Screening and Evaluating Novel Autoantibodies Based on Proteomic Chips in Diagnosis of Esophageal Squamous Cell Carcinoma

Abstract Background More and more studies have confirmed that TAAbs could be used as potential biomarkers for tumor patients. The aim of this study is to identify novel TAAbs through proteomic chips and construct a diagnostic model to discriminate esophageal squamous cell carcinoma (ESCC) cases from benign esophageal diseases cases and normal controls (NCs). Methods The human proteomic chips were used to screen TAAbs. Enzyme-linked immunosorbent assay (ELISA) was adopted to verify and validate the candidate TAAbs which were screened by the chips in verification phase (90 ESCC cases and 90 NCs) and validation phase (126 ESCC cases, 237 benign esophageal diseases cases and 126 NCs). Based on the candidate TAAbs, then the diagnostic model for ESCC was constructed by logistic regression analysis in the training group and validated in the testing group. Results Firstly, thirteen potential candidate TAAbs were identified by proteomic chips. In verification phase, the titers of six TAAbs (anti-MAGEA1, anti-VCL, anti-PRKCZ, anti-TP53, anti-NFKB1 and anti- MAGEA4) in ESCC cases were higher than those in NCs while other seven TAAbs showed no difference. Subsequently, six candidate TAAbs were validated further in validation phase. Finally, the logistic regression model with 3 TAAbs (anti-MAGEA1, anti-VCL, anti-TP53) could discriminate ESCC cases from NCs with area under curve(AUC)of 0.80 in the training group and 0.73 in the testing group, respectively. Meanwhile, the model could discriminate ESCC cases from benign esophageal diseases cases with AUC of 0.74. Conclusion The study has identified six novel TAAbs through protein chips and constructed a diagnostic model. The panel showed great performance to distinguish ESCC cases from benign esophageal diseases cases and NCs.

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A prognostic model for stratification of stage IB/IIA esophageal squamous cell carcinoma: a retrospective study

BMC Gastroenterology ◽

10.1186/s12876-021-01636-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lei-Lei Wu ◽

Qi-Long Ma ◽

Wei Huang ◽

Xuan Liu ◽

Li-Hong Qiu ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

High Risk ◽

Esophageal Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Prognostic Score ◽

Training Group ◽

Low Risk ◽

Stage Ib ◽

Ptnm Stage

Abstract Background To explore the postoperative prognosis of esophageal squamous cell carcinoma (ESCC) patients with stage IB/IIA, using a prognostic score (PS). Methods Stage IB/IIA ESCC patients who underwent esophagectomy from 1999 to 2010 were included. We retrospectively recruited 153 patients and extracted their medical records. Moreover, we analyzed the programmed death ligand-1 (PD-L1) expression of their paraffin tissue. The cohort were randomly divided into a training group (N = 123) and a validation group (N = 30). We selected overall survival (OS) as observed endpoint. Prognostic factors with a multivariable two-sided P < 0.05 met standard of covariate inclusion. Results Univariable and multivariable analyses identified pTNM stage, the number of lymph nodes (NLNs) and PD-L1 expression as independent OS predictors. Primary prognostic score which comprised above three covariates adversely related with OS in two cohorts. PS discrimination of OS was comparable between the training and internal validation cohorts (C-index = 0.774 and 0.801, respectively). In addition, the PS system had an advantage over pTNM stage in the identification of high-risk patients (C-index = 0.774 vs. C-index = 0.570, P < 0.001). Based on PS cutoff, training and validation datasets generated low-risk and high-risk groups with different OS. Our three-factor PS predicted OS (low-risk subgroup vs. high-risk subgroup 60-month OS, 74% vs. 23% for training cohort and 83% vs. 45% for validation cohort). Conclusion Our study suggested a PS for significant clinical stratification of IB/IIA ESCC to screen out subgroups with poor prognosis.

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CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy

Journal of Radiation Research ◽

10.1093/jrr/rrz027 ◽

2019 ◽

Vol 60 (4) ◽

pp. 538-545 ◽

Cited By ~ 9

Author(s):

Zhining Yang ◽

Binghui He ◽

Xinyu Zhuang ◽

Xiaoying Gao ◽

Dandan Wang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Logistic Regression ◽

Regression Analysis ◽

Esophageal Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Logistic Regression Analysis ◽

Neoadjuvant Chemoradiotherapy ◽

Model Performance ◽

Training Cohort

Abstract The objective of this study was to build models to predict complete pathologic response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC) patients using radiomic features. A total of 55 consecutive patients pathologically diagnosed as having ESCC were included in this study. Patients were divided into a training cohort (44 patients) and a testing cohort (11 patients). The logistic regression analysis using likelihood ratio forward selection was performed to select the predictive clinical parameters for pCR, and the least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomic predictors in the training cohort. Model performance in the training and testing groups was evaluated using the area under the receiver operating characteristic curves (AUC). The multivariate logistic regression analysis identified no clinical predictors for pCR. Thus, only radiomic features selected by LASSO were used to build prediction models. Three logistic regression models for pCR prediction were developed in the training cohort, and they were able to predict pCR well in both the training (AUC, 0.84–0.86) and the testing cohorts (AUC, 0.71–0.79). There were no differences between these AUCs. We developed three predictive models for pCR after nCRT using radiomic parameters and they demonstrated good model performance.

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Modeling the Risk of Radiation Pneumonitis in Esophageal Squamous Cell Carcinoma Treated with Definitive Chemoradiotherapy

10.21203/rs.3.rs-120618/v1 ◽

2020 ◽

Author(s):

Kaiqi Lan ◽

Cheng Xu ◽

Shiliang Liu ◽

Jinhan Zhu ◽

Yadi Yang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Logistic Regression ◽

Esophageal Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Radiation Pneumonitis ◽

Smoking Status ◽

Characteristic Curve ◽

Smoking History ◽

Prediction Ability

Abstract Purpose: To develop and validate a nomogram for the prediction of symptomatic radiation pneumonitis (RP) in patients with esophageal squamous cell carcinoma (ESCC) who received definitive concurrent chemoradiotherapy.Methods: Clinical factors, dose-volume histogram parameters, and pulmonary function parameters were collected from 402 ESCC patients between 2010 and 2017, including 321 patients in the primary cohort and 81 in the validation cohort. The end-point was the occurrence of symptomatic RP (grade ≥2) within the first 12 months after radiotherapy. Univariate and multivariate logistic regression analyses were applied to evaluate the predictive value of each factor for RP. A prediction model was generated in the primary cohort, which was internally validated to assess its performance.Results: In the primary cohort, 31 patients (9.7%) experienced symptomatic RP. Based on logistic regression model, patients with larger planning target volumes (PTVs) or higher lung V20 had a higher predictive risk of RP, whereas the overall risk was substantially higher for three-dimensional conformal radiotherapy (3DCRT) than intensity-modulated radiotherapy. On multivariate analysis, independent predictive factors for RP were smoking history (P=0.018), radiotherapy modality (P<0.001), PTV (P=0.014), and lung V20 (P=0.002), which were incorporated into the nomogram. The areas under the receiver operating characteristic curve of the nomogram in the primary and validation cohorts were 0.772 and 0.900, respectively, which were superior to each predictor alone.Conclusions: Non-smoking status, 3DCRT, lung V20 (>27.5%), and PTV (≥713.0 cc) were significantly associated with a higher risk of RP. A nomogram was built with satisfactory prediction ability.

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