Machine learning-driven mast cell gene signatures for prognostic and therapeutic prediction in prostate cancer
Abudukeyoumu Maimaitiyiming,
Hengqing An,
Chen Xing,
Xiaodong Li,
Zhao Li,
Junbo Bai,
Cheng Luo,
Tao Zhuo,
Xin Huang,
Aierpati Maimaiti,
Abudushalamu Aikemu,
Yujie Wang
Affiliations
Abudukeyoumu Maimaitiyiming
The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China; Department of Urological, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
Hengqing An
The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China; Department of Urological, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China; Xinjiang Clinical Research Center of Urogenital Diseases, Urumqi, China; Corresponding author. An Department of Urology, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Chen Xing
The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China; Department of Urological, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China; Xinjiang Clinical Research Center of Urogenital Diseases, Urumqi, China
Xiaodong Li
The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China; Department of Urological, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China; Xinjiang Clinical Research Center of Urogenital Diseases, Urumqi, China
Zhao Li
Department of Abdominal Ultrasonography, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
Junbo Bai
Department of Pediatric Urology, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
Cheng Luo
The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China; Department of Urological, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
Tao Zhuo
The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China; Department of Urological, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
Xin Huang
The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China; Department of Urological, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
Aierpati Maimaiti
The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China; Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
Abudushalamu Aikemu
Department of Emergency Medicine, Youai Hospital, Urumqi, Xinjiang, China
Yujie Wang
The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China; Department of Urological, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China; Xinjiang Clinical Research Center of Urogenital Diseases, Urumqi, China; Corresponding author. Department of Urology, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Background: The role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy. Methods: This study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to develop an effective MCMGS from candidate genes. Subsequently, an immunological assessment of MCMGS was conducted to provide new insights into the evaluation of immunotherapy responses and prognostic assessments. Additionally, we utilized Gene Set Enrichment Analysis (GSEA) and pathway analysis to explore the biological pathways and mechanisms associated with MCMGS. Results: MCMGS incorporated 13 marker genes and was successful in segregating patients into distinct high- and low-risk categories. Prognostic efficacy was confirmed by survival analysis incorporating MCMGS scores, alongside clinical parameters such as age, T stage, and Gleason scores. High MCMGS scores were correlated with upregulated pathways in fatty acid metabolism and β-alanine metabolism, while low scores correlated with DNA repair mechanisms, homologous recombination, and cell cycle progression. Patients classified as low-risk displayed increased sensitivity to drugs, indicating the utility of MCMGS in forecasting responses to immune checkpoint inhibitors. Conclusion: The combination of MCMGS with a robust machine learning methodology demonstrates considerable promise in guiding personalized risk stratification and informing therapeutic decisions for patients with PCA.
