<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>13</volume><submitter>Liu X</submitter><pubmed_abstract>&lt;h4>Background&lt;/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.&lt;h4>Methods&lt;/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.&lt;h4>Results&lt;/h4>Eleven c</pubmed_abstract><journal>Frontiers in immunology</journal><pagination>1012303</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9606610</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq.</pubmed_title><pmcid>PMC9606610</pmcid><pubmed_authors>Liu X</pubmed_authors><pubmed_authors>Xing J</pubmed_authors><pubmed_authors>Li J</pubmed_authors><pubmed_authors>Wang Q</pubmed_authors><pubmed_authors>Li S</pubmed_authors><pubmed_authors>Hu X</pubmed_authors><pubmed_authors>Bai L</pubmed_authors><pubmed_authors>Li Q</pubmed_authors></additional><is_claimable>false</is_claimable><name>Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq.</name><description>&lt;h4>Background&lt;/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.&lt;h4>Methods&lt;/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.&lt;h4>Results&lt;/h4>Eleven c</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022</publication><modification>2025-04-22T02:06:34.496Z</modification><creation>2025-04-05T20:13:41.284Z</creation></dates><accession>S-EPMC9606610</accession><cross_references><pubmed>36311759</pubmed><doi>10.3389/fimmu.2022.1012303</doi></cross_references></HashMap>