Zixuan Zhai’s research while affiliated with First Affiliated Hospital of China Medical University and other places

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Publications (1)

Identification of prognostic differentially expressed genes between tumor and adjacent non-tumor cells based on scRNA-seq data. A The UMAP algorithm was applied for dimensionality reduction, and five cell clusters were successfully classified and annotated with SingleR and CellMarker according to the composition of marker genes. B Expression levels of marker genes for each cell cluster. C The UMAP algorithm was applied to the epithelial cells for dimensionality reduction, and the epithelial cells were successfully classified into adjacent non-tumor (green dots), normal (orange dots), and tumor (blue dots) cells according to the pseudo-temporal analysis. D–E The pseudo-temporal analysis of epithelial cells. F Volcano plot of DEGs between tumor cells and adjacent non-tumor cells. P < 0.05 and |log2FoldChange|> 0.5 were identified as significant DEGs. The red dots represent upregulated genes and the blue dots represent downregulated genes. G Identification of DEGs with prognosis in TCGA-LIHC. The blue part represents DEGs, the yellow part represents prognostic genes, and the overlapped part represents DEGs with prognostic significance
Development and validation of the 11-gene model in multiple cohorts. A LASSO coefficient profile, from which the optimal λ is chosen based on the plot. The two dashed lines indicate two specific λ values: lambda.min and lambda.1se. The λ values between these two are considered suitable. The model built with lambda.1se is the simplest, using fewer genes, while the model built with lambda.min has slightly higher accuracy, using more genes. By default, lambda.min is selected. B Survival curve between high-risk and low-risk groups in the training set. C ROC curve of 1 year, 3 year, and 5 year survival of patients in the training set. D–E Survival curves between high-risk and low-risk groups in the testing set D and the TCGA-LIHC cohort E. F–G ROC curves of 1 year, 3 year, and 5 year survival of patients in the testing set F and the TCGA-LIHC cohort G. H–I Survival curves between high-risk and low-risk groups in the CHCC-HBV cohort H and the GSE20140 cohort I
Multivariable analysis A and nomogram B based on risk score, age, gender, TNM stage, and tumor stage. Calibration curves C were used to validate the predictive ability of the nomogram for 1 year (red line), 3 year (yellow line), and 5 year (green line) survival in HCC
Molecular differences between patients with high and low risk scores in the TCGA-LIHC cohort. A Volcano plot of DEGs between high-risk and low-risk groups. P < 0.05 and |log2FoldChange|> 0.5 were identified as significant DEGs. The red dots represent upregulated genes and the blue dots represent downregulated genes. B GO analysis of the upregulated genes. C GO analysis of the downregulated genes. D Forest plot of mutations between high-risk and low-risk groups. E Significant differences in mutations between high-risk and low-risk groups by Fisher test. An odds ratio greater than 1 indicates that the high-risk group has more mutations in certain genes, while an odds ratio smaller than 1 indicates the opposite. F Heatmap of immune cells that are significantly different in infiltration between high-risk and low-risk groups with P < 0.05. G The immune score and infiltration of CD8 + naive T cells, Th2 cells, and natural killer T cells between high-risk and low-risk groups. Significant differences tested by Wilcoxon test
Drug identification in the GDSC database. A Heatmap of the 20 drugs with significantly different IC50 values between the high-risk and low-risk groups. B Comparison of fourteen drugs that have significantly lower IC50 values in the high-risk group compared to the low-risk group. Significant differences tested by Wilcoxon test
Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer
  • Article
  • Full-text available

November 2024

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10 Reads

Human Genomics

Qunfang Zhou

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Jingqiang Wu

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Jiaxin Bei

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Mingyu Liu

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.

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