
Faculty, Staff and Student Publications
Publication Date
10-22-2024
Journal
International Journal of Molecular Sciences
Abstract
Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting seven-gene signature (KMT5C, DPP4, TYMS, CDC25B, IRF5, MEN1, and DNMT3B) was validated across independent cohorts and patient-derived xenograft (PDX) models. This signature demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, and overall survival. Importantly, the signature not only identified specific NEPC subtypes, such as large-cell neuroendocrine carcinoma, which is associated with very poor outcomes, but also predicted a poor prognosis for PCa cases that exhibit this molecular signature, even when they were not histopathologically classified as NEPC. This dual prognostic and classifier capability makes the seven-gene signature a robust tool for personalized medicine, providing a valuable resource for predicting disease progression and guiding treatment strategies in PCa management.
Keywords
Humans, Machine Learning, Male, Prostatic Neoplasms, Disease Progression, Prognosis, Gene Expression Regulation, Neoplastic, Animals, Transcriptome, Biomarkers, Tumor, Mice, Carcinoma, Neuroendocrine, Gene Expression Profiling, Neoplastic Stem Cells, prostate cancer, stemness, gene signature, prognosis, machine learning, neuroendocrine transdifferentiation, large cell neuroendocrine carcinoma
DOI
10.3390/ijms252111356
PMID
39518911
PMCID
PMC11545501
PubMedCentral® Posted Date
10-22-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons