Anillin actin-binding protein (ANLN) is crucially involved in cell proliferation and migration. Moreover, ANLN is significantly in tumor progression in several types of human malignant tumors; however, it remains unclear whether ANLN acts through common molecular pathways within different tumor microenvironments, pathogeneses, prognoses and immunotherapy contexts. Therefore, this study aimed to perform bioinformatics analysis to examine the correlation of ANLN with tumor immune infiltration, immune evasion, tumor progression, immunotherapy, and tumor prognosis. We observed increased ANLN expression in multiple tumors, which could be involved in tumor cell proliferation, migration, infiltration, and prognosis. The level of ANLN methylation and genetic alteration was associated with prognosis in numerous tumors. ANLN facilitates tumor immune evasion through different mechanisms, which involve T-cell exclusion in different cancer types and tumor-infiltrating immune cells in colon adenocarcinoma, kidney renal clear cell carcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma. Additionally, ANLN is correlated with immune or chemotherapeutic outcomes in malignant cancers. Notably, ANLN expression may be a predictive biomarker for the response to immune checkpoint inhibitors. Taken together, our findings suggest that ANLN can be used as an onco-immunological biomarker and could serve as a hallmark for tumor screening, prognosis, individualized treatment design, and follow-up.
Hepatocellular carcinoma (HCC) is a malignant tumor associated with a high recurrence rate after hepatectomy. Recently, preoperative inflammatory and liver function reserve indices were found to predict increased risk of recurrence and decreased survival in HCC patients. This study aims to evaluate the ability of the γ-glutamyl transpeptidase-to-albumin ratio (GAR) and aspartate aminotransferase-to-lymphocyte ratio (ALRI), individually and in combination, to predict the prognosis of HCC patients after hepatectomy. We retrospectively reviewed 206 HCC patients who underwent radical resection at the General Hospital of Ningxia Medical University from January 2011 to November 2016. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off value for GAR and ALRI. The Pearson Chi-Squared test was used to analyze the correlations between GAR, ALRI and clinicopathological characteristics. Univariate and multivariate analyses were used to determine the predictive value of these factors for disease-free survival (DFS) and overall survival (OS). Survival rates were drawn according to the Kaplan-Meier method and differences between subgroups were compared by the log-rank statistics. GAR and ALRI were significantly correlated with gender, history of smoking, prothrombin time, tumor diameter, T stage and early intrahepatic recurrence by the Pearson Chi-Squared test (all P < .05). Univariate analysis indicated that T stage, GAR and ALRI were significantly correlated with DFS and OS in HCC patients after hepatectomy. Multivariate analysis illustrated that GAR and ALRI were independently related to DFS and OS in HCC patients. Preoperative GAR > 0.946 or ALRI > 18.734 predicted poor prognosis in HCC patients after hepatectomy. Additionally, the predictive scope of GAR combined with ALRI was more sensitive than that of either individual measurement alone. Our data indicate that there is a close association between the clinicopathological characteristics in HCC patients and increased GAR or ALRI. Higher levels of GAR and ALRI could sensitively and specifically predict a poor prognosis in HCC patients after hepatectomy. Furthermore, combined usage of GAR and ALRI could improve the accuracy of this prediction.
Glucose-6-phosphate dehydrogenase (G6PD) plays an important role in the metabolic and immunological aspects of tumors. In hepatocellular carcinoma (HCC), the alteration of tumor microenvironment influences recurrence and metastasis. We extract G6PD expression information from TCGA and GEO databases in liver cancer tissues and normal tissues, validated by immunohistochemistry, and the correlation between G6PD expression and clinical features is analyzed, and the clinical significance of G6PD in liver cancer is assessed by Kaplan-Meier, Cox regression and prognostic line graph models. Functional enrichment analysis is performed by protein-protein interaction (PPI) network, GO/KEGG, GSEA and G6PD-associated differentially expressed genes (DEGs). TIMER and ssGSEA packages are used to assess the correlation between expression and the level of immune cell infiltration. Analysis of TCGA and GEO datasets revealed that G6PD expression is significantly upregulated in hepatocellular carcinoma tissues (P < 0.001). G6PD expression is associated with histological grade, pathological stage, T-stage, vascular infiltration and AFP level (P < 0.05); HCC patients in the low G6PD expression group had longer overall survival and better prognosis compared with the high G6PD expression group (P < 0.05). The level of G6PD expression affects the levels of macrophages, unactivated dendritic cells, B cells, and follicular helper T cells in the tumor microenvironment. High expression of G6PD is a potential biomarker for poor prognosis of hepatocellular carcinoma, and G6PD may be a target for immunotherapy of HCC.
Background: Glucose-6-phosphate dehydrogenase (G6PD) plays an important role in the metabolic and immunological aspects of tumors. In hepatocellular carcinoma (HCC), the alteration of tumor microenvironment influences recurrence and metastasis. We extracted G6PD-related data from public databases of HCC tissues and used a bioinformatics approach to explore the correlation between G6PD expression and clinicopathological features and prognosis of immune cell infiltration in HCC.Methods: We extract G6PD expression information from TCGA and GEO databases in liver cancer tissues and normal tissues, validated by immunohistochemistry, and the correlation between G6PD expression and clinical features is analyzed, and the clinical significance of G6PD in liver cancer is assessed by Kaplan-Meier, Cox regression and prognostic line graph models. Functional enrichment analysis is performed by protein-protein interaction (PPI) network, GO/KEGG, GSEA and G6PD-associated differentially expressed genes (DEGs). TIMER and ssGSEA packages are used to assess the correlation between expression and the level of immune cell infiltration.Results: Our results show that G6PD expression is significantly upregulated in hepatocellular carcinoma tissues (P < 0.001). G6PD expression is associated with histological grade, pathological stage, T-stage, vascular infiltration and AFP level (P < 0.05); HCC patients in the low G6PD expression group had longer overall survival and better prognosis compared with the high G6PD expression group (P < 0.05). The level of G6PD expression also affects the levels of macrophages, unactivated dendritic cells, B cells, and follicular helper T cells in the tumor microenvironment.Conclusion: High expression of G6PD is a potential biomarker for poor prognosis of hepatocellular carcinoma, and G6PD may be a target for immunotherapy of HCC.
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