Detailed genes in brown module using WGCNA.

BackgroundLiver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC.MethodsThe single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively. Then, we explored the association of stemness indicators (CytoTRACE score and stemness index, mRNAsi) with survival outcomes and clinical characteristics by combining clinical information and survival analyses. Subsequently, weighted co-expression network analysis (WGCNA) and Cox were applied to assess mRNAsi-related genes in bulk LIHC data and construct a prognostic model for LIHC patients. Single-sample gene-set enrichment analysis (ssGSEA), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Tumor Immune Estimation Resource (TIMER) analysis were employed for immune infiltration assessment. Finally, the potential immunotherapeutic response was predicted by the Tumor Immune Dysfunction and Exclusion (TIDE), and the tumor mutation burden (TMB). Additionally, pRRophetic package was applied to evaluate the sensitivity of high and low-risk groups to common chemotherapeutic drugs.ResultsA total of four genes (including STIP1, H2AFZ, BRIX1, and TUBB) associated with stemness score (CytoTRACE score and mRNAsi) were identified and constructed a risk model that could predict prognosis in LIHC patients. It was observed that high stemness cells occurred predominantly in the late stages of LIHC and that poor overall survival in LIHC patients was also associated with high mRNAsi scores. In addition, pathway analysis confirmed the biological uniqueness of the two risk groups. Personalized treatment predictions suggest that patients with a low risk benefited more from immunotherapy, while those with a high risk group may be conducive to chemotherapeutic drugs.ConclusionThe current study developed a novel prognostic risk signature with genes related to CSCs, which provides novel ideas for the diagnosis, prognosis and treatment of LIHC.</div

Construction of a prognostic gene risk model associated with CSCs and its survival analysis.

(A) Venn plot of CytoTRACE and mRNAsi prognosis-related genes; (B) Univariate analysis of 16 prognostic genes associated with CSCs; (C) Multivariate analysis of 4 prognostically critical genes in LIHC; (D) ROC Curves predict prognosis in LIHC patients at 1,3, and 5 years; (E-H) KM (E), DSS (F), DFI (G), and PFI (H) survival curves based on the TCGA-LIHC cohort; (I) Differences in survival status of patients in different risk groups in the TCGA-LIHC cohort; (J-K) ROC curves (J) and KM curves (K) based on HCCDB18 cohorts; (L) Differences in survival status of patients in different risk groups in the HCCDB18 cohort.</p

Analysis of clinical characteristics and pathway differences between LIHC risk groups.

Correlation of risk scores with mRNAsi in the TCGA cohort (A) and the HCCDB18 cohort (B); Difference in risk score distribution between AJCC stage (C) and grade (D) in TCGA cohort; (E) Comparison of the distribution of risk score in HCCDB18 cohort across stages; (F) Pathway differences between risk groups in the TCGA cohort; (G) Comparison of GSEA analysis between risk groups in the TCGA cohort.</p

A nomogram formed based on stem cells index-related risk score signature with clinical features.

Univariate (A) and multivariate (B) cox analysis of risk scores and clinical information; (C) A nomogram based on risk scores and clinical stage of AJCC; Nomogram-based calibration curves (D) and decision curves (E); (F) Differential expression of prognostic critical genes across risk groups in the TCGA cohort; (G) Expression distribution of prognostic critical genes in single-cell profile.</p

Profiling of cells in LIHC at the scRNA transcript level and cell stemness analysis.

Cells were clustered using optimal resolution 1. (A) Seven cell types in LIHC were annotated as the primary markers of the cluster; (B) Expression of marker genes in different cell types; Proportion of immune cells (C) and non-immune cells (D) in scRNA-seq data from LIHC; (E) UMAP plot of the distribution of epithelial cells of primary tumors at different stages; (F) CytoTRACE analysis in primary tumor epithelial cells; (G) Percentage of each cell type in different stages; (H) Top 10 Genes related to CytoTRACE score. *p < 0.05.</p

The number of genes in each module using WGCNA.

BackgroundLiver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC.MethodsThe single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively. Then, we explored the association of stemness indicators (CytoTRACE score and stemness index, mRNAsi) with survival outcomes and clinical characteristics by combining clinical information and survival analyses. Subsequently, weighted co-expression network analysis (WGCNA) and Cox were applied to assess mRNAsi-related genes in bulk LIHC data and construct a prognostic model for LIHC patients. Single-sample gene-set enrichment analysis (ssGSEA), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Tumor Immune Estimation Resource (TIMER) analysis were employed for immune infiltration assessment. Finally, the potential immunotherapeutic response was predicted by the Tumor Immune Dysfunction and Exclusion (TIDE), and the tumor mutation burden (TMB). Additionally, pRRophetic package was applied to evaluate the sensitivity of high and low-risk groups to common chemotherapeutic drugs.ResultsA total of four genes (including STIP1, H2AFZ, BRIX1, and TUBB) associated with stemness score (CytoTRACE score and mRNAsi) were identified and constructed a risk model that could predict prognosis in LIHC patients. It was observed that high stemness cells occurred predominantly in the late stages of LIHC and that poor overall survival in LIHC patients was also associated with high mRNAsi scores. In addition, pathway analysis confirmed the biological uniqueness of the two risk groups. Personalized treatment predictions suggest that patients with a low risk benefited more from immunotherapy, while those with a high risk group may be conducive to chemotherapeutic drugs.ConclusionThe current study developed a novel prognostic risk signature with genes related to CSCs, which provides novel ideas for the diagnosis, prognosis and treatment of LIHC.</div

Immunotherapy and drug sensitivity in different risk groups of LIHC.

(A) TIDE algorithm assesses immune escape potential in different risk groups; (B) Analysis of differences in immune response between high and low risk groups; (C) There was no significant difference in TMB levels between the high and low risk groups; (D) KM curves in the high- and low-risk groups after combination with TMB; (E) Expression levels of immunomodulator-related genes in different risk groups; (F) Analysis of differences in drug sensitivity.</p

Immune infiltration analysis for two risk groups of LIHC.

(A) Differences in innate and adaptive immunity between high and low risk groups; (B) The ssGSEA algorithm assesses the extent of immune cell infiltration in different risk groups; (C) CIBERSORT analysis; (D) TIMER analysis. *p < 0.05, **p < 0.01, ***p <0.001, ****p <0.0001, ns, no signicance.</p

Construction of mRNAsi related module genes using WGCNA.

(A) Analysis of scale-free exponent and average connectivity of various soft threshold powers; (B) Cluster dendrogram of the co-expression network modules; (C) Module thresholds for WGCNA analysis; (D) The number of genes in each module. (PDF)</p

Association of mRNAsi with OS and clinical characteristics of LIHC patients.

(A-B) Relationship between mRNAsi and OS in the HCCDB18 (A) TCGA (B) datasets; Association of mRNAsi with tumor stage (C) and grade (D) in the TCGA dataset; (E) Relationship between mRNAsi and tumor stage in the HCCDB8 dataset.</p