Bladder cancer (BC) is the most common urinary malignancy; however accurate diagnosis and prediction of recurrence after therapies remain elusive. This study aimed to develop a biosignature of immunotherapy-based responses using gene expression data. Publicly available BC datasets were collected, and machine learning (ML) approaches were applied to identify a novel biosignature to differentiate patient subgroups. Immune phenotyping of BC in the IMvigor210 dataset included three subtypes: inflamed, excluded, and desert immune. Immune phenotypes were analyzed with gene expressions using traditional but powerful classification methods such as random forests, Deep Neural Networks (DNN), Support Vector Machines (SVM) together with boosting and feature selection methods. Specifically, DNN yielded the highest area under the curve (AUC) with precision and recall (PR) curves and receiver operating characteristic (ROC) curves for each phenotype ([Formula: see text] and [Formula: see text], respectively) resulting in the identification of gene expression features useful for immune phenotype classification. Our results suggest significant potential to further develop and utilize machine learning algorithms for analysis of BC and its precaution. In conclusion, the findings from this study present a novel gene expression assay that can accurately discriminate BC patients from controls. Upon further validation in independent cohorts, this gene signature could be developed into a predictive test that can support clinical evaluation and patient care.
IEEE open journal of engineering in medicine and biology. 2022 Apr 15*** epublish ***
Hyuna Cho, Feng Tong, Sungyong You, Sungyoung Jung, Won Hwa Kim, Jayoung Kim
Graduate School of Artificial Intelligence (GSAI)Pohang University of Science and Technology Pohang 37673 South Korea., Department of Computer Science and EngineeringUniversity of Texas at Arlington Arlington TX 76019 USA., Department of Surgery and Biomedical SciencesCedars-Sinai Medical Center Los Angeles CA 90048 USA., Department of Electrical EngineeringUniversity of Texas at Arlington Arlington TX 76019 USA.