LAT, HOXD3 and NFE2L3 identified as novel DNA methylation-driven genes and prognostic markers in human clear cell renal cell carcinoma by integrative bioinformatics approaches.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31777602", "kind": "science-record", "revision": 8, "content_hash": "sha256:d68c60c3238d04b4e38398c621c2116d33af3e12dbcf8fe5aa65368942868c8f", "created_at": "2026-11-01T15:40:52Z", "updated_at": "2026-11-01T22:01:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "LAT, HOXD3 and NFE2L3 identified as novel DNA methylation-driven genes and prognostic markers in human clear cell renal cell carcinoma by integrative bioinformatics approaches.", "doi": "10.7150/jca.35641", "pmid": "31777602", "authors": ["Jie Wang", "Hu Zhao", "Huiyue Dong", "Ling Zhu", "Shuiliang Wang", "Ping Wang", "Qun Ren", "Hehuan Zhu", "Junqiu Chen", "Zhijie Lin", "Yuanhang Cheng", "Benjiang Qian", "Yi Zhang", "Ruxue Jia", "Weizheng Wu", "Jun Lu", "Jianming Tan"], "journal": "Journal of Cancer", "year": 2019, "abstract": "Background: Abnormal DNA methylation of is one of the important mechanisms leading to tumor pathogenesis. The purpose of this study was to explore differentially methylated genes that may drive the development of renal clear cell carcinoma through a comprehensive analysis of the TCGA database. Materials and methods: Methylation data and RNA-seq data for clear cell renal cell carcinoma were downloaded from The Cancer Genome Atlas (TCGA). Differentially methylated genes and the differential genes associated with survival were then screened by MethylMix R package and univariate Cox proportional-hazards model, respectively. Their common genes were then intersected and obtained for further analysis. Correlation of gene expression and methylation levels, gene set enrichment analysis (GSEA) enrichments, survival curve, and ROC curve plotting for DNA methylation-driven genes were finally performed. The methylation alterations of the three genes were validated via two GEO datasets (GSE70303 and GSE113501), and the genes expression level was verified through two GEO datasets (GSE6344 and GSE53757). Results: Three novel DNA methylation-driven genes LAT, HOXD3 and NFE2L3 were identified in clear cell renal cell carcinoma. Expression analysis further revealed that hypomethylation levels of LAT and NFE2L3 showed higher gene expression levels, while HOXD3 exhibited opposite methylation-expression pattern. The CpG sites of LAT (cg16462073), HOXD3 (cg24000528) and NFE2L3 (cg16882373) that may affect respective gene expressions were also identified. For the survival analysis, we found that hypomethylation and over-expression of LAT and NFE2L3 were correlated with poor survival, while hypermethylation and low-expression HOXD3 was correlated with poor survival of clear cell renal cell carcinoma patients. In addition, GSEA KEGG analysis and biological processes of these genes were also enriched for functional analysis. Kaplan-Meier survival and ROC analyses of these genes showed an average risk score of 0.9140593, AUC = 0.692, which suggested a good clinical application value. Finally, the opposite methylation-expression pattern of these three genes were verified in GEO datasets. Conclusions: In this study, we successfully exhibited the potential DNA methylation-driven genes LAT, HOXD3, and NFE2L3 involved in clear cell renal cell carcinoma. Moreover, gene functions and prognostic risk models were also elucidated, which facilitated the expansion of the current study on the role of methylation in the pathology process of clear cell renal cell carcinoma.", "claims": [{"id": "claim-data-access", "statement": "Firstly, RNA-seq data (N = 72, T = 539), DNA methylation data (N = 160, T = 325) and clinical data of the original ccRCC patients were downloaded from the TCGA database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31777602", "locator": "sections[3] \"Data collection and preprocessing\" paragraph 2", "quote": "Firstly, RNA-seq data (N = 72, T = 539), DNA methylation data (N = 160, T = 325) and clinical data of the original ccRCC patients were downloaded from the TCGA database."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "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:31777602"], "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. 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