Bcl9 Depletion Modulates Endothelial Cell in Tumor Immune Microenvironment in Colorectal Cancer Tumor

Abstract

Tumor endothelial cells are an important part of the tumor microenvironment, and angiogenesis inhibitory therapy has shown potential in tumor treatment. However, which subtypes of tumor endothelial cells are distributed in tumors, what are the differences between tumor endothelial cells and normal endothelial cells, and what is the mechanism of angiogenesis inhibitory therapy at the histological level, are all need to be resolved urgently. Using single-cell mRNA sequencing, we analyzed 12 CT26 colon cancer samples from mice, and found that knockdown of the downstream factor BCL9 in the Wnt signaling pathway or inhibitor-mediated functional inhibition can modulate tumor endothelial cells at a relatively primitive stage, inhibiting their differentiation into further extracellular matrix construction and angiogenesis functions. Furthermore, we propose a BCL9-endo-Score based on the differential expression of cells related to different states of BCL9 functions. Using published data sets with normal endothelial cells, we found that this score can characterize endothelial cells at different stages of differentiation. Finally, in the The Cancer Genome Atlas (TCGA) pan-cancer database, we found that BCL9-endo-Score can well predict the prognosis of diseases including colon cancer, kidney cancer and breast cancer, and identified the markers of these tumor subtypes, provide a basis for the prognosis prediction of patients with such types of tumor. Our data also contributed knowledge for tumor precision treatment with angiogenesis inhibitory therapy by targeting the Wnt signaling pathway.

Tumor-infiltrated activated B cells suppress liver metastasis of colorectal cancers

“…We therefore further analyzed the balance of Ig at the clinical and genetic levels. We constructed the HeavytoLight score to evaluate the balance of Ig light-and heavy-chain gene expression (Wei et al, 2020(Wei et al, , 2021. The liver-metastatic group had lower HeavytoLight scores than the nonmetastatic group (p = 0.037) (Figure S7A), which was consistent with our scRNA-seq observations.…”

Inhibition of BCL9 Modulates the Cellular Landscape of Tumor-Associated Macrophages in the Tumor Immune Microenvironment of Colorectal Cancer

“…Among them, cluster 23 was composed mainly of BRCA, and cluster 5 was composed mainly of BRCA, Uterine Corpus Endometrial Carcinoma (UCEC), and Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). According to our previous reports, cluster 5 had marker genes TYR and MLANA (Wei et al, 2020). These results suggested that TYR and MLANA may be used as pathological criteria for prognostic prediction with TAM-to-NK score.…”

“…After completing the principal component analysis of genes, UMAP, which is a dimensionality reduction method based on manifold analysis, grouped the cells together according to the similarity of gene expression (Figure 2A). Through the clustering analysis of all cells, we found that these cells can be divided into eight clusters corresponding to tumor cells, tumor-associated monocytes, TAMs, tumor-associated endothelial cells, fibroblasts, and T cells (Figure 2A), these classifications are based on known markers or bioinformatics research (Wei et al, 2020). To study the potential differences in TAMs, we extracted the TAM population based on the cell information we previously reported (Wei et al, 2020), and performed re-clustering analysis (Step 1 re-clustering; Figures 2B-E).…”

“…Through the clustering analysis of all cells, we found that these cells can be divided into eight clusters corresponding to tumor cells, tumor-associated monocytes, TAMs, tumor-associated endothelial cells, fibroblasts, and T cells (Figure 2A), these classifications are based on known markers or bioinformatics research (Wei et al, 2020). To study the potential differences in TAMs, we extracted the TAM population based on the cell information we previously reported (Wei et al, 2020), and performed re-clustering analysis (Step 1 re-clustering; Figures 2B-E). Most cells showed a notable overlap in the reduced dimension maps between different samples or treatment groups (Figures 2A-C) large number of cells continuously distributed topological structure (cells circled by red dashed line; Figure 2B).…”

“…Among them, Breast invasive carcinoma (BRCA) and Head and Neck squamous cell carcinoma (HNSC) were the most prominent (Figures 8C,E). Because tumors classified according to their location of incidence have large gene expression pattern unpredictability, we previously reported a method to cluster the samples of the TCGA pan-cancer dataset according to their gene expression patterns (Wei et al, 2020). The advantage of this method is that it can eliminate the differences between the samples as much as possible, so that the comparison of specific gene sets or clinical prognosis are based on a highly consistent population.…”