GOLM1 and FAM49B: Potential Biomarkers in HNSCC Based on Bioinformatics and Immunohistochemical Analysis.
Yue XiTiange ZhangWei SunRuobing LiangSridha GaneshHonglei ChenPublished in: International journal of molecular sciences (2022)
Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide. We aimed to identify potential genetic markers that could predict the prognosis of HNSCC. A total of 44 samples of GSE83519 from Gene Expression Omnibus (GEO) datasets and 546 samples of HNSCC from The Cancer Genome Atlas (TCGA) were adopted. The differently expressed genes (DEGs) of the samples were screened by GEO2R. We integrated the expression information of DEGs with clinical data from GES42743 using the weighted gene co-expression network analysis (WGCNA). A total of 17 hub genes were selected by the module membership (|MM| > 0.8), and the gene significance (|GS| > 0.3) was selected from the turquoise module. GOLM1 and FAM49B genes were chosen based on single-gene analysis results. Survival analysis showed that the higher expression of GOLM1 and FAM49B genes was correlated with a worse prognosis of HNSCC patients. Immunohistochemistry and multiplex immunofluorescence techniques verified that GOLM1 and FAM49B genes were highly expressed in HNSCC cells, and high expressions of GOLM1 were associated with the pathological grades of HNSCC. In conclusion, our study illustrated a new insight that GOLM1 and FAM49B genes might be used as potential biomarkers to determine the development of HNSCC, while GOLM1 and FAM49B have the possibility to be prognostic indicators for HNSCC.
Keyphrases
- genome wide
- genome wide identification
- bioinformatics analysis
- dna methylation
- network analysis
- gene expression
- poor prognosis
- genome wide analysis
- copy number
- end stage renal disease
- chronic kidney disease
- machine learning
- squamous cell carcinoma
- signaling pathway
- induced apoptosis
- magnetic resonance imaging
- electronic health record
- cell death
- high throughput
- climate change
- deep learning
- patient reported outcomes
- prognostic factors
- human health
- cell cycle arrest
- young adults