Background: Hepatocellular carcinoma (HCC) is a prevalent type of cancer with high incidence and mortality rates. It is the third most common cause of cancer-related deaths. CD8⁺ T cell exhaustion (TEX) is a progressive decline in T cell function due to sustained T cell receptor stimulation from continuous antigen exposure. Studies have shown that CD8⁺ TEX plays an important role in the anti-tumor immune process and is significantly correlated with patient prognosis. The aim of the research is to establish a reliable CD8⁺ TEX-based signature using single-cell RNA sequencing (scRNA-seq) and high-throughput RNA sequencing (RNA-seq), providing a new approach to evaluate HCC patient prognosis and immune microenvironment. Methods: The RNA-seq data of HCC patients were download from three different databases: The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC). HCC’s 10× scRNA data were acquired from GSE149614. Based on single-cell sequencing data, CD8⁺ TEX-related genes were identified using uniform manifold approximation and projection (UMAP) algorithm, singleR, and marker gene methods. Afterwards, we proceeded to construct CD8⁺ TEX signature using differential gene analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis. We also validated the CD8⁺ TEX signature in GEO and ICGC external cohorts and investigated clinical characteristics, chemotherapy sensitivity, mutation landscape, functional analysis, and immune cell infiltration in different risk groups. Results: The CD8⁺ TEX signature, consisting of 13 genes (HSPD1, UBB, DNAJB4, CALM1, LGALS3, BATF, COMMD3, IL7R, FDPS, DRAP1, RPS27L, PAPOLA, GPR171), was found to have a strong predictive effect on the prognosis of HCC. The Kaplan-Meier (KM) analysis showed that the overall survival (OS) rate of patients in the low-risk group was higher than that of patients in the high-risk group across different datasets and specific populations. The research findings suggested that the risk score was an independent predictor of HCC prognosis. The model based on clinical features and risk score has a strong predictive effect. We observed significant differences among various risk groups in terms of clinical characteristics, functional analysis, mutation landscape, chemotherapy sensitivity, and immune cell infiltration. Conclusions: We constructed a CD8⁺ TEX signature to predict the survival probability of patients with HCC. We also found that the model could predict the sensitivity of targeted drugs and immune cell infiltration, and the risk score was negatively correlated with CD8⁺ T cell infiltration. In summary, the CD8⁺ TEX signature of HCC was constructed for the prediction of prognosis and immune microenvironment by integrated analysis of bulk and scRNA-seq data.

Objectives: The purpose of this study was to develop and validate a radiomics nomogram for predicting thymidylate synthase (TYMS) status in hepatocellular carcinoma (HCC) by using Gd-DTPA contrast enhanced MRI. Methods: We retrospectively enrolled 147 consecutive patients with surgically confirmed HCC and randomly allocated to training and validation set (7:3). The TYMS status was immunohistochemical determined and classified into low TYMS (positive cells ≤ 25%) and high TYMS (positive cells > 25%) groups. Radiomics features were extracted from the arterial phases and portal venous phase of Gd-DTPA contrast enhanced MRI. Least absolute shrinkage and selection operator (LASSO) were applied for generating the Rad score. Clinical data and MRI findings were assessed to build a clinical model. Rad score combined with clinical features was used to construct radiomics nomogram. Results: A total of 2260 features were extracted and reduced to 7 features as the most important discriminators to build the Rad score. InAFP was identified as the only independent clinical factors for TYMS status. The radiomics nomogram showed good discrimination in training (AUC, 0.759; 95% CI 0.665–0.838) and validation set (AUC, 0.739; 95% CI 0.585–0.860), and showed better discrimination capability (P < 0.05) compared with clinical model in training (AUC, 0.656; 95% CI 0.555–0.746) and validation set (AUC, 0.622; 95% CI 0.463–0.764). Conclusions: The radiomics nomogram shows favorable predictive efficacy for TYMS status in HCC, which might be helpful for the personalized treatment of HCC.

Hepatocellular carcinoma (HCC) is a particularly heterogeneous tumor. It has a very poor prognosis. Pyroptosis has been demonstrated in recent years to be an inflammatory form of programmed cell death. However, the relationship between the expression of pyroptosis related genes (PRGs) and prognosis of HCC is still unclear. The development of a specific PRGs prognostic model is important if we want to improve therapeutic effect of tumor. In this study, we identified 42 PRGs that were differentially expressed between HCC and peripheral normal tissues and exhibited the mutation frequency, classification, the location of copy number variation (CNV) alteration and the CNV variation frequency of PRGs. Two clusters were distinguished by the consensus clustering analysis based on the 42 differentially expressed genes (DEGs). The result show that there were significant differences in clinical features (including T stage, grade, gender, stage) among different clusters. KM curve analysis show that cluster 1 had a better prognosis than cluster 2. The prognostic value of PRGs for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. By applying the univariate analysis and multivariate analysis method, a 10-gene signature was built and all HCC patients in the TCGA cohort were divided into low-risk group and high-risk group. HCC patients in the high-risk group showed significantly lower survival possibilities than those in the low-risk group (P<0.001). Utilizing the median risk score from the TCGA cohort, HCC patients from Gene Expression Omnibus (GEO) cohort (GSE14520) were divided into two risk subgroups. The result showed that overall survival (OS) time was decreased in the high-risk group (P=0.027). Combined with the clinical characteristics, the risk score was found to be an independent factor for predicting the OS of HCC patients. Then, for evaluating the prognostic prediction value of the model, ROC curve and survival analysis were performed. Finally, we constructed a PRGs clinical characteristics nomogram to furtherly predict HCC patient survival probability. There were significant differences in immune cell infiltration, GSEA enrichment pathway, IC50 of chemotherapeutics, PRGs mutation frequency, GO and KEGG analysis between high-risk group and low-risk group. This work suggests PRGs signature plays a crucial role in HCC. The exploration may assist in identifying novel biomarkers and assist HCC patients in predicting their prognosis, clinical diagnosis, and management.

Background: Hepatocellular carcinoma (HCC) is a prevalent type of cancer with high incidence and mortality rates. It is the third most common cause of cancer-related deaths. CD8+ T cell exhaustion (TEX) is a progressive decline in T cell function due to sustained T cell receptor stimulation from continuous antigen exposure. Studies have shown that CD8+ TEX plays an important role in the anti-tumor immune process and is significantly correlated with patient prognosis. The aim of the research is to establish a reliable CD8+ TEX-based signature using single-cell RNA sequencing (scRNAseq) and high-throughput RNA sequencing, providing a new approach to evaluate HCC patient prognosis and immune microenvironment. Method: The RNA-seq data of hepatocellular carcinoma (HCC) patients is download from three different databases: The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC). HCC's 10x scRNA data is acquired from GSE149614. Based on single-cell sequencing data, CD8+ TEX-related genes were identified using UMAP algorithm, singleR, and marker gene methods. Afterwards, we proceeded to construct CD8+ TEX signature using differential gene analysis, univariate COX regression analysis, LASSO regression, and multivariate COX regression analysis. We also validated CD8+ TEX signature in GEO and ICGC external cohorts and investigated clinical characteristics, chemotherapy sensitivity, mutation landscape, functional analysis, and immune cell infiltration in different risk groups. Finally, we identified genes that regulate CD8+ T cells through correlation analysis. Result: The CD8+ TEX signature, consisting of 13 genes (HSPD1, UBB, DNAJB4, CALM1, LGALS3, BATF, COMMD3, IL7R, FDPS, DRAP1, RPS27L, PAPOLA, GPR171), has been found to have a strong predictive effect on the prognosis of HCC. The KM analysis shows that the overall survival rate of patients in the low-risk group is higher than that of patients in the high-risk group across different datasets and specific populations. The research findings suggest that the risk score is an independent predictor of HCC prognosis. The model based on clinical features and risk score has a strong predictive effect. We have observed significant differences among various risk groups in terms of clinical characteristics, functional analysis, mutation landscape, chemotherapy sensitivity, and immune cell infiltration. Finally, it was discovered that BATF is a critical gene involved in the regulation of CD8+ T cells. Conclusion: We constructed a CD8+ T cell exhaustion signature to predict the survival probability of patients with HCC. We also found that the model could predict the sensitivity of targeted drugs and immune cell infiltration, and the risk score was negatively correlated with CD8+ T cell infiltration. In summary, this is the first CD8+ T cell exhaustion signature of HCC for the prediction of prognosis and immune microenvironment by integrated analysis of bulk and single-cell RNA sequencing data.