A novel nomogram integrated with PDL1 and CEA to predict the prognosis of patients with gastric cancer

Introduction This study aimed to develop a prognostic nomogram for patients with gastric cancer (GC) based on the levels of programmed death 1 ligand 1 (PDL1) and carcinoembryonic antigen (CEA). Methods The nomogram was developed using data from a primary cohort of 247 patients who had been clinicopathologically diagnosed with GC, as well as a validation cohort of 63 patients. Furthermore, the nomogram divided the patients into three different risk groups for overall survival (OS)—the low-risk, middle-risk, and high-risk groups. Univariate and multivariate Cox hazard analyses were used to determine all of the factors included in the model. Decision curve analysis and receiver operating characteristic (ROC) curves were used to assess the accuracy of the nomogram. Results The Kaplan–Meier survival analysis revealed that metastasis stage, clinical stage, and CEA and PDL1 levels were predictors for progress-free survival (PFS) and OS of patients with GC. Metastasis stage, clinical stage, and CEA and PDL1 levels were found to be independent risk factors for the PFS and OS of patients with GC in a multivariate analysis, and the nomogram was based on these factors. The concordance index of the nomogram was 0.763 [95% confidence interval (CI) 0.740–0.787]. The area under the concentration–time curve of the nomogram model was 0.81 (95% CI 0.780–0.900). According to the decision curve analysis and ROC curves, the nomogram model had a higher overall net efficiency in forecasting OS than clinical stage, CEA and PDL1 levels. Conclusion In conclusion, we proposed a novel nomogram that integrated PDL1 and CEA, and the proposed nomogram provided more accurate and useful prognostic predictions for patients with GC (AU)

A novel nomogram integrated with PDL1 and CEA to predict the prognosis of patients with gastric cancer.

INTRODUCTION: This study aimed to develop a prognostic nomogram for patients with gastric cancer (GC) based on the levels of programmed death 1 ligand 1 (PDL1) and carcinoembryonic antigen (CEA). METHODS: The nomogram was developed using data from a primary cohort of 247 patients who had been clinicopathologically diagnosed with GC, as well as a validation cohort of 63 patients. Furthermore, the nomogram divided the patients into three different risk groups for overall survival (OS)-the low-risk, middle-risk, and high-risk groups. Univariate and multivariate Cox hazard analyses were used to determine all of the factors included in the model. Decision curve analysis and receiver operating characteristic (ROC) curves were used to assess the accuracy of the nomogram. RESULTS: The Kaplan-Meier survival analysis revealed that metastasis stage, clinical stage, and CEA and PDL1 levels were predictors for progress-free survival (PFS) and OS of patients with GC. Metastasis stage, clinical stage, and CEA and PDL1 levels were found to be independent risk factors for the PFS and OS of patients with GC in a multivariate analysis, and the nomogram was based on these factors. The concordance index of the nomogram was 0.763 [95% confidence interval (CI) 0.740-0.787]. The area under the concentration-time curve of the nomogram model was 0.81 (95% CI 0.780-0.900). According to the decision curve analysis and ROC curves, the nomogram model had a higher overall net efficiency in forecasting OS than clinical stage, CEA and PDL1 levels. CONCLUSION: In conclusion, we proposed a novel nomogram that integrated PDL1 and CEA, and the proposed nomogram provided more accurate and useful prognostic predictions for patients with GC.

High AKAP8L expression predicts poor prognosis in esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a severe disease with high mortality, and is associated with poor prognosis and frequent lymphatic metastasis. Therefore, prognostic indicators for ESCC are urgently needed. A-kinase anchor-protein 8-like (AKAP8L) is a member of the A kinase anchor-protein (AKAPs) family and is overexpressed in many cancers. However, the role of AKAP8L in ESCC remains unclear. The aim of this study is to investigate the expression patterns and prognostic value of AKAP8L in ESCC. METHODS: The mRNA expression of AKAP8L was analyzed from the dataset of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Immunohistochemistry was applied to detect the AKAP8L expression in tissue microarray. Pearson's chi-square test was carried out for the correlation analysis of clinicopathological features and AKAP8L expression. The prognostic significance of clinicopathological features and AKAP8L expression was determined by univariate and multivariate Cox hazard models. Kaplan-Meier survival curve was used for survival analysis. RESULTS: We found that the mRNA level of AKAP8L was higher in tumor tissues than in adjacent tissues in TCGA and GEO dataset. High AKAP8L expression was associated with poor overall survival (OS) in ESCC patients (p = 0.0039). Besides, AKAP8L expression was highly expressed in patients with lymph node metastasis detected by ESCC tissue microarray (p = 0.0014). The comparison of the different clinicopathological features of ESCC between high and low AKAP8L expression groups revealed that high AKAP8L expression was related to lymph node stage (p = 0.041). Kaplan-Meier survival analysis revealed that high AKAP8L expression indicates an unfavorable progression-free survival (PFS) and OS in ESCC patients (p < 0.0001). Univariate and multivariate analyses confirmed that AKAP8L was an independent prognostic factor for PFS and OS in ESCC (p = 0.003 and p < 0.0001). CONCLUSIONS: In conclusion, this study demonstrated that high expression of AKAP8L is associated with poor prognosis of ESCC and can be considered an independent risk factor for ESCC.