Machine learning-based development of cuproptosis-related lncRNA signature for prognosis and immunotherapy exploration in colon cancer

Yuchao Liu; Ruiyu Zhu

doi:10.1117/12.3031359

19 July 2024 Machine learning-based development of cuproptosis-related lncRNA signature for prognosis and immunotherapy exploration in colon cancer

Yuchao Liu, Ruiyu Zhu

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131817O (2024) https://doi.org/10.1117/12.3031359
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

This study aimed to construct a cuproptosis-related lncRNA signature through an integrated machine learningbased model to predict prognosis and response to immunotherapy in CC. Methods Raw RNA-seq data and corresponding clinicopathological information of patients with CC were retrieved from The Cancer Genome Atlas (TCGA) database. A gene-lncRNA co-expression analysis was applied to obtain cuproptosis-related lncRNAs. A prognostic risk model composed of cuproptosis-related lncRNAs was constructed using univariable Cox regression analysis and a machine learning-based integrative model. The clinicopathological samples were divided into high- and low-risk groups according to the median risk scores. Immunotherapy response was assessed across the groups using the Tumor Immune Dysfunction and Exclusion (TIDE) scores, while immune-related functions with single-sample gene set enrichment analysis (ssGSEA). Results The prognostic risk model was constructed based on 11 cuproptosis-related lncRNAs. The high-risk group exhibited higher immune-related function scores and a greater likelihood of immune evasion but a worse prognosis and immunotherapy outcomes. Conclusion The cuproptosis-related lncRNA signature could be utilized as an independent prognostic factor for CC to guide clinical treatment.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuchao Liu and Ruiyu Zhu "Machine learning-based development of cuproptosis-related lncRNA signature for prognosis and immunotherapy exploration in colon cancer", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131817O (19 July 2024); https://doi.org/10.1117/12.3031359

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