Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Oct 31;26(12):108367.
doi: 10.1016/j.isci.2023.108367. eCollection 2023 Dec 15.

Large-scale analysis of cell-cell communication reveals angiogenin-dependent tumor progression in clear cell renal cell carcinoma

Lucile Massenet-Regad  1   2 , Justine Poirot  1   2 , Margaret Jackson  1 , Caroline Hoffmann  3   4 , Elise Amblard  1   5 , Fanny Onodi  1 , Fatiha Bouhidel  6 , Malika Djouadou  7 , Idir Ouzaid  8 , Evanguelos Xylinas  1   8 , Jasna Medvedovic  1 , Vassili Soumelis  1   4   9
Affiliations

Large-scale analysis of cell-cell communication reveals angiogenin-dependent tumor progression in clear cell renal cell carcinoma

Lucile Massenet-Regad et al. iScience. .

Abstract

Cellular crosstalk in the tumor microenvironment (TME) is still largely uncharacterized, while it plays an essential role in shaping immunosuppression or anti-tumor response. Large-scale analyses are needed to better decipher cell-cell communication in cancer. In this work, we used original and publicly available single-cell RNA sequencing (scRNAseq) data to characterize in-depth the communication networks in human clear cell renal cell carcinoma (ccRCC). We identified 50 putative communication channels specifically used by cancer cells to interact with other cells, including two novel angiogenin-mediated interactions. Expression of angiogenin and its receptors was validated at the protein level in primary ccRCC. Mechanistically, angiogenin enhanced ccRCC cell line proliferation and down-regulated secretion of IL-6, IL-8, and MCP-1 proinflammatory molecules. This study provides novel biological insights into molecular mechanisms of ccRCC, and suggests angiogenin and its receptors as potential therapeutic targets in clear cell renal cancer.

Keywords: Cancer; Microenvironment; Transcriptomics.

PubMed Disclaimer

Conflict of interest statement

V.S. and C.H. are currently employed by Owkin. C.H. serves as an advisor and has received honoraria from Nanobiotix. J.P. is currently employed by Aguettant. The remaining authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
Landscape of communication molecule expression in clear cell renal cell carcinoma (A) Experimental strategy for scRNAseq data generation from 3 freshly resected ccRCC tumors and juxtatumors. After mechanical and enzymatic dissociation, a fraction of the cell suspension was enriched in CD45−cells. All fractions were then stained to sort specific cell populations before sequencing. Cell sorting gating strategy is provided in Figure S1. Illustrations created with BioRender.com. (B) Uniform Manifold Approximation and Projection (UMAP) visualization of the original scRNAseq (n = 3 patients, tumors and juxtatumors), pooled together (left) or displayed separately according to tissue of origin (right), colored according to cluster identity. From the 27,963 pre-processed cells, 23 clusters were identified and manually annotated. NK: natural killer; Treg: regulatory T cell; cDC: conventional dendritic cell; pDC: plasmacytoid dendritic cell. (C) Expression levels of selected genes used for the cell type assignment of the clusters. Color corresponds to the intensity of average expression, and dot size to the percentage of cells expressing the gene in the cluster. (D) Global expression score computed for each family of molecules (cytokines, immune checkpoints, chemokines, growth factors and cell adhesion molecules) considering cells from tumor samples only, grouped by cell types (average expression profile of the cell type). For each family of molecules, global expression scores were centered-reduced as z-scores. Cell types were ordered according to Ward hierarchical clustering using Euclidean distance. (E) Differences of global expression score between cells from tumors and juxtatumors, considering separately each cell type (average expression profile of the cell type in each tissue) and each family of molecules. Significant differences according to Wilcoxon tests with FDR correction and with log2 fold change (log2FC) greater than 0.25 are highlighted with stars. ∗∗∗: p value <0.001.See also Figure S1, Tables S1, and S2.
Figure 2
Figure 2
Characterization of ccRCC cancer cell-specific vocabulary within the TME (A) UMAP of the cancer cells from tumor samples. Two sub-clusters were identified at a resolution of 0.1: ccRCC1 (pink), and ccRCC2 (blue). Proportions of each sub-cluster among the 3 patients are represented later in discussion the UMAP. (B) Proportion of expressed genes belonging to the ribosomal gene family (in percentage), in the function of the total number of expressed genes, for each cancer cell subcluster. (C) Expression of CA9 (ccRCC cancer cell marker) for each cancer cell subcluster (left), and enrichment score of communication genes in each cancer cell subcluster (right). The scores were compared by Wilcoxon tests. (D) Functional enrichment analysis of ccRCC1 compared to ccRCC2 cells (left), or conversely (right). Genes differentially expressed between ccRCC1 and ccRCC2 cells and filtered for minimum log2FC of 0.5 and p < 0.05 were considered. See also Table S4. (E) Expression of genes significantly upregulated by ccRCC2 compared two-by-two to all other non-tumoral cell clusters of the TME. Color corresponds to the intensity of average expression, and dot size to the percentage of cells expressing the gene in the cluster. (F) Expression of VEGFα, EGFR, TGFα, ANG, CD70, OPN, PLXNB2 by two ccRCC cell lines (786-O, Caki2), and a proximal tubule primary cell line (RPTEC), at protein levels. Expression of surface markers is quantified by flow cytometry using specific mean fluorescence intensity (MFI), corresponding to the MFI obtained with specific antibody subtracted by those with the corresponding isotype (n = 5 up to 7 biologically independent replicates). Secreted molecules were measured from cell lines supernatants (n = 8 biologically independent replicates). Data are represented as mean values ±SEM. Conditions were compared using Kruskal–Wallis statistical tests combined with a Dunn’s post-hoc. ns, not significant; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. See also Figure S2.
Figure 3
Figure 3
Identification of communication channels specifically used by cancer cells to interact with ccRCC TME (A) Description of the cell-cell communication analysis to identify ccRCC2 specific interactions in the TME and upregulated as compared to proximal tubules (PT). Equations 1 and 2 are detailed in STAR methods. (B) Barplot representing global communication scores between ccRCC2 and the other cell types included in the scRNAseq dataset. (left) represents the outgoing communication scores, meaning ligand expressed by ccRCC2 and receptors expressed by the other cell types. Conversely, (right) represents the incoming communication scores. The contribution of each family of molecules to the communication scores is represented by the color code. See also Figure S2. (C and D) Intersection of cancer cell-specific ligand/receptor interactions in 3 datasets. (C) represents outgoing communication (ccRCC2 as emitter cell), and (D) incoming communication (ccRCC2 as receiver cell). Based on original dataset, shared interactions between the original and public datasets are represented at the bottom as an heatmap. The heatmap displays the interaction score (from 0 to 100) between ccRCC2 and the other cell type (horizontal axis). See also Figures S3 and S4 and Table S6.
Figure 4
Figure 4
Angiogenin, EGFR and Plexin-B2 protein expression by ccRCC cancer cells (A and B) Angiogenin (red) tissue distribution in three representative ccRCC tumors (A) or juxtatumors (B) by immunofluorescence. Scale bar corresponds to 100 μm. An isotype anti-IgG is used as a control of anti-ANG antibody specificity. See also Figure S5. (C) ANG (red) and CA9 (green) expression on ccRCC human tumors. Scale bar corresponds to 20 μm. (D) Quantification of ANG positive cells normalized by total number of cells in ccRCC tumors (n = 25 images from 12 biologically independent samples) or juxtatumors (n = 19 images from 10 biologically independent samples). Data are represented as mean values ±SEM. Conditions were compared using Mann-Whitney statistical test. (E) Quantification of ANG positive cells normalized by CA9+ positive cells in tumors or PDZK1IP1+ cells in juxtatumors. Data are represented as mean values ±SEM. Conditions were compared using Mann-Whitney statistical test. (F) Surface expression of PLXNB2 (left) or EGFR (right) by immune cells (CD45+), endothelial cells (CD45−CD31+), cancer cells or PT (CD45−CD31−CD13+), and other cells (CD45−CD31−CD13−) in human ccRCC tumors and juxtatumors (n = 9 and n = 8 biologically independent samples). Expression is quantified as specific mean fluorescence intensity (MFI). Data are represented as mean values ±SEM. Conditions were compared using Kruskal–Wallis statistical tests combined with a Dunn’s post-hoc. Gating strategy is provided in Figure S5.
Figure 5
Figure 5
Angiogenin silencing in vitro inhibits cancer cell proliferation and increases pro-inflammatory molecules secretion (A) Secretion levels of ANG of Caki1, Caki2, A498 and 786-O cell lines upon ANG silencing (siANG) or control (siControl) condition (biologically independent replicates, n = 8 for Caki1, Caki2 and A498, n = 6 for 786-O). Conditions were compared using Wilcoxon matched-pairs signed rank tests. Data are presented as mean values ±SEM. (B) Analysis of Caki1, Caki2, A498 and 786-O cell proliferation after transfection with siRNA against ANG (siANG) or control condition (siControl). The proliferation is measured by Incucyte to quantify cell confluency over time. Each independent experiments were run in triplicates (n = 6 independent experiments for A498 and Caki2, n = 7 for Caki1, and n = 3 for 786-O). Data are presented as mean values ±SEM. Conditions were compared using repeated measures two-way ANOVA with Geisser Greenhouse correction combined with a Šidák post hoc. See also Figure S6. (C) Secretion levels IL-6, IL-8, MCP-1 (CCL2) and VEGF-α for Caki2 and 786-O cell lines upon siControl or siANG perturbation (biologically independent replicates, n = 8 for Caki1, Caki2 and A498, n = 6 for 786-O). Data are presented as mean values ±SEM. Conditions were compared using Wilcoxon matched-pairs signed rank tests.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. West N.R., McCuaig S., Franchini F., Powrie F. Emerging cytokine networks in colorectal cancer. Nat. Rev. Immunol. 2015;15:615–629. doi: 10.1038/nri3896. - DOI - PubMed
    1. Berraondo P., Sanmamed M.F., Ochoa M.C., Etxeberria I., Aznar M.A., Pérez-Gracia J.L., Rodríguez-Ruiz M.E., Ponz-Sarvise M., Castañón E., Melero I. Cytokines in clinical cancer immunotherapy. Br. J. Cancer. 2019;120:6–15. doi: 10.1038/s41416-018-0328-y. - DOI - PMC - PubMed
    1. Demasure S., Spriet I., Debruyne P.R., Laenen A., Wynendaele W., Baldewijns M., Dumez H., Clement P.M., Wildiers H., Schöffski P., et al. Overall survival improvement in patients with metastatic clear-cell renal cell carcinoma between 2000 and 2020: a retrospective cohort study. Acta Oncol. 2022;61:22–29. doi: 10.1080/0284186X.2021.1989720. - DOI - PubMed
    1. Rathmell W.K., Rumble R.B., Van Veldhuizen P.J., Al-Ahmadie H., Emamekhoo H., Hauke R.J., Louie A.V., Milowsky M.I., Molina A.M., Rose T.L., et al. Management of Metastatic Clear Cell Renal Cell Carcinoma: ASCO Guideline. J. Clin. Orthod. 2022;40:2957–2995. doi: 10.1200/JCO.22.00868. - DOI - PubMed
    1. Şenbabaoğlu Y., Gejman R.S., Winer A.G., Liu M., Van Allen E.M., de Velasco G., Miao D., Ostrovnaya I., Drill E., Luna A., et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. 2016;17:231. doi: 10.1186/s13059-016-1092-z. - DOI - PMC - PubMed

LinkOut - more resources