Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven 7-Gene Stemness Signature that Predicts Progression DOI Creative Commons
Agustina Sabater, Pablo Sanchis, Rocio Seniuk

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 25, 2024

ABSTRACT Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate (NEPC). This study developed and validated stemness-associated gene signature using advanced machine learning techniques, including Random Forest Lasso regression, applied large-scale transcriptomic datasets. The resulting 7-gene ( KMT5C, MEN1, TYMS, IRF5, DNMT3B, CDC25B DPP4 ) was across independent cohorts patient-derived xenograft (PDX) models. demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, overall survival. Importantly, the not only identified specific NEPC subtypes, such as large-cell carcinoma, which is associated with very poor outcomes, but also predicted prognosis PCa cases that exhibit this molecular signature, even when they were histopathologically classified NEPC. dual classifier capability makes robust tool personalized medicine, providing valuable resource predicting disease guiding treatment strategies in management.

Language: Английский

Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression DOI Open Access
Agustina Sabater, Pablo Sanchis, Rocio Seniuk

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(21), P. 11356 - 11356

Published: Oct. 22, 2024

Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate (NEPC). This study developed and validated stemness-associated gene signature using advanced machine learning techniques, including Random Forest Lasso regression, applied large-scale transcriptomic datasets. The resulting seven-gene (KMT5C, DPP4, TYMS, CDC25B, IRF5, MEN1, DNMT3B) was across independent cohorts patient-derived xenograft (PDX) models. demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, overall survival. Importantly, the not only identified specific NEPC subtypes, such as large-cell carcinoma, which is associated with very poor outcomes, but also predicted prognosis PCa cases that exhibit this molecular signature, even when they were histopathologically classified NEPC. dual classifier capability makes robust tool personalized medicine, providing valuable resource predicting disease guiding treatment strategies in management.

Language: Английский

Citations

1
Stemness regulation in prostate cancer: prostate cancer stem cells and targeted therapy DOI Creative Commons
Hao Liang, Bin Zhou, Pei-Xin Li

et al.

Annals of Medicine, Journal Year: 2024, Volume and Issue: 57(1)

Published: Dec. 23, 2024

Background Increasing evidence indicates that cancer stem cells (CSCs) and stem-like form a special subpopulation of are ubiquitous in tumors. These exhibit similar characteristics to those normal tissues; moreover, they capable self-renewal differentiation, as well high tumorigenicity drug resistance. In prostate (PCa), it is difficult kill these using androgen signaling inhibitors chemotherapy drugs. Consequently, the residual (PCSCs) mediate tumor recurrence progression.

Language: Английский

Citations

1
Oncogenic Role of SATB2 In Vitro: Regulator of Pluripotency, Self-Renewal, and Epithelial–Mesenchymal Transition in Prostate Cancer DOI Creative Commons
Wei Yu, Rashmi Srivastava,

Shivam Srivastava

et al.

Cells, Journal Year: 2024, Volume and Issue: 13(11), P. 962 - 962

Published: June 3, 2024

Special AT-rich sequence binding protein-2 (SATB2) is a nuclear matrix protein that binds to attachment regions and involved in chromatin remodeling transcription regulation. In stem cells, it regulates the expression of genes required for maintaining pluripotency self-renewal epithelial–mesenchymal transition (EMT). this study, we examined oncogenic role SATB2 prostate cancer assessed whether overexpression human normal epithelial cells (PrECs) induces properties (CSCs). The results demonstrate highly expressed cell lines CSCs, but not PrECs. Overexpression PrECs cellular transformation which was evident by formation colonies soft agar spheroids suspension. also resulted induction markers (CD44 CD133), pluripotency-maintaining factors (cMYC, OCT4, SOX2, KLF4, NANOG), CADHERIN switch, EMT-related factors. Chromatin immunoprecipitation assay demonstrated can directly bind promoters BCL-2, BSP, NANOG, MYC, XIAP, HOXA2, suggesting capable regulating pluripotency/self-renewal, survival, proliferation. Since CSCs play crucial initiation, progression, metastasis, effects knockdown on stemness. inhibited spheroid formation, viability, colony motility, migration, invasion compared their scrambled control groups. upregulated E-CADHERIN N-CADHERIN, SNAIL, SLUG, ZEB1. significantly higher adenocarcinoma tissues. Overall, our data suggest acts as an factor where inducing malignant changes CSC characteristics.

Language: Английский

Citations

0
Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven 7-Gene Stemness Signature that Predicts Progression DOI Creative Commons
Agustina Sabater, Pablo Sanchis, Rocio Seniuk

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 25, 2024

ABSTRACT Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate (NEPC). This study developed and validated stemness-associated gene signature using advanced machine learning techniques, including Random Forest Lasso regression, applied large-scale transcriptomic datasets. The resulting 7-gene ( KMT5C, MEN1, TYMS, IRF5, DNMT3B, CDC25B DPP4 ) was across independent cohorts patient-derived xenograft (PDX) models. demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, overall survival. Importantly, the not only identified specific NEPC subtypes, such as large-cell carcinoma, which is associated with very poor outcomes, but also predicted prognosis PCa cases that exhibit this molecular signature, even when they were histopathologically classified NEPC. dual classifier capability makes robust tool personalized medicine, providing valuable resource predicting disease guiding treatment strategies in management.

Language: Английский

Citations

0