Liu Siyuan, Hu Yi, Liu Fei, Jiang Yizheng, Wang Hongrui, Wu Xusheng, Hu Dehua
School of Life Sciences, Central South University, Changsha 410013, China.
Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, China.
Biomedicines. 2024 Sep 23;12(9):2157. doi: 10.3390/biomedicines12092157.
Androgen deprivation therapy (ADT) is the mainstay of treatment for prostate cancer, yet dynamic molecular changes from hormone-sensitive to castration-resistant states in patients treated with ADT remain unclear. In this study, we combined the dynamic network biomarker (DNB) method and the weighted gene co-expression network analysis (WGCNA) to identify key genes associated with the progression to a castration-resistant state in prostate cancer via the integration of single-cell and bulk RNA sequencing data. Based on the gene expression profiles of CRPC in the GEO dataset, the DNB method was used to clarify the condition of epithelial cells and find out the most significant transition signal DNB modules and genes included. Then, we calculated gene modules associated with the clinical phenotype stage based on the WGCNA. IHC was conducted to validate the expression of the key genes in CRPC and primary PCa patients Results:Nomograms, calibration plots, and ROC curves were applied to evaluate the good prognostic accuracy of the risk prediction model. By combining single-cell RNA sequence data and bulk RNA sequence data, we identified a set of DNBs, whose roles involved in androgen-associated activities indicated the signals of a prostate cancer cell transition from an androgen-dependent state to a castration-resistant state. In addition, a risk prediction model including the risk score of four key genes (SCD, NARS2, ALDH1A1, and NFXL1) and other clinical-pathological characteristics was constructed and verified to be able to reasonably predict the prognosis of patients receiving ADT. In summary, four key genes from DNBs were identified as potential diagnostic markers for patients treated with ADT and a risk score-based nomogram will facilitate precise prognosis prediction and individualized therapeutic interventions of CRPC.
雄激素剥夺疗法（ADT）是前列腺癌治疗的主要手段，但接受ADT治疗的患者从激素敏感状态转变为去势抵抗状态时的动态分子变化仍不清楚。在本研究中，我们结合动态网络生物标志物（DNB）方法和加权基因共表达网络分析（WGCNA），通过整合单细胞和批量RNA测序数据，识别与前列腺癌进展至去势抵抗状态相关的关键基因。基于GEO数据集中CRPC的基因表达谱，使用DNB方法阐明上皮细胞的状况，并找出最显著的转变信号DNB模块和所含基因。然后，我们基于WGCNA计算与临床表型阶段相关的基因模块。进行免疫组化以验证关键基因在CRPC和原发性PCa患者中的表达。结果：应用列线图、校准图和ROC曲线评估风险预测模型的良好预后准确性。通过结合单细胞RNA序列数据和批量RNA序列数据，我们鉴定出一组DNB，其在雄激素相关活动中的作用表明前列腺癌细胞从雄激素依赖状态转变为去势抵抗状态的信号。此外，构建了一个包括四个关键基因（SCD、NARS2、ALDH1A1和NFXL1）的风险评分以及其他临床病理特征的风险预测模型，并验证其能够合理预测接受ADT治疗患者的预后。总之，从DNB中鉴定出的四个关键基因被确定为接受ADT治疗患者的潜在诊断标志物，基于风险评分的列线图将有助于CRPC的精确预后预测和个体化治疗干预。
