Dataset Information

Single-cell multiomics analysis reveals regulatory programs in clear cell renal cell carcinoma.

ABSTRACT: The clear cell renal cell carcinoma (ccRCC) microenvironment consists of many different cell types and structural components that play critical roles in cancer progression and drug resistance, but the cellular architecture and underlying gene regulatory features of ccRCC have not been fully characterized. Here, we applied single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to generate transcriptional and epigenomic landscapes of ccRCC. We identified tumor cell-specific regulatory programs mediated by four key transcription factors (TFs) (HOXC5, VENTX, ISL1, and OTP), and these TFs have prognostic significance in The Cancer Genome Atlas (TCGA) database. Targeting these TFs via short hairpin RNAs (shRNAs) or small molecule inhibitors decreased tumor cell proliferation. We next performed an integrative analysis of chromatin accessibility and gene expression for CD8⁺ T cells and macrophages to reveal the different regulatory elements in their subgroups. Furthermore, we delineated the intercellular communications mediated by ligand-receptor interactions within the tumor microenvironment. Taken together, our multiomics approach further clarifies the cellular heterogeneity of ccRCC and identifies potential therapeutic targets.

SUBMITTER: Long Z

PROVIDER: S-EPMC9296597 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Renal cell carcinoma (RCC) is one of the leading causes of cancer-related death worldwide. Tumour metastasis and heterogeneity lead to poor survival outcomes and drug resistance in patients with metastatic RCC (mRCC). In this study, we aimed to assess intratumoural heterogeneity (ITH) in mRCC cells by performing a combined analysis of bulk data and single-cell RNA-sequencing data, and develop novel biomarkers for prognosis prediction on the basis of the potential molecular mechanisms underlying tumorigenesis. Eligible single-cell cohorts related to mRCC were acquired using the Gene Expression Omnibus (GEO) dataset to identify potential mRCC subpopulations. We then performed gene set variation analysis to understand the differential function in primary RCC and mRCC samples. Subsequently, we applied weighted correlation network analysis to identify coexpressing gene modules that were related to the external trait of metastasis. Protein-protein interactions were used to screen hub subpopulation-difference (sub-dif) markers (ACTG1, IL6, CASP3, ACTB and RAP1B) that might be involved in the regulation of RCC metastasis and progression. Cox regression analysis revealed that ACTG1 was a protective factor (HR < 1), whereas the other four genes (IL6, CASP3, ACTB and RAP1B) were risk factors (HR > 1). Kaplan-Meier survival analysis suggested the potential prognostic value of these sub-dif markers. The expression of sub-dif markers in mRCC was further evaluated in clinical samples by immunohistochemistry (IHC). Additionally, the genetic features of sub-dif marker expression patterns, such as genetic variation profiles, correlations with tumour-infiltrating lymphocytes (TILs), and targeted signalling pathway activities, were assessed in bulk RNA-seq datasets. In conclusion, we established novel subpopulation markers as key prognostic factors affecting EMT-related signalling pathway activation in mRCC, which could facilitate the implementation of a treatment for mRCC patients.

Project description:BackgroundShugoshin-1 (SGOL1) is a mammalian ortholog of Shugoshin in yeast and is essential for precise chromosome segregation during mitosis and meiosis. Aberrant SGOL1 expression was reported to be closely correlated with the malignant progression of various tumors. However, the expression pattern and biological function of SGOL1 in clear cell renal cell carcinoma (ccRCC) are unclear.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provide mRNA expression data and outcome information for ccRCC patients. Immunohistochemistry (IHC) of ccRCC tissue chips verified SGOL1 protein expression in ccRCC patients. Data processing and visualization were performed with the UALCAN, TISIDB, TIMER, GSCA, LinkedOmics, and starBase databases. Gene Ontology (GO) annotation and gene set enrichment analysis (GSEA) were used to identify SGOL1-related biological functions and signaling pathways. Immune infiltration analysis was performed using the TISIDB database, ssGSEA algorithm, and TCGA-KIRC cohort. The biological role of SGOL1 in ccRCC was investigated using a series of in vitro cytological assays, including the MTT assay, EdU staining assay, flow cytometry analysis, Transwell assay, and wound healing assay.ResultsSGOL1 was highly expressed in ccRCC and linked to adverse clinicopathological parameters and unfavorable prognosis. Multivariate logistic regression and nomogram calibration suggested that SGOL1 might serve as an independent and reliable prognostic predictor of ccRCC. Functional enrichment analysis indicated that SGOL1 may be involved in the cell cycle, the p53 pathway, DNA replication, and T-cell activation. Furthermore, tumor microenvironment (TME) analysis suggested that SGOL1 was positively associated with Treg infiltration and immune checkpoint upregulation. In addition, we identified a potential SNHG17/PVT1/ZMIZ1-AS1-miR-23b-3p-SGOL1 axis correlated with ccRCC carcinogenesis and progression. Finally, we demonstrated that SGOL1 promoted ccRCC cell proliferation, migratory capacity, and invasion in vitro.ConclusionsSGOL1 potentially functions as an oncogene in ccRCC progression and might contribute to the immunosuppressive TME by increasing Treg infiltration and checkpoint expression, suggesting that targeting SGOL1 could be a novel therapeutic strategy for the treatment of ccRCC patients.

Project description:Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous cancer that poses great challenge to clinical treatment and prognostic prediction. Characterizing the cellular landscape of ccRCC in a single-cell dimension can help better understand the tumor heterogeneity and molecular mechanisms of ccRCC. This study analyzed single-cell profiles in ccRCC samples and para-tumor samples from Gene Expression Omnibus and identified a highly heterogeneous subcluster of renal tubule cells. Single-cell regulatory network inference and clustering analyses and cell communication analysis were performed to develop transcription factor-target gene regulatory networks and cell-cell interactions. Additionally, the distribution and prognostic risk of renal tubule cells from spatial transcriptome data (GSM6415706) and The Cancer Genome Atlas-Kidney Clear Cell Carcinoma data were analyzed. A total of 10 cell types were identified in ccRCC and para-tumor samples. The ccRCC renal tubule cells showed a high expression of the oncogene nicotinamide N-methyltransferase and a significantly high degree of tumor heterogeneity. We further identified 6 cell subclusters with specific expression of BEX2, PTHLH, SFRP2, KLRB1, ADGRL4, and HGF from the ccRCC renal tubule cells. ADGRL4+ renal tubule cells had highly metastatic and angiogenesis-inducing characteristics, with more ADGRL4+ renal tubule cells indicating a worse survival. ADGRL4+ renal tubule cells regulated the metastasis of other renal tubule cells through metastasis-related receptor-ligand communication. We also found that ADGRL4+ renal tubule cells clustered around the glomeruli but the rest of the renal tubule cell subclusters rarely localized in ccRCC tissues. ETS1 and ELK3 -dominant GRNs were remarkably activated in ADGRL4+ renal tubule cells, functionally, knockdown of ELK3 in A498 significantly disturbedaffected the cell migration and invasion. ADGRL4+ renal tubule cells, which were highly metastatic and invasive, might be an essential cell subcluster for ccRCC, and ADGRL4 could be used a novel therapeutic target.

Project description:Bilateral renal clear cell carcinoma (BRCC) is a rare type of renal cell carcinoma (RCC) that accounts for only 1-5% of RCC cases and has a poor clinical prognosis. The origin, tumor microenvironment, cellular molecular features, and intra-tumoral heterogeneity of BRCC are still unclear. We downloaded BRCC single-cell transcriptome sequencing data from the gene expression omnibus database biochip GSE171306, containing 3,575 cells from left-sided clear cell renal cell carcinoma (ccRCC) and 3,568 cells from right-sided ccRCC, and used a series of R packages for data quality control (QC) and subsequent analysis of BRCC single-cell transcriptome data, including the use of the R packages Seurat and scCancer for cell QC, identification of major cell types, and cell annotation; R package scran for calculation of cell cycle scores; R package infercnv for malignancy scoring of tumor cells; R package ReactomeGSA for functional enrichment analysis; R package Monocle 2 for the analysis of cell differentiation trajectories; and R package CellphoneDB for the analysis of intercellular interactions. In this study, by analyzing the high-quality single-cell transcriptome data of BRCC, we identified 18 cell types and found that left- and right-sided ccRCC were approximately the same in terms of cell type and the number of each cell but differed significantly in terms of tumor cell malignancy score, tumor microenvironment, and cell stemness score. In the cell differentiation trajectory analysis of BRCC, we found that endothelial cells and macrophages play an extremely important role in its tumor progression. Further cell communication analysis was performed, and we found that it may signal through ligand-receptors, such as vascular endothelial growth factor-vascular endothelial growth factor receptor1 (VEGF-VEGFR1), MIF-(CD74-CXCR4), and growth arrest-specific protein 6-AXL, to influence the development of BRCC. The analysis of single-cell transcriptomic data of human BRCC suggests that left- and right-sided ccRCC may be of the same tumor origin, but the left-sided ccRCC is more malignant and has a better immune response.

Dataset Information

Single-cell multiomics analysis reveals regulatory programs in clear cell renal cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets