Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in cholangiocarcinoma.

ABSTRACT:

Background

Cholangiocarcinoma is a kind of epithelial cell malignancy with high mortality. Intratumor heterogeneity (ITH) is involved in tumor progression, aggressiveness, treatment resistance, and disease recurrence.

Methods

Integrative machine learning procedure including 10 methods (random survival forest, elastic network, Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox, supervised principal components, generalized boosted regression modeling, and survival support vector machine) was performed to construct an ITH-related signature (IRS) for cholangiocarcinoma. Single cell analysis was performed to clarify the communication between immune cell subtypes. Cellular experiment was used to verify the biological function of hub gene.

Results

SUBMITTER: Chen X 

PROVIDER: S-EPMC10899030 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature