{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Mehra R"],"funding":["NCI NIH HHS"],"pagination":["e2300565"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11569832"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["8"],"pubmed_abstract":["<h4>Purpose</h4>Develop and validate gene expression-based biomarker associated with recurrent disease to facilitate risk stratification of clear cell renal cell carcinoma (ccRCC).<h4>Materials and methods</h4>We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (<i>discovery</i> cohort). Patients who recurred were matched on the basis of grade/stage to patients without recurrence. Capture whole-transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence-free survival/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNA-seq data for each p"],"journal":["JCO precision oncology"],"pubmed_title":["Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma."],"pmcid":["PMC11569832"],"funding_grant_id":["P30 CA046592","R37 CA283857"],"pubmed_authors":["Nallandhighal S","Knuth Z","Su F","Cotta B","Kasputis A","Cao X","Wang R","Zhang Y","Cieslik MP","Salami SS","Mehra R","Udager AM","Dhanasekaran SM","Morgan TM"],"additional_accession":[]},"is_claimable":false,"name":"Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma.","description":"<h4>Purpose</h4>Develop and validate gene expression-based biomarker associated with recurrent disease to facilitate risk stratification of clear cell renal cell carcinoma (ccRCC).<h4>Materials and methods</h4>We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (<i>discovery</i> cohort). Patients who recurred were matched on the basis of grade/stage to patients without recurrence. Capture whole-transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence-free survival/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNA-seq data for each p","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 May","modification":"2026-06-02T20:43:30.377Z","creation":"2026-04-20T03:10:35.078Z"},"accession":"S-EPMC11569832","cross_references":{"pubmed":["38810179"],"doi":["10.1200/PO.23.00565","10.1200/po.23.00565"]}}