| Background Clear cell Renal cell carcinoma(cc RCC),accounting for 70%-80% of Renal cell carcinoma(RCC),is the most common pathological type of RCC.Because of its high invasiveness,chemoresistance and poor response to radiotherapy,about one third of patients have recurrence and metastasis after radical resection.Tumor heterogeneity is one of the main reasons for the above characteristics.The formation of tumor heterogeneity,involves multiple links and multi-step biological pathway dysfunction such as genome and epigenetics.The information of transcriptome can not reveal the whole biological process of cells and the reasons behind the differences of gene expression.Previous studies of cc RCC mostly elucidated its molecular mechanism from the average level of tissue,but could not reveal the heterogeneity between cancer cells.The development of single-cell sequencing technology has promoted a breakthrough in the study of tumor heterogeneity,and numerous studies have been applied to cc RCC.There have been reports on the immune microenvironment and origin of RCC,but there are few studies on the heterogeneity,drug sensitivity and relationship with prognosis in cancer cells.The development of sc ATAC-seq technology allows us to study the accessibility of cell type specific chromatin in samples containing heterogeneous cell populations from the cellular level,and can reflect the whole genome map of open chromatin region.Objective This study demonstrates the molecular characteristics of cc RCC from the two aspects of single-cell transcriptome and chromatin accessibility,focuses on the heterogeneity of tumor cells and its relationship with clinical prognosis,and finds potential targeted drugs,which will lay a foundation for the clinical individualized and accurate treatment of cc RCC.Part 1:Single Cell Transcriptome Sequencing Reveals the Heterogeneity of cc RCC and Clinical SignificanceMethods In this part,we performed sc RNA-seq on ccRCC and adjacent normal kidney samples.Firstly,the cancer cell was identified by calculating the copy number variation of epithelial cells by infer CNV and the expression of characteristic genes(CA9 and NDUFA4L2).Then,cancer cells were re clustered and analyzed,and five cancer cell subsets were defined according to the functional differences of GSVA.Using the clinical data of cc RCC in TCGA database,the cancer cell subsets significantly related to the prognosis were determined by deconvolution algorithm,and then beyondcell analysis was used to screen the drugs sensitive to cancer cell subsets.The cell origin and development trajectory were speculated by RNA velocity and monocle3,and the activity score of transcription factors was evaluated by SCENIC.It also shows the heterogeneity of NK/T cells,endothelial cells and fibroblasts in the tumor microenvironment(TME).Results Through quality control,48247 high-quality cells were obtained for downstream analysis.After dimensionality reduction and clustering,they were divided into 7 different main cell types.The identified cancer cells were re clustered,and cancer cells were divided into five different functional subgroups according to differentially expressed genes and GSVA analysis.Among them,we found C2_Cancer cell expresses cancer stem gene and function,can differentiate into other cancer cells,is related to poor prognosis,and drug-resistant C5_Cancer cell has been found insensitive to most targeted drugs.Then,we also predicted two drugs that are sensitive to cancer cell populations,pranabulin and Mevastatin.At the same time,we found and preliminarily verified CP and REG1 A,two tumor specific markers of cc RCC,and identified new cell types with important functions in the TME of cc RCC.Conclusion This study comprehensive demonstrated the heterogeneity of cc RCC and TME through sc RNA-seq,and provided a valuable single-cell transcriptome map.In addition,we also provide targets related to the survival of cc RCC,which lays a foundation for the research of precision treatment of cc RCC in the future.Part 2: Single Cell Staining Accessibility Atlas and Tumor Heterogeneity of cc RCCMethods We extracted the nuclei of two cases of cc RCC and sequenced them by sc ATAC.After data quality control,Text Frequency Inverse Text Frequency(TF-IDF),Singular Value Decomposition(SVD)and Uniform Manifold Approximation and Projection(UMAP)are used to standardize and reduce the dimension of the data.The gene activity matrix of sc ATAC-seq data and the method of cross pattern integration and label transmission based on sc RNA cell type reference were used to annotate the sc ATAC cell groups.Subsequently,motif and footprint analysis were performed by Signca R package to find cell subsets specific transcription factors.At the same time,we analyzed the corresponding relationship between GWAS-SNP of cc RCC and the open area detected by sc ATAC.Results Through certain quality control standards,we finally retained 6670 high-quality cells from two samples for subsequent analysis,and divided the cells into five types through cross mode integration and label transmission methods,gene activity of common markers and chromatin open peak,GWAS association analysis showed that 14:72812712 and 8:127877125|8:127877126 were closely related to cancer cells.Through motif analysis and transcription factor footprint analysis,cancer cell specific transcription factors including HNF1 A,HNF1B,HNF4 A,HNF4G and POU6F1 were found.In addition,based on the results of sc RNA-seq,we found the specific motif and its transcription factors of each cell type,and further revealed the heterogeneity of cancer cells.Conclusion This part of study describes the single-cell chromatin accessibility landscape of cc RCC for the first time,integrates the single-cell transcriptome information,and reveals the heterogeneity of cc RCC.While supporting the relevant conclusions of single-cell transcriptome,we also explored the epigenetic heterogeneity of cc RCC at the single-cell level,and found specific regulatory factors based on cell type,which provides a reference for the epigenetic study of cc RCC based on sc ATAC-seq. |
PDF Full Text Request