Maimaitiyiming Abudukeyoumu, An Hengqing, Xing Chen, Li Xiaodong, Li Zhao, Bai Junbo, Luo Cheng, Zhuo Tao, Huang Xin, Maimaiti Aierpati, Aikemu Abudushalamu, Wang Yujie
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.
Heliyon. 2024 Jul 26;10(15):e35157. doi: 10.1016/j.heliyon.2024.e35157. eCollection 2024 Aug 15.
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.
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.
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.
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.
在前列腺癌（PCA）预后不可预测以及免疫治疗效果参差不齐的背景下，肥大细胞的作用尚未得到充分研究。我们的研究旨在采用全面的计算方法，评估来自1091例PCA患者全球队列的肥大细胞标记基因特征（MCMGS）。该方法旨在识别一种可靠的生物标志物，以辅助预后评估和预测免疫治疗反应。
本研究首先从六个国际队列的前列腺腺癌（PRAD）患者中识别肥大细胞相关生物标志物。我们采用了多种机器学习技术，包括随机森林、支持向量机（SVM）、套索回归和Cox比例风险模型，从候选基因中开发出有效的MCMGS。随后，对MCMGS进行了免疫学评估，为免疫治疗反应评估和预后评估提供新的见解。此外，我们利用基因集富集分析（GSEA）和通路分析来探索与MCMGS相关的生物学通路和机制。
MCMGS纳入了13个标记基因，并成功地将患者分为不同的高风险和低风险类别。通过纳入MCMGS评分的生存分析以及年龄、T分期和Gleason评分等临床参数，证实了预后效果。高MCMGS评分与脂肪酸代谢和β-丙氨酸代谢通路上调相关，而低评分与DNA修复机制、同源重组和细胞周期进程相关。被归类为低风险的患者对药物显示出更高的敏感性，表明MCMGS在预测免疫检查点抑制剂反应方面的实用性。
MCMGS与强大的机器学习方法相结合，在指导PCA患者的个性化风险分层和为治疗决策提供依据方面显示出巨大潜力。
