Dataset Information

Reclassification of Hepatocellular Cancer With Neural-Related Genes.

ABSTRACT: Neural infiltration is a critical component of the tumor microenvironment; however, owing to technological limitations, its role in hepatocellular cancer remains obscure. Herein, we obtained the RNA-sequencing data of liver hepatocellular carcinoma (LIHC) from The Cancer Genome Atlas database and performed a series of bioinformatic analyses, including prognosis analysis, pathway enrichment, and immune analysis, using the R software packages, Consensus Cluster Plus and Limma. LIHC could be divided into two subtypes according to the expression of neural-related genes (NRGs); moreover, there are statistic differences in the prognosis, stage, and immune regulation between the two subtypes. The prognostic model showed that high expression of NRGs correlated with a poor survival prognosis (P<0.05). Further, CHRNE, GFRA2, GFRA3, and GRIN2D was significantly correlated with LIHC clinical prognosis, clinical stage, immune infiltration, immune response, and vital signaling pathways. There was nerve-cancer crosstalk in LIHC. A reclassification of LIHC based on NRG expression may prove beneficial to clinical practice. CHRNE, GFRA2, GFRA3, and GRIN2D may serve as potential biomarker for liver cancer prognosis or immune response.

SUBMITTER: Zhang YG

PROVIDER: S-EPMC9136183 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Reclassification of Hepatocellular Cancer With Neural-Related Genes.

Zhang Yi-Gan YG Jin Ming-Zhu MZ Zhu Xiao-Ran XR Jin Wei-Lin WL

Frontiers in oncology 20220513

Neural infiltration is a critical component of the tumor microenvironment; however, owing to technological limitations, its role in hepatocellular cancer remains obscure. Herein, we obtained the RNA-sequencing data of liver hepatocellular carcinoma (LIHC) from The Cancer Genome Atlas database and performed a series of bioinformatic analyses, including prognosis analysis, pathway enrichment, and immune analysis, using the R software packages, Consensus Cluster Plus and Limma. LIHC could be divide ...[more]

PMID: 35646712

Similar Datasets

Project description:Human papillomavirus (HPV) is a clear etiology of cervical cancer (CC). However, the associations between HPV infection and DNA methylation have not been thoroughly investigated. Additionally, it remains unknown whether HPV-related methylation signatures can identify subtypes of CC and stratify the prognosis of CC patients. DNA methylation profiles were obtained from The Cancer Genome Atlas to identify HPV-related methylation sites. Unsupervised clustering analysis of HPV-related methylation sites was performed to determine the different CC subtypes. CC patients were categorized into cluster 1 (Methylation-H), cluster 2 (Methylation-M), and cluster 3 (Methylation-L). Compared to Methylation-M and Methylation-L, Methylation-H exhibited a significantly improved overall survival (OS). Gene set enrichment analysis (GSEA) was conducted to investigate the functions that correlated with different CC subtypes. GSEA indicated that the hallmarks of tumors, including KRAS signaling, TNFα signaling via NF-κB, inflammatory response, epithelial-mesenchymal transition, and interferon-gamma response, were enriched in Methylation-M and Methylation-L. Based on mutation and copy number variation analyses, we found that aberrant mutations, amplifications, and deletions among the MYC, Notch, PI3K-AKT, and RTK-RAS pathways were most frequently detected in Methylation-H. Additionally, mutations, amplifications, and deletions within the Hippo, PI3K-AKT, and TGF-β pathways were presented in Methylation-M. Genes within the cell cycle, Notch, and Hippo pathways possessed aberrant mutations, amplifications, and deletions in Methylation-L. Moreover, the analysis of tumor microenvironments revealed that Methylation-H was characterized by a relatively low degree of immune cell infiltration. Finally, a prognostic signature based on six HPV-related methylation sites was developed and validated. Our study revealed that CC patients could be classified into three heterogeneous clusters based on HPV-related methylation signatures. Additionally, we derived a prognostic signature using six HPV-related methylation sites that stratified the OS of patients with CC into high- and low-risk groups.

Dataset Information

Reclassification of Hepatocellular Cancer With Neural-Related Genes.

Publications

Reclassification of Hepatocellular Cancer With Neural-Related Genes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets