ArticleCell genomics2024
ERCC2 mutations alter the genomic distribution pattern of somatic mutations and are independently prognostic in bladder cancer.
Article in Cell genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Multi-omics identification of a programmed cell death-related signature and potential target P4HB for bladder cancer based on a 101-combination machine learning and experimental validation.Clinical and experimental medicine · 2026Article
- Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification.Discover oncology · 2025Review
- Case Report: Genomic profiling in an invasive solid papillary carcinoma patient with liver metastasis and a history of invasive lobular carcinoma.Pathology oncology research : POR · 2025Article
- Genomic patterns of somatic mutations provide new prognostic, therapeutic, and biological insights in cancer.Cell genomics · 2024Article
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11 authors.
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Abstract
Excision repair cross-complementation group 2 (ERCC2) encodes the DNA helicase xeroderma pigmentosum group D, which functions in transcription and nucleotide excision repair. Point mutations in ERCC2 are putative drivers in around 10% of bladder cancers (BLCAs) and a potential positive biomarker for cisplatin therapy response. Nevertheless, the prognostic significance directly attributed to ERCC2 mutations and its pathogenic role in genome instability remain poorly understood. We first demonstrated that mutant ERCC2 is an independent predictor of prognosis in BLCA. We then examined its impact on the somatic mutational landscape using a cohort of ERCC2 wild-type (n = 343) and mutant (n = 39) BLCA whole genomes. The genome-wide distribution of somatic mutations is significantly altered in ERCC2 mutants, including T[C>T]N enrichment, altered replication time correlations, and CTCF-cohesin binding site mutation hotspots. We leverage these alterations to develop a machine learning model for predicting pathogenic ERCC2 mutations, which may be useful to inform treatment of patients with BLCA.
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