Search Results (109)

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications. Full article

Introduction: Huntington’s disease (HD) disrupts cortico-striato-thalamocortical circuits decades before clinical onset. Electroencephalography (EEG) offers millisecond temporal resolution, low cost, and broad accessibility, yet its mechanistic and biomarker potential in HD remains underexplored. We conducted a mechanistic review to synthesize half a century of EEG findings, identify reproducible electrophysiological signatures, and outline translational next steps. Methods: Two independent reviewers searched PubMed, Scopus, Google Scholar, ResearchGate, and the Cochrane Library (January 1970–April 2025) using the terms “EEG” OR “electroencephalography” AND “Huntington’s disease”. Clinical trials published in English that reported raw EEG (not ERP-only) in human HD gene carriers were eligible. Abstract/title screening, full-text appraisal, and cross-reference mining yielded 22 studies (~700 HD recordings, ~600 controls). We extracted sample characteristics, acquisition protocols, spectral/connectivity metrics, and neuroclinical correlations. Results: Across diverse platforms, a consistent spectral trajectory emerged: (i) presymptomatic carriers show a focal 7–9 Hz (low-alpha) power loss that scales with CAG repeat length; (ii) early-manifest patients exhibit widespread alpha attenuation, delta–theta excess, and a flattened anterior-posterior gradient; (iii) advanced disease is characterized by global slow-wave dominance and low-voltage tracings. Source-resolved studies reveal early alpha hypocoherence and progressive delta/high-beta hypersynchrony, microstate shifts (A/B ↑, C/D ↓), and rising omega complexity. These electrophysiological changes correlate with motor burden, cognitive slowing, sleep fragmentation, and neurovascular uncoupling, and achieve 80–90% diagnostic accuracy in shallow machine-learning pipelines. Conclusions: EEG offers a coherent, stage-sensitive window on HD pathophysiology—from early thalamocortical disinhibition to late network fragmentation—and fulfills key biomarker criteria. Translation now depends on large, longitudinal, multi-center cohorts with harmonized high-density protocols, rigorous artifact control, and linkage to clinical milestones. Such infrastructure will enable the qualification of alpha-band restoration, delta-band hypersynchrony, and neurovascular coupling as pharmacodynamic readouts, fostering precision monitoring and network-targeted therapy in Huntington’s disease. Full article

Background/Objectives: Lung cancer recurrence, particularly in NSCLC, remains a major challenge, with 30–70% of patients relapsing post-treatment. Traditional predictors like TNM staging and histopathology fail to account for tumor heterogeneity and immune dynamics. This review evaluates AI models integrating gene biomarkers (TP53, KRAS, FOXP3, PD-L1, and CD8) to enhance the recurrence prediction and improve the personalized risk stratification. Methods: Following the PRISMA guidelines, we systematically reviewed AI-driven recurrence prediction models for lung cancer, focusing on genomic biomarkers. Studies were selected based on predefined criteria, emphasizing AI/ML approaches integrating gene expression, radiomics, and clinical data. Data extraction covered the study design, AI algorithms (e.g., neural networks, SVM, and gradient boosting), performance metrics (AUC and sensitivity), and clinical applicability. Two reviewers independently screened and assessed studies to ensure accuracy and minimize bias. Results: A literature analysis of 18 studies (2019–2024) from 14 countries, covering 4861 NSCLC and small cell lung cancer patients, showed that AI models outperformed conventional methods. AI achieved AUCs of 0.73–0.92 compared to 0.61 for TNM staging. Multi-modal approaches integrating gene expression (PDIA3 and MYH11), radiomics, and clinical data improved accuracy, with SVM-based models reaching a 92% AUC. Key predictors included immune-related signatures (e.g., tumor-infiltrating NK cells and PD-L1 expression) and pathway alterations (NF-κB and JAK-STAT). However, small cohorts (41–1348 patients), data heterogeneity, and limited external validation remained challenges. Conclusions: AI-driven models hold potential for recurrence prediction and guiding adjuvant therapies in high-risk NSCLC patients. Expanding multi-institutional datasets, standardizing validation, and improving clinical integration are crucial for real-world adoption. Optimizing biomarker panels and using AI trustworthily and ethically could enhance precision oncology, enabling early, tailored interventions to reduce mortality. Full article

Background/Objectives: The prediction of cancer types is primarily reliant on driver genes and their specific mutations. The advancement in novel omics technologies has led to the acquisition of additional genetic data. When integrated with artificial intelligence models, there is considerable potential for this to enhance the accuracy of cancer diagnosis. As mutational signatures can provide insights into repair mechanism malfunctions, they also have the potential for more accurate cancer diagnosis. Methods: First, we compared unsupervised and supervised machine learning approaches to predict cancer types. We employed deep and artificial neural network architectures with an explainable component like layerwise relevance propagation to extract the most relevant features for the cancer-type prediction. Ten-fold cross-validation and an extensive grid search were used to optimize the neural network architecture using driver gene mutations, mutational signatures and topological mutation information as input. The PCAWG dataset was used as input to discriminate between 17 primary sites and 24 cancer types. Results: Overall, our approach showed that the most relevant mutation information to discriminate between cancer types is increased by >10% using the whole genome or intergenic and intronic genome regions instead of exome information. Furthermore, the most relevant features for most cancer types, except for two, are in the mutational signatures and not the topological mutation information. Conclusions: Informative mutational signatures outperformed the prediction of cancer types in comparison to driver gene mutations and added a new layer of diagnostic information. As the degree of information within the mutational signatures is not solely based on the frequency of occurrence, it is even possible to separate cancer types from the same primary site by the different relevant mutations. Furthermore, the comparison of informative mutational signatures allowed the cancer-type assignment of specific impaired repair mechanisms. Full article

Physical activity (PA) and body composition are important lifestyle factors that influence public health. Research suggests that DNA regions (CpG site locations) are differentially methylated in a physically active population. This meta-analysis aimed to identify CpG sites associated with various levels of PA and associated metabolic pathways. The meta-analysis followed PRISMA guidelines using PubMed, SportDISCUS, Embase, Scopus, Cochrane and Web of Science. Epigenomic analyses performed on DNA of participants with no underlying health conditions were included. Articles were screened using Rayyan AI and extracted CpG sites, and their location were confirmed using the EWAS catalogue. Six studies comprising 770 subjects were included in this meta-analysis. The meta-analysis was performed on clinical metrics extracted from the six studies and showed that BMI, blood pressure, insulin and glucose testing are significantly improved upon PA intervention. Amongst the included studies, a total of 257 CpG sites were differentially methylated in physically active participants, with 134 CpGs located in 92 genes associated with obesity-related pathways. The identified differentially methylated genes either belonged to the lipid metabolism or insulin signalling pathway. The genes which were differentially regulated in multiple tissue types and studies are JAZF1 (insulin signalling, and lipid and carbohydrate metabolism pathways) and NAV1 (mTOR signalling pathway). In conclusion, the current epigenomic meta-analysis showed that PA levels induce differential DNA methylation signatures on genes that affect metabolism. To understand the positive molecular effects of PA, further research on the above candidate genes needs to be conducted amongst various levels of a physically active population. Full article

Over the last few decades, the prevalence of metabolic diseases such as obesity, diabetes, non-alcoholic fatty liver disease, hypertension, and hyperuricemia has surged, primarily due to high-fat diet (HFD). The pathologies of these metabolic diseases show disease-specific alterations in the composition and function of their gut microbiome. How HFD alters the microbiome and its metabolite to mediate adipose tissue (AT) inflammation and obesity is not well known. Thus, this study aimed to identify the changes in the gut microbiome and metabolomic signatures induced by an HFD to alter obesity. To explore the changes in the gut microbiota and metabolites, 16S rRNA gene amplicon sequencing and metabolomic analyses were performed after HFD and normal diet (ND) feeding. We noticed that, at taxonomic levels, the number of operational taxonomic units (OTUs), along with the Chao and Shannon indexes, significantly shifted in HFD-fed mice compared to those fed a ND. Similarly, at the phylum level, an increase in Firmicutes and a decrease in Bacteroidetes were noticed in HFD-fed mice. At the genus level, an increase in Lactobacillus and Ruminococcus was observed, while Allobaculum, Clostridium, and Akkermansia were markedly reduced in the HFD group. Many bacteria from the Ruminococcus genus impair bile acid metabolism and restrict weight loss. Firmicutes are efficient in breaking down complex carbohydrates into short-chain fatty acids (SCFAs) and other metabolites, whereas Bacteroidetes are involved in a more balanced or efficient energy extraction. Thus, an increase in Firmicutes over Bacteroidetes enhances the absorption of more calories from food, which may contribute to obesity. Taken together, the altered gut microbiota and metabolites trigger AT inflammation, which contributes to metabolic dysregulation and disease progression. Thus, this study highlights the potential of the gut microbiome in the development of therapeutic strategies for obesity and related metabolic disorders. Full article

The extract of Scenedesmus deserticola JD052 has been reported to exhibit anti-aging effects on the skin, with research indicating an increase in loliolide, a major active component, through heterotrophic cultivation. In this study, we evaluated the effects of extracts obtained from both photoautotrophic (PE) and heterotrophic (HE) cultures on hair-inductive properties in human dermal papilla (HDP) cells. Biochemical assays demonstrated that both extracts enhanced HDP cell viability and increased the size of three-dimensional dermal papilla (DP) spheres. Notably, the activation of β-catenin, a crucial marker associated with hair growth, was assessed using a luciferase reporter assay, revealing that HE exhibited a significantly higher efficacy than PE. Further analyses indicated that HE promoted the translocation of β-catenin into the nucleus through the phosphorylation and activation of AKT, which also elevated the expression levels of DP signature genes and hair-growth-related autocrine factors. Additionally, conditioned media from HE-treated HDP cells enhanced keratinocyte migration and increased the expression of growth factors, including VEGF and IGF-1. HPLC-MS analysis showed no significant difference in loliolide content; however, specific peaks in HE were identified as pheophorbide A and linolelaidic acid. Thus, HE may enhance hair growth inductivity via AKT/β-catenin signaling. Full article

Complex RNA-seq signatures involving the transcription factors ASCL1, NEUROD1, and POU2F3 classify Small Cell Lung Cancer (SCLC) into four subtypes: SCLC-A, SCLC-N, SCLC-P, and SCLC-I (triple negative or inflamed). Preliminary studies suggest that identifying these subtypes can guide targeted therapies and potentially improve outcomes. This study aims to evaluate whether the expression levels of these three key transcription factors can effectively classify SCLC subtypes, comparable to the use of individual antibodies in immunohistochemical (IHC) analysis of formalin-fixed, paraffin-embedded (FFPE) tumor samples. We analyzed preclinical models of increasing complexity, including eleven human and five mouse SCLC cell lines, six patient-derived xenografts (PDXs), and two circulating tumor cell (CTC)-derived xenografts (CDXs) generated in our laboratory. RT-qPCR conditions were established to detect the expression levels of ASCL1, NEUROD1, and POU2F3. Additionally, protein-level analysis was performed using Western blot for cell lines and IHC for FFPE samples of PDX and CDX tumors, following our experience with patient tumor samples from the CANTABRICO trial (NCT04712903). We found that the analyzed SCLC cell line models predominantly expressed ASCL1, NEUROD1, and POU2F3, or showed no expression, as identified by RT-qPCR, consistently matching the previously assigned subtypes for each cell line. The classification of PDX and CDX models demonstrated consistency between RT-qPCR and IHC analyses of the transcription factors. Our results show that single-gene analysis by RT-qPCR from FFPE-extracted RNA simplifies SCLC subtype classification. This approach provides a cost-effective alternative to IHC staining or expensive multi-gene RNA sequencing panels, making SCLC subtyping more accessible for both preclinical research and clinical applications. Full article

Background and Objectives: Persistent exposure to airborne fine dust (FD) particles contributing to air pollution has been linked to various human health issues, including respiratory inflammation, allergies, and skin diseases. We aimed to identify potential seaweed anti-inflammatory bioactive reagents and determine their effects on systemic inflammatory responses induced by FD particles. Materials and Methods: While exploring anti-inflammatory bioactive reagents, we purified compounds with potential anti-inflammatory effects from the seaweed extracts of Ecklonia cava, Ecklonia stolonifera, and Codium fragile. Structural analyses of the purified compounds siphonaxanthin (Sx), fucoxanthin (Fx), dieckol (Dk), and phlorofucofuroeckol-A (PFF-A) were performed using NMR and LC-MS/MS. Results: Notably, these compounds, especially PFF-A, showed significant protective effects against FD-induced inflammatory responses in RAW 264.7 cells without cytotoxicity. Further investigation of inflammatory-associated signaling demonstrated that PFF-A inhibited IL-1β expression by modulating the NF-κB/MAPK signal pathway in FD-induced RAW 264.7 cells. Additionally, gene profiling revealed the early activation of various signature genes involved in the production of inflammatory cytokines and chemokines using gene profiling. Treatment with PFF-A markedly reduced the expression levels of pro-inflammatory and apoptosis-related genes and even elevated the Bmp gene families. Conclusions: These results suggested that PFF-A is a potential natural therapeutic candidate for managing FD-induced inflammatory response. Full article

Background/Objectives: Predicting the behavior of clear cell renal cell carcinoma (ccRCC) is challenging using standard-of-care histopathologic examination. Indeed, pathologic RCC tumor grading, based on nuclear morphology, performs poorly in predicting outcomes of patients with International Society of Urological Pathology/World Health Organization grade 2 and 3 tumors, which account for most ccRCCs. Methods: We applied spatial point process modeling of H&E-stained images of patients with grade 2 and grade 3 ccRCCs (n = 72) to find optimum separation into two groups. Results: One group was associated with greater spatial randomness and clinical metastasis (p < 0.01). Notably, spatial analysis outperformed standard pathologic grading in predicting clinical metastasis. Moreover, cell-to-cell interaction distances in the metastasis-associated group were significantly greater than those in the other patient group and were also greater than expected by the random distribution of cells. Differential gene expression between the two spatially defined groups of patients revealed a matrisome signature, consistent with the extracellular matrix’s crucial role in tumor invasion. The top differentially expressed genes (with a fold change > 3) stratified a larger, multi-institutional cohort of 352 ccRCC patients from The Cancer Genome Atlas into groups with significant differences in survival and TNM disease stage. Conclusions: Our results suggest that the spatial distribution of ccRCC tumor cells can be extracted from H&E-stained images and that it is associated with metastasis and with extracellular matrix genes that are presumably driving these tumors’ aggressive behavior. Full article

Alterations in epigenetic modifications, like DNA methylation, in peripheral blood could serve as a useful, minimally invasive biomarker of the effects of anti-aging interventions. This study explores this potential with a water extract of the botanical Centella asiatica (CAW). Eighteen-month-old mice were treated with CAW in their drinking water for 5 weeks alongside vehicle-treated eighteen-month-old C57BL6 mice. Reduced representation bisulfite sequencing (RRBS) was used to identify genome-wide differential methylation in the blood of CAW-treated aged mice compared to vehicle-treated aged mice. Our results showed a distinct enrichment of differentially methylated regions (DMRs) nearby genes involved in biological processes relevant to aging (i.e., antioxidant response, metabolic regulation, cellular metabolism). A distinct difference was observed between males and females in both the number of methylation sites and the state of methylation. Moreover, genes nearby or overlapping DMRs were found to be enriched for biological processes related to previously described cellular effects of CAW in the mouse brain (i.e., antioxidant response, metabolic regulation, calcium regulation, and circadian rhythm). Together, our data suggest that the peripheral blood methylation signature of CAW in the blood could be a useful, and readily accessible, biomarker of CAW’s effects in aging. Full article

According to the Humane Society, 25 to 40 percent of pet dogs in the United States are adopted from animal shelters. Shelter dogs can harbor bacterial, viral, fungal, and protozoal pathogens, posing risks to canine and human health. These bacterial pathogens may also carry antibiotic resistance genes (ARGs), serving as a reservoir for antimicrobial resistance (AMR) transmission. This study aimed to utilize whole metagenome sequencing (WMS) to screen for microbial pathogens and assess the resistome in healthy shelter dogs. Fecal samples from 58 healthy shelter dogs across 10 shelters in Kentucky, Tennessee, and Virginia were analyzed using WMS. Genomic DNA was extracted, and bioinformatics analyses were performed to identify pathogens and ARGs. The WMS detected 53 potentially zoonotic or known pathogens including thirty-eight bacterial species, two protozoa, five yeast species, one nematode, four molds, and three viruses. A total of 4560 ARGs signatures representing 182 unique genes across 14 antibiotic classes were detected. Tetracycline resistance genes were most abundant (49%), while β-lactam resistance genes showed the highest diversity with 75 unique ARGs. ARGs were predominantly detected in commensal bacteria; however, nearly half (18/38, 47.4%) of known bacterial pathogens detected in this study carried ARGs for resistance to one or more antibiotic classes. This study provides evidence that healthy shelter dogs carry a diverse range of zoonotic and antibiotic-resistant pathogens, posing a transmission risk through fecal shedding. These findings highlight the value of WMS for pathogen detection and AMR surveillance, informing therapeutic and prophylactic strategies to mitigate the transmission of pathogens among shelter dog populations and the risk associated with zoonoses. Full article

Mulberry branch extracts (MBEs) have garnered significant attention as natural feed additives and antioxidants; however, their antioxidant properties in meat post-slaughter and their influence on muscle-related metabolic processes remain largely unexplored. Herein, we evaluated the effects of MBEs on the antioxidant capacity and metabolic processes of breast muscle in yellow-feather broilers by adding 0 g/kg, 1.5 g/kg, 3.0 g/kg, and 4.5 g/kg of MBEs to their diets. The results demonstrate that MBEs enhanced the activity of antioxidant enzymes in muscle tissue. Specifically, a real-time quantitative PCR analysis revealed that MBEs increased the expression of antioxidant enzyme genes in a dose-dependent manner, activated the Nrf2 signaling pathway, and upregulated the expression of the Nrf2 gene and its downstream targets at doses of up to 3.0 g/kg. Furthermore, the results of widely targeted metabolomics indicate that the dietary supplementation of MBEs changed the amino acid profile of the muscle, increasing the levels of amino acids and small peptides that contribute to antioxidant properties while reducing the contents of oxidized lipids and carnitine (C5:1) and partially reducing the content of lysophosphatidylcholine (LPC). Notably, at doses of up to 3 g/kg, the levels of five signature bile acids increased in correlation with the added dose. A KEGG analysis indicated that the differential metabolites were predominantly enriched in the metabolism of xenobiotics by cytochrome P450, suggesting that the function of MBEs may be associated with the expression of P450 enzymes. In summary, this study demonstrates that MBEs are effective, safe, and natural antioxidants, offering a viable solution to mitigating oxidative stress in the yellow-feather broiler farming industry and even in livestock farming. Full article

An accumulating number of studies suggest the potential of circulating cell-free microbial DNA (cfmDNA) as a non-invasive biomarker in various diseases, including cancers. However, its value in the prediction or prognosis of clinical outcomes of immune checkpoint inhibitors (ICIs) is poorly explored. The circulating cfmDNA pool may also reflect the translocation of various microbial ligands to the circulatory system and may be associated with the increased release of soluble CD14 (sCD14) by myeloid cells. In the present study, blood samples were collected from advanced melanoma patients (n = 66) before and during the anti-PD-1 therapy (approximately 3 and 12 months after the start). Then, V3-V4 16S rRNA gene sequencing was performed to analyze the circulating cfmDNA extracted from plasma samples. Moreover, the concentration of plasma sCD14 was measured using ELISA. As a result, the differences in the circulating cfmDNA profiles were found between patients with favorable and unfavorable clinical outcomes of the anti-PD-1 and baseline signatures correlated with progression-free survival and overall survival. Moreover, there was a higher concentration of plasma sCD14 in patients with unfavorable clinical outcomes. High baseline sCD14 level and its increase during the therapy prognosticated worse survival outcomes. Taken together, this preliminary study indicates the potential of circulating cfmDNA signatures and plasma sCD14 levels as biomarkers of clinical outcomes of ICIs. Full article

Background: Serous tubal intraepithelial carcinoma (STIC) is an early-stage cancerous lesion found in the fallopian tubes, often at the fimbrial end. It is strongly associated with high-grade serous carcinoma (HGSC), a highly aggressive type of ovarian cancer. STIC is considered a precursor to many HGSC cases, originating in the fallopian tubes. Its development is frequently linked to mutations in the TP53 gene, leading to the formation of a p53 signature, an early abnormality that may progress to HGSC. This signature is more common in BRCA mutation carriers, explaining the higher incidence of STIC in this group. The aim of this review is to evaluate the literature on the incidence of serous tubal intraepithelial carcinoma in patients (both BRCA-positive and BRCA-negative) undergoing preventive salpingo-oophorectomy, analysing the available data and identifying associations between specific characteristics and the onset of STIC. Methods: A comprehensive review of the literature from 2016 to 2023 was conducted using PubMed, focusing on studies analysing the incidence of STIC in BRCA-positive patients undergoing preventive salpingo-oophorectomy. Data on patient characteristics, interventions, outcomes, and incidence of STIC were extracted and analysed. Results: Nine international studies were included in the review, reporting varying incidences of STIC among patients undergoing salpingo-oophorectomy. The overall incidence of STIC in all the women included in the studies was 7.31%, while that in the BRCA-mutated women was approximately 6.08%. Notably, the presence of the TP53 signature was significantly associated with the occurrence of STIC. Conclusions: The etiopathogenesis of STIC involves complex interactions between genetic, environmental, and molecular factors. Further research is needed to fully understand its mechanisms and identify additional risk factors beyond BRCA mutations. Establishing a national database of STIC cases could facilitate future research and improve patient outcomes. Full article