Aberrant methylation of Fragile Histidine Triad gene is associated with poor prognosis in early stage esophageal squamous cell carcinoma

2006 ◽  
Vol 42 (7) ◽  
pp. 972-980 ◽  
Author(s):  
Eun Ju Lee ◽  
Bo Bin Lee ◽  
Jin Wook Kim ◽  
Young Mog Shim ◽  
I. Hoseok ◽  
...  
2016 ◽  
Vol Volume 9 ◽  
pp. 4289-4293 ◽  
Author(s):  
Wei-Wei Tang ◽  
Zihao Liu ◽  
Tongxin Yang ◽  
Hanjin Wang ◽  
Xiufeng Cao

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaofeng Duan ◽  
Xiaobin Shang ◽  
Jie Yue ◽  
Zhao Ma ◽  
Chuangui Chen ◽  
...  

Abstract Background A nomogram was developed to predict lymph node metastasis (LNM) for patients with early-stage esophageal squamous cell carcinoma (ESCC). Methods We used the clinical data of ESCC patients with pathological T1 stage disease who underwent surgery from January 2011 to June 2018 to develop a nomogram model. Multivariable logistic regression was used to confirm the risk factors for variable selection. The risk of LNM was stratified based on the nomogram model. The nomogram was validated by an independent cohort which included early ESCC patients underwent esophagectomy between July 2018 and December 2019. Results Of the 223 patients, 36 (16.1%) patients had LNM. The following three variables were confirmed as LNM risk factors and were included in the nomogram model: tumor differentiation (odds ratio [OR] = 3.776, 95% confidence interval [CI] 1.515–9.360, p = 0.004), depth of tumor invasion (OR = 3.124, 95% CI 1.146–8.511, p = 0.026), and tumor size (OR = 2.420, 95% CI 1.070–5.473, p = 0.034). The C-index was 0.810 (95% CI 0.742–0.895) in the derivation cohort (223 patients) and 0.830 (95% CI 0.763–0.902) in the validation cohort (80 patients). Conclusions A validated nomogram can predict the risk of LNM via risk stratification. It could be used to assist in the decision-making process to determine which patients should undergo esophagectomy and for which patients with a low risk of LNM, curative endoscopic resection would be sufficient.


Tumor Biology ◽  
2014 ◽  
Vol 35 (8) ◽  
pp. 7743-7754 ◽  
Author(s):  
Hai-Wei Xie ◽  
Qing-Quan Wu ◽  
Bin Zhu ◽  
Fang-Jun Chen ◽  
Lv Ji ◽  
...  

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