RT Journal Article
SR Electronic
T1 Transcriptome-based Deep Learning Model for Predicting Gemcitabine and Cisplatin Chemotherapy Response in Urothelial Carcinoma: Development and External Validation
JF Cancer Genomics - Proteomics
JO Cancer Genomics Proteomics
FD International Institute of Anticancer Research
SP 546
OP 557
DO 10.21873/cgp.20589
VO 23
IS 3
A1 KANG, JUWON
A1 LEE, HYUN JUNG
A1 OH, SANG-BO
A1 NAM, JONG KIL
A1 KIM, TAE UN
A1 RYU, HWASEONG
A1 KI, YONG KAN
A1 KANG, JIHOON
A1 KIM, YI RANG
A1 LEE, JEONG HOON
A1 CHOI, JUNJEONG
A1 HONG, YUN JEONG
A1 PARK, KWONOH
YR 2026
UL http://cgp.iiarjournals.org/content/23/3/546.abstract
AB Background/Aim: Chemotherapy with gemcitabine and cisplatin remains the cornerstone of treatment for advanced urothelial carcinoma (UC), yet response rates vary significantly among patients. Predicting treatment response is crucial to avoid unnecessary toxicity and optimize therapeutic strategies. This study aims to develop a deep learning model leveraging RNA sequencing data to predict chemotherapy response in UC patients.Materials and Methods: We developed a deep learning model using RNA sequencing gene expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus to predict chemotherapy (gemcitabine and cisplatin) response in UC patients. The model was externally validated using an independent cohort from the Pusan National University Yangsan Hospital. Model interpretation was performed through gene ontology and survival analyses using predictions from TCGA samples not included in the training set.Results: The deep learning model demonstrated excellent predictive performance, achieving 94.7% accuracy in the training dataset and 90.0% accuracy in external validation. Gene ontology analysis revealed four key functional clusters associated with chemotherapy response: DNA damage response, cell cycle regulation, kinesins/microtubule dynamics, and mitotic cytokinesis. Notably, the model showed significant prognostic value in early-stage, with predicted responders displaying markedly better survival outcomes (p=0.019).Conclusion: Our transcriptome-based deep learning approach offers a promising computational strategy for predicting chemotherapy response in urothelial carcinoma. By integrating high-dimensional RNA-seq data and advanced machine learning techniques, we provide a potential decision-support tool for personalized treatment planning.