We aimed to identify prognostic methylation genes associated with lymph node metastasis (LNM) in lung squamous cell carcinoma (LUSC). Bioinformatics methods were used to obtain optimal prognostic genes for risk model construction using data from the Cancer Genome Atlas database. ROC curves were adopted to predict the prognostic value of the risk model. Multivariate regression was carried out to identify independent prognostic factors and construct a prognostic nomogram. The differences in overall survival, gene mutation and pathways between high- and low-risk groups were analyzed. Finally, the expression and methylation level of the optimal prognostic genes among different LNM stages were analyzed. FGA, GPR39, RRAD and TINAGL1 were identified as the optimal prognostic genes and were applied to establish a prognostic risk model. Significant differences were found among the different LNM stages. The risk model could predict overall survival, showing a moderate performance with AUC of 0.64-0.68. The model possessed independent prognostic value, and could accurately predict 1-, 3- and 5-year survival. Patients with a high risk score showed poorer survival. Lower gene mutation frequencies and enrichment of leukocyte transendothelial migration and the VEGF signaling pathway in the high-risk group may lead to the poor prognosis. This study identified several specific methylation markers associated with LNM in LUSC and generated a prognostic model to predict overall survival for LUSC patients.
Keyphrases
- lymph node metastasis
- squamous cell carcinoma
- poor prognosis
- genome wide
- papillary thyroid
- prognostic factors
- dna methylation
- signaling pathway
- long non coding rna
- emergency department
- gene expression
- ejection fraction
- genome wide identification
- big data
- locally advanced
- oxidative stress
- deep learning
- vascular endothelial growth factor
- peripheral blood
- patient reported outcomes
- pi k akt
- endoplasmic reticulum stress
- induced apoptosis
- squamous cell
- high density