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. 2022 Nov 23;15(1):123.
doi: 10.1186/s13048-022-01059-0.

Establishment of an ovarian cancer omentum metastasis-related prognostic model by integrated analysis of scRNA-seq and bulk RNA-seq

Dongni Zhang  1 , Wenping Lu  2 , Shasha Cui  1 , Heting Mei  1 , Xiaoqing Wu  1 , Zhili Zhuo  1
Affiliations

Establishment of an ovarian cancer omentum metastasis-related prognostic model by integrated analysis of scRNA-seq and bulk RNA-seq

Dongni Zhang et al. J Ovarian Res. .

Abstract

Objective: 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.

Methods: 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.

Results: 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 = 0.307*TIMP3 + 3.516*FBN1-0.109*IGKC + 0.209*RPL21 + 0.870*UCHL1 + 0.365*RARRES1. 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.

Conclusion: 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.

Keywords: 6-OMAGs; Omentum metastasis; Ovarian cancer; Prognosis; Tumor microenvironment; scRNA-seq.

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Conflict of interest statement

None.

Figures

Fig. 1
Fig. 1
The technical workflow
Fig. 2
Fig. 2
scRNA-seq data processing and analysis. A The correlation analysis between sequencing depth and mitochondrial genes using Pearson’s method, NA represents no correlation; B The correlation analysis between the number of genes and sequencing depth using Pearson’s method, the Pearson correlation coefficient was 0.89; C The sequencing depth of 9885 cells from 6 ovarian cancer patients; D The number of genes of 9885 cells from 6 ovarian cancer patients; E Detection of the highly variable genes across the cells in volcano plot, the top 10 genes were marked out; F PCA plot of scRNA-seq samples from 6 patients; G The p values of PCs from 1–20 calculated by JackStraw function; H The standard deviation of 1–30 PCs calculated using ElbowPlot function; I Calculation of the cumulative percentages for each PC, 18 is the last point where change of % of variation is more than 0.1%; H The t-SNE algorithm divided the cells into 18 clusters by 18 PCs; I 18 cell clusters were annotated into 14 cell types
Fig. 3
Fig. 3
Reconstruction of differentiation trajectories of ovarian cancer omentum metastasis sites. A The trajectory plot of 18 clusters using monocle analysis; B The trajectory plot of 5 cell states; C The trajectory plot in pseudotime, the darker the color is, the default starting point is represented, and the lighter the color is, the farther it is from the starting point of the pseudotimeline; D The trajectory plot of 14 cell types in 5 states
Fig. 4
Fig. 4
The relevance between cell state characteristic genes and the clinical features of ovarian cancer. A K-M survival curve of 3 clusters; B Age distribution in each cluster; C Proportions of tumor stages in each cluster; D Distribution of sample sources in each cluster; E Distribution of anatomical sites in each cluster; F-O Expression of upregulated and downregulated characteristic genes in 5 states of patients in 3 clusters
Fig. 5
Fig. 5
Analysis of the TME, immune cell infiltration and immune checkpoints. A The proportion of 22 immune cells built on 3 clusters; B The difference of the fraction of 22 immune cells in 3 clusters; C The relationship between the proportion of dendritic cells resting and survival; D The relationship between the proportion of macrophages M2 cells and survival; E-G The relationship between the expression level of PDCD1, CD274, CTLA4 and survival; H The expression of 6 immune checkpoint genes with significant differences among the 3 clusters; I-L The ESTIMATE Score, Immune Score, Stromal Score, Tumor Purity were significantly different between 3 clusters. (*p < 0.05, **p < 0.01, ***p < 0.001, ns: nonsignificance)
Fig. 6
Fig. 6
Construction of a nomogram model based on the OMAG risk signature and clinicopathological characteristics. A The confidence interval under each lambda; B The trajectory of each independent variable: the horizontal axis represents the log value of the independent variable lambda, and the vertical axis represents the coefficient of the independent variable. C Survival difference in high- and low-risk scores of the training set (TCGA-OV); D Survival difference in high- and low-risk scores of the test set (GSE132342); E The prognostic value of the 6-OMAGs signature was evaluated using the ROC curves in the training set (TCGA-OV); F The prognostic value of the 6-OMAGs signature was evaluated using the ROC curves in the test set (GSE132342). G Univariate Cox regression analyses of the 6-OMAGs and clinicopathological data; H Multivariate Cox regression analysis of the 6-OMAGs and clinicopathological data; I The nomogram model was constructed to predict the 3-, 5-, and 10-year survival of ovarian cancer patients. CStatus for person neoplasm cancer status, 0 for tumor free, 1 for with tumor; POutcome for primary therapy outcome success, 1 for complete remission/response, 2 for partial remission/response, 3 for stable disease, 4 for progressive disease. J The calibration curve of the nomogram at 1, 3, and 5 years; K The ROC curve of the nomogram at 5 years
Fig. 7
Fig. 7
Analysis of the expression and function of 6-OMAGs. A Bubble plot of the 6-OMAGs expression level in 18 cell clusters; B-G tSNE maps of the expression of 6-OMAGs in 18 cell clusters
Fig. 8
Fig. 8
The expression distribution of 6-OMAGs mRNA in 45 human ovarian cancer cell lines. The x-axis represents the expression distribution of mRNA, the y-axis represents different cell lines, different colors and the size of dots represent expression
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