Evidence map›Paper›PMID 42734769›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2027

Integrative Transcriptomic Network Modeling Coupled with Patient-Derived Validation for Circulating lncRNA Biomarker Discovery in NAFLD: A Comprehensive Workflow.

Mohamed Ali Hussein, Anwar Abdelnaser

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Article in Methods in molecular biology (Clifton, N.J.), 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mohamed Ali HusseinInstitute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, New Cairo, 11835, Egypt.
Anwar AbdelnaserInstitute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, New Cairo, 11835, Egypt. anwar.abdelnaser@aucegypt.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

.: 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.

Indexed as

Gene Expression ProfilingGene Regulatory NetworksNon-alcoholic Fatty Liver DiseaseRNA, Long NoncodingTranscriptomeAnimalsBiomarkersComputational BiologyHumansMiceMicroRNAsWorkflowBiomarkersMicroRNAsRNA, Long NoncodingCirculating RNAlncRNA–miRNA–mRNA regulatory networkLong noncoding RNA (lncRNA)MicroRNA (miRNA)Network analysisNonalcoholic fatty liver disease (NAFLD)Nonalcoholic steatohepatitis (NASH)Noninvasive biomarkers

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.