IDENTIFICATION OF MOLECULAR BIOMARKERS FOR EARLY DIAGNOSIS OF CHRONIC DISEASES IN CLINICAL PRACTICE

Abstract

The early diagnosis of chronic diseases remains a critical challenge in clinical practice due to the absence of sensitive and specific diagnostic markers at the preclinical stage. This study aimed to identify potential molecular biomarkers for early disease detection by analyzing gene expression profiles derived from publicly available datasets, including TCGA and GEO. Differential gene expression analysis was performed to compare tumor and normal samples, leading to the identification of significantly dysregulated genes associated with disease conditions. The results revealed a subset of genes that exhibited consistent upregulation or downregulation across multiple datasets, indicating their robustness and potential as reliable biomarkers. Functional interpretation suggested that these genes are involved in key biological processes such as cell proliferation, apoptosis, immune response, and metabolic regulation, which are critical in disease progression. The identified biomarkers demonstrated strong discriminatory power between diseased and healthy samples, supporting their potential application in early diagnosis. Furthermore, the integration of molecular data with emerging technologies, such as biosensors and computational approaches, highlights new opportunities for improving diagnostic accuracy and clinical decision-making. Despite certain limitations, including the need for further experimental validation, the findings of this study contribute to the growing body of research on molecular diagnostics and biomarker discovery. Overall, this study underscores the importance of gene expression-based biomarkers in advancing early detection strategies and facilitating precision medicine in the management of chronic diseases.