Diffuse large B-cell lymphoma (DLBC) is a kind of tumor with rapid progress and poor prognosis. Therefore, it is necessary to explore new biomarkers or therapeutic targets to assist in diagnosis or treatment. This study is aimed at screening hub genes by weighted gene coexpression network analysis (WGCNA) and exploring the significance of overall survival (OS) in DLBC patients. Statistical data using WGCNA to analyze mRNA expression in DLBC patients came from The Cancer Genome Atlas (TCGA) dataset. After analyzing with clinical information, the biological functions of hub genes were detected. Survival analysis, Cox regression detection, and correlation analysis of the hub genes were carried out. The potential function of the hub gene related to prognosis was predicted by gene set enrichment analysis (GSEA). The results showed that APOE, CTSD, LGALS2, and TMEM176B expression in normal tissues was significantly higher than that in cancer tissues (P < 0.01). Survival analysis showed that patients with high APOE and CTSD were associated with better OS (P < 0.01). APOE and CTSD genes were mainly enriched in the regulation of ROS and oxidative stress. The two hub genes related to the prognosis of DLBC were identified and verified based on WGCNA. Survival analysis showed that the overexpression of APOE and CTSD in DLBC might be beneficial to the prognosis. These findings identified vital pathways and genes that may become new therapeutic targets and contribute to prognostic indicators.
Keyphrases
- network analysis
- genome wide
- genome wide identification
- diffuse large b cell lymphoma
- bioinformatics analysis
- poor prognosis
- end stage renal disease
- genome wide analysis
- oxidative stress
- copy number
- dna methylation
- epstein barr virus
- ejection fraction
- chronic kidney disease
- transcription factor
- magnetic resonance
- long non coding rna
- cognitive decline
- newly diagnosed
- peritoneal dialysis
- high fat diet
- gene expression
- type diabetes
- induced apoptosis
- cell death
- smoking cessation
- signaling pathway
- healthcare
- cell proliferation
- skeletal muscle
- lymph node metastasis
- big data
- data analysis
- binding protein
- risk assessment
- replacement therapy
- endoplasmic reticulum stress
- artificial intelligence
- climate change
- sensitive detection
- squamous cell