Systematical identification of key genes and regulatory genetic variants associated with prognosis of esophageal squamous cell carcinoma.
Linglong GuXinying YueSiyuan NiuJialing MaShasha LiuMiaoxin PanLina SongQianqian SuYuqian TanYueping LiJiang ChangPublished in: Molecular carcinogenesis (2024)
Esophageal squamous cell carcinoma (ESCC) stands as a highly lethal malignancy characterized by pronounced recurrence and metastasis, resulting in a bleak 5-year survival rate. Despite extensive investigations, encompassing genome-wide association studies, the identification of robust prognostic markers has remained elusive. In this study, leveraging four independent data sets comprising 404 ESCC patients, we conducted a systematic analysis to unveil pivotal genes influencing overall survival. our meta-analysis identified 278 genes significantly associated with ESCC prognosis. Further exploration of the prognostic landscape involved an examination of expression quantitative trait loci for these genes, leading to the identification of six tag single nucleotide polymorphisms predictive of overall survival in a cohort of 904 ESCC patients. Notably, functional annotation spotlighted rs11227223, residing in the enhancer region of nuclear paraspeckle assembly transcript 1 (NEAT1), as a crucial variant likely exerting a substantive biological role. Through a series of biochemistry experiments, we conclusively demonstrated that the rs11227223-T allele, indicative of a poorer prognosis, augmented NEAT1 expression. Our results underscore the substantive role of NEAT1 and its regulatory variant in prognostic predictions for ESCC. This comprehensive analysis not only advances our comprehension of ESCC prognosis but also unveils a potential avenue for targeted interventions, offering promise for enhanced clinical outcomes.
Keyphrases
- bioinformatics analysis
- genome wide
- end stage renal disease
- systematic review
- newly diagnosed
- ejection fraction
- chronic kidney disease
- poor prognosis
- transcription factor
- prognostic factors
- free survival
- dna methylation
- binding protein
- genome wide association
- genome wide identification
- big data
- high resolution
- climate change
- single cell
- meta analyses
- cancer therapy
- case control
- artificial intelligence
- drug delivery
- human health
- patient reported