Potentially functional genetic variants of the notch signaling pathway genes predict survival of Chinese patients with esophageal squamous cell carcinoma

Abstract

Background The Notch signaling pathway is involved in the progression of esophageal squamous cell carcinoma (ESCC), although the roles of single nucleotide polymorphisms (SNPs) of the Notch signaling pathway genes in the process remain unknown. Methods The present study included 1009 patients with histopathologically diagnosed ESCC at Fudan University Shanghai Cancer Center. Two‐stage multivariate Cox proportional hazards regression analysis was used to estimate associations between 13,248 SNPs in 103 Notch signaling pathway genes and overall survival of the patients. Results We found that overall survival of the patients was significantly associated with genotypes of HDAC9 rs1729318 (AT+TT vs. AA: hazard ratio = 1.44, 95% confidence interval = 1.16–1.80, pcombined = 0.001) and HDAC9 rs1339555498 (GT + TT vs. GG: hazard ratio = 1.38, 95% confidence interval = 1.10–1.74, pcombined = 0.005). Further receiver operator characteristic (ROC) curve analysis indicated that the model with both available clinical factors and these two SNPs improved the area under the ROC curve compared to the model with clinical factors only (1‐year: 0.66 vs. 0.64, p = 0.034). Additional expression quantitative trait loci analysis showed that the rs1729318 T variant genotypes were associated with increased mRNA expression levels of HDAC9 in normal esophageal muscular tissue (p = 0.003). Conclusions The results suggest that these two potential functional SNPs on HDAC9 may serve as biomarkers for predicting survival of ESCC patients. However, further studies are needed to confirm these findings.

FN1 and cancer‐associated fibroblasts markers influence immune microenvironment in clear cell renal cell carcinoma

“…[8][9][10][11] The need to elucidate the underlying mechanisms is gaining more momentum in complex diseases such as cancer as a result of intricate and extensive investigations. [12][13][14][15][16] The present study applied elaborate systems biology methodology encompassing differential gene expression, GIN and functional enrichment analysis (FEA) to determine the underlying mechanisms promoting metastasis. We also emphasized the role of TME with screened hubs to identify potential TME-based biomarkers for ccRCC.…”

“…A systems biology‐based gene interaction network (GIN) comprising curated genes (organism‐specific) can help to provide a better understanding of the underlying mechanisms in deregulated biological systems through enrichment and statistical parameters 8–11 . The need to elucidate the underlying mechanisms is gaining more momentum in complex diseases such as cancer as a result of intricate and extensive investigations 12–16 . The present study applied elaborate systems biology methodology encompassing differential gene expression, GIN and functional enrichment analysis (FEA) to determine the underlying mechanisms promoting metastasis.…”