Identifying Genetic Biomarkers to Differentiate Subtypes of LIHC

Overview

For this bioinformatics project, I analyzed genomic, clinical, and expression datasets across 352 patients to identify two distinct molecular subtypes (Subtype A and Subtype B) of Liver Hepatocellular Carcinoma (LIHC) and isolate key gene biomarkers for early diagnosis and targeted treatment. My technical and analytical contributions focused on building the project infrastructure, processing clinical data, performing survival analyses, executing pathway mapping, and authoring technical documentation. Specifically, I established the Git repository, coordinated workflows, imported necessary R packages, and processed clinical variables to assess survival outcomes against patient demographics and attributes like age, gender, weight, and race. Following patient clustering based on gene expression profiles, I conducted Kaplan-Meier survival evaluations that revealed a statistically significant lower overall survival rate in Subtype B compared to Subtype A (p = 0.0056). I also demonstrated that tumor stage (p = 0.0059) and genetic ancestry (p = 0.029) significantly affected survival outcomes in Subtype A, whereas these factors showed no significant impact on Subtype B. To uncover the underlying biological mechanisms, I utilized the GAGE and Pathview R packages to map differentially expressed genes (DEGs) onto functional pathways, identifying critical pathways such as Purine Metabolism and Caffeine Metabolism. Our findings revealed 18 DEGs that clearly differentiate the two molecular subtypes. High expression of genes such as GCK, CLEC4M, CYP1A2, and BMP10 characterizes Subtype A, whereas high expression of HMGA2, SLC6A14, SST, and CADPS characterizes Subtype B. Interestingly, despite diverging gene-level expression profiles and distinct mutational landscapes—such as a higher CTNNB1 mutation frequency in Subtype A—the pathway-level impacts remained identical across both subtypes. Ultimately, I drafted the Methods and Results sections, refined document formatting, and presented the technical methodology during project delivery.

Key Deliverables

• Processed clinical data for 352 patients and executed Kaplan-Meier evaluations
• Isolated 18 key gene biomarkers separating the two subtypes and mapped shared functional pathways using GAGE and Pathview.