Search Results (348)

IgA nephropathy (IgAN) is a primary glomerular disease with various clinical features, disease progression, and therapeutic responses that affects people worldwide. Currently, risk stratification depends primarily on clinical variables, together with the Oxford MEST-C classification, which indicates structural damage. However, these approaches only provide a limited explanation of the molecular mechanisms responsible for the disease. Recent proteomic discoveries have enabled researchers to characterize proteins not only in blood plasma and urine but also in kidney tissues, opening new avenues for understanding the underlying biology of IgAN. Our review addresses existing findings in plasma proteomic studies and, at the same time, brings into the picture developments in urinary and tissue proteomic profiling, thereby demonstrating that molecular profiling has been essential for further understanding of IgAN pathogenesis. Several research studies highlight complement system dysregulation, immune system overactivity, extracellular matrix remodeling, and metabolic disturbances as the leading factors linked to disease activity and progression. Even after many years in biomarker discovery, the development and clinical application of single plasma-based molecules as markers for disease detection remain challenging. Proteomic signatures based on the various processes involved in a single disease consistently outperform single protein identification in describing complexity and distinguishing molecular endotypes. We also present the current status of proteomic-based profiling methods, cross-linking proteomics with other data sources, and the remaining clinical application barriers. Through the development of this technology, proteomics has not only enabled the discovery of new biomarkers but also provided a framework for viewing IgAN as a heterogeneous disease comprising distinct molecular endotypes. Overall, current evidence indicates that proteomics is evolving from biomarker discovery toward molecular disease classification, with the potential to improve prognostication, guide mechanism-based therapeutic selection, and advance precision nephrology in IgAN. Full article

This review summarizes current data on the role of small nucleolar RNAs (snoRNAs) and their host genes (SNHGs) in the development of bladder cancer (BC). It examines snoRNA biogenesis, classical functions (rRNA modification), and non-canonical oncogenic mechanisms, including microRNA sponging, sdRNA production, and protein interactions (EZH2, DNMT3A, hnRNPK). The factors involved in the deregulation of snoRNA/SNHG expression during tumour transformation are described, such as amplifications, epigenetic changes, and transcriptional control (c-Myc, p53). Studies have shown that in BC, the majority of snoRNAs/SNHGs (SNHG1, SNHG3, SNHG6, SNHG13, SCARNA12) act as oncogenes, activating the PI3K/AKT, Wnt/β-catenin, NF-κB, and c-Myc pathways, thereby enhancing proliferation, EMT, invasion, and metastasis. Suppressor molecules (SNHG2/GAS5) are also discussed. The clinical potential of snoRNAs as prognostic signatures (SNORS), diagnostic biomarkers (SNHG1 in urine), and therapeutic targets (e.g., SNHG3) is analyzed. Thus, snoRNAs and SNHGs represent a promising class of molecules for the development of new diagnostic and therapeutic approaches for BC, although further investigation in prospective studies is required. Full article

Renal cell carcinoma (RCC) accounts for the majority of kidney cancers, with approximately 80,000 new diagnoses and over 14,000 deaths annually in the United States. Risk stratification is essential for prognostication, treatment selection, and clinical trial design across all disease stages. In localized and locally advanced RCC, pathological stage, histology, and grade remain the primary prognostic factors, while the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria serve as the standard risk stratification tool in the metastatic setting. However, current models rely predominantly on clinical and pathologic variables that act as indirect surrogates of tumor biology and do not account for the molecular heterogeneity inherent to RCC. This narrative review synthesizes and compares established and emerging risk stratification and prognostic models across all stages of RCC. Established models such as the IMDC criteria and the stage, size, grade, and necrosis (SSIGN) score demonstrate robust prognostic performance but are limited by their reliance on clinical and pathologic variables alone. Emerging biomarkers—including circulating tumor DNA, methylated DNA, artificial intelligence-based radiomics, and tissue-based molecular signatures—show promise for improving risk discrimination. The molecular heterogeneity of RCC underscores an urgent need for integrated molecular–clinical–pathologic prognostic tools tailored to specific histologic subtypes to enable more precise, individualized care. Full article

Melanoma incidence rates have also been steadily increasing, emphasizing the need for improved prognostic and diagnostic tools with the goal of enhancing patients’ outcomes. Biomarkers in melanoma have emerged as an important component of melanoma management, offering insight into disease progression, tumor biology, and the potential for judging treatment responses. Traditionally, blood and immunohistochemical markers such as lactate dehydrogenase (LDH), S100 calcium-binding protein (S100B), human melanoma black-45 (HMB-45), and SRY-box transcription factor 10 (SOX10) have been widely used in melanoma diagnosis, staging, and monitoring. However, their clinical use has been limited because of their low specificity, especially in patients with early-stage disease. This has led to the development of molecular and genetic biomarkers, including BRAF, NRAS, and KIT mutations, which improved patients’ risk stratification and enabled targeted therapies, and gene expression signature assays such as DecisionDx (Castle Biosciences) and SkylineDx (Merlin) that are already used in clinics to help with surgical decisions and to assess patients’ prognosis. Other circulating biomarkers, including microRNAs, circulating tumor DNA and circulating tumor cells, have been developed to provide minimally invasive approaches to monitor tumor evolution and detect recurrence. However, none of these new approaches are used in clinics due to their low specificity and/or sensitivity. Additionally, nomograms or predictive models have been created using biomarkers and clinicopathologic data to assess patients’ outcomes and survival. While significant progress has been made, the integration of melanoma biomarkers into routine clinical practice remains limited. This review summarizes current advancements in melanoma biomarkers, including traditional serum and immunohistochemical markers, as well as developments in molecular, genetic, circulating, and predictive biomarker approaches. Full article

Neuroinflammation in multiple sclerosis (MS) is driven by the infiltration of myelin-reactive T cells into the central nervous system (CNS). Interferon-β (IFN-β) is one of the earliest disease-modifying treatments (DMTs) approved for MS and remains widely used in special populations (pregnant and elderly patients) owing to its favorable safety profile. However, the exact mechanism of action of this drug and reliable biomarkers of treatment response remain unclear. Transcriptomic profiling and data integration approaches offer powerful tools for investigating complex patterns of regulation and molecular mechanisms underlying therapeutic efficacy. In this study, we performed an integrative analysis of openly available transcriptomic datasets to characterize IFN-β-induced gene expression changes in MS patients. By combining data from large independent cohorts, we identified a 43-gene transcriptional signature consistently associated with IFN-β treatment across disease stages, including progressive MS. To explore the relevance of this signature, we cross-referenced the 43-gene signature with publicly available expression quantitative trait loci (eQTL) datasets to determine whether these genes could be influenced by known MS-associated risk variants highlighting Interferon-Induced Transmembrane Protein 3 (IFITM3) as a candidate molecular mediator of MS. This integrative approach provides new insights into IFN-β-driven immune modulation and supports the development of therapeutic strategies for MS. Full article

Background: Severe malaria remains a major cause of morbidity and mortality, yet the conserved molecular signatures underlying complicated infections across Plasmodium vivax (P. vivax) and Plasmodium falciparum (P. falciparum) are not well characterized. Identifying shared transcriptional biomarkers and host–parasite interaction networks is crucial for improving diagnosis and discovering new therapeutic targets. Methods: Public transcriptomic datasets (GSE55644, GSE59844, GSE34404) were analyzed using GEO2R to identify differentially expressed genes (DEGs). Volcano plots, Venn diagrams, and KEGG mapping were used to identify conserved DEGs. Principal Component Analysis (PCA) and Support Vector Machine (SVM) models were used to assess predictive performance. Host–parasite cross-species correlation analysis integrated parasite DEGs with host hub-genes. Functional enrichment and network module analysis were performed using Cytoscape v3.10.2 and GO/KEGG annotation tools. Results: A total of 3363 DEGs were identified in P. vivax (GSE55644) and only one DEG in P. falciparum (GSE59844) using adjusted p-values, though 772 DEGs emerged with unadjusted p-values. Cross-dataset comparison revealed 18 common DEGs, with eight upregulated genes—TIM9, NUF2, SRP68, HDAC1, GRP94, DHHC8, PPM9, and RPL27—showing robust predictive performance (AUC = 1.000; CA = 1.000) for distinguishing complicated from uncomplicated malaria in both species. Host analysis identified 1719 DEGs and six hub-genes (TNF, IL6, TLR4, CR1, CD40LG, ICAM1) linked to apoptosis, Toll-like receptor signaling, complement cascades, and cell adhesion. SVM validation predicted parasitemia levels with 75.5–84.0% accuracy. Cross-species correlation revealed strong positive interactions between parasite HDAC1/GRP94 and host IL6/TNF and negative correlations involving NUF2, TIM9, ICAM1, and CR1. Functional enrichment analysis highlighted ER stress, immune activation, and erythrocyte adhesion pathways, which together form three major host–parasite modules. Conclusion: These findings highlight conserved biomarkers and potential therapeutic candidates for future validation, demonstrating that combined DEG profiling and machine-learning approaches can provide a powerful framework for improving diagnostics and intervention strategies for severe malaria. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy. The identification and management of precursor lesions, particularly the increasingly common intraductal papillary mucinous neoplasms (IPMNs), pose a significant challenge, creating a profound clinical dilemma between intercepting pancreatic ductal adenocarcinoma and avoiding surgical overtreatment. This literature review aims to synthesize the latest evidence to facilitate a transition from purely morphology-based surveillance toward a biologically informed risk stratification paradigm. This approach could provide a personalized risk-stratification algorithm that optimizes therapeutic management and enables timely intervention for pancreatic cancer. By using PubMed, Embase, Scopus, and Web of Science, we analyzed and summarized key findings from recent literature (2020–2025), including cohort studies, mechanistic analyses, evidence-based guidelines, and systematic reviews on cyst fluid biomarkers (CEA panels, DNA/RNA sequencing), and emerging AI applications. Prospective and multicenter studies consistently report that NOD is independently associated with high-risk stigmata, cyst progression, and malignant transformation. Mechanistic research suggests a bidirectional interplay between the evolving neoplasia and pancreatic endocrine dysfunction. Updated guidelines underscore the need for more precise diagnostic algorithms. Recent work demonstrates that advanced cyst fluid markers—CEA panels, DNA/RNA sequencing, and multi-omic signatures—significantly improve diagnostic accuracy. Furthermore, explainable AI models show encouraging performance in predicting malignancy and assisting patient triage. Risk stratification in PCLs is shifting from morphology-based assessment toward integrated, multimodal approaches combining clinical, endocrine, imaging, molecular, and computational data. Recent evidence positions new-onset diabetes as a clinically accessible and biologically plausible marker of high-risk IPMNs. Similarly, molecular assays and AI-enhanced analytics provide an additional layer of diagnostic precision. The development of personalized risk prediction algorithms could improve early detection of malignancy while reducing unnecessary surgical resections. Full article

Background: Hearing loss is a widespread sensory disorder affecting over 1.5 billion people worldwide, with the number projected to exceed 700 million by 2050. It imposes social and economic burdens across all ages and regions. Approximately half of adult cases are preventable, but the underlying causes are complex, with 75–80% due to autosomal recessive genetic factors and key roles for mutations in genes such as GJB2. Advances in sequencing technologies have accelerated gene discovery, but challenges remain in interpreting variants. Epigenetic mechanisms such as DNA methylation and histone modifications are increasingly recognized as crucial in auditory biology and could offer new biomarkers and therapeutic targets. Integrating epidemiological, genetic, and epigenomic data is essential to developing targeted prevention and treatment strategies to reduce the global burden of hearing loss. Methods: This narrative review examines recent genomic and epigenomic advances in hearing loss, with particular emphasis on molecular mechanisms, emerging diagnostic applications, and translational therapeutic opportunities. A comprehensive review of current epidemiological data, genetic studies, and epigenomic research was conducted using the peer-reviewed literature from international databases. Key areas of interest include inheritance patterns, molecular pathways, and recent advances in omics technologies. Results: Epigenetic mechanisms, including DNA methylation and histone modifications, are increasingly recognized as important regulators of cochlear development and hair cell survival, although much of the current evidence remains preclinical. Studies suggest that peripheral epigenetic signatures may serve as biomarkers for early diagnosis and risk stratification. Conclusions: Integrating established screening pathways with epidemiological trends and molecular knowledge offers a promising path toward precision medicine in hearing care. Connecting these domains is essential to developing equitable and effective interventions and addressing persistent global disparities in hearing health. This review highlights the evolving landscape of auditory genetics and epigenetics and outlines future directions for translational research and personalized therapy. Full article

Immune checkpoint receptors (ICRs) play a pivotal role in modulating antitumor immunity and have become central targets in the immunotherapy of genitourinary (GU) malignancies. This review provides a comprehensive overview of the fundamental mechanisms of ICR signaling, the expression and pathophysiological roles of these receptors in GU cancers (kidney, bladder, prostate, testicular, and penile), and the evolving therapeutic landscape. Key ICRs, including PD-1, CTLA-4, LAG-3, TIM-3, and TIGIT, orchestrate complex signaling cascades that can lead to T-cell exhaustion and tumor immune evasion. Their expression varies significantly across GU cancer types, histological subtypes, and tumor stages, influencing prognosis and therapeutic response. Immune checkpoint inhibitors (ICIs) reinvigorate antitumor immunity by disrupting these inhibitory pathways and remodeling the tumor microenvironment (TME); however, resistance mechanisms (primary, adaptive, and acquired) and immune-related adverse events (irAEs) pose significant clinical challenges. Established biomarkers such as PD-L1 expression, tumor mutational burden (TMB), and microsatellite instability (MSI)/deficient mismatch repair (dMMR) status guide ICI use, but their predictive power has limitations. Consequently, emerging tissue-based (e.g., immune cell signatures, multiplex IHC/IF, spatial transcriptomics), liquid biopsy-based (e.g., ctDNA, CTCs, exosomes), and imaging-based (radiomics, AI-driven analysis) biomarkers are under active investigation to refine patient selection and monitor treatment efficacy. The therapeutic armamentarium is rapidly expanding with novel ICIs targeting new receptors, bispecific antibodies, and innovative combination strategies involving ICIs with chemotherapy, targeted therapies, radiotherapy, and other immunotherapies. Furthermore, ICIs are increasingly explored in neoadjuvant, adjuvant, and maintenance settings. This review highlights the dynamic progress in understanding ICR biology and its clinical translation, emphasizing the ongoing efforts to develop more personalized and effective immunotherapeutic strategies for patients with genitourinary tumors. Full article

In the human reproductive system, extracellular vesicles (EVs) have been recognized as playing a vital role in mediating cell–cell communication. They are considered critical for embryo development, implantation, gamete interaction, and fertilization. The various cargoes carried by EVs, depending on the physiological and pathological state of the cell, include proteins, lipids, nucleic acids, and mitochondrial components. EVs are recognized as critical carriers of redox-related signals and mitochondrial components, linking oxidative stress (OS) to reproductive failure and influencing gamete quality and embryo competence. Although considerable progress has been made, research remains poorly integrated, despite individual omics technologies providing valuable molecular insights. The use of multi-omics technologies, including transcriptomics, proteomics, metabolomics, and microbiome analysis, has been proposed as a global approach to understanding the complexities associated with EVs and discovering new biomarkers associated with infertility. ML and AI have been proposed to identify predictive signatures linked to ART effectiveness and reproductive outcomes, with a strong capacity to handle high-dimensional data. The review aims to provide an overview of current knowledge on EV-mediated redox–mitochondrial signaling in human reproduction, while highlighting the importance of emerging multi-omics and AI technologies for EV-mediated biomarker development. The review discusses the promise of EVs in the development of minimally invasive diagnostic approaches and therapeutic interventions, as well as the challenges in the standardization, integration, and clinical translation of EV-mediated research. In addition, the review proposes integrating computational approaches to better understand molecular pathways involved in the development of next-generation precision medicine in human reproduction. Full article

Despite major advances in early breast cancer detection and therapeutic strategies, locoregional and distant recurrences, as well as the development of a second primary breast cancer, remain major clinical challenges. Current prognostic tools primarily rely on tumor-specific features, such as the histological grade, hormone receptor status, and proliferative index, and, more recently, on molecular signatures aimed at improving risk stratification and predicting recurrence. However, these approaches remain imperfect, and there is an urgent need to develop complementary strategies. Growing attention has been focused on the tumor microenvironment and the surrounding non-tumoral tissue, which may harbor clinically relevant molecular alterations. Emerging evidence suggests that DNA methylation changes can be detected in the adjacent and contralateral breast tissue and reflect early steps of carcinogenesis or predisposition to tumor development. This phenomenon, often referred to as field cancerization, raises new questions about the dynamics of cancer development. The aim of this work is to provide an integrative overview of DNA methylation alterations in normal breast tissue, including peritumoral and contralateral areas, and to examine their potential as predictive biomarkers of recurrence, based on the available data from tumoral tissue. In theory, these applications seem promising, but their role needs to be confirmed in large prospective trials, in order to overcome barriers to clinical implementation. The currently available evidence does not support a role for DNA methylation in the selection of locoregional and systemic treatment strategies, particularly with a view to reducing the rising number of uni- and bilateral mastectomies performed without any demonstrated survival benefit. Full article

Changes in the maternal serum cytokine landscape occur throughout pregnancy, representing immune adaptations and modifications to support a healthy pregnancy. A better understanding of normal cytokine patterns can help improve the management of pregnancy and potentially predict complications. Using maternal serum samples collected at four different timepoints from 29 subjects, we characterized the longitudinal serum cytokine patterns throughout normal pregnancy with a multiplex ELISA of 96 cytokines. Based on unsupervised principal component analyses of the 96-cytokine data, we developed an integrated panel that incorporated the data of 21 key cytokines. This integrated immune signature allowed us to distinguish serum samples collected at different stages of healthy pregnancy and evaluate their chemotactic properties in vitro. We also evaluated the potential of using integrated cytokine scores for identifying pathological condition like preeclampsia before clinical signs are presented. This study explored new approaches of developing serum biomarker panels for immune profiling and early detection of pathological conditions during pregnancy. Full article

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy characterized by profound molecular heterogeneity and high relapse rates, posing significant clinical challenges. Programmed cell death (PCD), encompassing diverse regulated modalities such as apoptosis, necroptosis, and ferroptosis, plays a key role in leukemogenesis and therapeutic response; however, a comprehensive prognostic framework integrating multi-modal PCD pathways in AML remains elusive. In this study, we performed a systematic transcriptomic analysis of 1624 genes associated with 13 distinct PCD forms. A novel computational pipeline combining a variational autoencoder (VAE) for dimensionality reduction and a multilayer perceptron (MLP) for classification was employed to identify robust PCD-related biomarkers, interpreted via SHapley Additive exPlanations (SHAP) analysis. This approach identified 48 candidate genes with discriminative potential between AML and normal bone marrow. Unsupervised consensus clustering based on these genes delineated two molecular subtypes exhibiting divergent clinical outcomes and immune microenvironment profiles. The subtype demonstrated an immunosuppressive phenotype, characterized by enriched regulatory T cells, M2 macrophages, and elevated expression of inhibitory immune checkpoints, correlating with inferior survival. We developed an 8-gene prognostic signature (SORL1, PIK3R5, RIPK3, ELANE, GPX1, VNN1, CD74, and IL3RA) that effectively categorized patients into high- and low-risk groups with notable survival differences, validated across independent cohorts. A prognostic nomogram combining the risk score, age, and cytogenetic risk enhanced the prediction accuracy for overall survival. Our study presents an integrative model that connects multi-modal PCD pathways to AML prognosis, offering a new molecular subtyping system and a clinically applicable risk assessment tool for improved prognostication and personalized treatment strategies. Full article

To better understand the complexity of biological systems, research has shifted from a reductionist to a holistic approach, expanding the focus from single genes to a genome-scale view of gene activity and regulation. This is known as transcriptomics, a continuously growing field generating gene expression signatures from different technologies. A comparable paradigm shift has occurred in computational systems biology with the implementation of Artificial Intelligence (AI) learning models for gene expression analysis and integration. These models enable transcriptome-based profiling to address challenges of data heterogeneity, integration, and updating, assisting human intelligence and enhancing their ability to retrieve, analyze, integrate, and generate data recursively, thanks to their intrinsic predictive, inferential, reinforcement, and generative capabilities. Additionally, while scientists worldwide are still learning how to leverage AI methods that can maintain the human-in-the-loop, a new fundamental change is emerging: agentic AI, which can autonomously act and employ other AI methods to pursue its objectives. As a futuristic perspective, the proposed data analysis pipeline imagines agentic AI systems allowing the automated retrieval and pre-processing of heterogeneous transcriptomics data, analysis and integration with other omics datasets, performed with an incremental updating and recurrent analysis (IURA) model that could allow the detection of guideline updates (e.g., disease reclassification) and the generation of new hypotheses, such as candidate biomarkers or transcriptome–phenotype correlations. Since personalized medicine could derive profound benefits from its use, this scenario also raises important considerations regarding the advantages and concerns associated with the use of scientific AI agents in research and clinical practice. Full article

Oxidative stress is a hallmark of carcinogenesis, yet the epigenetic mechanisms linking the lifestyle-based Oxidative Balance Score (OBS) to cancer risk remain poorly understood. This study investigated the epigenetic bridge between OBS and pan-cancer susceptibility using a multi-cohort approach integrating population-based and cancer genomic data. We calculated OBS based on 16 dietary and lifestyle factors (including dietary fiber, vitamins, minerals, physical activity, smoking, alcohol, and BMI) for 2749 participants from the Korean Genome and Epidemiology Study (KoGES) and identified OBS-associated CpG sites via epigenome-wide association analysis. These markers were validated against The Cancer Genome Atlas (TCGA) pan-cancer dataset using a novel Hybrid Pi-score (HyPi) to quantify the directional consistency between OBS-driven methylation in healthy individuals and cancer-specific epigenetic alterations across three clinical comparisons: normal vs. tumor, survival outcomes, and tumor stage. We observed profound sex-specific epigenetic signatures, with zero overlap in the top 200 OBS-associated CpG sites between males and females, underscoring fundamental sexual dimorphism in oxidative stress-epigenome interactions. Notably, the top 20 OBS-associated CpGs demonstrated strong directional consistency with multiple cancer types in TCGA, particularly in kidney renal clear cell carcinoma and lung adenocarcinoma, exhibiting methylation patterns inversely correlated with tumorigenesis. Mechanistically, these findings support the role of one-carbon metabolism and vitamin C-dependent DNA demethylation pathways in mediating OBS effects. Our study provides the first evidence of an epigenetic link between lifestyle-based oxidative balance and pan-cancer risk, highlighting the utility of the HyPi score as a novel sex-specific predictive biomarker for cancer prevention. These results suggest that optimizing oxidative balance through precision nutrition may epigenetically modulate cancer susceptibility, opening new avenues for personalized prevention strategies. Full article