repair, and inflammatory and immune responses. Conclusions: These findings suggest altered salivary proteomic signatures in MRONJ for APOB, APOA2, SERPIND1, and KRT16 proteins. Full article

Background/Objectives: The Indian star tortoise (Geochelone elegans) is a protected species for which physiological and molecular health indicators remain poorly characterized. This study aimed to monitor and analyze plasma proteome profiles and biochemical parameters in captive adult Indian star tortoises and to identify potential diagnostic biomarkers. Methods: Plasma samples from nine clinically healthy adult Indian star tortoises (four males and five females) maintained in captivity were subjected to biochemical profiling and proteomic analysis. Sex-related differences in biochemical parameters were evaluated, and differentially expressed proteins were mapped to Homo sapiens Reactome pathways to identify significantly enriched biological processes. Results: Plasma biochemical profiling established baseline reference values, indicating stable hepatic and metabolic function in captive tortoises. Creatinine and urea concentrations were significantly higher in females than in males (p < 0.05), suggesting sex-related differences in protein metabolism or renal function. No significant sex-related differences were observed in hepatic enzymes (ALP, ALT, AST, and GGT), muscle-associated enzymes (CK and LDH), glucose, cholesterol, triglycerides, total proteins, albumin, or electrolyte concentrations (Na, K, Ca, Mg, Cl, P, and Fe). Proteomic analysis identified 12 differentially expressed proteins, including nine upregulated and three downregulated proteins. Functional pathway analysis revealed 90 significantly enriched Reactome pathways (FDR < 0.05). Upregulated proteins were primarily associated with cytoskeletal organization (KRT75, KRT5, and KRT17), lipid transport and remodeling (APOB), coagulation (F10), extracellular transport (TTR), immune response (WFDC3), transmembrane signaling (KCP), and gamete interaction (ZAN). Downregulated proteins (C7, SERPING1, and PZP) were linked to complement activation and acute-phase response pathways. Conclusions: Captive Indian star tortoises exhibited increased cytoskeletal remodeling and coagulation activity together with reduced complement activation. These findings provide novel insights into the plasma proteome of this species and identify candidate biomarkers that may support future health assessment, physiological monitoring, and diagnostic applications in Indian star tortoises. Full article

Hantaviruses are emerging zoonotic pathogens responsible for two severe clinical syndromes: (i) haemorrhagic fever with renal syndrome (HFRS) and (ii) hantavirus cardiopulmonary syndrome (HCPS), collectively causing more than 200,000 human cases annually worldwide. Despite their public-health importance, the molecular mechanisms governing the host response and the population-level dynamics of rodent-to-human spillover remain incompletely characterised. The timeliness of this framework is underscored by the April–May 2026 outbreak of Andes orthohantavirus aboard the MV Hondius cruise ship, the first such cluster in a maritime setting, with three deaths reported across multiple countries. This event revealed critical gaps in existing models that treat humans solely as dead-end spillover hosts. Our coupled Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model assumes no human-to-human transmission and is therefore designed for hantavirus strains where spillover does not lead to secondary human cases, specifically Hantaan virus (HTNV), Puumala virus (PUUV), Sin Nombre virus (SNV), and Dobrava-Belgrade virus (DOBV). The Andes virus (ANDV) outbreak aboard the MV Hondius is used as a real-world case study to assess the boundaries of our model and to motivate future extensions, not as a direct validation target for its quantitative predictions. Here, we present an integrated computational study combining three complementary analyses. First, we performed a preliminary phylogenetic analysis of the viral sequence, identifying Orthohantavirus andesense as the likely etiological agent responsible for the vessel-associated outbreak. Second, we carried out a downstream transcriptomic analysis of Hantaan virus (HTNV)-infected human umbilical vein endothelial cells (HUVECs), using publicly available RNA-seq data (GEO accession GSE133751, $n=3$ per group). This analysis identified 184 upregulated and 19 downregulated genes, highlighting a transcriptional response dominated by interferon-stimulated genes (ISGs), including CXCL10, CXCL11, MX2, DDX58, IRF7, STAT1, OASL, and CMPK2. We then constructed a protein–protein interaction (PPI) network using STRING, comprising 176 nodes and 3210 edges, and applied a composite network centrality score to rank putative regulatory hubs. This analysis identified ISG15, IRF1, CXCL10, STAT1, and DDX58 as the most central nodes. Pathway enrichment analysis confirmed a strong activation of interferon signalling (Reactome, $p=1.3\times {10}^{-63}$), antiviral defence mechanisms (Gene Ontology, $p=3.8\times {10}^{-58}$), and NF-$\kappa$B-related pathways, together with a concurrent suppression of ribosomal translation. Finally, we developed a coupled SEIRD epidemiological model that explicitly represents rodent-to-rodent and rodent-to-human transmission with logistic rodent population growth. Preliminary simulation analysis demonstrates that reducing human exposure to rodent excreta is substantially more effective than rodent population control alone for reducing human disease burden, and that rodent control in isolation can paradoxically increase human cases through a dilution-like effect. The integrated framework provides molecular and epidemiological insights relevant to hantavirus surveillance, therapeutic target identification, and public-health intervention design. Full article

Background/Objectives: Due to the utility of knowing the pathway involvement of metabolites detected in biological experiments, knowledgebases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and MetaCyc have annotated compound entries to specific pathways defined by the knowledgebase. However, these compound-pathway annotations are largely incomplete and are costly to obtain experimentally or curate from published scientific literature. This metabolite-pathway annotation incompleteness problem is amenable to machine learning (ML)-based solutions. But to date, no machine learning model has been trained on all three knowledgebases to maximize its performance and robustness. This may be due to inconsistencies in chemical structure representation that can confuse a model and greatly reduce generalizability. Methods: We constructed a new training dataset with roughly 50,000,000 entries using compound-pathway annotations derived from KEGG, Reactome, and MetaCyc. We trained and tested a multitask classification, graph convolutional neural network-like model that classifies compound involvement with 8056 pathways that have unique pathway representations, based on annotated compound chemical structures represented with chemical substructure features. While the initial dataset contained inconsistencies in chemical structure representations across knowledgebases, we alleviated this issue by standardizing chemical structure representation using InChI (IUPAC International Chemical Identifier) canonicalization. We compared the performance of the non-standardized versus the standardized dataset and quantified their generalizability by comparing training set compounds to their knowledgebase cross-references. Results: While the non-standardized dataset scored a mean Matthews correlation coefficient (MCC) of 0.8725 ± 0.0064, the standardized dataset scored an MCC of 0.9036 ± 0.0033. When comparing model generalizability, the non-standardized chemical structure representations had a huge 0.2687 drop in mean MCC, while the standardized chemical structure representations only had a 0.0384 drop in mean MCC. Conclusions: We constructed the largest ML-ready dataset for predicting compound-pathway involvement to date. Next, we constructed, trained, and evaluated the highest performing ML model capable of predicting the highest number of pathway annotations to date. We discovered that standardizing chemical structure representation is an essential step when predicting novel chemical structures. Full article

Coffee consumption has been associated with metabolic and cardiovascular health, but the molecular mechanisms underlying these associations remain unclear. This study investigated the association between coffee intake and circulating proteomic profiles across metabolic conditions using a pooled-serum, exploratory design. Participants were classified into four groups: normal weight (NW), normal weight with coffee intake (NWC), obese with hypertension (OBHT), and obese with hypertension with coffee intake (OBHTC). Differentially expressed proteins (DEPs) were identified using volcano plot criteria (|log₂FC| ≥ 1, FDR < 0.05), followed by Reactome pathway enrichment, Gene Ontology (GO) molecular function, and Enrichr-derived protein–protein interaction (PPI) analyses. Results: In NW vs. NWC, coffee intake was associated with proteins enriched in receptor-mediated signaling and phosphoinositide pathways. In OBHT vs. OBHTC, DEPs were linked to mitochondrial respiration and oxidoreductase activity. The NW vs. OBHT comparison showed downregulation of metabolic and signaling proteins with enrichment of mitochondrial and stress-response functions. In NWC vs. OBHTC, proteins related to cytokine signaling and vascular function were reduced, while redox-associated regulators were increased. PPI networks highlighted interconnected hubs integrating signaling, metabolism, and immune responses. Conclusion: These findings suggest context-dependent proteomic patterns associated with coffee intake. Given the pooled design and small sample size, results are hypothesis-generating and require validation. Full article

Background: Ovarian cancer (OVCA) remains the most lethal gynecologic malignancy, with poor prognosis largely due to late-stage diagnosis and therapy resistance. Pyroptosis, a pro-inflammatory form of programmed cell death, has recently emerged as a regulator of tumor progression and immune regulation. This study aimed to systematically profile pyroptosis-related genes and identify robust biomarkers for OVCA. Methods: Microarray data from the GSE54388 dataset were analyzed to characterize pyroptosis-related gene expression. Immune cell infiltration was assessed using xCell, and pathway enrichment was performed via Gene Set Enrichment Analysis (GSEA). Weighted Gene Co-expression Network Analysis (WGCNA) identified hub genes, followed by Gene Ontology (GO) and Reactome enrichment. Machine learning algorithms (Support Vector Machine, XGBoost, and Generalized Linear Model) were employed for feature selection and biomarker identification. Validation was conducted across independent bulk and scRNA-seq datasets, with GEPIA2 used to compare OVCA and normal samples and KMplot for survival analysis. Results: OVCA samples showed significantly reduced infiltration of CD4⁺ and CD8⁺ T cells, mast cells, monocytes, neutrophils, and immature dendritic cells compared to normal samples. GSEA revealed enrichment of cell cycle-related pathways, implicating pyroptosis-related genes as key regulators of mitotic progression. From 1097 differentially expressed genes, 22 pyroptosis-related DEGs (PYRDEGs) were identified, with nine hub genes (CASP1, CEP55, CHMP4C, HTRA1, IL18, MELK, PKM, PTX3, TNFSF13B) strongly associated with OVCA. Functional enrichment linked these genes to cytokinesis, inflammasome activity, and immune signaling. Machine learning consistently identified CEP55 as the core biomarker, demonstrating high diagnostic accuracy (AUC up to 0.972) and significant upregulation in OVCA samples. Correlation analysis linked CEP55 expression to altered immune cell populations, including positive associations with Th1 and class-switched memory B-cells and negative associations with iDCs, Tregs, and M2 macrophages. CEP55 was highly expressed across bulk and scRNA-seq datasets (cancer epithelial and CD8+ TEMRA cells) and negatively correlated with overall survival (OS) and progression-free survival (PFS). Conclusions: Pyroptosis-related genes play pivotal roles in OVCA pathogenesis. CEP55 emerges as a promising biomarker for early detection and a potential therapeutic target, bridging cell cycle regulation with immune modulation. Full article

Background/Objectives: Amplitude-modulated radiofrequency (AMRF) fields have emerged as promising non-temperature-induced strategies in oncology. While conventional hyperthermia (HT) relies on thermal stress, the biological impact of AMRF, particularly in combination with radiotherapy (RT), remains insufficiently characterized. Methods: We assessed RF and AMRF, alone or with RT, using phenotypic analyses of proliferation, apoptosis, and necrosis across four cancer cell lines (HT29, SW620, U343, U138). Transcriptomic profiling with Kyoto Encyclopedia of Genes and Genomes (KEGG), GO:BP, and Reactome enrichment was performed in SW620 and U138 cells, selected for their strong phenotypic responses. Results: Across the panel, AMRF was associated with broader cytotoxic responses than RF or HT in most but not all cell lines. AMRF+RT produced the strongest necrotic responses, with cell-line-specific exceptions identified explicitly in the Results (the absence of a significant AMRF+RT apoptotic effect in SW620 and the absence of a significant AMRF+RT necrotic response in U343). In SW620 cells, AMRF was associated with extensive transcriptional reprogramming involving immune modulation, extracellular matrix remodeling, and cell cycle regulation, whereas RF alone showed narrower and delayed effects. In contrast, U138 cells showed elevated apoptosis and necrosis but limited transcriptional changes—a phenotype–transcriptome divergence that points to mechanisms operating downstream of transcription and warrants functional investigation in dedicated follow-up studies. Conclusions: AMRF and AMRF+RT emerge as promising non-temperature-induced anticancer modalities in the cell-line models profiled here, with the pattern of response varying between cell lines. These findings expand the biological impact of RF-based treatments and set the grounds for further investigation in mechanistic and translational studies. Full article