Oncology Department, China Academy of Chinese Medical Sciences Guang'anmen Hospital, Beijing, China.
J Ovarian Res. 2022 Nov 23;15(1):123. doi: 10.1186/s13048-022-01059-0.
Ovarian cancer has the highest mortality rate among gynecological malignant tumors, and it preferentially metastasizes to omental tissue, leading to intestinal obstruction and death. scRNA-seq is a powerful technique to reveal tumor heterogeneity. Analyzing omentum metastasis of ovarian cancer at the single-cell level may be more conducive to exploring and understanding omentum metastasis and prognosis of ovarian cancer at the cellular function and genetic levels.
The omentum metastasis site scRNA-seq data of GSE147082 were acquired from the GEO (Gene Expression Omnibus) database, and single cells were clustered by the Seruat package and annotated by the SingleR package. Cell differentiation trajectories were reconstructed through the monocle package. The ovarian cancer microarray data of GSE132342 were downloaded from GEO and were clustered by using the ConsensusClusterPlus package into omentum metastasis-associated clusters according to the marker genes gained from single-cell differentiation trajectory analysis. The tumor microenvironment (TME) and immune infiltration differences between clusters were analyzed by the estimate and CIBERSORT packages. The expression matrix of genes used to cluster GSE132342 patients was extracted from bulk RNA-seq data of TCGA-OV (The Cancer Genome Atlas ovarian cancer), and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to establish an omentum metastasis-associated gene (OMAG) signature. The signature was then tested by GSE132342 data. Finally, the clinicopathological characteristics of TCGA-OV were screened by univariate and multivariate Cox regression analysis to draw the nomogram.
A total of 9885 cells from 6 patients were clustered into 18 cell clusters and annotated into 14 cell types. Reconstruction of differentiation trajectories divided the cells into 5 branches, and a total of 781 cell trajectory-related characteristic genes were obtained. A total of 3769 patients in GSE132342 were subtyped into 3 clusters by 74 cell trajectory-related characteristic genes. Kaplan-Meier (K-M) survival analysis showed that the prognosis of cluster 2 was the worst, P < 0.001. The TME analysis showed that the ESTIMATE score and stromal score in cluster 2 were significantly higher than those in the other two clusters, P < 0.001. The immune infiltration analysis showed differences in the fraction of 8 immune cells among the 3 clusters, P < 0.05. The expression data of 74 genes used for GEO clustering were extracted from 379 patients in TCGA-OV, and combined with survival information, 10 candidates for OMAGs were filtered by LASSO. By using multivariate Cox regression, the 6-OMAGs signature was established as RiskScore = 0.307TIMP3 + 3.516FBN1-0.109IGKC + 0.209RPL21 + 0.870UCHL1 + 0.365RARRES1. Taking TCGA-OV as the training set and GSE132342 as the test set, receiver operating characteristic (ROC) curves were drawn to verify the prognostic value of 6-OMAGs. Screened by univariate and multivariate Cox regression analysis, 3 (age, cancer status, primary therapy outcome) of 5 clinicopathological characteristics were used to construct the nomogram combined with risk score.
We constructed an ovarian cancer prognostic model related to omentum metastasis composed of 6-OMAGs and 3 clinicopathological features and analyzed the potential mechanism of these 6-OMAGs in ovarian cancer omental metastasis.
卵巢癌是妇科恶性肿瘤中死亡率最高的肿瘤，其优先转移至网膜组织，导致肠梗阻和死亡。scRNA-seq 是揭示肿瘤异质性的强大技术。在单细胞水平上分析卵巢癌的网膜转移可能更有利于探索和理解卵巢癌在细胞功能和遗传水平上的网膜转移和预后。
从 GEO（基因表达综合数据库）数据库中获取 GSE147082 的网膜转移部位 scRNA-seq 数据，使用 Seruat 包对单细胞进行聚类，并使用 SingleR 包进行注释。通过 monocle 包重建细胞分化轨迹。从 GEO 下载 GSE132342 的卵巢癌微阵列数据，使用 ConsensusClusterPlus 包根据单细胞分化轨迹分析获得的标记基因将其聚类为与网膜转移相关的簇。使用 estimate 和 CIBERSORT 包分析簇之间的肿瘤微环境（TME）和免疫浸润差异。从 TCGA-OV（癌症基因组图谱卵巢癌）的批量 RNA-seq 数据中提取用于聚类 GSE132342 患者的基因表达矩阵，使用最小绝对收缩和选择算子（LASSO）和多变量 Cox 回归建立与网膜转移相关的基因（OMAG）特征。然后使用 GSE132342 数据进行测试。最后，通过单变量和多变量 Cox 回归分析筛选 TCGA-OV 的临床病理特征，绘制列线图。
从 6 名患者的 9885 个细胞中聚类成 18 个细胞簇，并注释成 14 种细胞类型。分化轨迹的重建将细胞分为 5 个分支，共获得 781 个与细胞轨迹相关的特征基因。GSE132342 中的 3769 名患者使用 74 个与细胞轨迹相关的特征基因分为 3 个簇。Kaplan-Meier（K-M）生存分析显示，第 2 簇的预后最差，P<0.001。TME 分析表明，第 2 簇的 ESTIMATE 评分和基质评分明显高于其他 2 个簇，P<0.001。免疫浸润分析显示 3 个簇之间 8 种免疫细胞的比例存在差异，P<0.05。从 TCGA-OV 的 379 名患者中提取用于 GEO 聚类的 74 个基因的表达数据，并结合生存信息，使用 LASSO 筛选出 10 个 OMAG 候选基因。通过多变量 Cox 回归，建立了由 6 个 OMAGs 组成的 RiskScore=0.307TIMP3+3.516FBN1-0.109IGKC+0.209RPL21+0.870UCHL1+0.365RARRES1 的风险评分模型。以 TCGA-OV 为训练集，GSE132342 为测试集，绘制受试者工作特征（ROC）曲线验证 6-OMAGs 的预后价值。通过单变量和多变量 Cox 回归分析筛选出 5 个临床病理特征中的 3 个（年龄、癌症状态、原发性治疗效果），结合风险评分构建了列线图。
我们构建了一个由 6 个 OMAGs 和 3 个临床病理特征组成的与卵巢癌网膜转移相关的预后模型，并分析了这些 6-OMAGs 在卵巢癌网膜转移中的潜在机制。
