Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients
Background: Liver hepatocellular carcinoma (LIHC) is a solid primary malignancy with poor prognosis. This study discovered key prognostic genes based on T cell exhaustion and used them to develop a prognostic prediction model for LIHC. Methods: SingleR's annotations combined with Seurat was use...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024041872 |
| _version_ | 1797224112921247744 |
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| author | Yu Zhou Wanrui Wu Wei Cai Dong Zhang Weiwei Zhang Yunling Luo Fujing Cai Zhenjing Shi |
| author_facet | Yu Zhou Wanrui Wu Wei Cai Dong Zhang Weiwei Zhang Yunling Luo Fujing Cai Zhenjing Shi |
| author_sort | Yu Zhou |
| collection | DOAJ |
| description | Background: Liver hepatocellular carcinoma (LIHC) is a solid primary malignancy with poor prognosis. This study discovered key prognostic genes based on T cell exhaustion and used them to develop a prognostic prediction model for LIHC. Methods: SingleR's annotations combined with Seurat was used to automatically annotate the single-cell clustering results of the LIHC dataset GSE166635 downloaded from the Gene Expression Omnibus (GEO) database and to identify clusters related to exhausted T cells. Patients were classified using ConsensusClusterPlus package. Next, weighted gene co-expression network analysis (WGCNA) package was employed to distinguish key gene module, based on which least absolute shrinkage and selection operator (Lasso) and multi/univariate cox analysis were performed to construct a RiskScore system. Kaplan-Meier (KM) analysis and receiver operating characteristic curve (ROC) were employed to evaluate the efficacy of the model. To further optimize the risk model, a nomogram capable of predicting immune infiltration and immunotherapy sensitivity in different risk groups was developed. Expressions of genes were measured by quantitative real-time polymerase chain reaction (qRT-PCR), and immunofluorescence and Cell Counting Kit-8 (CCK-8) were performed for analyzing cell functions. Results: We obtained 18,413 cells and clustered them into 7 immune and non-immune cell subpopulations. Based on highly variable genes among T cell exhaustion clusters, 3 molecular subtypes (C1, C2 and C3) of LIHC were defined, with C3 subtype showing the highest score of exhausted T cells and a poor prognosis. The Lasso and multivariate cox analysis selected 7 risk genes from the green module, which were closely associated with the C3 subtype. All the patients were divided into low- and high-risk groups based on the medium value of RiskScore, and we found that high-risk patients had higher immune infiltration and immune escape and poorer prognosis. The nomogram exhibited a strong performance for predicting long-term LIHC prognosis. In vitro experiments revealed that the 7 risk genes all had a higher expression in HCC cells, and that both liver HCC cell numbers and cell viability were reduced by knocking down MMP-9. Conclusion: We developed a RiskScore model for predicting LIHC prognosis based on the scRNA-seq and RNA-seq data. The RiskScore as an independent prognostic factor could improve the clinical treatment for LIHC patients. |
| first_indexed | 2024-04-24T13:47:57Z |
| format | Article |
| id | doaj.art-28a1b17adcab4092a6dc9cff05c4c774 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T13:47:57Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
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| series | Heliyon |
| spelling | doaj.art-28a1b17adcab4092a6dc9cff05c4c7742024-04-04T05:06:56ZengElsevierHeliyon2405-84402024-03-01106e28156Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patientsYu Zhou0Wanrui Wu1Wei Cai2Dong Zhang3Weiwei Zhang4Yunling Luo5Fujing Cai6Zhenjing Shi7Department of Infectious, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, ChinaDepartment of Vasointerventional, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, ChinaDepartment of Infectious, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, ChinaDepartment of Infectious, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, ChinaDepartment of Infectious, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, ChinaDepartment of Infectious, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, ChinaDepartment of Infectious, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China; Corresponding author.Department of Vasointerventional, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China; Corresponding author.Background: Liver hepatocellular carcinoma (LIHC) is a solid primary malignancy with poor prognosis. This study discovered key prognostic genes based on T cell exhaustion and used them to develop a prognostic prediction model for LIHC. Methods: SingleR's annotations combined with Seurat was used to automatically annotate the single-cell clustering results of the LIHC dataset GSE166635 downloaded from the Gene Expression Omnibus (GEO) database and to identify clusters related to exhausted T cells. Patients were classified using ConsensusClusterPlus package. Next, weighted gene co-expression network analysis (WGCNA) package was employed to distinguish key gene module, based on which least absolute shrinkage and selection operator (Lasso) and multi/univariate cox analysis were performed to construct a RiskScore system. Kaplan-Meier (KM) analysis and receiver operating characteristic curve (ROC) were employed to evaluate the efficacy of the model. To further optimize the risk model, a nomogram capable of predicting immune infiltration and immunotherapy sensitivity in different risk groups was developed. Expressions of genes were measured by quantitative real-time polymerase chain reaction (qRT-PCR), and immunofluorescence and Cell Counting Kit-8 (CCK-8) were performed for analyzing cell functions. Results: We obtained 18,413 cells and clustered them into 7 immune and non-immune cell subpopulations. Based on highly variable genes among T cell exhaustion clusters, 3 molecular subtypes (C1, C2 and C3) of LIHC were defined, with C3 subtype showing the highest score of exhausted T cells and a poor prognosis. The Lasso and multivariate cox analysis selected 7 risk genes from the green module, which were closely associated with the C3 subtype. All the patients were divided into low- and high-risk groups based on the medium value of RiskScore, and we found that high-risk patients had higher immune infiltration and immune escape and poorer prognosis. The nomogram exhibited a strong performance for predicting long-term LIHC prognosis. In vitro experiments revealed that the 7 risk genes all had a higher expression in HCC cells, and that both liver HCC cell numbers and cell viability were reduced by knocking down MMP-9. Conclusion: We developed a RiskScore model for predicting LIHC prognosis based on the scRNA-seq and RNA-seq data. The RiskScore as an independent prognostic factor could improve the clinical treatment for LIHC patients.http://www.sciencedirect.com/science/article/pii/S2405844024041872Single cell profileT cell exhaustionPrognosis signatureLiver hepatocellular carcinoma (LIHC) |
| spellingShingle | Yu Zhou Wanrui Wu Wei Cai Dong Zhang Weiwei Zhang Yunling Luo Fujing Cai Zhenjing Shi Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients Heliyon Single cell profile T cell exhaustion Prognosis signature Liver hepatocellular carcinoma (LIHC) |
| title | Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients |
| title_full | Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients |
| title_fullStr | Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients |
| title_full_unstemmed | Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients |
| title_short | Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients |
| title_sort | prognostic prediction using a gene signature developed based on exhausted t cells for liver cancer patients |
| topic | Single cell profile T cell exhaustion Prognosis signature Liver hepatocellular carcinoma (LIHC) |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024041872 |
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