Department of Urology & Andrology, Minimally Invasive Surgery Center, Guangdong Provincial Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Front Endocrinol (Lausanne). 2023 Jan 5;13:1005916. doi: 10.3389/fendo.2022.1005916. eCollection 2022.
A huge focus is being placed on the development of novel signatures in the form of new combinatorial regimens to distinguish the neuroendocrine (NE) characteristics from castration resistant prostate cancer (CRPC) timely and accurately, as well as predict the disease-free survival (DFS) and progression-free survival (PFS) of prostate cancer (PCa) patients.
Single cell data of 4 normal samples, 3 CRPC samples and 3 CRPC-NE samples were obtained from GEO database, and CellChatDB was used for potential intercellular communication, Secondly, using the "limma" package (v3.52.0), we obtained the differential expressed genes between CRPC and CRPC-NE both in single-cell RNA seq and bulk RNA seq samples, and discovered 12 differential genes characterized by CRPC-NE. Then, on the one hand, the diagnosis model of CRPC-NE is developed by random forest algorithm and artificial neural network (ANN) through Cbioportal database; On the other hand, using the data in Cbioportal and GEO database, the DFS and PFS prognostic model of PCa was established and verified through univariate Cox analysis, least absolute shrinkage and selection operator (Lasso) regression and multivariate Cox regression in R software. Finally, somatic mutation and immune infiltration were also discussed.
Our research shows that there exists specific intercellular communication in classified clusters. Secondly, a CRPC-NE diagnostic model of six genes ( and ) has been established and verified, the area under the ROC curve (AUC) is as high as 0.952 (95% CI: 0.882-0.994). The mutation landscape shows that these six genes are rarely mutated in the CRPC and NEPC samples. In addition, NE-DFS signature ( and ) and NE-PFS signature () are good predictors of DFS and PFS in PCa patients and better than other clinical features. Lastly, the infiltration levels of plasma cells, T cells CD4 naive, Eosinophils and Monocytes were significantly different between the CRPC and NEPC groups.
This study revealed the heterogeneity between CRPC and CRPC-NE from different perspectives, and developed a reliable diagnostic model of CRPC-NE and robust prognostic models for PCa.
目前的研究重点是开发新的组合方案，以新型组合方案的形式形成新的组合方案，及时、准确地从去势抵抗性前列腺癌（CRPC）中区分神经内分泌（NE）特征，并预测前列腺癌（PCa）患者的无病生存期（DFS）和无进展生存期（PFS）。
从 GEO 数据库中获取 4 个正常样本、3 个 CRPC 样本和 3 个 CRPC-NE 样本的单细胞数据，使用 CellChatDB 进行潜在的细胞间通讯；其次，使用“limma”包（v3.52.0），我们在单细胞 RNA seq 和批量 RNA seq 样本中获得了 CRPC 和 CRPC-NE 之间差异表达的基因，并发现了 12 个具有 CRPC-NE 特征的差异基因。然后，一方面，通过 Cbioportal 数据库，利用随机森林算法和人工神经网络（ANN）建立和验证 CRPC-NE 的诊断模型；另一方面，利用 Cbioportal 和 GEO 数据库的数据，在 R 软件中通过单因素 Cox 分析、最小绝对收缩和选择算子（Lasso）回归和多因素 Cox 回归建立和验证 PCa 的 DFS 和 PFS 预后模型。最后，还讨论了体细胞突变和免疫浸润。
我们的研究表明，在分类簇中存在特定的细胞间通讯。其次，建立并验证了一个由 6 个基因（和）组成的 CRPC-NE 诊断模型，ROC 曲线下面积（AUC）高达 0.952（95%CI：0.882-0.994）。突变景观表明，这 6 个基因在 CRPC 和 NEPC 样本中很少发生突变。此外，NE-DFS 特征（和）和 NE-PFS 特征（）是 PCa 患者 DFS 和 PFS 的良好预测因子，优于其他临床特征。最后，CRPC 和 NEPC 组之间浆细胞、T 细胞 CD4 幼稚、嗜酸性粒细胞和单核细胞的浸润水平有显著差异。
本研究从不同角度揭示了 CRPC 和 CRPC-NE 之间的异质性，并开发了一种可靠的 CRPC-NE 诊断模型和稳健的 PCa 预后模型。
