Usher syndrome, a rare genetic disorder causing both hearing and vision loss, presents significant diagnostic and therapeutic challenges due to its complex genetic basis. The identification of reliable biomarkers for early detection and intervention is crucial for improving patient outcomes. In this study, we present a machine learning-based hybrid sequential feature selection approach to identify key mRNA biomarkers associated with Usher syndrome. Beginning with a dataset of 42,334 mRNA features, our approach successfully reduced dimensionality and identified 58 top mRNA biomarkers that distinguish Usher syndrome from control samples. We employed a combination of feature selection techniques, including variance thresholding, recursive feature elimination, and Lasso regression, integrated within a nested cross-validation framework. The selected biomarkers were further validated using multiple machine learning models, including Logistic Regression, Random Forest, and Support Vector Machines, demonstrating robust classification performance. To assess the biological relevance of the computationally identified mRNA biomarkers, we experimentally validated candidates from the top 10 selected mRNAs using droplet digital PCR (ddPCR). The ddPCR results were consistent with expression patterns observed in the integrated transcriptomic metadata, reinforcing the credibility of our machine learning-driven biomarker discovery framework. Our findings highlight the potential of machine learning-driven biomarker discovery to enhance the detection of Usher syndrome.

Usher syndrome (USH) is a rare genetic disorder affecting vision, hearing, and balance. Identifying reliable biomarkers is crucial for early diagnosis and understanding disease mechanisms. MicroRNAs (miRNAs), key regulators of gene expression, hold promise as biomarkers for USH. This study aimed to identify a minimal subset of miRNAs that could serve as biomarkers to effectively differentiate USH from controls. We employed ensemble feature selection techniques to select the top miRNAs appearing in at least three algorithms. Machine learning models were trained and tested using this subset, followed by validation on an independent 10% sample. Our approach identified 10 key miRNAs as potential biomarkers for USH. To further validate their biological relevance, we conducted pathway analysis, which revealed significant pathways associated with USH. Furthermore, our approach achieved high classification performance, with an accuracy of 97.7%, sensitivity of 98%, specificity of 92.5%, F1 score of 95.8%, and an AUC of 97.5%. These findings demonstrate that combining ensemble feature selection with machine learning provides a robust strategy for miRNA biomarker discovery, advancing USH diagnosis and molecular understanding.

Usher syndrome (USH) is an inherited disorder characterized by sensorineural hearing loss (SNHL), retinitis pigmentosa (RP)-related vision loss, and vestibular dysfunction. USH presents itself as three distinct clinical types, 1, 2, and 3, with no biomarker for early detection. This study aimed to explore whether microRNA (miRNA) expression in USH cell lines is dysregulated compared to the miRNA expression pattern in a cell line derived from a healthy human subject. Lymphocytes from USH patients and healthy individuals were isolated and transformed into stable cell lines using Epstein–Barr virus (EBV). DNA from these cell lines was sequenced using a targeted panel to identify gene variants associated with USH types 1, 2, and 3. Microarray analysis was performed on RNA from both USH and control cell lines using NanoString miRNA microarray technology. Dysregulated miRNAs identified by the microarray were validated using droplet digital PCR technology. DNA sequencing revealed that two USH patients had USH type 1 with gene variants in USH1B (MYO7A) and USH1D (CDH23), while the other two patients were classified as USH type 2 (USH2A) and USH type 3 (CLRN-1), respectively. The NanoString miRNA microarray detected 92 differentially expressed miRNAs in USH cell lines compared to controls. Significantly altered miRNAs exhibited at least a twofold increase or decrease with a p value below 0.05. Among these miRNAs, 20 were specific to USH1, 14 to USH2, and 5 to USH3. Three miRNAs that are known as miRNA-183 family which are crucial for inner ear and retina development, have been significantly downregulated as compared to control cells. Subsequently, droplet digital PCR assays confirmed the dysregulation of the 12 most prominent miRNAs in USH cell lines. This study identifies several miRNA signatures in USH cell lines which may have potential utility in Usher syndrome identification.

Circulating miRNA has recently emerged as important biomolecules with potential clinical values as diagnostic markers for several diseases. However, to be used as such, it is critical to accurately quantify miRNAs in the clinic. Yet, preanalytical factors that can affect an error-free quantification of these miRNAs have not been explored. This study aimed at investigating several of these preanalytical factors that may affect the accurate quantification of miRNA-451a, miRNA-423-5p and miRNA-199a-3p in human blood samples. We initially evaluated levels of these three miRNAs in red blood cells (RBCs), white blood cells (WBCs), platelets, and plasma by droplet digital PCR (ddPCR). Next, we monitored miRNA levels in whole blood or platelet rich plasma (PRP) stored at different temperatures for different time periods by ddPCR. We also investigated the effects of hemolysis on miRNA concentrations in platelet-free plasma (PFP). Our results demonstrate that more than 97% of miRNA-451a and miRNA-423-5p in the blood are localized in RBCs, with only trace amounts present in WBCs, platelets, and plasma. Highest amount of the miRNA-199a-3p is present in platelets. Hemolysis had a significant impact on both miRNA-451a and miRNA-423-5p concentrations in plasma, however miRNA-199a levels remain unaffected. Importantly, PRP stored at room temperature (RT) or 4°C showed a statistically significant decrease in miRNA-451a levels, while the other two miRNAs were increased, at days 1, 2, 3 and 7. PFP at RT caused statistically significant steady decline in miRNA-451a and miRNA-423-5p, observed at 12, 24, 36, 48 and 72 hours. Levels of the miRNA-199a-3p in PFP was stable during first 72 hours at RT. PFP stored at -20°C for 7 days showed declining stability of miRNA-451a over time. However, at -80°C miRNA-451a levels were stable up to 7 days. Together, our data indicate that hemolysis and blood storage at RT, 4°C and -20°C may have significant negative effects on the accuracy of circulating miRNA-451a and miRNA-423-5p quantification.

Background: The thioredoxin system (TrxS) is crucial for maintaining redox balance by regulating cellular thiol levels and is involved in various biological processes, including cancer progression. Thioredoxin reductase (TrxR), a key component of TrxS, reduces oxidized thioredoxin (Trx) using NADPH. This study investigates the inhibitory effects of 2-bromo-2-nitro-1,3-propanediol (Bronopol, BP), a preservative, on TrxR activity and its impact on cellular thiols and cell viability. Methods: Purified recombinant TrxR and noncancer and cancer cells were treated with different concentrations of BP and TrxR activity measured. BP-treated cells were examined for effects of BP on total cellular thiol level and GSH/GSSG ratio. Results: BP effectively inhibited TrxR in a dose-dependent manner, an effect that was reversible with dithiothreitol (DTT). BP treatment significantly reduced total thiol levels, decreased the GSH/GSSG ratio, and increased reactive oxygen species (ROS) in cells. Additionally, BP decreased cell viability and induced apoptosis, as indicated by morphological changes and increased c-fos mRNA expression. Conclusions: These findings highlight BP’s potential as a TrxR inhibitor and its cytotoxicity toward both noncancer and cancer cells. The observed effects—TrxR inhibition, thiol oxidation, GSH/GSSG imbalance, and ROS accumulation—may underlie BP’s cytotoxicity. Further research is needed to explore the precise molecular mechanisms by which BP exerts these effects.

DNA packaged into cancer cell-derived EV is not well appreciated. Here, we uncovered signatures of EV DNA secreted by pancreatic cancer cells. The cancer cells and non-cancer counterparts exhibit distinct low vs. high molecular weight (LMW vs. HMW) EV DNA fragments distribution, respectively. Genome sequencing and Single Nucleotide Variants analysis revealed that 95% of reads and 94% of SNVs map to noncoding regions of the genome. Given that ~1% of the human genome represents coding regions, the 5% mapping rate to coding regions suggests a non-random enrichment of certain coding regions and mutations. The LMW DNA fragments not only set cancer cells apart, but also harbor cancer specific enrichment of unique coding regions, the top nine being FAM135B, COL22A1, TSNARE1, KCNK9, ZFAT, JRK, MROH5, GSDMD, and MIR3667HG. Additionally, the cancer cells’ LMW DNA fragments exhibit dense centromeric mapping more strikingly on chromosomes 3, 7, 9, 10, 11, 13, 17, and 20. Mutational profiling turned up close to 200 mutations specific for the cancer cells. Altogether, our analyses suggest that centromeric regions might hold clues to EV DNA content from pancreatic cancer, the molecular, mutational signatures thereof, and rationalizes the need for a new approach to DNA biomarker research.