单细胞测序技术已成为肿瘤机制和异质性研究中不可或缺的工具。胰腺腺癌（PAAD）缺乏早期特异性症状，对PAAD进行全面的生物信息学分析有助于发育机制的研究。我们对PAAD的单细胞测序数据GSE165399进行降维分析，得到了特定的细胞簇。然后，通过加权共表达网络分析，我们获得了与细胞簇相关的基因模块，并通过轨迹分析在PAAD中识别了与肿瘤发生相关的细胞簇和基因模块。将PAAD的肿瘤相关基因与细胞簇标记基因和签名模块进行交集，得到与PAAD发生相关的基因，以构建一个基于COX模型的预后风险评估工具。模型的性能通过Kaplan-Meier（K-M）曲线和接收者操作特征（ROC）曲线进行评估。内分泌途径得分通过ssGSEA分析进行评估。对PAAD的单细胞数据集GSE165399进行过滤和降采样，最终过滤出17个细胞亚群和标记17个细胞簇。WGCNA分析表明，棕色模块与肿瘤发生最相关。其中，棕色模块与C11和C14细胞簇显著相关。C11和C14细胞簇分别属于成纤维细胞和循环胎儿细胞，轨迹分析显示成纤维细胞的异质性较低，而循环胎儿细胞的异质性极高。接下来，通过差异分析，我们发现C11细胞簇内的基因与肿瘤发生高度相关。最后，我们构建了RiskScore系统，K-M曲线和ROC曲线显示RiskScore具有客观的临床预后潜力，并在多个数据集中展现了一致的稳健性。低风险组呈现出更高的内分泌代谢和较低的免疫浸润状态。我们确定了由APOL1、BHLHE40、CLMP、GNG12、LOX、LY6E、MYL12B、RND3、SOX4和RiskScore组成的预后模型，RiskScore显示出有前途的临床价值。RiskScore可能具有PAAD的可靠临床预后潜力。版权所有 © 2023 Xu、Chen、Liu、Chu 和 Wang。

Single-cell sequencing technology has become an indispensable tool in tumor mechanism and heterogeneity studies. Pancreatic adenocarcinoma (PAAD) lacks early specific symptoms, and comprehensive bioinformatics analysis for PAAD contributes to the developmental mechanisms.We performed dimensionality reduction analysis on the single-cell sequencing data GSE165399 of PAAD to obtain the specific cell clusters. We then obtained cell cluster-associated gene modules by weighted co-expression network analysis and identified tumorigenesis-associated cell clusters and gene modules in PAAD by trajectory analysis. Tumor-associated genes of PAAD were intersected with cell cluster marker genes and within the signature module to obtain genes associated with PAAD occurrence to construct a prognostic risk assessment tool by the COX model. The performance of the model was assessed by the Kaplan-Meier (K-M) curve and the receiver operating characteristic (ROC) curve. The score of endocrine pathways was assessed by ssGSEA analysis.The PAAD single-cell dataset GSE165399 was filtered and downscaled, and finally, 17 cell subgroups were filtered and 17 cell clusters were labeled. WGCNA analysis revealed that the brown module was most associated with tumorigenesis. Among them, the brown module was significantly associated with C11 and C14 cell clusters. C11 and C14 cell clusters belonged to fibroblast and circulating fetal cells, respectively, and trajectory analysis showed low heterogeneity for fibroblast and extremely high heterogeneity for circulating fetal cells. Next, through differential analysis, we found that genes within the C11 cluster were highly associated with tumorigenesis. Finally, we constructed the RiskScore system, and K-M curves and ROC curves revealed that RiskScore possessed objective clinical prognostic potential and demonstrated consistent robustness in multiple datasets. The low-risk group presented a higher endocrine metabolism and lower immune infiltrate state.We identified prognostic models consisting of APOL1, BHLHE40, CLMP, GNG12, LOX, LY6E, MYL12B, RND3, SOX4, and RiskScore showed promising clinical value. RiskScore possibly carries a credible clinical prognostic potential for PAAD.Copyright © 2023 Xu, Chen, Liu, Chu and Wang.