Title:Integrated Machine Learning Algorithms for Stratification of Patients with Bladder Cancer
Volume: 19
Issue: 10
Author(s): Yuanyuan He, Haodong Wei, Siqing Liao, Ruiming Ou, Yuqiang Xiong, Yongchun Zuo*Lei Yang*
Affiliation:
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
- Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology
Co., Ltd. Hohhot, 010010, China
- Inner Mongolia International Mongolian Hospital, Hohhot, 010065, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
Keywords:
Bladder cancer, prognosis, machine learning, drug sensitivity, immunotherapy, algorithms.
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 datasets sourced from the Gene Expression
Omnibus (GEO), the Cancer Genome Atlas (TCGA) databases, and IMvigor210 dataset. The machine
learning based on feature selection algorithms were used to generate 96 models for establishing the
risk score for each patient. Based on the risk score, all the patients were 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.
