Identification of Differentially Methylated Genes Associated with Clear Cell Renal Cell Carcinoma and Their Prognostic Values.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36793506", "kind": "science-record", "revision": 4, "content_hash": "sha256:3b2714b03ce1f8d583f67a297f17cc697e86bd81cea03eae7c0e58ef6fdef822", "created_at": "2026-11-01T19:01:23Z", "updated_at": "2026-11-01T22:51:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Differentially Methylated Genes Associated with Clear Cell Renal Cell Carcinoma and Their Prognostic Values.", "doi": "10.1155/2023/8405945", "pmid": "36793506", "authors": ["Bin Wan", "Yang Yang", "Zhuo Zhang"], "journal": "Journal of environmental and public health", "year": 2023, "abstract": "Objective: Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes among which clear cell RCC (ccRCC) accounts for 70% of all RCC cases. DNA methylation constitutes a main part of the molecular mechanism of cancer evolution and prognosis. In this study, we aim to identify differentially methylated genes related to ccRCC and their prognostic values.\nMethods: The GSE168845 dataset was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between ccRCC tissues and paired tumor-free kidney tissues. DEGs were submitted to public databases for functional and pathway enrichment analysis, protein-protein interaction (PPI) analysis, promoter methylation analysis, and survival correlation analysis.\nResults: In the setting of |log2FC| ≥ 2 and adjusted p value <0.05 during differential expression analysis of the GSE168845 dataset, 1659 DEGs between ccRCC tissues and paired tumor-free kidney tissues were sorted out. The most enriched pathways were \"T cell activation\" and \"cytokine-cytokine receptor interaction.\" After PPI analysis, 22 hub genes related to ccRCC stood out, among which CD4, PTPRC, ITGB2, TYROBP, BIRC5, and ITGAM exhibited higher methylation levels, and BUB1B, CENPF, KIF2C, and MELK exhibited lower methylation levels in ccRCC tissues compared with paired tumor-free kidney tissues. Among these differentially methylated genes, TYROBP, BIRC5, BUB1B, CENPF, and MELK were significantly correlated with the survival of ccRCC patients (p < 0.001).\nConclusion: Our study indicates the DNA methylation of TYROBP, BIRC5, BUB1B, CENPF, and MELK may be promising results for the prognosis of ccRCC.", "claims": [{"id": "claim-method", "statement": "Differential gene analysis was performed using the limma Bioconductor R package, and the p value was corrected by using the Benjamini–Hochberg false discovery rate (FDR).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36793506", "locator": "sections[3] \"2.1. Analysis of Microarray Data\" paragraph 2", "quote": "Differential gene analysis was performed using the limma Bioconductor R package, and the p value was corrected by using the Benjamini–Hochberg false discovery rate (FDR)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GSE168845 datasets were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo) that is a public database. Other data used to support the findings of this study are included within the article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36793506", "locator": "sections[15] \"Data Availability\" paragraph 2", "quote": "The GSE168845 datasets were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo) that is a public database. Other data used to support the findings of this study are included within the article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified 22 ccRCC-related hub genes and found that TYROBP, BIRC5, BUB1B, CENPF, and MELK were significantly correlated with ccRCC patient survival (p < 0.001).", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "Results of survival analysis, it was found that TYROBP, BIRC5, BUB1B, CENPF, and MELK were significantly correlated with the survival of ccRCC patients (Figure 5, p < 0.001)."}, "evidence": [{"asset": "source:pmid:36793506", "locator": "sections[11] \"3.3. Identification of Hub Genes Related to ccRCC\" paragraph 2", "quote": "There were 22 genes with degree values not less than 50 and they were deemed as hub genes related to ccRCC."}, {"asset": "source:pmid:36793506", "locator": "sections[13] \"3.5. Survival Correlation Analysis of Hub Genes with ccRCC\" paragraph 2", "quote": "Results of survival analysis, it was found that TYROBP, BIRC5, BUB1B, CENPF, and MELK were significantly correlated with the survival of ccRCC patients (Figure 5, p < 0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified 22 ccRCC-related hub genes and found that TYROBP, BIRC5, BUB1B, CENPF, and MELK were significantly correlated with ccRCC patient survival (p < 0.001).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36793506"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE168845"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {"fdr_threshold": "with Benjamini–Hochberg false discovery rate correction, together wi"}, "inputs": ["data:geo:GSE168845"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36793506", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2023/8405945", "sha256": "sha256:4b15076a97d1f82d5cf1d28466d8c8c75fdb6e124c5c9df054dd1330832c0568", "bytes": 17244, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2023/8405945", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9925242", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE168845", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE168845", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE168845", "version": null, "note": "The GSE168845 datasets were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo) that is a public database. 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Other data used to support the findings of this study are included within the article.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM5171251", "GSM5171252", "GSM5171253", "GSM5171254", "GSM5171255", "GSM5171256", "GSM5171257", "GSM5171258"], "sample_count": 8, "group_sizes": null, "group_sizes_basis": null, "platform": "21185", "taxon": "Homo sapiens", "title": "Gene expression profile of cancerous and non-cancerous renal tissues", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE168845 not downloaded", "asset:data:geo:GSE168845 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {"fdr_threshold": "with Benjamini–Hochberg false discovery rate correction, together wi"}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Differential gene analysis was performed using the limma Bioconductor R package, and the p value was corrected by using the Benjamini–Hochberg false discovery rate (FDR)."}}, {"id": "method-2", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-3", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "build_graph", "expected_value": 0.001, "unit": "p-value", "tolerance": null, "state": "complete", "note": "The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold."}], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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