Robust Cancer Biomarker Identification From Matched Transcriptomic Data Via Bootstrapped Regularized Conditional Logistic Regression.

ABSTRACT:

Objectives

With the increasing application of high-throughput transcriptomic data in cancer research, identifying reliable cancer biomarkers in high-dimensional settings remains a major challenge. This study aims to systematically evaluate various regularized conditional logistic regression (CLR) methods under a matched case-control (MCC) design, focusing on their performance in variable selection, parameter estimation, and predictive accuracy. Special emphasis is placed on the importance of the matching design in reducing confounding effects and improving model interpretability.

Methods

We utilize RNA-seq data from The Cancer Genome Atlas (TCGA), specifically datasets for liver, thyroid, and lung cancers, which include paired tumor and adjacent normal tissue samples. In our

SUBMITTER: Wang JH 

PROVIDER: S-EPMC12709001 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature