1. Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer
- Author
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Qunfang Zhou, Jingqiang Wu, Jiaxin Bei, Zixuan Zhai, Xiuzhen Chen, Wei Liang, Jing Meng, and Mingyu Liu
- Subjects
scRNA-seq ,Liver cancer ,Molecular model ,Tumor cell characteristics ,Drug exploration ,Medicine ,Genetics ,QH426-470 - Abstract
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
- Published
- 2024
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