Predicting prognosis in resected esophageal squamous cell carcinoma using a clinical nomogram and recursive partitioning analysis

Harnessing Calmodulin-Related Genes to Build a Prognostic Model in Esophageal Squamous Cell Carcinoma for a Comprehensive Analysis of Single-Cell Immune Characteristics and Drug Efficacy

SUMMARY

Calmodulin (CALM) has a bearing on the prognosis of various cancers. However, the prognostic value of CALM in esophageal squamous cell carcinoma (ESCC) remains unelucidated. Differentially expressed genes (DEGs) were screened between normal and tumor groups of TCGA-ESCC sets. The intersection of DEGs with calmodulin-related genes (CRGs) yielded differentially expressed CRGs (DE-CRGs). A prognostic model was established using LASSO Cox regression analysis and multivariate Cox regression analysis. qPCR validated the expression of prognostic feature genes. Analysis of gene expression patterns of different cellular clusters was based on single-cell sequencing data. Lastly, GSEA enrichment, immune infiltration, mutational profiling, drug sensitivity, and molecular docking as well as cellular thermal shift assay (CETSA) were conducted for ESCC patients. A prognosis model with excellent predictive capability was created based on 4 feature genes (ATP2B3, CALB1, KCNQ1, and MYO1G). The qPCR results demonstrated that ATP2B3 and KCNQ1 were significantly downregulated in human ESCC cells, whereas CALB1 and MYO1G were upregulated (P<0.05). Single-cell analysis uncovered that MYO1G and KCNQ1 were mainly expressed in different cell clusters. Furthermore, this risk model was strongly associated with functional pathway enrichment, immune cell infiltration, and somatic mutations. We also identified AZD-8055 may be potential therapy for ESCC patients. The CETSA experiment demonstrated the existence of a favorable binding thermal stability between AZD-8055 and MYO1G. This research may identify potential biomarkers for predicting the prognosis of ESCC patients.

A nomogram to predict long-term survival after resection for esophageal cancer: An observational study in northeast China

OBJECTIVE

This study aims to create a prognostic nomogram by combining clinicopathologic variables that are linked to the overall survival following the surgical removal of esophageal squamous cell carcinoma.

METHODS

A total of 224 patients with esophageal cancer who underwent surgical R0 resection were included. The construction of the nomogram involved using a multivariable Cox proportional hazards regression model. To evaluate the model's effectiveness, Kaplan-Meier curves and calibration plots were used for discrimination and calibration, respectively.

RESULTS

Nearly half of the patients were >60 years old (45.1%), and 95.5% of the patients were male. After esophageal cancer resection, 35.7% of patients experienced complications, with 23.7% developing anastomotic stenosis and 4.5% developing a fistula. Using the backward selection of clinically relevant variables, we found that tumor located in middle thoracic (hazard ratio 2.299, 95% confidence interval 1.008-5.244), anastomotic fistula (3.028, 1.436-6.384), and vascular invasion (2.175, 1.496-3.108) were independently associated with mortality (all P < .05), whereas lymph node clearance ≥15 nodes is associated with longer survival (0.444, 0.278-0.710) (P = .001). On the basis of these factors, a nomogram was created to predict survival of esophageal squamous cell carcinoma after resection. Discrimination using Kaplan-Meier curves, calibration curves, and bootstrap cross-validation revealed good predictive abilities (C index, 0.673).

CONCLUSIONS

A nomogram was created based on the experience from northeast China to forecast overall survival following resection for esophageal squamous cell carcinoma. The validation process demonstrated accurate distinction and calibration, indicating the practical value of the nomogram in enhancing personalized survival predictions for patients who undergo esophageal squamous cell carcinoma resection in this study population.

Recursive partitioning staging system based on the log odds of the negative lymph node/T stage ratio in colon mucinous adenocarcinoma

Background

This study aimed to investigate the prognostic significance of the log odds of negative lymph nodes/T stage ratio (LONT) and develop an efficient prognostic staging system using LONT in patients with colon mucinous adenocarcinoma (MAC).

Methods

This study included 5,236 patients diagnosed with colon MAC obtained from the Surveillance, Epidemiology, and End Results database. The Kaplan-Meier method, subgroup analysis, receiver operating characteristic (ROC) curve, and Cox proportional hazard regression model were used to determine the clinical outcomes. Recursive partitioning analysis (RPA) was used to develop a novel prognostic system.

Results

The 1-, 3-, and 5-year ROC curves, used to predict cancer-specific survival (CSS) and overall survival (OS), demonstrated that the areas under the ROC curve for LONT were superior to those of pT, pN, and pTNM stages. Additionally, a lower LONT was correlated with worse clinical outcomes. The LONT classification efficiently differentiated the prognosis of patients in terms of OS and CSS. Multivariate Cox analyses revealed that LONT was an independent prognostic factor for both CSS and OS. Based on the pT stage and LONT, a novel prognostic staging system was developed using RPA, demonstrating a good prognostic predictive performance.

Conclusion

A lower LONT was associated with worse survival in patients with colon MAC. The pT stage and LONT-based prognostic staging system facilitated risk stratification in these patients.

Survival risk stratification based on prognosis nomogram to identify patients with esophageal squamous cell carcinoma who may benefit from postoperative adjuvant therapy

OBJECTIVE

The purpose of the study is to develop a prognosis nomogram for esophageal squamous cell carcinoma (ESCC) patients with radical resection and to identify patients who may benefit from postoperative adjuvant radiotherapy/chemoradiotherapy through survival risk stratification.

METHODS

We retrospectively enrolled patients who underwent esophagectomy in the First Affiliated Hospital of Nanjing Medical University from July 2015 to June 2017. Patients with stage I-III esophageal squamous cell carcinoma who received radical R0 resection with or without postoperative adjuvant radiotherapy/chemoradiotherapy were included. Further, patients were randomly allocated into two groups (training and validation cohorts) with a distribution ratio of 7:3. The prognosis nomogram was constructed based on independent factors determined by univariate and multivariate Cox analyses. The area under the receiver operating characteristic curve (AUC) and calibration curve were adopted to evaluate the discriminative ability and reliability of the nomogram. The accuracy and clinical practicability were respectively assessed by C-index values and decision curve analysis (DCA), and further contrasted the nomogram model and the eighth edition of the American Joint Committee on Cancer (AJCC) TNM staging system. In addition, survival risk stratification was further performed according to the nomogram, and the effect of postoperative adjuvant therapy on each risk group was appraised by the Kaplan-Meier survival analysis.

RESULTS

A total of 399 patients with esophageal squamous cell carcinoma were recruited in this study, including the training cohort (n = 280) and the validation cohort (n = 119). The nomogram-related AUC values ​​for 1, 3, and 5-year OS were 0.900, 0.795, and 0.802, respectively, and 0.800, 0.865, 0.829 in the validation cohort, respectively. The slope of the calibration curve for both cohorts was close to 1, indicating good consistency. The C-index value of the nomogram was 0.769, which was higher than that of the AJCC 8th TNM staging system by 0.061 (p < 0.001). Based on the prognosis nomogram, patients were stratified into three risk groups (low, medium, and high), and there were obvious differences in prognosis among the groups (p < 0.001). Furthermore, postoperative adjuvant therapy has been shown to enhance the 5-year survival rate by over 15% among patients classified as medium- and high-risk.

CONCLUSION

The constructed nomogram as developed resulted in accurate and effective prediction performance in survival outcomes for patients with stage I-III esophageal squamous cell carcinoma who underwent radical R0 resection, which is superior to the AJCC 8th TNM staging system. The survival risk stratification had potential clinical application to guide further personalized adjuvant therapy.

Development and validation of a prognostic nomogram for esophageal cancer patients based on SEER Asian population

This study aims to develop and validate a nomogram for predicting overall survival (OS) in Asian patients with Esophageal Cancer (EC). Data from Asian EC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for initial variable selection, followed by multivariate Cox regression analysis to identify independent prognostic factors. A nomogram was subsequently constructed based on these factors. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves and calibration curves, while the clinical utility of the nomogram was assessed through decision curve analysis (DCA). The LASSO regression and multivariate Cox regression analysis identified age, sex, marital status, tumor size, M stage, surgery, and chemotherapy as independent prognostic factors. The ROC curve results demonstrated that the area under the curve (AUC) values for predicting 1-year, 3-year, and 5-year OS in the training cohort were 0.770, 0.756, and 0.783, respectively. In the validation cohort, the AUC values were 0.814, 0.763, and 0.771, respectively. Calibration curves indicated a high concordance between predicted and actual OS. The DCA demonstrated that the nomogram has significant clinical applicability. This nomogram provides reliable predictions and valuable guidance for personalized survival estimates and high-risk patient identification.

Construction of prognostic nomogram based on the SEER database for esophageal cancer patients

Currently, the incidence of esophageal cancer continues to rise around the world. Because of its good early prognosis, it is of great significance to establish an effective model for predicting the survival of EC patients. The purpose of this study was to predict survival after diagnosis in Esophageal Cancer (EC) patients by constructing a valid clinical nomogram. In this study, 5037 EC patient samples diagnosed from 2010 to 2015 were screened by accessing the SEER database, and 8 independent prognostic factors were screened by various methods, and Cox multivariate regression was included to construct a prognostic model and nomogram for esophageal cancer. to estimate esophageal cancer recurrence and overall survival. Calibration of the nomogram predicted probabilities of 1-year, 3-year and 5-year survival probability, which were closely related to actual survival. In conclusion, this study validated that the column-line graphical model can be considered an individualized quantitative tool for predicting the prognosis of patients with EC in order to assist clinicians in making therapeutic decisions.

A novel nomogram and prognostic factor for metastatic soft tissue sarcoma survival

Background

This study represented the inaugural effort to develop predictive survival nomograms for metastatic soft tissue sarcoma (mSTS) patients in the era of immune checkpoint inhibitors.

Method

From the Surveillance, Epidemiology, and End Results (SEER) program database, we extracted 3078 eligible patients with mSTS between 2016 and 2022. Kaplan-Meier survival analysis, univariate and multivariable Cox analyses, and univariate and multivariable logistic analyses were conducted. Subsequently, predictive nomograms were constructed. Clinical effectiveness was validated using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA) methods.

Results

We used the SEER database to include 3078 eligible patients with mSTS between 2016 and 2022. All the eligible patients were randomly allocated in a ratio of 6:4 and stratified into a training group (n = 1846) and a validation group (n = 1232). In the multivariate Cox analysis, age, race, marital status, pathological grade, histologic subtype, surgery, and chemotherapy were identified as independent prognostic factors. These factors were used to construct the nomogram to predict the 1-, 3-, and 5-year OS of mSTS patients. The C-index for the training cohort and the validation cohort was 0.722(95% confidence interval [CI]: 0.708-0.736), and 0.716(95% CI: 0.698-0.734), respectively. The calibration curves for 1-, 3-, and 5-year OS probability demonstrated excellent calibration between the predicted and the actual survival. The AUC values of the nomogram at 1-, 3-, and 5-year were 0.785, 0.767, and 0.757 in the training cohort, 0.773, 0.754, and 0.751 in the validation cohort, respectively. Furthermore, DCA indicated the favorable clinical utility of the nomogram in both cohorts. The risk stratification system was constructed using the established nomogram, which enhanced prediction accuracy, aided clinicians in identifying high-risk patients and informing treatment decisions.

Conclusion

This study marked the inaugural effort in constructing predictive survival nomograms mSTS patients in the era of immune checkpoint inhibitors. The robustly constructed nomograms, alongside actual outcomes, offered valuable insights to inform follow-up management strategies.

A novel staging system based on deep learning for overall survival in patients with esophageal squamous cell carcinoma

PURPOSE

We developed DeepSurv, a deep learning approach for predicting overall survival (OS) in patients with esophageal squamous cell carcinoma (ESCC). We validated and visualized the novel staging system based on DeepSurv using data from multiple cohorts.

METHODS

Totally 6020 ESCC patients diagnosed from January 2010 to December 2018 were included in the present study from the Surveillance, Epidemiology, and End Results database (SEER), randomly assigned to the training and test cohorts. We developed, validated and visualized a deep learning model that included 16 prognostic factors; then a novel staging system was further constructed based on the total risk score derived from the deep learning model. The classification performance at 3-year and 5-year OS was assessed by the receiver-operating characteristic (ROC) curve. Calibration curve and the Harrell's concordance index (C-index) were also used to comprehensively assess the predictive performance of the deep learning model. Decision curve analysis (DCA) was utilized to assess the clinical utility of the novel staging system.

RESULTS

A more applicable and accurate deep learning model was established, which outperformed the traditional nomogram in predicting OS in the test cohort (C-index: 0.732 [95% CI 0.714-0.750] versus 0.671 [95% CI 0.647-0.695]). The ROC curves at 3-year and 5-year OS for the model also showed good discrimination ability in the test cohort (Area Under the Curve [AUC] at 3-/5-year OS = 0.805/0.825). Moreover, using our novel staging system, we observed a clear survival difference among different risk groups (P < 0.001) and a significant positive net benefit in the DCA.

CONCLUSIONS

A novel deep learning-based staging system was constructed for patients with ESCC, which performed a significant discriminability for survival probability. Moreover, an easy-to-use web-based tool based on the deep learning model was also implemented, offering convenience for personalized survival prediction. We developed a deep learning-based system that stages patients with ESCC according to their survival probability. We also created a web-based tool that uses this system to predict individual survival outcomes.

Development and validation of a ligand-receptor pairs signature to predict outcome and provide a therapeutic strategy in gastric cancer

BACKGROUND

An important factor in tumor development and progression is the tumor microenvironment (TME), which is heterogeneous. Previous studies have mainly investigated the expression profile and prognostic values of genes in gastric cancer (GC) at the cell population level but neglected the interactions and heterogeneity between cells.

METHODS

The pattern of ligand-receptor (LR) interactions was delineated on a scRNA-seq dataset containing 44,953 cells from nine GC patients and a fourth bulk RNA-seq dataset including data from 1159 GC patients. We then constructed an LR.Score scoring model to comprehensively evaluate the influence of LR-pairs on the TME, overall survival, and immunotherapy response in GC patients from several cohorts.

RESULTS

Cell communication network among 13 cell types was constructed based on the LR-pairs. We proposed a new molecular subtyping model for GC based on the LR-pairs and revealed the differences in prognosis, pathophysiologic features, mutation characteristics, function enrichment, and immunological characteristics among the three subtypes. Finally, an LR.Score model based on LR-pairs was developed and validated on several datasets.

CONCLUSIONS

Based on the selected LR-pairs, we successfully constructed a novel prediction model and observed its well performance on molecular subtyping, target and pathway screening, prognosis judging, and immunotherapy response predicting.

Establishment and validation of a prognostic risk classification for patients with stage T1-3N0M0 esophageal squamous cell carcinoma

INTRODUCTION

At present, clinical factors and hematological indicators have been proved to have great potential in predicting the prognosis of cancer patients, and no one has combined these two valuable indicators to establish a prognostic model for esophageal squamous cell carcinoma (ESCC) patients with stage T1-3N0M0 after R0 resection. To verify, we aimed to combine these potential indicators to establish a prognostic model.

METHODS

Stage T1-3N0M0 ESCC patients from two cancer centers (including training cohort: N = 819, and an external validation cohort: N = 177)-who had undergone esophagectomy in 1995-2015 were included. We integrated significant risk factors for death events by multivariable logistic regression methods and applied them to the training cohort to build Esorisk. The parsimonious aggregate Esorisk score was calculated for each patient; the training set was divided into three prognostic risk classes according to the 33rd and 66th percentiles of the Esorisk score. The association of Esorisk with cancer-specific survival (CSS) was assessed using Cox regression analyses.

RESULTS

The Esorisk model was: [10 + 0.023 × age + 0.517 × drinking history - 0.012 × hemoglobin-0.042 × albumin - 0.032 × lymph nodes]. Patients were grouped into three classes-Class A (5.14-7.26, low risk), Class B (7.27-7.70, middle risk), and Class C (7.71-9.29, high risk). In the training group, five-year CSS decreased across the categories (A: 63%; B: 52%; C: 30%, Log-rank P < 0.001). Similar findings were observed in the validation group. Additionally, Cox regression analysis showed that Esorisk aggregate score remained significantly associated with CSS in the training cohort and validation cohort after adjusting for other confounders.

CONCLUSIONS

We combined the data of two large clinical centers, and comprehensively considered their valuable clinical factors and hematological indicators, established and verified a new prognostic risk classification that can predict CSS of stage T1-3N0M0 ESCC patients.

Prognostic nomogram and risk factors for predicting survival in patients with pT2N0M0 esophageal squamous carcinoma

This study analyzed the impact of factors affecting overall survival in patients with pT2N0M0 esophageal squamous carcinoma (ESCC) and developed a nomogram to predict overall survival (OS). We reviewed the clinical data of 413 patients with pathological T2N0M0 ESCC after radical esophagectomy in two hospitals. Data from one institution was used as the training cohort. A nomogram was established using Cox proportional hazard regression for identifying the prognostic factors affecting for OS in ESCC patients. The area under the curve (AUC), calibration curves and decision curve analysis (DCA) were used to evaluate prognostic efficacy, which was validated in an independent validation cohort. In the training cohort (N = 304), the median OS was 69.33 months, and the 3-, 5- and 10-year OS rates were 76.80%, 67.00% and 56.90%, respectively. The median OS of the validation cohort (N = 109) was 73.50 months, and the 3-, 5- and 10-year OS rates were 77.00%, 67.80% and 55.60%, respectively. According to Cox univariate and multivariate analyses, sex, age, tumor length and the number of resected lymph nodes were identified as predictors of OS. We developed nomograms and performed internal and external validation. The time-dependent receiver operating characteristic (ROC) curve and area under the curve (AUC) value, calibration curve and decision curve analysis (DCA) showed good prediction ability of the nomogram. The developed nomogram can effectively predict OS after esophagectomy in patients with pT2N0M0 ESCC.

Nomogram for prognosis of patients with esophageal squamous cell cancer after minimally invasive esophagectomy established based on non-textbook outcome

BACKGROUND

Non-textbook outcome (non-TO) represents a new prognostic evaluation index for surgical oncology. The present study aimed to develop new nomograms based on non-TO to predict the mortality and recurrence rate in patients with esophageal squamous cell cancer (ESCC) after minimally invasive esophagectomy (MIE).

METHODS

The study involved a retrospective analysis of 613 ESCC patients, from the prospectively maintained database from January 2011 to December 2018. All the included ESCC patients underwent MIE, and they were randomly (1:1) assigned to the training cohort (307 patients) and the validation cohort (306 patients). Kaplan-Meier survival analysis was used to analyze the differences recorded between overall survival (OS) and disease-free survival (DFS). In the case of the training cohort, the nomograms based on non-TO were developed using Cox regression, and the performance of these nomograms was calibrated and evaluated in the validation cohort.

RESULTS

Significant differences were recorded for 5-year OS and DFS between non-TO and TO groups (p < 0.05). Multivariate cox analysis revealed that non-TO, intraoperative bleeding, T stage, and N stage acted as independent risk factors that affected OS and DFS (p < 0.05). The results for multivariate regression were used to build non-TO-based nomograms to predict OS and DFS of patients with ESCC, the t-AUC curve analysis showed that the nomograms predicting OS and DFS were more accurate as compared to TNM staging, during the follow-up period in the training cohort and validation cohort. Further, the nomogram score was used to divide ESCC patients into low-, middle-, and high-risk groups and significant differences were recorded for OS and DFS between these three groups (p < 0.001).

CONCLUSIONS

Non-TO was identified as an independent prognostic factor for ESCC patients. The nomograms based on non-TO could availably predict OS and DFS in ESCC patients after MIE.

Deep-learning-based classification of desmoplastic reaction on H&E predicts poor prognosis in oesophageal squamous cell carcinoma

AIMS

Desmoplastic reaction (DR) categorisation has been shown to be a promising prognostic factor in oesophageal squamous cell carcinoma (ESCC). The usual DR evaluation is performed using semiquantitative scores, which can be subjective. This study aimed to investigate whether a deep-learning classifier could be used for DR classification. We further assessed the prognostic significance of the deep-learning classifier and compared it to that of manual DR reporting and other pathological factors currently used in the clinic.

METHODS AND RESULTS

From 222 surgically resected ESCC cases, 31 randomly selected haematoxylin-eosin-digitised whole slides of patients with immature DR were used to train and develop a deep-learning classifier. The classifier was trained for 89 370 iterations. The accuracy of the deep-learning classifier was assessed to 30 unseen cases, and the results revealed a Dice coefficient score of 0.81. For survival analysis, the classifier was then applied to the entire cohort of patients, which was split into a training (n = 156) and a test (n = 66) cohort. The automated DR classification had a higher prognostic significance for disease-specific survival than the manually classified DR in both the training and test cohorts. In addition, the automated DR classification outperformed the prognostic accuracy of the gold-standard factors of tumour depth and lymph node metastasis.

CONCLUSIONS

This study demonstrated that DR can be objectively and quantitatively assessed in ESCC using a deep-learning classifier and that automatically classed DR has a higher prognostic significance than manual DR and other features currently used in the clinic.

A Visualized Dynamic Prediction Model for Lymphatic Metastasis in Ewing's Sarcoma for Smart Medical Services

Background

This study aims to predict the lymphatic metastasis in Ewing's sarcoma (ES) patients by nomogram. The risk of lymphatic metastasis in patients with ES was predicted by the built model, which provided guidance for the clinical diagnosis and treatment planning.

Methods

A total of 929 patients diagnosed with ES were enrolled from the year of 2010 to 2016 in the Surveillance, Epidemiology, and End Results (SEER) database. The nomogram was established to determine predictive factors of lymphatic metastasis according to univariate and multivariate logistic regression analysis. The validation of the model performed using multicenter data (n = 51). Receiver operating characteristics (ROC) curves and calibration plots were used to evaluate the prediction accuracy of the nomogram. Decision curve analysis (DCA) was implemented to illustrate the practicability of the nomogram clinical application. Based on the nomogram, we established a web calculator to visualize the risk of lymphatic metastases. We further plotted Kaplan-Meier overall survival (OS) curves to compare the survival time of patients with and without lymphatic metastasis.

Results

In this study, the nomogram was established based on six significant factors (survival time, race, T stage, M stage, surgery, and lung metastasis), which were identified for lymphatic metastasis in ES patients. The model showed significant diagnostic accuracy with the value of the area under the curve (AUC) was 0.743 (95%CI: 0.714–0.771) for SEER internal validation and 0.763 (95%CI: 0.623–0.871) for multicenter data external validation. The calibration plot and DCA indicated that the model had vital clinical application value.

Conclusion

In this study, we constructed and developed a nomogram with risk factors to predict lymphatic metastasis in ES patients and validated accuracy of itself. We found T stage (Tx OR = 2.540, 95%CI = 1.433–4.503, P < 0.01), M stage (M1, OR = 2.061, 95%CI = 1.189–3.573, P < 0.05) and survival time (OR = 0.982, 95%CI = 0.972–0.992, P < 0.001) were important independent factors for lymphatic metastasis in ES patients. Furthermore, survival time in patients with lymphatic metastasis or unclear situation (P < 0.0001) was significantly lower. It can help clinicians make better decisions to provide more accurate prognosis and treatment for ES patients.

A novel prognostic model: which group of esophageal squamous cell carcinoma patients could benefit from adjuvant chemotherapy

BACKGROUND

This study aimed to establish a reliable model for predicting the overall survival (OS) of esophageal squamous cell carcinoma (ESCC) patients and identifying the potential beneficiaries of adjuvant chemotherapy after esophagectomy.

METHODS

This retrospective study included 819 ESCC patients who underwent esophagectomy as the training cohort. We constructed a prognostic model named GTLN2. Both internal and external validation tests were performed. Potential beneficiaries were defined as ESCC patients who obtained a significantly longer OS after adjuvant chemotherapy. Propensity score matching (PSM) was utilized in the subgroup analysis to screen ESCC beneficiaries of adjuvant chemotherapy.

RESULTS

We enrolled a total of 819 cT1b-3 patients in the training cohort. Multiple prognostic factors were associated with adjuvant chemotherapy. Using uni-/multivariate analysis, histological grade (G), tumor invasion depth (T), regional lymph node metastasis (N), and the number of cleared lymph nodes (NCLNs) were identified as independent prognostic factors. Then, we developed the GTLN2 model based on these predictors and validated it using internal calculations [the 1-, 3- and 5-year area under the curves (AUCs) were 0.692, 0.685 and 0.680, respectively; P<0.001] and external cohorts (the 1-, 3-, and 5-year AUCs were 0.651, 0.619 and 0.650, respectively; P<0.001). ESCC patients were categorized into high- and low-risk groups based on their assigned risk scores. After 1:1 patient pairing was performed by PSM in the high-risk group, better OS was noted in patients receiving adjuvant chemotherapy (P=0.024).

CONCLUSIONS

Differentiating high- and low-risk patient groups via a novel mathematical prediction model allows physicians to identify patients in need of adjuvant chemotherapy accurately.

A Survival Prediction Nomogram for Esophageal Squamous Cell Carcinoma Treated with Neoadjuvant Chemoradiotherapy Followed by Surgery

BACKGROUND

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is a component of the standard treatment for resectable locally advanced esophageal squamous cell carcinoma (ESCC), and the parameters for survival prediction are not clear yet. Our study aimed to construct a survival prediction nomogram for ESCC with NCRT followed by surgery.

METHODS

We analyzed hematological parameters and related-derivative indexes from 122 ESCC patients treated with NCRT followed by surgery. Univariate and multivariate Cox survival analyses were performed to identify independent prognostic factors to establish a nomogram and predict overall survival (OS). The predictive value of the nomogram for OS was evaluated by the concordance index (C-index), decision curve analysis (DCA), the clinical impact curve (CIC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

The pretreatment nutritional candidate, prognostic nutrition index, inflammation-related absolute monocyte count and TNM staging were entered into the nomogram for ESCC with NCRT followed by surgery. The C-index of the nomogram for OS was 0.790 (95% CI = 0.688-0.893), which was higher than that of TNM staging (0.681; 95% CI = 0.565-0.798, P = 0.026). The DCA, CIC, NRI, and IDI of the nomogram showed moderate improvement in predicting survival. Based on the cut point calculated according to the constructed nomogram, the high-risk group had poorer OS than that of the low-risk group (P < 0.05).

CONCLUSION

A novel nomogram based on nutrition- and inflammation-related indicators might help predict the survival of ESCC treated with NCRT followed by surgery.

Development and Validation of a Clinical Prognostic Nomogram for Esophageal Adenocarcinoma Patients

Background

Clinical staging is essential for clinical decisions but remains imprecise. We purposed to construct a novel survival prediction model for improving clinical staging system (cTNM) for patients with esophageal adenocarcioma (EAC).

Methods

A total of 4180 patients diagnosed with EAC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and included as the training cohort. Significant prognostic variables were identified for nomogram model development using multivariable Cox regression. The model was validated internally by bootstrap resampling, and then subjected to external validation with a separate cohort of 886 patients from 2 institutions in China. The prognostic performance was measured by concordance index (C-index), Akaike information criterion (AIC) and calibration plots. Different risk groups were stratified by the nomogram scores.

Results

A total of six variables were determined related with survival and entered into the nomogram construction. The calibration curves showed satisfied agreement between nomogram-predicted survival and actual observed survival for 1-, 3-, and 5-year overall survival. By calculating the AIC and C-index values, our nomogram presented superior discriminative and risk-stratifying ability than current TNM staging system. Significant distinctions in survival curves were observed between different risk subgroups stratified by nomogram scores.

Conclusion

The established and validated nomogram presented better risk-stratifying ability than current clinical staging system, and could provide a convenient and reliable tool for individual survival prediction and treatment strategy making.

Risk factors for lymph node metastasis in rectal neuroendocrine tumors: A recursive partitioning analysis based on multicenter data

BACKGROUND

The well-differentiated rectal neuroendocrine tumors (RNETs) can also have lymph node metastasis (LNM). Large multicenter data were reviewed to explore the risk factors for LNM in RNETs. Further, we developed a model to predict the risk of LNM in RNETs.

METHODS

In total, 223 patients with RNETs from the Fujian Medical University Union Hospital, the First Affiliated Hospital of Fujian Medical University, and the First Affiliated Hospital of Xiamen University were retrospectively enrolled. Logistic regression analysis was performed to study the factors affecting LNM, and recursive partitioning analysis (RPA) was performed to stratify the risk of LNM.

RESULTS

Among the 223 patients diagnosed with RNETs, the incidence of LNM was 10.8%. Univariate and multivariate regression analyses revealed that tumor size, World Health Organization (WHO) grade, and depth of tumor invasion were independent risk factors for LNM (p < 0.05). The area under the curve was 0.948 (95% confidence interval: 0.890-1.000). Furthermore, the incidence of LNM in patients divided into low- and high-risk groups according to RPA was 1.1% and 56.4%, respectively.

CONCLUSION

Compared with tumor size, the depth of tumor invasion and WHO grade are more important factors in predicting LNM. Then, we developed a model based on RPA to predict the risk of LNM in RNETs and identify patients who are suitable for local resection.

Survival risk prediction model for patients with pT1-3 N0M0 esophageal squamous cell carcinoma after R0 esophagectomy with two-field lymphadenectomy for therapeutic purposes

BACKGROUND

The overall survival (OS) remains unsatisfactory in patients with esophageal squamous cell carcinoma (ESCC) after extended esophagectomy with two-field lymphadenectomy. Therefore, this retrospective study aimed to identify the risk factors that contribute to the low survival of patients with pT_1-3N₀M₀ ESCC.

METHODS

Patients with pT_1-3N₀M₀ ESCC who only underwent R0 esophagectomy with two-field lymphadenectomy in our department from January 2008 to December 2012 were retrospectively enrolled in this study and medical records were reviewed. Postoperative OS, disease-free survival (DFS), recurrence-free survival (RFS), and locoregional recurrence-free survival (LRFS) were analyzed sequentially.

RESULTS

This study recruited a total of 488 patients, whose follow-up visits were completed at the end of December 2019. The five-year OS, DFS, RFS and LRFS rates were 62.1, 53.1, 58.3 and 65.6%, respectively. Multivariate Cox analysis identified patient age, site of the lesion, small mediastinal lymph nodes in CT imaging (SLNs in CT), dissected lymph nodes (LNs), and stage of esophageal malignancy as independent risk factors for OS of the patients. Of these factors, the site of the lesion, SLNs in CT and stage of the cancer were determined to be independent factors for DFS, RFS and LRFS. Based on all five factors, the recursive partitioning analysis (RPA) score system was developed to stratify the patients into low-, medium- and high-risk groups, which were found to possess significantly different rates of OS, DFS, RFS and LRFS (p < 0.001).

CONCLUSIONS

Several factors were associated with the survival of patients with pT_1-3 N₀M₀ ESCC who underwent extended esophagectomy with two-field lymphadenectomy. These factors contributed to the RPA scoring system, which could stratify the risk of postoperative survival and may expedite the initiation of postoperative adjuvant therapy.

Prognostic nomograms and risk-stratifying systems for predicting survival in patients with resected pT2-4aN0M0 esophageal carcinoma

Background

According to the National Comprehensive Cancer Network (NCCN) guidelines, surveillance or adjuvant chemoradiation is recommended for patients with completely resected pT2-4aN0M0 esophageal carcinoma (EC). Due to this population’s variant prognosis, we developed novel nomograms to define the high-risk patients who may need closer follow-up or even post-operative therapy.

Methods

Cases with resected pT2-4aN0M0 EC from the Surveillance, Epidemiology, and End Results (SEER) database and the Sun Yat-sen University Cancer Center (SYSUCC) were enrolled in the study. The SEER database cases were randomly assigned into the training cohort (SEER-T) and the internal validation cohort (SEER-V). Cases from the SYSUCC served as the external validation cohort (SYSUCC-V). Overall survival (OS) and cancer specific survival (CSS) were compared between groups. Multivariate analyses were applied to identify the prognostic factors. Nomograms and risk-classifying systems were developed. The nomograms’ performances were evaluated by concordance index (C-index), calibration plots and decision curve analysis (DCA).

Results

A total of 2,441 eligible EC cases (SEER-T, n=839; SEER-V, n=279; SYSUCC-V, n=1,323) were included. Age, sex, chemotherapy, lymph node harvested (LNH) and T stage were identified as the independent predictors for CSS. Regarding OS, it also included the prognostic factor of histology. Nomograms were formulated. For CSS, the C-index was 0.68 [95% confidence interval (CI): 0.66–0.71], 0.67 (95% CI: 0.63–0.71) and 0.61 (95% CI: 0.59–0.63) for the SEER-T, SEER-V, and SYSUCC-V, respectively. For OS, the C-index was 0.69 (95% CI: 0.66–0.72), 0.64 (95% CI: 0.59–0.69) and 0.62 (95% CI: 0.61–0.63) for the SEER-T, SEER-V, and SYSUCC-V, respectively. The calibration curves and DCA showed good performances of the nomograms. In further analyses, risk-classification systems stratified pT2-4aN0M0 EC into low-risk and high-risk subgroup. The OS and CSS curves of these 2 subgroups, in the full analysis set or stratified by TNM stage, histology, T stage and LNH categories, showed significant distinctions.

Conclusions

The novel prognostic nomograms and risk-stratifying systems which separated resected pT2-4aN0M0 esophageal carcinoma patients into the low-risk and high-risk prognostic groups were developed. It may help clinicians estimate individual survival and develop individualized treatment strategies.

Nomogram based on nutritional and inflammatory indicators for survival prediction of small cell carcinoma of the esophagus

OBJECTIVES

Small cell carcinoma of the esophagus (SCCE) is a rare type of esophageal cancer, and the parameters for prediction of SCCE outcome are unclear. This study aimed to construct a nomogram to predict the outcome of SCCE.

METHODS

Patients who underwent treatments at the Sun Yat-Sen University Cancer Center were recruited and divided randomly into training and validation cohorts (61 and 32 patients, respectively). A Cox regression analysis was utilized to identify independent prognostic factors to establish a nomogram and predict overall survival (OS) and disease-free survival (DFS).

RESULTS

Information on pretreatment nutritional candidate hemoglobin and inflammation-related neutrophil-to-lymphocyte ratio and platelet count were entered into the nomogram. In the training cohort, the concordance index of the nomogram for OS was 0.728, higher than that obtained by tumor/node/metastasis staging (0.614; P = 0.014). A significant difference was observed in the nomogram for DFS (0.668 vs tumor/node/metastasis stage: 0.616; P = 0.014). Similar results were found in the validation group. The decision curve analysis, net reclassification improvement, and integrated discrimination improvement showed moderate improvement of the nomogram in predicting survival. Based on the cut point calculated according to the constructed nomogram, the high-risk group had poorer OS and DFS than the low-risk group in both cohorts (all P < 0.05). Moreover, the DFS of patients receiving surgery in the high-risk group was better than that of patients receiving single radiation therapy or chemotherapy (P = 0.0111).

CONCLUSIONS

A nomogram based on nutrition- and inflammation-related indicators was developed to predict the survival of patients with SCCE.

Correlates of Long-Term Survival of Patients with pN+ Esophageal Squamous Cell Carcinoma after Esophagectomy

Esophageal squamous cell carcinoma (ESCC) is the most common pathological type of esophageal cancer in China. Patients with ESCC have poor long-term survival, especially those with lymphatic metastasis (pN + ESCC). In this retrospective study, we evaluated the correlates of long-term survival time of patients with pN + ESCC. A total of 453 patients with pN + ESCC who underwent surgical R0 resection between Jan 2008 and Sep 2011 were enrolled. The follow-up ended on December 2019. The clinical, pathological, inflammation-related factors and general survival data of these patients were analyzed using SPSS 22.0 software. The 1-, 3-, and 5-year overall survival (OS) rates were 73.7%, 34.6%, and 25.6%, respectively; the 1-, 3-, and 5-year disease-free survival (DFS) rates were 45.0%, 26.3%, and 20.4%, respectively. The median OS and DFS were 23 and 14 months, respectively. On multivariate analyses, gender, site of lesion, number of dissected lymph nodes, stage pTNM, adjuvant therapy, and neutrophil lymphocyte ratio were independent predictors of OS. Site of lesion, stage pTNM, and adjuvant therapy were independent predictors of DFS. Recursive partitioning analysis (RPA) scores of each patient were calculated based on the independent predictors of OS, and the patients were divided into 3 classes: low-risk, medium-risk, and high-risk. The OS, DFS, and local recurrence-free survival were significantly different among these three RPA classes (P < 0.001). Several factors showed an independent association with long-term postoperative survival of pN + ESCC patients after radical surgery. RPA scores can potentially be used to predict the prognosis of ESCC.

Development and validation of a new clinical staging system to predict survival for esophageal squamous cell carcinoma patients: Application of the nomogram

INTRODUCTION

Survival of patients with the same clinical stage varies widely and effective tools to evaluate the prognosis utilizing clinical staging information is lacking. This study aimed to develop a clinical nomogram for predicting survival of patients with Esophageal Squamous Cell Carcinoma (ESCC).

MATERIALS AND METHODS

On the basis of data extracted from the SEER database (training cohort, n = 3375), we identified and integrated significant prognostic factors for nomogram development and internal validation. The model was then subjected to external validation with a separate dataset obtained from Jinling Hospital of Nanjing Medical University (validation cohort, n = 1187). The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index), Akaike information criterion (AIC) and calibration curves. And risk group stratification was performed basing on the nomogram scores.

RESULTS

On multivariable analysis of the training cohort, seven independent prognostic factors were identified and included into the nomogram. Calibration curves presented good consistency between the nomogram prediction and actual observation for 1-, 3-, and 5-year OS. The AIC value of the nomogram was lower than that of the 8th edition American Joint Committee on Cancer TNM (AJCC) staging system, whereas the C-index of the nomogram was significantly higher than that of the AJCC staging system. The risk groups stratified by CART allowed significant distinction between survival curves within respective clinical TNM categories.

CONCLUSIONS

The risk stratification system presented better discriminative ability for survival prediction than current clinical staging system and might help clinicians in decision making.

The role of endoscopic tumor length in resected esophageal squamous cell carcinoma: a retrospective study

Background

In esophageal squamous cell carcinoma (ESCC), tumor status is assessed on the basis of latitudinal invasion. Endoscopic tumor length (ETL) may represent the longitudinal scope of the primary tumor, and whether it affects tumor stage or prognosis is not entirely clear. In this study, we evaluated the role of ETL in patients with resected ESCC.

Methods

The relationships of ETL with pathological parameters (pT status and pN status) and overall survival (OS) were analyzed using data from patients with resected ESCC who were treated at Fujian Cancer Hospital between January 1997 and December 2013. Odds ratios (ORs) and hazard ratios (HRs) were fitted with locally weighted scatterplot smoothing, and the structural breakpoints for ETL were determined using the Chow test.

Results

A total of 721 patients with resected ESCC were enrolled. As the ETL increased in these patients, a rise in the risk of advanced pT status, nodal metastasis, and mortality was observed. Cutpoint analysis showed a breakpoint of 7.0 cm. A negative impact of ETL ≥7.0 cm was also found (adjusted HR, 1.335; 95% CI, 1.004–1.774). Seven independent prognostic factors, including sex, age, number of nodes dissected, T stage, N stage, tumor location, and ETL, were identified and entered into the nomogram. The calibration curves for 1-, 3-, and 5-year OS showed optimal agreement between nomogram prediction and actual observation (c-index: 0.688).

Conclusions

Longer tumor length, with ETL ≥7.0 cm as the breakpoint, is a negative prognostic factor in patients with ESCC.

Serum exosomal microRNA-766-3p expression is associated with poor prognosis of esophageal squamous cell carcinoma

The aim was to analyze the association between exosomal microRNA (miR)‐766‐3p expression levels in serum and the prognosis of esophageal squamous cell carcinoma (ESCC). The serum global exosomal miRNA expression of ESCC patients was measured by microRNA microarray. Quantitative real‐time PCR was used to analyze the expression levels of candidate miRNAs in both serum and tissues from ESCC patients. Wilcoxon tests were applied to evaluate clinical characteristics and their association with serum levels of exosomal miR‐766‐3p. A Cox regression model was used to identify prognostic factors. The effects of miR‐766‐3p expression on cell migration and invasion were examined using Transwell assays, and CCK‐8 assays were carried out to measure cell proliferation. The TNM stage was associated with high serum exosomal miR‐766‐3p levels of ESCC patients (P = .030). Higher serum exosomal miR‐766‐3p expression levels were associated with poor prognosis (for overall survival, hazard ratio [HR] [95% confidence interval (CI)], 2.21 [1.00, 4.87]; for disease‐free survival, HR [95% CI], 2.15 [1.01, 4.59]). However, we found no association between the expression of miR‐766‐3p in tissue and ESCC prognosis. In vitro results showed that miR‐766‐3p promotes cell migration and invasion, but not cell proliferation. By using dual‐luciferase reporter assay, HOXA13 was confirmed as a direct target gene of miR‐766‐3p. The ESCC patients with highly expressed serum exosomal miR‐766‐3p had a significantly worse survival. Therefore, serum exosomal miR‐766‐3p could serve as a prognostic marker for the assessment of ESCC.

Development and Validation of an Individualized Nomogram for Predicting Survival in Patients with Esophageal Carcinoma after Resection

An accurate estimation of prognosis of the esophageal carcinoma patients after surgery is urgently needed. Clinical nomogram has been developed to quantify risk by incorporating prognostic factors for individual patient. Based on the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2013, a total of 4566 patients were selected. Of those, 3198 patients were assigned to training set to construct the nomogram, which incorporated age, gender, histology, grade, T stage, N stage, nodes examined, radiation and chemotherapy. The calibration curve for probability of survival showed good agreement between prediction by nomogram and actual observation. The C-index of the nomogram was 0.71(95%CI 0.70-0.72), which was statistically higher than the TNM staging system. The results were then validated using bootstrap resampling and a validation set of 1368 patients in the SEER database. Besides, in the esophageal squamous cell carcinoma and esophageal adenocarcinoma subgroups, the nomogram discrimination was superior to the TNM staging system. It is likely that these results would play a supplementary role in the current staging system and help to identify the high risk population after surgery.

lncRNA MIAT promotes esophageal squamous cell carcinoma progression by regulating miR-1301-3p/INCENP axis and interacting with SOX2

Long noncoding RNAs (lncRNAs) have been reported to participate in the development of multiple cancers, including esophageal squamous cell carcinoma (ESCC). A growing number of studies have demonstrated that lncRNA myocardial infarction-associated transcript (MIAT) played an oncogenic role in several human malignancies, but its expression and function in ESCC remain unknown. In this study, we found that MIAT was significantly increased in ESCC tissues, as well as cell lines. Downregulation of MIAT suppressed ESCC cell proliferation, cell cycle, migration, and invasion. Mechanical studies revealed that MIAT promoted ESCC cell proliferation and cell cycle by acting as a competitively endogenous RNA (ceRNA) to upregulate the inner centromere protein (INCENP) expression through sponging miR-1301-3p. Furthermore, we uncovered that MIAT-SOX2 formed a positive feedback loop to facilitate cell proliferation, migration, and invasion of ESCC. Our findings indicated that MIAT promoted ESCC progression via targeting INCENP/miR-1301-3p axis and interacting with SOX2, suggesting novel potential therapeutic targets for ESCC.

Novel nomograms individually predict the survival of patients with soft tissue sarcomas after surgery

Background: The aim of the study was to build and validate practical nomograms to better predict the overall survival (OS) and cancer-specific survival (CSS) of the patients with soft tissue sarcomas (STS) who underwent surgery.

Methods: Patient data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified 8804 patients who underwent surgery with STS between 2007 and 2015, and randomly divided them into the training (n=6164) and validation (n=2640) cohorts. The Cox regression analysis and cumulative incidence function were performed to identify the independent prognostic factors associated with OS and CSS, respectively. The performance of the nomograms was evaluated using Harrell’s concordance index (C-index) and the calibration curves. Decision curve analysis (DCA) was introduced to compare the clinical practicality between the nomograms and the AJCC staging system.

Results: Eight independent prognostic factors for OS and seven for CSS were determined and then used to build the nomograms for 3- and 5-year OS and CSS, respectively. The C-indexes of the nomograms for predicting OS were 0.788 in the internal validation and 0.823 in external validation, significantly higher than C-index of the AJCC staging system (P<0.001). The similar results were obtained in the validation cohort. Internal and external calibration curves for the predicting 3- and 5-year OS and CSS showed excellent agreement between the prediction and the actual survival outcomes. In addition, DCA demonstrated that our nomograms were superior over the AJCC staging system with obtaining more clinical net benefits.

Conclusions: We established and validated the nomograms that could accurately predict the 3- and 5-year OS and CSS for STS patients who underwent surgery. The nomograms showed more robust and applicable performance than the AJCC staging system for predicting OS and CSS.

Low expression level of ASK1-interacting protein-1 correlated with tumor angiogenesis and poor survival in patients with esophageal squamous cell cancer

Objective

To investigate the expression of tumor suppressor protein ASK1-interacting protein-1 (AIP1) in human esophageal squamous cell carcinoma (ESCC) and its role in tumor progression, angiogenesis, and prognosis.

Methods

A total of 117 biopsy samples were obtained from ESCC patients. None of the patients had distant metastasis before surgery, and did not receive preoperative chemotherapy or radiotherapy. Immunohistochemistry was used to detect the expression of AIP1 protein and vascular endothelial growth factor receptor 2 (VEGFR2) in ESCC specimens collected from 117 patients who underwent esophageal cancer radical surgery. Microvessel density (MVD) was evaluated by immunohistochemical staining of vascular endothelial CD34. The correlation between AIP1 protein and clinicopathological characteristics, tumor angiogenesis, and prognosis was analyzed.

Results

The downregulation of AIP1 protein in esophageal carcinoma tissues was detected in 63 cases. This downregulation significantly correlated with lymph node metastasis, clinicopathological staging, and tumor MVD (P<0.05). Survival analysis showed that ESCC patients with a low expression of AIP1, a high expression of VEGFR2, and a high level of MVD had a lower 5-year survival rate (P<0.05). Multivariate analysis confirmed that the downregulation of AIP1 significantly affected patient survival.

Conclusion

The downregulation of AIP1 correlated with ESCC progression, tumor angiogenesis, and poor prognosis. AIP1 could be a promising biomarker for predicting ESCC prognosis and a potential target for anti-angiogenic therapy.