Search Results (571)

Posterior capsule opacification (PCO), a frequent complication of cataract surgery, arises from dysregulated wound healing and fibrotic transformation of residual lens epithelial cells. While transcriptomic and machine learning (ML) approaches have elucidated fibrosis-related pathways in other tissues, the molecular divergence between regenerative and fibrotic outcomes in the lens remains unclear. Here, we used an ex vivo chick lens injury model to simulate post-surgical conditions, collecting RNA from lenses undergoing either regenerative wound healing or fibrosis between days 1–3 post-injury. Bulk RNA sequencing data were normalized, log-transformed, and subjected to univariate filtering prior to training LASSO, SVM, and RF ML models to identify discriminatory gene signatures. Each model was independently validated using a held-out test set. Distinct gene sets were identified, including fibrosis-associated genes (VGLL3, CEBPD, MXRA7, LMNA, gga-miR-143, RF00072) and wound-healing-associated genes (HS3ST2, ID1), with several achieving perfect classification. Gene Set Enrichment Analysis revealed divergent pathway activation, including extracellular matrix remodeling, DNA replication, and spliceosome associated with fibrosis. RT-PCR in independent explants confirmed key differential expression levels. These findings demonstrate the utility of supervised ML for discovering lens-specific fibrotic and regenerative gene features and nominate biomarkers for targeted intervention to mitigate PCO. Full article

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

Background/Objectives: Alterations in DNA damage repair mechanisms can impair the therapeutic effectiveness of cisplatin. MicroRNAs (miRNAs), key regulators of DNA damage repair processes, have been proposed as promising biomarkers for predicting the response to platinum-based chemotherapy (CT) in non-small cell lung cancer (NSCLC). In this study, by using a bioinformatics approach, we identified six miRNAs, which were differentially expressed (DE) between NSCLC patients characterized as responders and non-responders to platinum-based CT. We further validated the differential expression of the selected miRNAs on tumor and matched normal tissues from patients with resected NSCLC. Methods: Two miRNA microarray expression datasets were retrieved from the Gene Expression Omnibus (GEO) repository, comprising a total of 69 NSCLC patients (N = 69) treated with CT and annotated data from their response to treatment. Differential expression analysis was performed using the Linear Models for Microarray Analysis (Limma) package in R to identify DE miRNAs between responders (N = 33) and non-responders (N = 36). Quantitative real-time PCR (qRT-PCR) was used to assess miRNA expression levels in clinical tissue samples (N = 20). Results: Analysis with the Limma package revealed 112 DE miRNAs between responders and non-responders. A random-effects meta-analysis further identified 24 miRNAs that were consistently up- or downregulated in at least two studies. Survival analysis using the Kaplan–Meier plotter (KM plotter) indicated that 22 of these miRNAs showed significant associations with prognosis in NSCLC. Functional and pathway enrichment analysis revealed that several of the identified miRNAs were linked to key pathways implicated in DNA damage repair, including the p53, Hippo, PI3K and TGF-β signaling pathways. We finally distinguished a six-miRNA signature consisting of miR-26a, miR-29c, miR-34a, miR-30e-5p, miR-30e-3p and miR-497, which were downregulated in non-responders and are involved in at least three DNA damage repair pathways. Comparative expression analysis on tumor and matched normal tissues from surgically treated NSCLC patients confirmed their differential expression in clinical samples. Conclusions: In summary, we identified a signature of six miRNAs that are suppressed in NSCLC and may serve as a predictor of cisplatin response in NSCLC. Full article

Exosomes are nanoscale extracellular vesicles (EVs) that carry biomolecular signatures reflective of their parent cells, making them powerful tools for non-invasive diagnostics and therapeutic monitoring. Despite their potential, clinical application is hindered by challenges such as low abundance, heterogeneity, and the complexity of biological samples. To address these limitations, plasmonic biosensing technologies—particularly propagating surface plasmon resonance (PSPR), localized surface plasmon resonance (LSPR), and surface-enhanced Raman scattering (SERS)—have been developed to enable label-free, highly sensitive, and multiplexed detection at the single-vesicle level. This review outlines recent advancements in nanoplasmonic platforms for exosome detection and profiling, emphasizing innovations in nanostructure engineering, microfluidic integration, and signal enhancement. Representative applications in oncology, neurology, and immunology are discussed, along with the increasingly critical role of artificial intelligence (AI) in spectral interpretation and diagnostic classification. Key technical and translational challenges—such as assay standardization, substrate reproducibility, and clinical validation—are also addressed. Overall, this review highlights the synergy between exosome biology and plasmonic nanotechnology, offering a path toward real-time, precision diagnostics via sub-femtomolar detection of exosomal miRNAs through next-generation biosensing strategies. Full article

Background: Breast cancer (BC) is highly heterogeneous, with varying molecular characteristics, such as reliance on autophagy. Autophagy is a critical cellular degradation process that helps cells survive under stress, but its regulation can be influenced by altered microRNA (miRNA) expression. Studying miRNA changes during starvation-induced autophagy in both mammary epithelial cells and BC cells could reveal potential molecular therapy targets. Methods: Rat mammary gland healthy epithelial and cancer cells were subjected to starvation, and differences in proliferation, migration, invasion, autophagy, and expression of autophagy-associated miRNAs were determined. Afterward, we assessed the effects of miR-218-5p modulation on the aforementioned processes. Results: Starvation-induced autophagy reduced the proliferation of all cells and increased the invasive and migratory capacity of cancer cells (p ≤ 0.05). We identified a miRNA signature related to starvation, comprising twenty-seven miRNAs. One miRNA had a significantly elevated baseline expression, while another six, including miR-218-5p, had a significantly lower basal expression in cancer cells compared to healthy cells (p ≤ 0.05). However, starvation caused significant miRNA expression changes, with miR-218-5p being upregulated specifically in cancer cells (p = 0.20–0.01). Functional studies on the role of miR-218-5p show that its inhibition decreases migration and leads to autophagosome accumulation. The study of miR-218-5p molecular targets has shown that its inhibition of sorting nexin 18 (SNX18) may act as an important regulator of the starvation-induced response in cancer cells. Conclusions: The baseline expression of miRNA related to starvation and autophagy differs between rat mammary gland cancer and healthy cells. The response to starvation also varies between cancer cells and normal cells. Starvation induces BC-specific miRNA dysregulation, affecting particularly miR-218-5p, which acts via SNX18, promoting the cancer cells’ survival. Full article

Background and Objectives: Nicotine is the most well-studied toxic substance in cigarette smoke and e-cigarette vape. However, smoke and vape are composed of other components that have a negative impact on health. The objective of this study is to investigate whether nicotine has a distinctive impact on molecular mechanisms in stem cells. Methods: The cellular impact of nicotine on the regenerative capacity of human dental pulp stem cells (DPSCs) and the microRNA (miRNA) profile was examined. Bioinformatic analysis was performed to identify miRNA-regulated cellular pathways associated with nicotine exposure. These pathways were then compared to those induced by cigarette smoke condensate (CSC). Results: Prolonged exposure to nicotine significantly impaired the regeneration of DPSCs and changed the expression of miRNAs. Nicotine upregulated the expression of hsa-miR-7977, hsa-miR-3178, and hsa-miR-10400-5p compared to vehicle control. Interestingly, nicotine did not change the expression of hsa-miR-29b-3p, hsa-miR-199b-5p, hsa-miR-26b-5p, or hsa-miR-26a-5p compared to the control. However, the expressions of these miRNAs were significantly altered when compared to CSC treatment. Further analysis revealed that nicotine was distinctively associated with certain miRNA-targeted pathways including apoptosis, ErbB, MAPK signaling, PI3K-Akt, TGF-b signaling, and Wnt signaling. Conclusions: Our work provides evidence on the distinctive miRNA signature induced by nicotine. The information will be important for identifying the unique molecular pathways downstream of nicotine from smoking and vaping in different individuals, providing a new direction for personalized disease prevention, prognosis, and treatment. Full article

Background: Endometriosis is a chronic gynecological condition marked by ectopic endometrial-like tissue, leading to inflammation, pain, and infertility. Diagnosis is often delayed by up to 10 years. Identifying non-invasive biomarkers could facilitate earlier detection. MicroRNAs, known for their stability in biological fluids and role in disease processes, have emerged as potential diagnostic tools. This pilot study investigated whether serum miRNA profiling can differentiate endometriosis from other causes of chronic pelvic pain. Methods: Serum samples from 52 patients (36 with laparoscopically confirmed endometriosis and 16 controls) treated for chronic pelvic pain at a University Endometriosis Centre were analyzed. High-throughput miRNA sequencing was performed. Feature selection reduced 4285 miRNAs to the 20 most informative MiRNAs. Machine learning models, including logistic regression, decision tree, random forest, and support vector machine, were trained and evaluated. Results: Among the tested machine learning models, support vector machine achieved the best overall performance (accuracy 0.71, precision 0.80), while logistic regression and random forest showed the highest AUC values (0.84 and 0.81, respectively), indicating strong diagnostic potential of serum miRNA profiling. Conclusions: This study demonstrates the feasibility of using serum miRNA profiling combined with machine learning for the non-invasive classification of endometriosis. The identified miRNA signature shows strong diagnostic potential and could contribute to earlier and more accurate detection of the disease. Full article

Background/Objectives: We examined the in vivo effects of successive treatments with the histone deacetylase (HDAC) inhibitor romidepsin in patients with cutaneous T-cell lymphoma (CTCL), using changes in gene expression in peripheral blood mononuclear cells (PBMCs). Methods: Exploiting data from a highly responsive CTCL patient through 12 months of treatment, we identified a malignant cell predictor (MCP), a gene signature associated with the diminishing numbers of circulating malignant cells. Results: The MCP was successfully validated in the patient’s relapse sample 9 months after treatment was terminated and via an independent set of CTCL patient samples. Conclusions: The MCP set of genes contained novel CTCL markers, including membrane-associated proteins not normally expressed in lymphocytes. A subclass of those markers was also detectable in residual malignant cells undetected by flow cytometry in remission samples from a patient who relapsed 10 months later. We identified a subset of transcriptional regulators, miRNAs and methylation patterns associated with the effect of progressive treatments revealing potential mechanisms of transcriptional dysregulation and functional effects in the malignant cells. We demonstrate a role for transcriptional activator HLF, over-expressed in malignant cells, and downregulated transcriptional-suppressor and immune-modulator NFIL3, as regulators of CTCL-specific genes. Full article

Background: To improve the prognosis of patients with lung adenocarcinoma (LUAD), revolutionary treatments for metastatic lesions are essential. Methods: To identify genes closely involved in LUAD-cell-derived metastasis, we used RNA sequencing to generate microRNA (miRNA) expression signatures of brain metastatic lesions. Once tumor-suppressive miRNAs are identified, it will be possible to explore the numerous tumor-promoting genes that are regulated by miRNAs. Results: By comparison with a previously created LUAD signature, we identified several miRNAs whose expression was significantly suppressed in brain metastases. We focused on both strands of pre-miR-195 (miR-195-5p and miR-195-3p), which were significantly downregulated in brain metastatic tissues, and confirmed by ectopic expression assays that both strands of pre-miR-195 attenuated the aggressive phenotypes (cell proliferation, migration, and invasion) of LUAD cells. These data suggest that both strands of pre-miR-195 have tumor-suppressive functions in LUAD cells. Next, we explored the target molecules that each miRNA strand regulates in LUAD cells. We identified 159 target genes regulated by miR-195-5p and miR-195-3p, of which 12 genes (ANLN, CDC6, CDCA2, CDK1, CEP55, CHEK1, CLSPN, GINS1, KIF23, MAD2L1, OIP5, and TIMELESS) affect cell cycle/cell division and the prognosis of LUAD patients. Finally, we focused on two genes, ANLN (miR-195-5p target) and MAD2L1 (miR-195-3p target), and demonstrated their oncogenic functions and the molecular pathways they regulate in LUAD cells. Conclusions: The miRNA signature derived from lung cancer brain metastasis will be a landmark in the field, and analysis of this miRNA signature will accelerate the identification of genes involved in lung cancer brain metastasis. Full article

Asthma places a significant burden at individual and societal levels, but there remains no gold-standard objective test for asthma diagnosis or asthma severity risk prediction. MicroRNAs (miRNAs) are short non-coding RNA sequences that are attracting interest as biological signatures of health and disease status. We sought to construct serum miRNA panels that could serve as potential biomarkers to aid in the diagnosis of asthma and predict asthma severity. Thirty-five asthma-related miRNAs were screened in the serum of three patient groups (never-asthma, mild-asthma, and severe-asthma; n = 50/group) drawn from two well-characterised cohorts. miRCURY LNA technology was used, followed by GeneGlobe analysis. The associations of miRNA expression with clinical outcomes of interest and diagnostic value of the proposed miRNA panels were assessed. We identified an asthma diagnosis panel comprising upregulated miR-223-3p, miR-191-5p, and miR-197-3p (area under curve (AUC) = 0.813, sensitivity 76% and specificity 72%). Compared with mild-asthma individuals, we also identified an asthma severity risk panel comprising upregulated miR-223-3p plus downregulated miR-30a-5p, miR-660-5p, and miR-125b-5p (AUC = 0.759, sensitivity 78%, specificity 64%). Individual miRNAs showed associations with worse clinical asthma severity and impaired quality of life. miRNA panels with high sensitivity and specificity offer potential as biomarkers for asthma diagnosis and asthma severity. Full article

Diabetic kidney disease (DKD) remains the leading cause of end-stage kidney disease (ESKD) globally. Despite advances in our understanding of its pathophysiology, current therapies are often insufficient to stop its progression. In recent years, microRNAs (miRNAs)—small, non-coding RNA molecules involved in post-transcriptional gene regulation—have emerged as critical modulators of key pathogenic mechanisms in DKD, including fibrosis, inflammation, oxidative stress, and apoptosis. Numerous studies have identified specific miRNAs that either exacerbate or mitigate renal injury in DKD. Among them, miR-21, miR-192, miR-155, and miR-34a are associated with disease progression, while miR-126-3p, miR-29, miR-146a, and miR-215 demonstrate protective effects. These molecules are also detectable in plasma, urine, and renal tissue, making them attractive candidates for diagnostic and prognostic biomarkers. Advances in therapeutic technologies such as antagomiRs, mimics, locked nucleic acids, and nanoparticle-based delivery systems have opened new possibilities for targeting miRNAs in DKD. Additionally, conventional drugs, including SGLT2 inhibitors, metformin, and GLP-1 receptor agonists, as well as dietary compounds like polyphenols and sulforaphane, may exert nephroprotective effects by modulating miRNA expression. Recent evidence also highlights the role of gut microbiota in regulating miRNA activity, linking metabolic and immune pathways relevant to DKD progression. Further research is needed to define stage-specific miRNA signatures, improve delivery systems, and develop personalized therapeutic approaches. Modulation of miRNA expression represents a promising strategy to slow DKD progression and improve patient outcomes. Full article

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types due to its late diagnosis, low survival rates, and high frequency of metastasis. Considering the molecular mechanism of PDAC development has not been fully elucidated, this study aimed to shed more light on the molecular regulatory signatures of circular RNAs (circRNAs) in PDAC progression and provide a different perspective to identify potential biomarkers as well as discover candidate repositioned drug molecules for the prevention or treatment of PDAC with network-based integrative analysis. The mRNA, miRNA, and circRNA expression profiles of PDAC were obtained from nine microarray datasets. Differentially expressed genes (DEGs), microRNAs (DEmiRNAs), and circular RNAs (DEcircRNAs) were identified. The competing endogenous RNA (ceRNA; DEG–DEmiRNA–DEcircRNA) regulatory network was constructed, which included 12 DEcircRNAs, 64 DEGs, and 6 miRNAs specific to PDAC. The ADAM12, MET, QKI, SEC23A, and ZEB2 were identified as hub genes and demonstrated significant survival probability for PDAC. In addition to providing novel biomarkers for diagnosis that can be detected non-invasively, the secretion levels of hub genes-associated proteins were found in plasma, serum, and oral epithelium. The drug repositioning analysis revealed vorinostat, meclocycline sulfosalicylate, and trichostatin A, which exhibited significant binding affinities to the hub genes compared to their inhibitors via molecular docking analysis. Full article

Endothelial inflammation and atherosclerosis remain leading drivers of cardiovascular disease, yet the post-transcriptional regulators orchestrating these events are not yet completely understood. In this review, we analyse recent preclinical and clinical studies to dissect microRNA (miRNA)-mediated control of vascular endothelial biology. We describe how miR-181b-5p and miR-223 modulate NLRP3 inflammasome activation and pyroptosis, how miR-615-5p, miR-138, and miR-133a coordinate endothelial nitric oxide synthase (eNOS) activity and nitric oxide bioavailability, and how miR-33a/b, miR-150, and miR-342-3p influence lipid efflux and foam-cell formation in atherogenesis. We also discuss miRNA signatures that correlate with endothelial dysfunction in human cohorts. By integrating mechanistic pathways with emerging biomarker data, this study underscores the relevance of miRNAs as both diagnostic and potential targets in vascular diseases. Full article

Vaccination with ChAdOx1 nCoV-19 is an important countermeasure to fight the COVID-19 pandemic. This vaccine enhances human immunoprotection against SARS-CoV-2 by inducing an immune response against the SARS-CoV-2 S protein. However, the immune-related genes induced by vaccination remain to be identified. This study employs feature ranking algorithms, an incremental feature selection method, and classification algorithms to analyze transcriptomic data from an experimental group vaccinated with the ChAdOx1 nCoV-19 vaccine and a control group vaccinated with the MenACWY meningococcal vaccine. According to different time points, vaccination status, and SARS-CoV-2 infection status, the transcriptomic data was divided into five groups, including a pre-vaccination group, ChAdOx1-onset group, MenACWY-onset group, ChAdOx1-7D group, and MenACWY-7D group. Each group contained samples with 13,383 RNA features and 1662 small RNA features. The results identified key genes that could indicate the efficacy of the ChAdOx1 nCoV-19 vaccine, and a classifier was developed to classify samples into the above groups. Additionally, effective classification rules were established to distinguish between different vaccination statuses. It was found that subjects vaccinated with ChAdOx1 nCoV-19 vaccine and infected with SARS-CoV-2 were characterized by up-regulation of HIST1H3G expression and down-regulation of CASP10 expression. In addition, IGHG1, FOXM1, and CASP10 genes were strongly associated with ChAdOx1 nCoV-19 vaccine efficacy. Compared with previous omics-driven studies, the machine learning algorithms used in this study were able to analyze transcriptome data faster and more comprehensively to identify potential markers associated with vaccine effect and investigate ChAdOx1 nCoV-19 vaccine-induced gene expression changes. These observations contribute to an understanding of the immune protection and inflammatory responses induced by the ChAdOx1 nCoV-19 vaccine during symptomatic episodes and provide a rationale for improving vaccine efficacy. Full article

Endometrial cancer is the fifth most common cancer worldwide, with one of the highest incidence and mortality rates. Its incidence is projected to increase 55% by 2030. Currently, the techniques used for its detection are heterogeneous and can be invasive and nonspecific. In this context, omics studies have gained relevance, providing solutions that have improved patient diagnosis and prognosis. In this study, we used data from the GSE268888 study as discovery cohort and data from the TCGA consortium as a validation cohort. Expression analyses were performed to identify miRNAs overexpressed in endometrial cancer. These miRNAs were then analyzed in relation to diagnostic and prognostic clinical variables. The target genes of these miRNAs were identified using bioinformatic tools, and functional enrichment analyses were conducted with this gene set to explore their involvement in relevant oncogenic signaling pathways. We also calculated the structural topology of the miRNA–target complexes and computed their correlation coefficients. We found that hsa-miR-182 and hsa-miR-760 had diagnostic and prognostic relevance and interacted with the p53 signaling pathway. Specifically, hsa-miR-449a was associated with diagnosis, but not with prognosis. Furthermore, we found that these miRNAs share TP53INP1 as a common target gene and estimated a high probability of complex formation, along with a positive correlation between these miRNAs and TP53INP1 in more advanced stages of the disease. These findings suggest that this miRNA signature has potential to be used as a diagnostic and prognostic biomarker and could serve as a foundation for future therapeutic strategies for endometrial cancer. However, further experimental validation is needed to confirm its clinical applicability. Full article