Mild dyserythropoiesis and β-like globin gene expression imbalance due to the loss of histone chaperone ASF1B

Identification of ASF1B as a prognostic marker for liver cancer by meta-analysis and its immune value revealed by a comprehensive pan-cancer analysis of 33 human cancers

Introduction

As one of the most common malignant tumours, liver cancer is difficult to detect in the early stage, with strong metastasis and poor prognosis. Anti-silencing function protein 1 was originally discovered in yeast as a histone H3-H4 chaperone, and studies have shown that ASF1B may be a target for inhibiting the growth of hepatocellular carcinoma cells.

Aim

To evaluate the diagnostic and prognostic significance of ASF1B expression in human LIHC on the basis of TCGA data.

Material and methods

A meta-analysis revealed that high ASF1B expression was strongly associated with better overall survival. A comprehensive pan-cancer analysis of 33 human cancers revealed the immunotherapeutic value of ASF1B.

Results

In this study, we observed a significant upregulation of ASF1B expression in LIHC samples compared to non-cancer samples. Clinical analysis showed that high expression of ASF1B was associated with age, tumour status, and clinical stage. Survival analysis showed that patients with high ASF1B expression had worse overall survival and progression-free survival than patients with low ASF1B expression. The AUCs of the 1-year, 3-year, and 5-year survival-related ROC curves were 0.672, 0.590, and 0.591, respectively.

Conclusions

Our study shows that ASF1B may provide new ideas for the diagnosis and prognosis of liver cancer patients, as well as providing a new direction for the application of ASF1B in tumour immunotherapy.

Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma

Purpose

Hepatitis B (HBV)-infected hepatocellular carcinoma is one of the most common cancers, and it has high incidence and mortality rates worldwide. The incidence of hepatocellular carcinoma has been increasing in recent years, and existing treatment modalities do not significantly improve prognosis. Therefore, it is important to find a biomarker that can accurately predict prognosis.

Methods

This study was analyzed using the The Cancer Genome Atlas (TCGA) database and validated by the International Cancer Genome Consortium (ICGC) database. The STRING database was used to construct a gene co-expression network and visualize its functional clustering using Cytoscape. A prognostic signature model was constructed to observe high and low risk with prognosis, and independent prognostic factors for HBV-infected hepatocellular carcinoma were identified by Cox regression analysis. The independent prognostic factors were then analyzed for expression and survival, and their pathway enrichment was analyzed using gene set enrichment analysis (GSEA).

Results

805 differentially expressed genes (DEGs) were obtained by differential analysis. Protein–protein interaction (PPI) showed that DEGs were mostly clustered in functional modules, such as cellular matrix response, cell differentiation, and tissue development. Prognostic characterization models showed that the high-risk group was associated with poor prognosis, while Cox regression analysis identified ASF1B as the only independent prognostic factor. As verified by expression and prognosis, ASF1B was highly expressed in HBV-infected hepatocellular carcinoma and led to a poor prognosis. GSEA showed that high ASF1B expression was involved in cell cycle-related signaling pathways.

Conclusion

Bioinformatic analysis identified ASF1B as an independent prognostic factor in HBV-infected hepatocellular carcinoma, and its high expression led to a poor prognosis. Furthermore, it may promote hepatocellular carcinoma progression by affecting cell cycle-related signaling pathways.

Comprehensive Pan-Cancer Analysis and the Regulatory Mechanism of ASF1B, a Gene Associated With Thyroid Cancer Prognosis in the Tumor Micro-Environment

Background

The incidence of thyroid cancer, whose local recurrence and metastasis lead to death, has always been high and the pathogenesis of papillary thyroid carcinoma (PTC) has not been clearly elucidated. Therefore, the research for more accurate prognosis-related predictive biomarkers is imminent, and a key gene can often be a prognostic marker for multiple tumors.

Methods

Gene expression profiles of various cancers in the TCGA and GTEx databases were downloaded, and genes significantly associated with the prognosis of THCA were identified by combining differential analysis with survival analysis. Then, a series of bioinformatics tools and methods were used to analyze the expression of the gene in each cancer and the correlation of each expression with prognosis, tumor immune microenvironment, immune neoantigens, immune checkpoints, DNA repair genes, and methyltransferases respectively. The possible biological mechanisms were also investigated by GSEA enrichment analysis.

Results

656 differentially expressed genes were identified from two datasets and 960 DEGs that were associated with disease-free survival in THCA patients were screened via survival analysis. The former and the latter were crossed to obtain 7 key genes, and the gene with the highest risk factor, ASF1B, was selected for this study. Differential analysis of multiple databases showed that ASF1B was commonly and highly expressed in pan-cancer. Survival analysis showed that high ASF1B expression was significantly associated with poor patient prognosis in multiple cancers. In addition, ASF1B expression levels were found to be associated with tumor immune infiltration in THCA, KIRC, LGG, and LIHC, and with tumor microenvironment in BRCA, LUSC, STAD, UCEC, and KIRC. Further analysis of the relationship between ASF1B expression and immune checker gene expression suggested that ASF1B may regulate tumor immune patterns in most tumors by regulating the expression levels of specific immune checker genes. Finally, GSEA enrichment analysis showed that ASF1B high expression was mainly enriched in cell cycle, MTORC1 signaling system, E2F targets, and G2M checkpoints pathways.

Conclusions

ASF1B may be an independent prognostic marker for predicting the prognosis of THCA patients. The pan-cancer analysis suggested that ASF1B may play an important role in the tumor micro-environment and tumor immunity and it has the potential of serving as a predictive biomarker for multiple cancers.