scholarly journals The value of complete blood count for the prognosis analysis of preoperative esophageal squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiang Lv ◽  
Songtao Han ◽  
Bin Xu ◽  
Yuqin Deng ◽  
Yangchun Feng
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Blood Count ◽  
Validation Cohort ◽  
Complete Blood Count ◽  
Training Cohort ◽  
Cox Regression Analysis

Abstract Objective To investigate the predictive value of preoperative complete blood count for the survival of patients with esophageal squamous cell carcinoma. Methods A total of 1587 patients with pathologically confirmed esophageal squamous cell carcinoma who underwent esophagectomy in the Cancer Hospital Affiliated to Xinjiang Medical University from January 2010 to December 2019 were collected by retrospective study. A total of 359 patients were as the validation cohort from January 2015 to December 2016, and the remaining 1228 patients were as the training cohort. The relevant clinical data were collected by the medical record system, and the patients were followed up by the hospital medical record follow-up system. The follow-up outcome was patient death. The survival time of all patients was obtained. The Cox proportional hazards regression model and nomogram were established to predict the survival prognosis of esophageal squamous cell carcinoma by the index, their cut-off values obtained the training cohort by the ROC curve. The Kaplan-Meier survival curve was established to express the overall survival rate. The 3-year and 5-year calibration curves and C-index were used to determine the accuracy and discrimination of the prognostic model. The decision curve analysis was used to predict the potential of clinical application. Finally, the validation cohort was used to verify the results of the training cohort. Results The cut-off values of NLR, NMR, LMR, RDW and PDW in complete blood count of the training cohort were 3.29, 12.77, 2.95, 15.05 and 13.65%, respectively. All indicators were divided into high and low groups according to cut-off values. Univariate Cox regression analysis model showed that age (≥ 60), NLR (≥3.29), LMR (< 2.95), RDW (≥15.05%) and PDW (≥13.65%) were risk factors for the prognosis of esophageal squamous cell carcinoma; multivariate Cox regression analysis model showed that age (≥ 60), NLR (≥3.29) and LMR (< 2.95) were independent risk factors for esophageal squamous cell carcinoma. Kaplan-Meier curve indicated that age <  60, NLR < 3.52 and LMR ≥ 2.95 groups had higher overall survival (p <  0.05). The 3-year calibration curve indicated that its predictive probability overestimate the actual probability. 5-year calibration curve indicated that its predictive probability was consistent with the actual probability. 5 c-index was 0.730 and 0.737, respectively, indicating that the prognostic model had high accuracy and discrimination. The decision curve analysis indicated good potential for clinical application. The validation cohort also proved the validity of the prognostic model. Conclusion NLR and LMR results in complete blood count results can be used to predict the survival prognosis of patients with preoperative esophageal squamous cell carcinoma.

scholarly journals The Value of Complete Blood Count For The Prognosis Analysis of Esophageal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Xiang Lv ◽  
Songtao Han ◽  
Bin Xu ◽  
Yuqin Deng ◽  
Feng Yangchun
Keyword(s):  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Blood Count ◽  
Cox Regression ◽  
Complete Blood Count ◽  
Analysis Model ◽  
Cox Regression Analysis

Abstract Objective To investigate the value of complete blood count in predicting the survival rate of patients with esophageal squamous cell carcinoma. Methods A total of 3587 patients with esophageal squamous cell carcinoma who were initially admitted to the Affiliated Cancer Hospital of Xinjiang Medical University from January 2010 to December 2017 were collected by retrospective study. The relevant clinical data were collected by the medical record system, and the patients were followed up by the hospital medical record follow-up system. The follow-up outcome was death. The survival time of all patients was obtained. The survival curve was established using the cut-off value of each index obtained by ROC curve. The Cox proportional hazards regression analysis model and nomogram were established to predict the survival prognosis of esophageal squamous cell carcinoma. The role of each index in the prognosis of patients with esophageal squamous cell carcinoma was studied Results The cut-off values of NLR, NMR, LMR, RDW and PDW in blood routine were 3.52, 10.22, 2.25, 13.85% and 12.05%, respectively. Survival curve analysis showed that patients aged < 60 years and NLR < 3.52 had survival. All indicators were divided into high and low groups according to ROC curve. Univariate Cox regression analysis model showed that RDW (≥ 13.85) and NLR (≥ 3.52) groups were risk factors for the prognosis of ESCC, with HR values of 1.099 (1.015–1.191; p = 0.020) and 1.340 (1.231–1.458; p < 0.001) compared with RDW (< 13.85) and NLR (< 3.52), respectively; multivariate Cox regression analysis model showed that NLR was significantly associated with the prognosis of ESCC, with HR of 1.342 (1.232–1.461; p < 0.001) for NLR (≥ 3.52) NLR (< 3.52). Conclusion NLR results in blood count can be used to predict the survival of patients with esophageal squamous cell carcinoma.

Establishment and Validation of a Prognostic Model in Patients with Stage T1-3N0M0 of Esophageal Squamous Cell Carcinoma (ESCC): An Observational Study from Two Centers

2020 ◽  
Author(s):  
Lei-Lei Wu ◽  
Yu-Xuan Ji ◽  
Yan Zheng ◽  
Xuan Liu ◽  
Yang-Yu Huang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Stage T1

Abstract PURPOSE: To explore the postoperative prognosis of patients diagnosed with esophageal squamous cell carcinoma (ESCC) of stage T1-3N0M0 using a survival prognostic model (SPM). PATIENTS AND METHODS: Patients diagnosed with stage T1-3N0M0 ESCC from two cancer centers—Sun Yat-sen University Cancer Center (SYSUCC-A/ training cohort: N = 555), SYSUCC-B/ internal validation cohort: N = 241) and Henan Cancer Hospital (HNCH/ external validation cohort: N = 170)—that had undergone esophagectomy between 1995 and 2015 were enrolled in this study. The primary clinical endpoint was overall survival (OS). We have identified and integrated significant prognostic factors for OS by univariate and multivariate Cox regression methods applied in the training cohort to build a SPM that could be validated in the validation cohorts.RESULTS: The OS evaluation by the SPM was comparable in three cohorts (SYSUCC-A: C-index 0.654, SYSUCC-B: C-index 0.630, HNCH: C-index 0.688). Discretization of patients was done using a fixed survival score cutoff of 136.434 based on our SPM determined from the training cohort divided into low- and high-risk subgroups with stratified OS in the validation cohort (SYSUCC-A: hazard ratio [HR] 1.009, 95% confidence intervals [CI], 1.006–1.011, P < 0.001; SYSUCC-B: HR 1.009, 95% CI, 1.004–1.013, P ˂ 0.001; HNCH: HR 1.010, 95% CI 1.005-1.015, P = 0.0017). The 48-month OS in the low-risk subgroup vs. that in the high-risk subgroup was 80.8% vs. 60.9% for SYSUCC-A, 86.4% vs. 60.7% for SYSUCC-B, and 89.7% vs. 64.1% for HNCH. CONCLUSION: We have established and validated a novel SPM that can predict the OS for T1-3N0M0 ESCC patients and could help clinicians to detect subgroups of patients with poor prognosis.

scholarly journals A Radiomics Nomogram for Non-Invasive Prediction of Progression-Free Survival in Esophageal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Ting Yan ◽  
lili liu ◽  
Meilan Peng ◽  
Zhenpeng Yan ◽  
Qingyu Wang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Validation Cohort ◽  
Progression Free Survival ◽  
Tnm Staging ◽  
Free Survival ◽  
Training Cohort ◽  
Radiomics Signature

Abstract Objectives: To construct a prognostic model for preoperative prediction based on computed tomography (CT) images of esophageal squamous cell carcinoma (ESCC). Methods: Radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) with high throughput radiomics features that extracted from the CT images of 272 patients (204 in training and 68 in validation cohort), who were pathologically confirmed ESCC. Multivariable logistic regression was adopted to build the radiomics signature and another predictive nomogram model, which was composed with radiomics signature, traditional TNM stage and clinical features. Then its performance was assessed by the calibration and decision curve analysis (DCA). Results: 16 radiomics features were selected from 954 to build a radiomics signature，which were significantly associated with progression-free survival (PFS) (p<0.001). The area under the curve (AUC) of performance was 0.891 (95% CI: 0.845-0.936) for training cohort and 0.706 (95% CI: 0.583-0.829) for validation cohort. The radscore of signatures’ combination showed significant discrimination for survival status in both two cohort. Kaplan-Meier survival curve further confirmed the radscore has a better prognostic performance in training cohort. Radiomics nomogram combined radscore with TNM staging showed significant improvement over TNM staging alone in training cohort (C-index, 0.802 vs 0.628; p<0.05), and it is the same with clinical data (C-index, 0.798 vs 0.660; p<0.05). Findings were confirmed in the validation cohort. DCA showed CT-based radiomics model will receive benefit when the threshold probability was between 0 and 0.9. Heat maps revealed associations between radiomics features and tumor stages.Conclusions: Multiparametric CT-based radiomics nomograms provided improved prognostic ability in ESCC.

scholarly journals Autophagy-Related Three-Gene Prognostic Signature for Predicting Survival in Esophageal Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Heyang Cui ◽  
Yongjia Weng ◽  
Ning Ding ◽  
Chen Cheng ◽  
Longlong Wang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Discovery Cohort ◽  
Cox Regression Analysis

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in China, and its prognosis remains poor. Autophagy is an evolutionarily conserved catabolic process involved in the occurrence and development of ESCC. In this study, we described the expression profile of autophagy-related genes (ARGs) in ESCC and developed a prognostic prediction model for ESCC patients based on the expression pattern of ARGs. We used four ESCC cohorts, GSE53624 (119 samples) set as the discovery cohort, The Cancer Genome Atlas (TCGA) ESCC set (95 samples) as the validation cohort, 155 ESCC cohort, and Oncomine cohort were used to screen and verify differentially expressed ARGs. We identified 34 differentially expressed genes out of 222 ARGs. In the discovery cohort, we divided ESCC patients into three groups that showed significant differences in prognosis. Then, we analyzed the prognosis of 34 differentially expressed ARGs. Three genes [poly (ADP-ribose) polymerase 1 (PARP1), integrin alpha-6 (ITGA6), and Fas-associated death domain (FADD)] were ultimately obtained through random forest feature selection and were constructed as an ARG-related prognostic model. This model was further validated in TCGA ESCC set. Cox regression analysis confirmed that the three-gene signature was an independent prognostic factor for ESCC patients. This signature effectively stratified patients in both discovery and validation cohorts by overall survival (P = 5.162E-8 and P = 0.052, respectively). We also constructed a clinical nomogram with a concordance index of 0.713 to predict the survival possibility of ESCC patients by integrating clinical characteristics and the ARG signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. In conclusion, we constructed a new ARG-related prognostic model, which shows the potential to improve the ability of individualized prognosis prediction in ESCC.

scholarly journals Serum Autoantibodies against STIP1 as a Potential Biomarker in the Diagnosis of Esophageal Squamous Cell Carcinoma

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yi-Wei Xu ◽  
Can-Tong Liu ◽  
Xin-Yi Huang ◽  
Li-Sheng Huang ◽  
Yu-Hao Luo ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Validation Cohort ◽  
Early Stage ◽  
Enzyme Linked Immunosorbent Assay ◽  
Characteristic Analysis ◽  
Training Cohort ◽  
Sensitivity Specificity

Esophageal squamous cell carcinoma (ESCC) remains one of the leading causes of cancer-related mortality around the world. The identification of novel serum biomarkers is required for early detection of ESCC. This study was designed to elucidate whether autoantibodies against STIP1 could be a diagnostic biomarker in ESCC. An enzyme-linked immunosorbent assay was performed to detect serum levels of STIP1 autoantibodies in a training cohort (148 ESCC patients and 111 controls) and a validation cohort (60 ESCC patients and 40 controls). Mann–Whitney’s U test showed that ESCC patients in two cohorts have higher levels of autoantibodies against STIP1 when compared to controls (P<0.001). According to receiver operating characteristic analysis, the sensitivity, specificity, and area under the curve (AUC) of autoantibodies against STIP1 in ESCC were 41.9%, 90.1%, and 0.682 in the training cohort and 40.0%, 92.5%, and 0.710 in the validation cohort, respectively. Moreover, detection of autoantibodies against STIP1 could discriminate early-stage ESCC patients from controls, with sensitivity, specificity, and AUC of 35.7%, 90.1%, and 0.684 in the training cohort and 38.5%, 92.5%, and 0.756 in the validation cohort, respectively. Our findings indicated that autoantibodies against STIP1 might be a useful biomarker for early-stage ESCC detection.

scholarly journals A Radiomics Nomogram for Non-Invasive Prediction of Progression-Free Survival in Esophageal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Ting Yan ◽  
Lili Liu ◽  
Meilan Peng ◽  
Zhenpeng Yan ◽  
Qingyu Wang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Validation Cohort ◽  
Progression Free Survival ◽  
Tnm Staging ◽  
Free Survival ◽  
Training Cohort ◽  
Radiomics Signature

Abstract To construct a prognostic model for preoperative prediction on computed tomography (CT) images of esophageal squamous cell carcinoma (ESCC), we constructed radiomics signature with high throughput radiomics features extracted from CT images of 272 patients (204 in training and 68 in validation cohort). Multivariable logistic regression was adopted to build the radiomics signature and another predictive nomogram model, which was composed with radiomics signature, traditional TNM stage and clinical features. 16 radiomics features were selected from 954 to build a radiomics signature，which were significantly associated with progression-free survival (PFS) (p<0.001). The area under the curve (AUC) of performance was 0.891 (95% CI: 0.845-0.936) for training cohort and 0.706 (95% CI: 0.583-0.829) for validation cohort. The radscore of signatures’ combination showed significant discrimination for survival status. Radiomics nomogram combined radscore with TNM staging showed significant improvement over TNM staging alone in training cohort (C-index, 0.802 vs 0.628; p<0.05), and it is the same with clinical data (C-index, 0.798 vs 0.660; p<0.05), which were confirmed in validation cohort. DCA showed the model will receive benefit when the threshold probability was between 0 and 0.9. Collectively, multiparametric CT-based radiomics nomograms provided improved prognostic ability in ESCC.

Computed tomography-based radiomics analysis to predict lymphovascular invasion in esophageal squamous cell carcinoma

2021 ◽  
Author(s):  
Hui Peng ◽  
Qiuxing Yang ◽  
Ting Xue ◽  
Qiaoling Chen ◽  
Manman Li ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Lymphovascular Invasion ◽  
Validation Cohort ◽  
Regression Method ◽  
Training Cohort ◽  
Volume Model ◽  
Single Slice ◽  
Full Volume

Objective The present study explored the value of preoperative CT radiomics in predicting lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC). Methods A retrospective analysis of 294 pathologically confirmed ESCC patients undergoing surgical resection and their preoperative chest-enhanced CT arterial images were used to delineate the target area of the lesion. All patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Radiomics features were extracted from single-slice, three-slice, and full-volume regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) regression method was applied to select valuable radiomics features. Radiomics models were constructed using logistic regression method and were validated using leave group out cross-validation (LGOCV) method. The performance of the three models was evaluated using the receiver characteristic curve (ROC) and decision curve analysis (DCA). Results A total of 1218 radiomics features were separately extracted from single-slice ROIs, three-slice ROIs, and full-volume ROIs, and 16, 13 and 18 features, respectively, were retained after optimization and screening to construct a radiomics prediction model. The results showed that the AUC of the full-volume model was higher than that of the single-slice and three-slice models. According to LGOCV, the full-volume model showed the highest mean AUC for the training cohort and the validation cohort. Conclusion The full-volume radiomics model has the best predictive performance and thus can be used as an auxiliary method for clinical treatment decision making. Advances in knowledge: LVI is considered to be an important initial step for tumor dissemination. CT radiomics features correlate with LVI in ESCC and can be used as potential biomarkers for predicting LVI in ESCC.

scholarly journals Development and Validation of a Ferroptosis-Related Gene Signature and Nomogram for Predicting the Prognosis of Esophageal Squamous Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiecheng Ye ◽  
Yining Wu ◽  
Heyuan Cai ◽  
Li Sun ◽  
Wanying Deng ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Group ◽  
Clinical Variable ◽  
Cox Regression Analysis

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor with high mortality and poor prognosis. Ferroptosis is a newly discovered form of cell death induced by iron-catalyzed excessive peroxidation of polyunsaturated fatty acids (PUFAs). However, the prognostic value of ferroptosis-related genes (FRGs) for ESCC remains unclear. Based on the ESCC dataset from the Gene Expression Omnibus (GEO) database, we identified 39 prognostic FRGs through univariate Cox regression analysis. After LASSO regression and multivariate Cox regression analyses, a multigene signature based on 10 prognostic FRGs was constructed and successfully divided ESCC patients into two risk groups. Patients in the low-risk group showed a significantly better prognosis than patients in the high-risk group. In addition, we combined the risk score with clinical predictors to construct a nomogram for ESCC. The predictive ability of the nomogram was further verified by ROC curves and calibration plots in both the training and validation sets. The predictive power of the nomogram was demonstrated to be better than that of either the risk score or clinical variable alone. Furthermore, functional analysis revealed that the 10-FRG signature was mainly associated with ferroptosis, differentiation and immune response. Connectivity map analysis identified potential compounds capable of targeting FRGs in ESCC. Finally, we demonstrated the prognostic value of SRC gene in ESCC using the clinical samples and found that SRC inhibition sensitized ESCC cells to ferroptosis inducers by in vitro experiments. In conclusion, we identified and verified a 10-FRG prognostic signature and a nomogram, which provide individualized prognosis prediction and provide insight into potential therapeutic targets for ESCC.

scholarly journals Comprehensive Analysis of the Functions, Prognostic and Diagnostic Values of RNA Binding Proteins in Head and Neck Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Xueping Ke ◽  
Zhen Fu ◽  
Jingjing Yang ◽  
Shijin Yu ◽  
Tingyuan Yan ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Training Cohort

Abstract Background: Increasing evidence has suggested that RNA binding protein (RBP) dysregulation plays an important part in tumorigenesis. Here, we sought to explore the potential molecular functions and clinical significance of RBP and develop diagnostic and prognostic signatures based on RBP in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The Limma package was applied to identify the differently expressed RBPs between HNSCC and normal samples with |log2 fold change (FC)|≥1 and false discovery rate (FDR)<0.05. the immunohistochemistry images from the Human Protein Atlas database The diagnostic signature based on RBP was built by LASSO-logistic regression and random forest and the prognostic signature based on RBP was constructed by LASSO and stepwise Cox regression analysis in training cohort and validated in validation cohort. All these analyses were performed using the R software.Results: A total of 84 aberrantly expressed RBPs were obtained, comprising 41 up-regulated and 43 down-regulated RBPs. Seven RBP genes (CPEB3, PDCD4, ENDOU, PARP12, DNMT3B, IGF2BP1, EXO1) were identified as diagnostic related hub gene and were used to establish a diagnostic RBP signature risk score (DRBPS) model by the coefficients in LASSO-logistic regression analysis and shown high specificity and sensitivity in the training (area under the receiver operating characteristic curve [AUC] = 0.998), and in all validation cohorts (AUC > 0.95 for all). Similarly, seven RBP genes (MKRN3, ZC3H12D, EIF5A2, AFF3, SIDT1, RBM24 and NR0B1) were identified as prognosis associated hub genes by least absolute shrinkage and selection operator (LASSO) and stepwise multiple Cox regression analyses and were used to construct the prognostic model named as PRBPS. The area under the curve of the time-dependent receiver operator characteristic curve of the prognostic model were 0.664 at 3 years and 0.635 at 5 years in training cohort and 0.720, 0.777 in the validation cohort, showing a favorable predictive effificacy for prognosis in HNSCC.Conclusions: Our results demonstrate the values of consideration of RBP in the diagnosis and prognosis for HNSCC and provide a novel insights to understand potential role of dysregulated RBP in HNSCC.

Prognostic value of programmed death-ligand 1 expression in patients with esophageal squamous cell carcinoma

2020 ◽  
Author(s):  
Qun Zhang ◽  
Feng Li ◽  
Peng Cai ◽  
Hongwei Li ◽  
Hongmei Yin ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cox Regression ◽  
Tumor Resection ◽  
Programmed Death Ligand 1 ◽  
Cox Regression Analysis ◽  
Programmed Death ◽  
L1 Protein

Abstract Background To investigate the expression of PD-L1(programmed death-ligand 1)in patients with esophageal squamous cell carcinoma (ESCC) and its clinical significance. Methods The tissue expression of PD-L1 protein in 139 cases of ESCC and 50 adjacent non-malignant epithelial tissues (> 5 cm from the tumor resection margins) were identified by immunohistochemical staining. Subsequently, the relationship between expression and the observed clinical characteristics was analyzed. Results The positive expression rate of PD-L1 protein was increased in tumor tissues compared to that of adjacent noncancerous mucosa tissues (40.3% vs. 22.0%, P < 0.05). The findings also indicated that PD-L1 protein expression had no significant correlation with age, gender, tumor location, differentiation and lymph node (N) status (P > 0.05). The 91 months follow-up Kaplan-Meier survival analysis showed that patients in positively expressed PD-L1 group experienced a lower survival rate compared to their negatively expressed PD-L1 counterparts (32.1% vs. 48.2%, P < 0.05). The COX regression analysis results suggested that PD-L1 represented an independent prognosis factor for ESCC. Conclusions The findings indicated that PD-L1 plays an important role in the progression of ESCC and might represent a potential therapeutic and prognostic target for ESCC patients.

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