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

Jie-Huei Wang¹, Zih-Han Wu¹, Hui-Chen Lu¹

¹Department of Mathematics, National Chung Cheng University, Chiayi, Taiwan.

Cancer Informatics

|December 19, 2025

Summary

This summary is machine-generated.

This study shows that using a matched case-control design with regularized conditional logistic regression improves biomarker discovery in high-dimensional cancer transcriptomic data, leading to more accurate and interpretable results.

Keywords:

TCGA conditional logistic regression gene importance matched case-control design precision medicine regularized regression

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Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

High-throughput transcriptomic data analysis is crucial for cancer research.
Identifying reliable cancer biomarkers in high-dimensional data is challenging.
Regularized conditional logistic regression (CLR) methods are used for biomarker discovery.

Purpose of the Study:

To systematically evaluate regularized CLR methods under a matched case-control (MCC) design.
To assess performance in variable selection, parameter estimation, and predictive accuracy.
To emphasize the importance of MCC design in reducing confounding and improving interpretability.

Main Methods:

Utilized RNA-seq data from The Cancer Genome Atlas (TCGA) for liver, thyroid, and lung cancers.
Applied four regularized CLR methods (clogitL1, pclogit, clogitLasso, penalizedclr) to over 20,000 gene expression features.
Evaluated performance using gene selection stability, predictive accuracy, interpretability, and bootstrap resampling for gene importance.

Main Results:

Incorporating the MCC design significantly enhanced feature selection by mitigating confounding noise.
Regularized CLR models identified well-established cancer-related genes with high consistency and significance.
Ignoring the matched design led to missed biomarkers or increased false positives.

Conclusions:

Integrating MCC design with regularized CLR methods improves analysis of high-dimensional transcriptomic data.
The framework offers enhanced accuracy, robustness, and biological relevance for cancer genomics.
This approach supports precision medicine and translational cancer research.