Biology subjects

Houri, H.

Publications and source records attributed to Houri, H..

3 recordsLinked to original sources

bioRxiv2025

Interplay of Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans along with Circulating miR-21 and miR-155 as Potential Biomarkers for Pancreatic Cancer

The associations between oral bacterial pathogens and the risk of pancreatic cancer (PC) have been reported in several epidemiological studies. In this study, we evaluated the diagnostic potential of periodontal pathogens Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans in combination with circulating oncomiRNAs, including miR-21 and miR-155. A total of 41 PC patients and 40 age- and sex-matched controls were recruited for the study. The salivary bacterial load of P. gingivalis and A. actinomycetemcomitans, along with the copy number of miR-21 and miR-155 in blood, were measured using quantitative real-time PCR. Subsequently, the predictive power of the selected biomarkers in PC diagnosis was determined. Elevated load of the periodontal pathogens P. gingivalis in females (OR=2.31; 95% CI 0.98-5.47) and A. actinomycetemcomitans in diabetic individuals (OR=3.66; 95% CI 0.47-6.68) were associated with a higher risk of PC. Moreover, the diagnostic model incorporating two salivary species and two circulating miRNAs demonstrated an AUC of 0.878 (95% CI 0.802-0.955). This study offers compelling new evidence supporting the idea that the combined analysis of salivary microbiota and circulating miRNAs serves as an informative avenue for the discovery of non-invasive biomarkers for PC.

molecular biology↗

bioRxiv2025

Investigating Alzheimers Disease Biomarkers by Applying Machine Learning Models

ObjectiveAlzheimers Disease (AD) is a debilitating neurodegenerative disorder characterized by memory loss, cognitive decline, and the accumulation of amyloid plaques and neurofibrillary tangles. This study investigates the interplay of various biomarkers and clinical features in diagnosing AD using machine learning (ML) techniques. MethodsWe analyzed data from 191 AD patients and 59 non-AD subjects, employing classifiers including Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). ResultsOur findings indicate that KNN, SVM, RF, and DT achieved high sensitivity (94%) and accuracy (92%), demonstrating their potential as effective diagnostic tools. Notably, significant differences in feature values between AD patients and non-AD subjects suggest that biomarker-driven approaches can enhance diagnostic precision. Key biomarkers such as neprilysin, alpha-secretase, beta-secretase, amyloid plaques and urinary formic acid emerged as critical elements. ConclusionOur results underscore the importance of selecting a targeted subset of features to streamline the diagnostic process, allowing for more efficient and cost-effective screening. While our study reveals valuable insights into AD pathology and diagnosis, future research with larger, longitudinal cohorts is essential to further elucidate these relationships and enhance our understanding of Alzheimers mechanisms, ultimately aiming for innovative therapeutic strategies.

bioinformatics↗

bioRxiv2024

Comprehensive Transcriptomic Analysis of Hepatocellular Carcinoma: Uncovering Shared and Unique Molecular Signatures Across Diverse Etiologies

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO), encompassing a diverse range of HCC etiologies, including HBV and HCV infections, cirrhosis, and normal liver comparisons. Our analysis identified over 9,000 differentially expressed genes (DEGs), with 125 genes consistently altered across multiple datasets, underscoring their potential as critical biomarkers for HCC. Notably, we observed significant dysregulation in pathways related to cell cycle regulation, immune response, and metabolic processes. The integration of these DEGs across various HCC subtypes provides novel insights into the molecular heterogeneity of HCC, offering promising avenues for the development of targeted therapies and personalized medicine. This extensive repository of DEGs serves as a valuable resource for the scientific community, facilitating further research into the underlying mechanisms of HCC and the pursuit of improved diagnostic and therapeutic strategies.

bioinformatics↗