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. 2024 Feb 12;19(2):e0298125.
doi: 10.1371/journal.pone.0298125. eCollection 2024.

Comprehensive analysis of single cell and bulk data develops a promising prognostic signature for improving immunotherapy responses in ovarian cancer

Affiliations

Comprehensive analysis of single cell and bulk data develops a promising prognostic signature for improving immunotherapy responses in ovarian cancer

Huanfei Ding et al. PLoS One. .

Abstract

The tumor heterogeneity is an important cause of clinical therapy failure and yields distinct prognosis in ovarian cancer (OV). Using the advantages of integrated single cell RNA sequencing (scRNA-seq) and bulk data to decode tumor heterogeneity remains largely unexplored. Four public datasets were enrolled in this study, including E-MTAB-8107, TCGA-OV, GSE63885, and GSE26193 cohorts. Random forest algorithm was employed to construct a multi-gene prognostic panel and further evaluated by receiver operator characteristic (ROC), calibration curve, and Cox regression. Subsequently, molecular characteristics were deciphered, and treatments strategies were explored to deliver precise therapy. The landscape of cell subpopulations and functional characteristics, as well as the dynamic of macrophage cells were detailly depicted at single cell level, and then screened prognostic candidate genes. Based on the expression of candidate genes, a stable and robust cell characterized gene associated prognosis signature (CCIS) was developed, which harbored excellent performance at prognosis assessment and patient stratification. The ROC and calibration curves, and Cox regression analysis elucidated CCIS could serve as serve as an independent factor for predicting prognosis. Moreover, a promising clinical tool nomogram was also constructed according to stage and CCIS. Through comprehensive investigations, patients in low-risk group were charactered by favorable prognosis, elevated genomic variations, higher immune cell infiltrations, and superior antigen presentation. For individualized treatment, patients in low-risk group were inclined to better immunotherapy responses. This study dissected tumor heterogeneity and afforded a promising prognostic signature, which was conducive to facilitating clinical outcomes for patients with OV.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig 1
Fig 1. Cell clusters and cell subpopulation of ovarian cancer tissues.
(A-B) The t-distributed stochastic neighbor embedding (tSNE) plot showing the cell clusters (A) and cell annotations (B) for distinct cell types in ovarian cancer. (C-E) The heatmap (C), bubble chart (D), and Violin plots (E) demonstrating the identity of each subpopulation through showing the expression of each cell type specific markers.
Fig 2
Fig 2. The dynamic change of macrophage cells and potential biological process in single cell level.
(A) Proportions of seven cell types originated from each ovarian cancer sample. (B) The t-SNE plot of only macrophage cells, displaying the cell clusters in ovarian cancer. (C-D) Differentiation trajectory of macrophage cells in HCC, with a color code for cell subpopulations (C) and pseudo-time (D). (E) Heatmap of 50 Hallmark gene sets among seven cell subpopulations using the gene set variation analysis (GSVA) algorithm.
Fig 3
Fig 3. Combination of scRNA-seq and bulk data develops a molecular signature.
(A-C) Kaplan-Meier curves of overall survival (OS) according to the signature in TCGA-OV (A), GSE63885 (B), and GSE26193 (C) cohorts, respectively. (D-F) Time-dependent ROC analysis for predicting OS at 1/3/5 years in TCGA-OV (D), GSE63885 (E), and GSE26193 (F) cohorts, respectively. (G-I) Calibration curves were employed to compare the actual probabilities and the predicted probabilities of OS in TCGA-OV (G), GSE63885 (H), and GSE26193 (I) cohorts, respectively.
Fig 4
Fig 4. The potential significance of clinical transformation.
(A) Kaplan-Meier curves of progression-free survival (PFS) according to the signature in TCGA-OV cohort. (B) Kaplan-Meier curves of disease-free survival (DFS) according to the signature in TCGA-OV cohort. (C-E) Multivariate Cox regression of OS (C), PFS (D), and DFS (E) in TCGA-OV cohort. (F) A prognostic nomogram based on two indicators included stage and risk group. (G) Based on the nomogram, the Time-dependent ROC analysis for predicting OS at 1/3/5 years.
Fig 5
Fig 5. The underlying biological behaviors and genomic variations.
(A) Based on GO and KEGG, exploring underlying biological characteristics by GSEA analysis according to the signature. (B-D) Composition mutated percentage of top three common somatic mutation gene, including TP53 (B), TTN (C), and CSMD3 (D) genes. (E) Distribution of tumor mutational burden (TMB) between high-risk and low-risk groups. (F-H) Distributions of fraction of genome alteration (F), fraction of genomic gained (G), and fraction of genome lost (H) between high-risk and low-risk groups. nsP> 0.05, **P< 0.01.
Fig 6
Fig 6. The landscape of copy number variation in ovarian cancer patients.
(A-D) Distributions of copy number variations at chromosome arm gain (A), arm loss (B), focal gain (C), and focal loss (D) level, respectively. (E) The gain and loss of copy number percentage in high-risk group. (F) The gain and loss of copy number percentage in low-risk group. nsP> 0.05, *P< 0.05, ***P< 0.001.
Fig 7
Fig 7. Evaluation of immune infiltration and antigen presentation.
(A) Infiltration abundance of 28 immune cell subsets (B) Comparison of leukocyte fraction between high-risk and low-risk groups. (C) The correlation between homologous recombination defects (HRD) and risk score, and the comparison of HRD between high-risk and low-risk groups. (D) The correlation between neoantigens and risk score, and the comparison of neoantigens between high-risk and low-risk groups. (E) Distribution of nine HLA molecular expressions. (F-G) Distribution of antigen presentation score (F) and immunophenoscore (G) between high-risk and low-risk groups. *P< 0.05, **P< 0.01, ***P< 0.001, ****P< 0.0001.
Fig 8
Fig 8. The assessment of immunotherapy and development of potential drugs.
(A) Distribution of co-stimulatory molecules between high-risk and low-risk groups. (B) Comparison of T cell inflammatory signature scores between high-risk and low-risk groups. (C) Distributions of TIDE score between high-risk and low-risk groups. (D) The evaluation of immunotherapy using Submap analysis. (E-I) The promising therapeutic drugs for patients in high-risk group, encompassing Pazopanib (E), Vinorelbine (F), Vorinostat (G), Shikonin (H), and FH535 (I). *P< 0.05, ***P< 0.001.

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