Unsupervised machine learning-based stratification and immune deconvolution of liver hepatocellular carcinoma.

ABSTRACT:

Background

Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer and a leading cause of cancer-related deaths globally. The tumour microenvironment (TME) influences treatment response and prognosis, yet its heterogeneity remains unclear.

Methods

The unsupervised machine learning methods- agglomerative hierarchical clustering, Multi-Omics Factor Analysis with K-means++, and an autoencoder with K-means++ - stratified patients using microarray data from HCC samples. Immune deconvolution algorithms estimated the proportions of infiltrating immune cells across identified clusters.

Results

Thirteen genes were found to influence HCC subtyping in both primary and validation datasets, with three genes-TOP2A, DCN, and MT1E-showing significant associations wi

SUBMITTER: Reierson MM 

PROVIDER: S-EPMC12066050 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature