Identification of Biomarkers Related to Immune Cell Infiltration in Hepatocellular Carcinoma Using Gene Co-Expression Network

The roles of NOP56 in cancer and SCA36

NOP56 is a highly conserved nucleolar protein. Amplification of the intron GGCCTG hexanucleotide repeat sequence of the NOP56 gene results in spinal cerebellar ataxia type 36 (SCA36). NOP56 contains an N-terminal domain, a coiled-coil domain, and a C-terminal domain. Nucleolar protein NOP56 is significantly abnormally expressed in a number of malignant tumors, and its mechanism is different in different tumors, but its regulatory mechanism in most tumors has not been fully explored. NOP56 promotes tumorigenesis in some cancers and inhibits tumorigenesis in others. In addition, NOP56 is associated with methylation in some tumors, suggesting that NOP56 has the potential to become a tumor-specific marker. This review focuses on the structure, function, related signaling pathways, and role of NOP56 in the progression of various malignancies, and discusses the progression of NOP56 in neurodegenerative and other diseases.

Bioinformatics Analysis Using ATAC-seq and RNA-seq for the Identification of 15 Gene Signatures Associated With the Prediction of Prognosis in Hepatocellular Carcinoma

Background

Gene expression (RNA-seq) and overall survival (OS) in TCGA were combined using chromosome accessibility (ATAC-seq) to search for key molecules affecting liver cancer prognosis.

Methods

We used the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) to analyse chromatin accessibility in the promoter regions of whole genes in liver hepatocellular carcinoma (LIHC) and then screened differentially expressed genes (DEGs) at the mRNA level by transcriptome sequencing technology (RNA-seq). We obtained genes significantly associated with overall survival (OS) by a one-way Cox analysis. The three were screened by taking intersection and further using a Kaplan–Meier (KM) for validation. A prognostic model was constructed using the obtained genes by LASSO regression analysis.The expression of these genes in hepatocellular carcinomas was then analysed. The protein expression of these genes was verified using the Human Protein Atlas(HPA) online datasets and immunohistochemistry.

Results

ATAC-seq, RNA-seq and survival analysis, combined with a LASSO prediction model, identified signatures of 15 genes (PRDX6, GCLM, HTATIP2, SEMA3F, UCK2, NOL10, KIF18A, RAP2A, BOD1, GDI2, ZIC2, GTF3C6 SLC1A5, ERI3 and SAC3D1), all of which were highly expressed in hepatocellular carcinoma. The LASSO prognostic model showed that this risk score had high predictive accuracy for the survival prognosis at 1, 3 and 5 years. A KM curve analysis showed that high expression of all 15 gene signatures was significantly associated with a poor prognosis in LIHC patients. HPA analysis of protein expression showed that PRDX6, GCLM, HTATIP2, NOL10, KIF18A, RAP2A and GDI2 were highly expressed in the hepatocellular carcinoma tissues compared with normal control tissues.

Conclusions

PRDX6, GCLM, HTATIP2, SEMA3F, UCK2, NOL10, KIF18A, RAP2A, BOD1, GDI2, ZIC2, GTF3C6, SLC1A5, ERI3 and SAC3D1 may affect the prognosis of LIHC.