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pmid:41053280
Transcriptome analysis combined with single-cell analysis identified that APOC1 influences cholesterol transport by macrophages in ccRCC.
Fengran Guo · Ziyang Ma · Hu Wang · Zhenwei Han
Scientific reports · 2025 doi:10.1038/s41598-025-18392-1
Paper
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Experiment
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Data
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Analysis
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Execution
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Complete file
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In this study, we obtained transcriptome RNA sequencing data from the Cancer Genome A", "their microenvironments25. By measuring the transcriptome at the single-cell level, scRNA-seq can more", "traditional methodologies26. The single-cell transcriptome dataset GSE121636 was retrieved from the GEO"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["SE121636. Various techniques, including PCR, Western blotting, migration assays, scratch wound assays, flo"], "order_basis": "paper_text_order:540", "id": "assay:2"}, {"role": "data_generation", "definition_id": "flow_cytometry", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ing, migration assays, scratch wound assays, flow cytometry, and immunofluorescence staining, were utili"], "order_basis": "paper_text_order:598", "id": "assay:3"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["s, scratch wound assays, flow cytometry, and immunofluorescence staining, were utilized to elucidate the fun"], "order_basis": "paper_text_order:618", "id": "assay:4"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["related genes were identified. Additionally, single-cell RNA sequencing data were integrated to determine the immune", "hment analysis on these genes. Additionally, single-cell RNA sequencing data were integrated to determine the immune", "ontrols and ccRCC samples The advancement of single-cell RNA sequencing (scRNA-seq) has facilitated a comprehensive", "rehensive immune profile was generated using single-cell RNA-seq data derived from 25,000 immune cells isolat"], "order_basis": "paper_text_order:912", "id": "assay:5"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["A total of 501 tumor samples were selected from 538 clear cell renal cell carcinoma (ccRCC) cases, with Kaplan–Meier method employed to generate survival curves. The log-rank test was applied to assess statistical significance, with a p-value threshold set at 0.05. The ggpubr package was loaded to perform correlation analysis between various scores and clinical pathological features, with statistical significance evaluated using the Wilcoxon rank sum test."], "order_basis": "consumes_earlier_output", "id": "assay:6"}]}