ArticleBiology direct2024
Machine learning-driven estimation of mutational burden highlights DNAH5 as a prognostic marker in colorectal cancer.
Article in Biology direct, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Construction and Validation of Multi-Omics Predictive Models for Colorectal Cancer Using Machine-Learning Approaches.Pharmacogenomics and personalized medicine · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundTumor Mutational Burden (TMB) have emerged as pivotal predictive biomarkers in determining prognosis and response to immunotherapy in colorectal cancer (CRC) patients. While Whole Exome Sequencing (WES) stands as the gold standard for TMB assessment, carry substantial costs and demand considerable time commitments. Additionally, the heterogeneity among high-TMB patients remains poorly characterized.
methodsWe employed eight advanced machine learning algorithms to develop gene-panel-based models for TMB estimation. To rigorously compare and validate these TMB estimation models, four external cohorts, involving 1,956 patients, were used. Furthermore, we computed the Pearson correlation coefficient between the estimated TMB and tumor neoantigen levels to elucidate their association. CD8
resultsThe TMB estimation model based on the Lasso algorithm, incorporating 20 genes, exhibiting satisfactory performance across multiple independent cohorts (R
conclusionsThe 20-gene model offers a cost-efficient approach to precisely estimating TMB, providing prognosis in patients with CRC. Incorporating DNAH5 within this model further refines the categorization of patients with elevated TMB. Utilizing the 20-gene model facilitates the stratification of patients with CRC, enabling more precise treatment planning.
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