Integrative Transcriptomic Network Modeling Coupled with Patient-Derived Validation for Circulating lncRNA Biomarker...

.: Nonalcoholic fatty liver disease (NAFLD) is a significant global health concern, impacting roughly 25% of people and leading to chronic liver conditions. It involves excess fat accumulation in the liver without significant alcohol intake and can develop into nonalcoholic steatohepatitis (NASH), fibrosis, or cirrhosis. While liver biopsy remains the gold standard for diagnosis, its invasive nature and associated risks restrict its routine use. Noninvasive biomarkers, such as serum ALT, AST, and various composite scores, are available; however, their clinical usefulness is often limited by variable sensitivity and specificity across different populations and disease stages. To overcome these limitations, this chapter offers a comprehensive, reproducible protocol for identifying and clinically validating circulating long noncoding RNA (lncRNA) biomarkers for NAFLD and NASH. The workflow integrates bioinformatic analysis of four Gene Expression Omnibus (GEO) transcriptomic datasets (two human and two murine cohorts) with network-based inference to construct a NAFLD-related lncRNA-miRNA-mRNA coregulatory network. This is followed by candidate prioritization based on cross-dataset evidence and a literature review. Candidate lncRNAs are then experimentally validated in patient-derived blood samples using quantitative PCR (qPCR), and their diagnostic performance is quantified using receiver operating characteristic (ROC) analysis, both as individual markers and multi-lncRNA panels. Circulating lncRNAs are detected in diverse biofluids, remain stable under standard preanalytical conditions, and are often tissue-specific. This integrated approach facilitates the development of more precise, scalable, and noninvasive biomarkers for NAFLD/NASH. The chapter further emphasizes essential translational steps, including preanalytical standardization, analytical validation, and validation in independent patient cohorts with relevant clinical endpoints.