Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq
BackgroundStudies 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.MethodsFrom the Gene Expression Omnibus (GEO) data...
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.1012303/full |
| _version_ | 1798021778632605696 |
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| author | Xiaorui Liu Jingjing Li Qingxiang Wang Lu Bai Jiyuan Xing Xiaobo Hu Shuang Li Qinggang Li |
| author_facet | Xiaorui Liu Jingjing Li Qingxiang Wang Lu Bai Jiyuan Xing Xiaobo Hu Shuang Li Qinggang Li |
| author_sort | Xiaorui Liu |
| collection | DOAJ |
| description | BackgroundStudies 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.MethodsFrom 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.ResultsEleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO–Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability.ConclusionTo precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells. |
| first_indexed | 2024-04-11T17:19:08Z |
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| id | doaj.art-7628540f4d8d425fa725857959dfc4d3 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-11T17:19:08Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-7628540f4d8d425fa725857959dfc4d32022-12-22T04:12:32ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-10-011310.3389/fimmu.2022.10123031012303Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seqXiaorui Liu0Jingjing Li1Qingxiang Wang2Lu Bai3Jiyuan Xing4Xiaobo Hu5Shuang Li6Qinggang Li7Department of Infection, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Infection, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of physical examination&Blood collection Xuchang Blood Center, Xuchang, ChinaDepartment of Infection, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Infection, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Infection, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaBioinformatics R&D Department, Hangzhou Mugu Technology Co., Ltd, Hangzhou, ChinaDepartment of Infection, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaBackgroundStudies 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.MethodsFrom 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.ResultsEleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO–Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability.ConclusionTo precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.https://www.frontiersin.org/articles/10.3389/fimmu.2022.1012303/fullhccScRNA-seqriskscoreautophagymolecular subtypes |
| spellingShingle | Xiaorui Liu Jingjing Li Qingxiang Wang Lu Bai Jiyuan Xing Xiaobo Hu Shuang Li Qinggang Li Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq Frontiers in Immunology hcc ScRNA-seq riskscore autophagy molecular subtypes |
| title | Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq |
| title_full | Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq |
| title_fullStr | Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq |
| title_full_unstemmed | Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq |
| title_short | Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq |
| title_sort | analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scrna seq and bulk rna seq |
| topic | hcc ScRNA-seq riskscore autophagy molecular subtypes |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.1012303/full |
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