{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["Qu G"],"funding":["Natural Science Foundation of Hunan Province"],"pubmed_abstract":["<b>Objective:</b> In order to predict the prognosis in patients with clear cell renal cell carcinoma (ccRCC) so as to understand cancer lipid metabolism and sensitivity to immune-targeting drugs, model algorithms were used to establish a risk coefficient model of long non-coding RNAs (lncRNAs) associated with lipid metabolism. <b>Methods:</b> The transcriptome data were retrieved from TCGA, and lncRNAs associated with lipid metabolism were obtained through Pearson correlation and differential expression analyses. Differentially expressed lipid metabolism-related lncRNAs and lipid metabolism-related lncRNA pairs were obtained using the R language software. The minimum absolute shrinkage method and the selector operation regression method were used to construct the model and draw the receive"],"journal":["Frontiers in genetics"],"pagination":["1040421"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9845405"],"repository":["biostudies-literature"],"pubmed_title":["Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model."],"pmcid":["PMC9845405"],"pubmed_authors":["Yang G","Liu L","Yi L","Tang C","Qu G","Chen D","Xu Y"],"additional_accession":[]},"is_claimable":false,"name":"Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model.","description":"<b>Objective:</b> In order to predict the prognosis in patients with clear cell renal cell carcinoma (ccRCC) so as to understand cancer lipid metabolism and sensitivity to immune-targeting drugs, model algorithms were used to establish a risk coefficient model of long non-coding RNAs (lncRNAs) associated with lipid metabolism. <b>Methods:</b> The transcriptome data were retrieved from TCGA, and lncRNAs associated with lipid metabolism were obtained through Pearson correlation and differential expression analyses. Differentially expressed lipid metabolism-related lncRNAs and lipid metabolism-related lncRNA pairs were obtained using the R language software. The minimum absolute shrinkage method and the selector operation regression method were used to construct the model and draw the receive","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-04T13:52:57.788Z","creation":"2025-04-04T13:52:57.788Z"},"accession":"S-EPMC9845405","cross_references":{"pubmed":["36685882"],"doi":["10.3389/fgene.2022.1040421"]}}