A Bioinformatics Analysis Based on Omics and Clinical Data for Graph-Based Patient Stratification in Hepatocellular...

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Hepatocellular carcinoma (HCC) is a leading cause of cancer death globally.
• HCC exhibits significant molecular and clinical heterogeneity, complicating accurate prognosis.
• Identifying distinct molecular subgroups is crucial for personalized treatment strategies.

Purpose of the Study:

• To identify molecular subgroups within Hepatocellular Carcinoma (HCC) with distinct prognostic profiles.
• To leverage multi-omics data integration for improved patient stratification.
• To establish a reproducible framework for prognostic assessment in HCC.

Main Methods:

• Applied Similarity Network Fusion (SNF) to integrate mRNA and miRNA sequencing data from the TCGA-LIHC cohort.
• Utilized spectral clustering on the fused similarity network to define patient subgroups.
• Employed log-rank tests, Cox regression, and differential expression analysis for subgroup characterization and validation.

Main Results:

• Identified six molecular clusters with significantly different survival trajectories (log-rank p-value = 3.6 × 10^-4).
• Characterized distinct prognostic profiles, with Cluster 1 showing the worst survival (median OS 22.6 months) and Cluster 4 demonstrating the best outcomes.
• Validated the prognostic relevance of identified HCC subtypes in an external cohort (GSE14520; log-rank p-value = 0.011).

Conclusions:

• Multi-omics data integration using SNF provides a robust method for prognostically informative patient stratification in HCC.
• The identified molecular subgroups and their associated transcriptional labels offer insights into HCC biology and potential therapeutic targets.
• This approach facilitates reproducible prognostic assessment and personalized medicine strategies for liver cancer.