结直肠癌(CRC)是全球导致肿瘤相关死亡的主要原因之一。单细胞转录组测序(scRNA-seq)可以为每个细胞提供准确的基因表达数据。本研究通过对CRC样品的scRNA-seq和批量转录组测序数据进行分析，构建了一个新的预后模型，以期深入了解CRC。CRC scRNA-seq数据从GSE161277数据库中下载，CRC批量RNA-seq数据从TCGA和GSE17537数据库中下载。使用scRNA-seq数据中的FindNeighbors和FindClusters功能对细胞进行聚类。应用CIBERSORTx检测批量RNA-seq表达矩阵中细胞群的丰度。使用表达轮廓构建TCGA-CRC基因共表达网络的WGCNA。接下来，我们使用十倍交叉检验构建模型，并使用Nomogram评估模型的独立性以进行临床应用。最后，我们通过qPCR和免疫组织化学检测未报道的模型基因的表达情况。使用克隆形成实验和原位结直肠肿瘤模型来检测未报道的模型基因的调节作用。经过质量控制后，总共包括43,851个细胞，并使用FindCluster()函数将其分类为20个细胞群。我们发现，在CRC肿瘤组织中，C1、C2、C4、C5、C15、C16和C19的丰度高，而C7、C10、C11、C13、C14和C17的丰度低。同时，生存分析结果表明，C4、C11和C13的丰度高，而C5和C14的丰度低与更好的生存相关。WGCNA结果表明，红色模块最相关于肿瘤和C14群集，其中包含615个基因。Lasso Cox回归分析发现了8个基因(PBXIP1、MPMZ、SCARA3、INA、ILK、MPP2、L1CAM和FLNA)，以构建风险模型。在模型中，风险得分特征对生存预测的影响最大，表明8个基因风险模型可以更好地预测预后。qPCR和免疫组织化学分析表明，CRC组织中MPZ、SCARA3、MPP2和PBXIP1的表达水平高。功能实验结果表明，MPZ、SCARA3、MPP2和PBXIP1可以促进体外CRC细胞的克隆形成能力和体内肿瘤发生率。我们基于scRNA-seq和批量RNA-seq数据构建了一个风险模型来预测CRC患者的预后，可供临床应用。我们还确定了4个先前未报道的模型基因(MPZ、SCARA3、MPP2和PBXIP1)作为CRC的新型致癌基因。这些结果表明，该模型可能潜在用于评估预后风险和为CRC患者提供潜在治疗靶点。©2022年作者（S）

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Single-cell transcriptome sequencing (scRNA-seq) can provide accurate gene expression data for individual cells. In this study, a new prognostic model was constructed by scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data of CRC samples to develop a new understanding of CRC.CRC scRNA-seq data were downloaded from the GSE161277 database, and CRC bulk RNA-seq data were downloaded from the TCGA and GSE17537 databases. The cells were clustered by the FindNeighbors and FindClusters functions in scRNA-seq data. CIBERSORTx was applied to detect the abundance of cell clusters in the bulk RNA-seq expression matrix. WGCNA was performed with the expression profiles to construct the gene coexpression networks of TCGA-CRC. Next, we used a tenfold cross test to construct the model and a nomogram to assess the independence of the model for clinical application. Finally, we examined the expression of the unreported model genes by qPCR and immunohistochemistry. A clone formation assay and orthotopic colorectal tumour model were applied to detect the regulatory roles of unreported model genes.A total of 43,851 cells were included after quality control, and 20 cell clusters were classified by the FindCluster () function. We found that the abundances of C1, C2, C4, C5, C15, C16 and C19 were high and the abundances of C7, C10, C11, C13, C14 and C17 were low in CRC tumour tissues. Meanwhile, the results of survival analysis showed that high abundances of C4, C11 and C13 and low abundances of C5 and C14 were associated with better survival. The WGCNA results showed that the red module was most related to the tumour and the C14 cluster, which contains 615 genes. Lasso Cox regression analysis revealed 8 genes (PBXIP1, MPMZ, SCARA3, INA, ILK, MPP2, L1CAM and FLNA), which were chosen to construct a risk model. In the model, the risk score features had the greatest impact on survival prediction, indicating that the 8-gene risk model can better predict prognosis. qPCR and immunohistochemistry analysis showed that the expression levels of MPZ, SCARA3, MPP2 and PBXIP1 were high in CRC tissues. The functional experiment results indicated that MPZ, SCARA3, MPP2 and PBXIP1 could promote the colony formation ability of CRC cells in vitro and tumorigenicity in vivo.We constructed a risk model to predict the prognosis of CRC patients based on scRNA-seq and bulk RNA-seq data, which could be used for clinical application. We also identified 4 previously unreported model genes (MPZ, SCARA3, MPP2 and PBXIP1) as novel oncogenes in CRC. These results suggest that this model could potentially be used to evaluate the prognostic risk and provide potential therapeutic targets for CRC patients.© 2022. The Author(s).