Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer
Abstract Background Liver cancer has a high global incidence, particularly in East Asia. Early detection difficulties lead to poor prognosis. Single-cell sequencing precisely identifies gene expression differences in specific cell types, making it valuable in tumor microenvironment research and immu...
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BMC
2024-11-01
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| Series: | Human Genomics |
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| Online Access: | https://doi.org/10.1186/s40246-024-00698-2 |
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| author | Qunfang Zhou Jingqiang Wu Jiaxin Bei Zixuan Zhai Xiuzhen Chen Wei Liang Jing Meng Mingyu Liu |
| author_facet | Qunfang Zhou Jingqiang Wu Jiaxin Bei Zixuan Zhai Xiuzhen Chen Wei Liang Jing Meng Mingyu Liu |
| author_sort | Qunfang Zhou |
| collection | DOAJ |
| description | Abstract Background Liver cancer has a high global incidence, particularly in East Asia. Early detection difficulties lead to poor prognosis. Single-cell sequencing precisely identifies gene expression differences in specific cell types, making it valuable in tumor microenvironment research and immune drug development. However, the characteristics of tumor cells themselves are equally important for patient prognosis and treatment. Methods We downloaded single-cell sequencing data from GSE189903, grouped cells by cluster markers, and classified epithelial cells into adjacent non-tumor, normal, and tumor cells. Differential gene and survival analyses identified significant differential genes. Using TCGA-LIHC data, we divided 370 patients into test and training sets. We constructed and validated a LASSO model based on these genes in both sets and two external datasets. Functional, immune infiltration, and mutation analyses were performed on high and low-risk groups. We also used RNA-seq and IC50 data of 15 liver cancer cell lines from GDSC, scoring them with our prognostic model to identify potential drugs for high-risk patients. Results Dimensionality reduction and clustering of 34 single-cell samples identified five subgroups, with epithelial cells further classified. Differential gene analysis identified 124 significant genes. An 11-gene prognostic model was constructed, effectively stratifying patient prognosis (p < 0.05) and achieving an AUC above 0.6 for 5 year survival prediction in multiple cohorts. Functional analysis revealed that upregulated genes in high-risk groups were enriched in cell adhesion pathways, while downregulated genes were enriched in metabolic pathways. Mutation analysis showed more TP53 mutations in the high-risk group and more CTNNB1 mutations in the low-risk group. Immune infiltration analysis indicated higher immune scores and less CD8 + naive T cell infiltration in the high-risk group. Drug sensitivity analysis identified 14 drugs with lower IC50 in the high-risk group, including clinically approved Sorafenib and Axitinib for treating unresectable HCC. Conclusion We established an 11-gene prognostic model that effectively stratifies liver cancer patients based on differentially expressed genes between tumor and adjacent non-tumor cells clustered by scRNA-seq data. The two risk groups had significantly different molecular characteristics. We identified 14 drugs that might be effective for high-risk HCC patients. Our study provides novel insights into tumor cell characteristics, aiding in research on tumor development and treatment. |
| format | Article |
| id | doaj-art-aaabbed2967c4032be225baf4d1699a7 |
| institution | DOAJ |
| issn | 1479-7364 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Human Genomics |
| spelling | doaj-art-aaabbed2967c4032be225baf4d1699a72025-08-20T02:51:16ZengBMCHuman Genomics1479-73642024-11-0118111210.1186/s40246-024-00698-2Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancerQunfang Zhou0Jingqiang Wu1Jiaxin Bei2Zixuan Zhai3Xiuzhen Chen4Wei Liang5Jing Meng6Mingyu Liu7Department of Interventional Radiology, Chinese PLA General HospitalDepartment of Radiology, Guangzhou Chest HospitalKey Laboratory of Surveillance of Adverse Reactions Related to CAR T Cell Therapy, Department of Immuno-Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical UniversityDepartment of Radiology, The Second Affiliated Hospital of Guangzhou Medical UniversityDepartment of Radiology, The Third Affiliated Hospital, Sun Yat-sen UniversityDepartment of Radiology, The First People’s Hospital of FoshanDepartment of Ophthalmology, The First Affiliated Hospital, Jinan UniversityDepartment of Interventional Radiology, The Affiliated Shunde Hospital of Jinan UniversityAbstract Background Liver cancer has a high global incidence, particularly in East Asia. Early detection difficulties lead to poor prognosis. Single-cell sequencing precisely identifies gene expression differences in specific cell types, making it valuable in tumor microenvironment research and immune drug development. However, the characteristics of tumor cells themselves are equally important for patient prognosis and treatment. Methods We downloaded single-cell sequencing data from GSE189903, grouped cells by cluster markers, and classified epithelial cells into adjacent non-tumor, normal, and tumor cells. Differential gene and survival analyses identified significant differential genes. Using TCGA-LIHC data, we divided 370 patients into test and training sets. We constructed and validated a LASSO model based on these genes in both sets and two external datasets. Functional, immune infiltration, and mutation analyses were performed on high and low-risk groups. We also used RNA-seq and IC50 data of 15 liver cancer cell lines from GDSC, scoring them with our prognostic model to identify potential drugs for high-risk patients. Results Dimensionality reduction and clustering of 34 single-cell samples identified five subgroups, with epithelial cells further classified. Differential gene analysis identified 124 significant genes. An 11-gene prognostic model was constructed, effectively stratifying patient prognosis (p < 0.05) and achieving an AUC above 0.6 for 5 year survival prediction in multiple cohorts. Functional analysis revealed that upregulated genes in high-risk groups were enriched in cell adhesion pathways, while downregulated genes were enriched in metabolic pathways. Mutation analysis showed more TP53 mutations in the high-risk group and more CTNNB1 mutations in the low-risk group. Immune infiltration analysis indicated higher immune scores and less CD8 + naive T cell infiltration in the high-risk group. Drug sensitivity analysis identified 14 drugs with lower IC50 in the high-risk group, including clinically approved Sorafenib and Axitinib for treating unresectable HCC. Conclusion We established an 11-gene prognostic model that effectively stratifies liver cancer patients based on differentially expressed genes between tumor and adjacent non-tumor cells clustered by scRNA-seq data. The two risk groups had significantly different molecular characteristics. We identified 14 drugs that might be effective for high-risk HCC patients. Our study provides novel insights into tumor cell characteristics, aiding in research on tumor development and treatment.https://doi.org/10.1186/s40246-024-00698-2scRNA-seqLiver cancerMolecular modelTumor cell characteristicsDrug exploration |
| spellingShingle | Qunfang Zhou Jingqiang Wu Jiaxin Bei Zixuan Zhai Xiuzhen Chen Wei Liang Jing Meng Mingyu Liu Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer Human Genomics scRNA-seq Liver cancer Molecular model Tumor cell characteristics Drug exploration |
| title | Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer |
| title_full | Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer |
| title_fullStr | Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer |
| title_full_unstemmed | Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer |
| title_short | Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer |
| title_sort | integration of single cell sequencing and drug sensitivity profiling reveals an 11 gene prognostic model for liver cancer |
| topic | scRNA-seq Liver cancer Molecular model Tumor cell characteristics Drug exploration |
| url | https://doi.org/10.1186/s40246-024-00698-2 |
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