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Polymorphisms in checkpoint kinase 2 may contribute to lymph node metastasis from esophageal cancer.

ABSTRACT: Esophageal cancer, which is commonly accompanied by lymph node metastasis, is among the deadliest of cancers and carries a grim prognosis. We investigated the association between genetic variation in checkpoint kinase 2 (CHEK2), which has been linked to metastasis in other cancers, and the risk of developing lymph node metastasis from esophageal cancer. CHEK2-122 G/C genotypes were determined by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in 296 subjects with esophageal cancer (67 cases with and 229 cases without lymph node metastasis). The associations between CHEK2 genotypes and the risk of lymph node metastasis from esophageal cancer were estimated by computing odds ratios (OR) and their 95% confidence intervals (CI). The CHEK2 GG, GC, and CC genotype frequencies in patients with and without lymph node metastasis were 47.8%, 40.3%, and 11.9% and 31.0%, 50.7%, and 18.3% respectively, and were statistically significant (?(2) =6.591, P=0.037). Logistic regression analyses revealed that the CHEK2-122 GC genotype significantly reduced the risk of lymph node metastasis (adjusted OR=0.54, 95% CI=0.29-0.93, P=0.028) compared to the GG genotype. Subsequently, we propose that the CHEK2-122 G/C polymorphism may play a protective role in preventing lymph node metastasis from esophageal cancer, and may also provide insight toward determining patient prognosis without the use of surgery.

SUBMITTER: Li XH

PROVIDER: S-EPMC4613028 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Polymorphisms in checkpoint kinase 2 may contribute to lymph node metastasis from esophageal cancer.

Li Xiao-Hui XH Li Xiang-Nan XN Pan Xue X Hou Xiao-Xu XX Liang Bao-Hui BH

International journal of clinical and experimental medicine 20150815 8

Esophageal cancer, which is commonly accompanied by lymph node metastasis, is among the deadliest of cancers and carries a grim prognosis. We investigated the association between genetic variation in checkpoint kinase 2 (CHEK2), which has been linked to metastasis in other cancers, and the risk of developing lymph node metastasis from esophageal cancer. CHEK2-122 G/C genotypes were determined by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in 296 su ...[more]

PMID: 26550343

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Project description:PURPOSE:To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS:A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS:Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell's Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION:The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.

Project description:Background: Lymph node (LN) metastasis is the most important prognostic factor in esophageal squamous cell carcinoma (ESCC). Traditional clinical factor and existing methods based on CT images are insufficiently effective in diagnosing LN metastasis. A more efficient method to predict LN status based on CT image is needed. Methods: In this multicenter retrospective study, 411 patients with pathologically confirmed ESCC were registered from two hospitals. Quantitative image features including handcrafted-, computer vision-(CV-), and deep-features were extracted from preoperative arterial phase CT images for each patient. A handcrafted-, CV-, and deep-radiomics signature were built, respectively. Then, multiple radiomics models were constructed by merging independent clinical risk factor into radiomics signatures. The performance of models were evaluated with respect to the discrimination, calibration, and clinical usefulness. Finally, an independent external validation cohort was used to validate the model's predictive performance. Results: Five, seven, and nine features were selected for building handcrafted-, CV-, and deep-radiomics signatures from extracted features, respectively. Those signatures were statistically significant different between LN-positive and LN-negative patients in all cohorts (p < 0.001). The developed multiple level CT radiomics model that integrates multiple radiomics signatures with clinical risk factor, was superior to traditional clinical factors and the results reported by existing methods, and achieved satisfactory discrimination performance with C-statistic of 0.875 in development cohort, 0.874 in internal validation cohort and 0.840 in independent external validation cohort. Nomogram and decision curve analysis (DCA) further confirmed our method may serve as an effective tool for clinicians to evaluate the risk of LN metastasis in patients with ESCC and further choose treatment strategy. Conclusions: The proposed multiple level CT radiomics model which integrate multiple level radiomics features into clinical risk factor can be used for preoperative predicting LN metastasis of patients with ESCC.

Dataset Information

Polymorphisms in checkpoint kinase 2 may contribute to lymph node metastasis from esophageal cancer.

Publications

Polymorphisms in checkpoint kinase 2 may contribute to lymph node metastasis from esophageal cancer.

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