Identification of key target genes and pathways in laryngeal carcinoma

Abstract

The purpose of the present study was to screen the key genes associated with laryngeal carcinoma and to investigate the molecular mechanism of laryngeal carcinoma progression. The gene expression profile of GSE10935 [Gene Expression Omnibus (GEO) accession number], including 12 specimens from laryngeal papillomas and 12 specimens from normal laryngeal epithelia controls, was downloaded from the GEO database. Differentially expressed genes (DEGs) were screened in laryngeal papillomas compared with normal controls using Limma package in R language, followed by Gene Ontology (GO) enrichment analysis and pathway enrichment analysis. Furthermore, the protein-protein interaction (PPI) network of DEGs was constructed using Cytoscape software and modules were analyzed using MCODE plugin from the PPI network. Furthermore, significant biological pathway regions (sub-pathway) were identified by using iSubpathwayMiner analysis. A total of 67 DEGs were identified, including 27 up-regulated genes and 40 down-regulated genes and they were involved in different GO terms and pathways. PPI network analysis revealed that Ras association (RalGDS/AF-6) domain family member 1 (RASSF1) was a hub protein. The sub-pathway analysis identified 9 significantly enriched sub-pathways, including glycolysis/gluconeogenesis and nitrogen metabolism. Genes such as phosphoglycerate kinase 1 (PGK1), carbonic anhydrase II (CA2), and carbonic anhydrase XII (CA12) whose node degrees were >10 were identified in the disease risk sub-pathway. Genes in the sub-pathway, such as RASSF1, PGK1, CA2 and CA12 were presumed to serve critical roles in laryngeal carcinoma. The present study identified DEGs and their sub-pathways in the disease, which may serve as potential targets for treatment of laryngeal carcinoma.

Identification of key pathways and genes in nonalcoholic fatty liver disease using bioinformatics analysis

“…Protein-protein interaction network analysis has been a useful tool to identify key regulators in human diseases, such as laryngeal carcinoma [20], colorectal cancer [21,22] and glioma [23]. Here, we for the first time constructed NAFLD associated PPI networks.…”

The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes

“…For example, in the context of myeloid leukemia[96], SVA enabled subsequent subtyping approaches to correctly identify previously validated subtypes. Removing batch effects via SVA has also helped subsequent unsupervised approaches to uncover overlap between inflammatory markers and common pathways implicated in many diseases[97,98], and identify functional components of tumor causing pathways[99].…”