{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["Liu X"],"pubmed_abstract":["<h4>Background</h4>Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics.<h4>Methods</h4>From the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed.<h4>Results</h4>Eleven c"],"journal":["Frontiers in immunology"],"pagination":["1012303"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9606610"],"repository":["biostudies-literature"],"pubmed_title":["Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq."],"pmcid":["PMC9606610"],"pubmed_authors":["Liu X","Xing J","Li J","Wang Q","Li S","Hu X","Bai L","Li Q"],"additional_accession":[]},"is_claimable":false,"name":"Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq.","description":"<h4>Background</h4>Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics.<h4>Methods</h4>From the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed.<h4>Results</h4>Eleven c","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-22T02:06:34.496Z","creation":"2025-04-05T20:13:41.284Z"},"accession":"S-EPMC9606610","cross_references":{"pubmed":["36311759"],"doi":["10.3389/fimmu.2022.1012303"]}}