Hepatocellular Carcinoma, a leading cause of cancer-relataed deaths worldwide, often goes undiagnosed until its later stages when treatment options are limited, and prognosis is poor. The primary reason for this late detection is the lack of sensitive and specific biomarkers for early-stage diagnosis. If we can identify reliable biomarkers for the early detection of HCC, it would allow for timely intervention when treatment is more feasible and effective.
This project focuses on identifying potential early-stage biomarkers for hepatocellular carcinoma (HCC) using gene expression data. The study leverages datasets from the Gene Expression Omnibus (GEO) to analyze differential gene expression between fetal liver, adult liver, and HCC samples. The goal is to identify genes associated with the dedifferentiation process of the adult liver into a fetal-like state, as one early hallmark of HCC is the dedifferentiation of adult hepatocytes into more highly proliferative state typical of the fetal liver.
The analysis follows these key steps:
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Data Acquisition: Microarray datasets were obtained from the GEO database, including fetal liver, adult liver, and HCC tissue samples.
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Preprocessing: The data was log2-transformed and normalized to ensure comparability across samples.
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Group Analysis: Differential gene expression analyses were conducted using UMAP plots, volcano plots, and Venn diagrams for the following groups:
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Group 1: Annotated early-stage HCC samples.
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Group 2: Healthy liver tissues vs. adjacent non- tumor tissues of HCC.
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Group 3: HCC tumor tissues vs. adjacent non- tumor tissues.
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Group 4: Fetal liver vs. adult liver samples.
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Enrichment Analysis: Identified genes underwent enrichment analysis to reveal their involvement in HCC-related pathways.
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Key Findings: The analysis identified three key genes—SQLE, AFP, and COL2A1—as potential early-stage HCC biomarkers.
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UMAP Plots: Showed clustering patterns among the various tissue groups, highlighting similarities between fetal liver and adult liver tissues in early-stage HCC.
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Volcano Plots: Revealed significant differentially expressed genes (DEGs) across the groups, with key genes identified for further analysis.
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Venn Diagrams: Used to identify common genes between the datasets, leading to the selection of SQLE, AFP, and COL2A1 as potential biomarkers.
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Enrichment Analysis: Supported the involvement of the identified genes in HCC-related pathways, highlighting their potential as early diagnostic markers.
This project was part of my Master's thesis research at Nottingham Trent University.
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