Integrated Machine Learning Algorithms for Stratification of Patients with Bladder Cancer


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Abstract

Background:Bladder cancer is a prevalent malignancy globally, characterized by rising incidence and mortality rates. Stratifying bladder cancer patients into different subtypes is crucial for the effective treatment of this form of cancer. Therefore, there is a need to develop a stratification model specific to bladder cancer.

Purpose:This study aims to establish a prognostic prediction model for bladder cancer, with the primary goal of accurately predicting prognosis and treatment outcomes.

Methods:We collected datasets from 10 bladder cancer samples sourced from the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA) databases, and IMvigor210 dataset. The machine learning based algorithms were used to generate 96 models for establishing the risk score for each patient. Based on the risk score, all the patients was classified into two different risk score groups.

Results:The two groups of bladder cancer patients exhibited significant differences in prognosis, biological functions, and drug sensitivity. Nomogram model demonstrated that the risk score had a robust predictive effect with good clinical utility.

Conclusion:The risk score constructed in this study can be utilized to predict the prognosis, response to drug treatment, and immunotherapy of bladder cancer patients, providing assistance for personalized clinical treatment of bladder cancer.

About the authors

Yuanyuan He

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Haodong Wei

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Siqing Liao

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Ruiming Ou

College of Basic Medicine, Harbin Medical University

Email: info@benthamscience.net

Yuqiang Xiong

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Yongchun Zuo

The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University

Author for correspondence.
Email: info@benthamscience.net

Lei Yang

College of Bioinformatics Science and Technology, Harbin Medical University

Author for correspondence.
Email: info@benthamscience.net

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