Prognosis Prediction Through an Integrated Analysis of Single-Cell and Bulk RNA-Sequencing Data in Triple-Negative Breast Cancer - Wang, Xiangru; Chen, Hanghang (Frontiers in Genetics, 2022)
Background: Due to the genomic and antigenic heterogeneity, it poses challenges to precisely predict the prognosis of triple-negative breast cancer (TNBC) patients. This study aimed to explore the crucial genes in cell differentiation and tumor malignant grade to predict the prognosis of TNBC patients through a comprehensive analysis of single cell RNA-sequencing data. Methods: We collected RNA-sequencing (RNA-seq) data and macroarray data of TNBC from the Molecular Taxonomy of Breast Cancer International Consortium and the Gene Expression Ominibus datasets. First, differentially expressed genes (DEGs) among trajectories from 1534 cells of 6 TNBC patients were identified though PCA, t-SNE and single cell trajectory analyses. Second, we further explore the grade- and tumor mutational burden-related DEGs through Weighted Correlation Network Analysis using the METABRIC data. Then the DEGs among trajectories were used to construct a prognostic signature and 4 independent GEO datasets were merged to validate it. Moreover, considering the differences of immune related passways between high- and low-risk groups through gene set variation analysis, we explored the immune differences between two groups. Results: A prognostic signature comprising 10 genes was developed to divide TNBC patients into high-risk and low-risk groups, and its prognostic performance was excellent in two independent patient cohorts (n=1053, 620). The high-risk group generally had lower immune cell infiltration and activities than those of the low-risk group. Conclusion: A 10-gene signature was successfully constructed to assess the prognosis of TNBC patients. Interestingly, the signature was also found closely associated with the tumor immune infiltration, which might suggest the crucial roles of immune cells in malignant progression of TNBC.