ABSTRACT
BACKGROUND: Nearly 66% of occurrences of gastric cancer (GC), which has the second-highest death rate of all cancers, arise in developing countries. In several cancers, the predictive significance of inflammatory markers has been established. AIM: To identify clinical characteristics and develop a specific nomogram to determine overall survival for GC patients. METHODS: Nine hundred and four GC patients treated at the First Affiliated Hospital of Anhui Medical University between January 2010 and January 2013 were recruited. Prognostic risk variables were screened for Cox analysis. The C index, receiver operator characteristic (ROC) curve, and decision curve analysis were used to evaluate the nomogram. RESULTS: Tumor node metastasis stage, carcinoembryonic antigen, systemic immune-inflammation index, and age were identified as independent predictive variables by multivariate analysis. Systemic immune-inflammation index value was superior to that of other inflammatory indicators. The ROC indicated the nomogram had a higher area under the curve than other factors, and its C-index for assessing the validation and training groups of GC patients was extremely reliable. CONCLUSION: We created a novel nomogram to forecast the prognosis of GC patients following curative gastrectomy based on blood markers and other characteristics. Both surgeons and patients can benefit significantly from this new scoring system.
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Background: We aimed to investigate the association between the number of examined lymph nodes (ELNs) and accurate nodal staging and long-term survival in Siewert type II-III Adenocarcinoma of the Esophagogastric Junction (AEG) by using large population-based databases and determined the optimal ELN number threshold. Methods: Data on Stage I-III Siewert type II-III AEG patients from 2010 to 2014 respectively from the United States (US) SEER database and a Chinese large medical center institutional registry were analyzed for correlation between the ELN number and stage migration (node negative-to-positive) and overall survival (OS) by using multivariable-adjusted logistic and Cox regression models, respectively. The series of odds ratios (ORs), and hazard ratios (HRs) were fitted with a LOWESS smoother, and the structural breakpoints were determined by Chow test. The selected optimal cut point was then validated with the 2015 to 2016 SEER database. Results: Both the US cohort(n=1387) and China cohort(n=981) showed significantly increases from node-negative to node-positive disease (ORtheUS1.032,95%CI 1.017-1.046;ORChina1.034,95%CI 1.002-1.065) and enhancements in overall survival (HRtheUS0.970,95%CI 0.961-0.979;HRChina0.960,95%CI 0.940-0.980) with the increasing ELN number after controlling for confounders. Associations for both stage migration and overall survival were still significant in most subgroups' stratification. Cut point analysis showed a threshold ELN number of 18, which was validated both in the cohorts where it originated and in an independent SEER data cohort(n=379). Conclusions: More ELNs are associated with accurate nodal staging(negative-to-positive) as well as higher overall survival in resected Siewert types II-III AEG, We recommend 18 ELNs as the optimal cut point for the quality assessment of postoperative lymph node examination or prognostic stratification in clinical practice.
ABSTRACT
BACKGROUND: In recent years, the incidence of types II and III adenocarcinoma of the esophagogastric junction (AEG) has shown an obvious upward trend worldwide. The prognostic prediction after radical resection of AEG has not been well established. AIM: To establish a prognostic model for AEG (types II and III) based on routine markers. METHODS: A total of 355 patients who underwent curative AEG at The First Affiliated Hospital of Anhui Medical University from January 2014 to June 2015 were retrospectively included in this study. Univariate and multivariate analyses were performed to identify the independent risk factors. A nomogram was constructed based on Cox proportional hazards models. The new score models was analyzed by C index and calibration curves. The receiver operating characteristic (ROC) curve was used to compare the predictive accuracy of the scoring system and tumor-node-metastasis (TNM) stage. Overall survival was calculated using the Kaplan-Meier curve amongst different risk AEG patients. RESULTS: Multivariate analysis showed that TNM stage (hazard ratio [HR] = 2.286, P = 0.008), neutrophil-to-lymphocyte ratio (HR = 2.979, P = 0.001), and body mass index (HR = 0.626, P = 0.026) were independent prognostic factors. The new scoring system had a higher concordance index (0.697), and the calibration curves of the nomogram were reliable. The area under the ROC curve of the new score model (3-year: 0.725, 95% confidence interval [CI]: 0.676-0.777; 5-year: 0.758, 95%CI: 0.708-0.807) was larger than that of TNM staging (3-year: 0.630, 95%CI: 0.585-0.684; 5-year: 0.665, 95%CI: 0.616-0.715). CONCLUSION: Based on the serum markers and other clinical indicators, we have developed a precise model to predict the prognosis of patients with AEG (types II and III). The new prognostic nomogram could effectively enhance the predictive value of the TNM staging system. This scoring system can be advantageous and helpful for surgeons and patients.
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At present, emerging evidence shows that non-coding RNAs (ncRNAs) play crucial roles for development of multiple tumors. Amongst these ncRNAs, long non-coding RNAs (lncRNAs) play prominent roles in physiological and pathological processes. LncRNAs are RNA transcripts larger than 200 nucleotides and have been shown to serve important regulatory roles in different types of cancer via interactions with DNA, RNA and proteins. Head and neck squamous cell carcinoma (HNSCC) is one of the most malignant tumors with low survival rates in advanced stages. Recently, lncRNAs have been demonstrated to be involved in a wide range of biological processes, including proliferation, metastasis, and prognosis of HNSCC. Therefore, this review describes molecular mechanisms of up- or down-regulation of lncRNAs and expounds their functions in pathology and clinical practices in HNSCC. It also highlights their potential clinical applications as biomarkers for the diagnosis, prognosis, and treatment of HNSCC. However, studies on lncRNAs are still not comprehensive, and more investigations are needed in the future.
