{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16(13)"],"submitter":["Zheng J"],"pubmed_abstract":["The genomic landscape of clear cell renal cell carcinoma (ccRCC) has a considerable intra-tumor heterogeneity, which is a significant obstacle in the field of precision oncology and plays a pivotal role in metastasis, recurrence, and therapeutic resistance of cancer. The mechanisms of intra-tumor heterogeneity in ccRCC have yet to be fully established. We integrated single-cell RNA sequencing (scRNA-seq) and transposase-accessible chromatin sequencing (scATAC-seq) data from a single-cell multi-omics perspective. Based on consensus non-negative matrix factorization (cNMF) algorithm, functionally heterogeneous cancer cells were classified into metabolism, inflammatory, and EMT meta programs, with spatial transcriptomics sequencing (stRNA-seq) providing spatial information of such disparate m"],"journal":["Aging"],"pagination":["10943-10971"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11272124"],"repository":["biostudies-literature"],"pubmed_title":["Unfolding the mysteries of heterogeneity from a high-resolution perspective: integration analysis of single-cell multi-omics and spatial omics revealed functionally heterogeneous cancer cells in ccRCC."],"pmcid":["PMC11272124"],"pubmed_authors":["Zheng J","Su C","Lu W","Wang C","Chen S","Zhang Q"],"additional_accession":[]},"is_claimable":false,"name":"Unfolding the mysteries of heterogeneity from a high-resolution perspective: integration analysis of single-cell multi-omics and spatial omics revealed functionally heterogeneous cancer cells in ccRCC.","description":"The genomic landscape of clear cell renal cell carcinoma (ccRCC) has a considerable intra-tumor heterogeneity, which is a significant obstacle in the field of precision oncology and plays a pivotal role in metastasis, recurrence, and therapeutic resistance of cancer. The mechanisms of intra-tumor heterogeneity in ccRCC have yet to be fully established. We integrated single-cell RNA sequencing (scRNA-seq) and transposase-accessible chromatin sequencing (scATAC-seq) data from a single-cell multi-omics perspective. Based on consensus non-negative matrix factorization (cNMF) algorithm, functionally heterogeneous cancer cells were classified into metabolism, inflammatory, and EMT meta programs, with spatial transcriptomics sequencing (stRNA-seq) providing spatial information of such disparate m","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jun","modification":"2026-06-01T10:02:31.35Z","creation":"2025-04-19T13:14:16.664Z"},"accession":"S-EPMC11272124","cross_references":{"pubmed":["38944814"],"doi":["10.18632/aging.205974"]}}