A Novel Causality-Based Method for Identifying Drivers of Breast Cancer Progression.

MOTIVATION: Identifying transcriptomic factors with potential causal effects on breast cancer progression is important for understanding disease mechanisms and prioritizing therapeutic targets. However, causal-effect estimation from high-dimensional gene-expression data remains challenging because of the large number of variables and potential unmeasured confounding. RESULTS: We propose CICIV, a causal inference framework that integrates PC-simple-based causal feature selection with conditional instrumental variable (CIV) representation learning. PC-simple first reduces the dimensionality of transcriptomic data by identifying candidate parent genes, after which CIV estimates and ranks their absolute causal effects. Applied to TCGA-BRCA, CICIV prioritized 40 breast cancer-related candidate genes and identified signals that were not captured by conventional correlation-based analyses. External validation using the independent METABRIC cohort showed consistent effect directions for 21 of the 40 genes, with four genes overlapping in the Top 10 and nine in the Top 20 rankings. Pathway enrichment and literature-based analyses further supported the biological relevance of the prioritized genes. AVAILABILITY AND IMPLEMENTATION: The CICIV benchmarking framework and source code are freely available at https://github.com/Zaiwen/CICIV. The software version and test data used in this study are archived at Zenodo (DOI: 10.5281/zenodo.22143976). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.