Methylated SEPT9 combined with AFP and PIVKA-II is effective for the detection of HCC in high-risk population.

BACKGROUND: The methylation SEPT9 (mSEPT9) appeared to be effective for hepatocellular carcinoma (HCC) detection. However, its performance in high-risk population has not been validated. We designed a pilot study and aimed to investigate the performance of mSEPT9, AFP, PIVKA-II and their combination in hepatic cirrhosis (HC) population. METHODS: A training cohort was established including 103 HCC and 114 HC patients. 10 ml blood was collected from each patient with K2EDTA tubes, and 3-4 ml plasma was extracted for subsequent tests. The performance of mSEPT9, AFP, PIVKA-II and their combination was optimized by the training cohort. Test performance was prospectively validated with a validation cohort, including 51 HCC and 121 HC patients. RESULTS: At the optimal thresholds in the training cohort, the sensitivity, specificity and area under curve (AUC) was 72.82%, 89.47%, 0.84, and 48.57%, 89.92%, 0.79, and 63.64%, 95.95%, 0.79 for mSEPT9, AFP and PIVKA-II, respectively. The combined test significantly increased the sensitivity to 84.47% (P < 0.05) at the specificity of 86.84% with an AUC of 0.91. Stage-dependent performance was observed with all single markers and their combination in plasma marker levels, positive detection rate (PDR) and AUC. Moderate correlation was found between mSEPT9 and AFP plasma levels (r = 0.527, P < 0.0001). Good complementarity was found between any two of the three markers, providing optimal sensitivity in HCC detection when used in combination. Subsequent validation achieved a sensitivity, specificity and AUC of 65.31%, 92.86%, 0.80, and 44.24%, 89.26%, 0.75, and 62.22%, 95.27%, 0.78 for mSEPT9, AFP and PIVKA-II, respectively. The combined test yielded a significantly increased sensitivity of 84.00% (P < 0.05) at 85.57% specificity, with an AUC at 0.89. CONCLUSIONS: The performance was optimal by the combination of mSEPT9, AFP, PIVKA-II compared with any single marker, and the combination may be effective for HCC opportunistic screening in HC population.

Unraveling the Heterogeneity of CD8+ T-Cell Subsets in Liver Cirrhosis: Implications for Disease Progression.

Background/Aims: : Liver cirrhosis involves chronic inflammation and progressive fibrosis. Among various immune cells, CD8+ T cells are considered a major contributor to hepatic inflammation and fibrosis. However, the exact molecular pathways governing CD8+ T-cell-mediated effects in cirrhosis remain unclear. Methods: : This study analyzed transcriptomic and single-cell sequencing data to elucidate CD8+ T-cell heterogeneity and implications in cirrhosis. Results: : Weighted gene co-expression analysis of bulk RNA-seq data revealed an association between cirrhosis severity and activated T-cell markers like HLA and chemokine genes. Furthermore, single-cell profiling uncovered eight CD8+ T-cell subtypes, notably, effector memory (Tem) and exhausted (Tex) T cells. Tex cells, defined by PDCD1, LAG3, and CXCL13 expression, were increased in cirrhosis, while Tem cells were decreased. Lineage tracing and differential analysis highlighted CXCL13+ Tex cells as a terminal, exhausted subtype of cells with roles in PD-1 signaling, glycolysis, and T-cell regulation. CXCL13+ Tex cells displayed T-cell exhaustion markers like PDCD1, HAVCR2, TIGIT, and TNFRSF9. Functional analysis implicated potential roles of these cells in immunosuppression. Finally, a CXCL13+ Tex-cell gene signature was found that correlated with cirrhosis severity and poorer prognosis of liver cancer. Conclusions: : In summary, this comprehensive study defines specialized CD8+ T-cell subpopulations in cirrhosis, with CXCL13+ Tex cells displaying an exhausted phenotype associated with immune dysregulation and advanced disease. Key genes and pathways regulating these cells present potential therapeutic targets.

Integrated Bioinformatics Analysis for the Screening of Hub Genes and Therapeutic Drugs in Hepatocellular Carcinoma.

BACKGROUND: Liver cancer is a major medical problem because of its high morbidity and mortality. Hepatocellular carcinoma (HCC) is the most common type of liver cancer. Currently, the mechanism of HCC is unclear, and the prognosis is poor with limited treatment. OBJECTIVE: The purpose of this study is to identify hub genes and potential therapeutic drugs for HCC. METHODS: We used the GEO2R algorithm to analyze the differential expression of each gene in 4 gene expression profiles (GSE101685, GSE62232, GSE46408, and GSE45627) between HCC and normal hepatic tissues. Next, we screened out the differentially expressed genes (DEGs) by corresponding calculation data according to adjusted P-value < 0.05 and | log fold change (FC) | > 1.0. Subsequently, we used the DAVID software to analyze the DEGs by GO and KEGG enrichment analysis. Then, we carried out the protein-protein interaction (PPI) network analysis of DEGs using the STRING tool, and the PPI network was constructed by Cytoscape software. MCODE plugin was used for module analysis, and the hub genes were screened out by the Cyto- Hubba plugin. Meanwhile, we used The Kaplan-Meier plotter, GEPIA2 and HPA databases to exert survival analysis and verify the expression alternation of hub genes. Furthermore, we used ENCORI, TargetScan, miRDB and miRWalk database to predict the upstream regulated miRNA of hub genes and construct a miRNA-hub genes network by Cytoscape software. Finally, we selected potential therapeutic drugs for HCC through DGIdb databases. RESULTS: A total of 415 DEGs were screened in HCC, including 196 up-regulated DEGs and 219 down-regulated DEGs. The results of KEGG pathway analysis suggested that the up-regulated DEGs can regulate the cell cycle, and DNA replication signal pathway, while the down-regulated DEGs were associated with metabolic pathways. In this study, we identified 11 hub genes (AURKA, BUB1B, TOP2A, MAD2L1, CCNA2, CCNB1, BUB1, KIF11, CDK1, CCNB2 and TPX2), which were independent risk factors of HCCand all up-regulated DEGs. We verified the expression difference of hub genes through the GEPIA2 and HPA database, which was consistent with the results of GEO data. We found that those hub genes were mutations in HCC according to the cBioPortal database. Finally, we used the DGIdb database to select 32 potential therapeutic targeting drugs for hub genes. CONCLUSION: In summary, our study provided a new perspective for researching the molecular mechanism of HCC. Hub genes, miRNAs, and candidate drugs provide a new direction for the early diagnosis and treatment of HCC.

Analysis of angiogenesis-related subtypes of hepatocellular carcinoma and tumor microenvironment infiltration feature in hepatocellular carcinoma.

PURPOSE: Hepatocellular carcinoma (HCC) is a highly vascularized tumor, and angiogenesis plays an important role in its progression. However, the role of angiogenesis in cell infiltration in the tumor microenvironment (TME) remains unclear. METHODS: We evaluated the associations of 35 angiogenesis-related genes (ARGs) with the clinicopathological features of 816 HCC patients. In addition, we assessed the associations between the ARGs and TME cell infiltration. A nomogram was constructed to determine the prognostic value of ARGs for HCC. The ARG score was used to distinguish angiogenic subtypes of HCC, and its usefulness for predicting the prognosis and treatment response of HCC patients was evaluated. RESULTS: We distinguished three ARG clusters differing in terms of TME cell infiltration, immune cell activation status, clinicopathological features, and clinical outcomes. There were significant associations of ARG expression with tumor immunity, the epithelial-mesenchymal transition (EMT), and transforming growth factor-ß expression. An ARG score model was constructed to generate a risk score for each patient based on differentially expressed genes between clusters. Furthermore, a high ARG score was associated with high expression of CTLA-4 and PD-L1/PD-1, and a low Tumor Immune Dysfunction and Exclusion score, indicating the usefulness of the ARG score for selecting patients for immunotherapy. Considering the relationship between ARGs and tumor immunity, immunotherapy combined with vascular-targeted therapy may be the best treatment for HCC. CONCLUSIONS: ARGs play an important role in TME diversity and complexity in HCC patients. The ARG score of HCC predicts TME invasion and can guide immunotherapy.