The incidences of upper third gastric cancer (UTGC) have been increasing. However, the prognostic factors for UTGC following radical surgical treatment remains largely unknown. This study was to investigate prognostic factors for overall survival (OS), lymph node metastasis and recurrence of UTGC.Clinicopathologic data of 126 UTGC patients who underwent radical surgical treatment were retrospectively analyzed. OS and univariate analysis were determined by Kaplan–Meier analysis and the significance of the difference between curves was calculated with the log-rank test. The Cox proportional hazards regression model was applied to perform multivariate analysis. Receiver operating characteristic (ROC) curve analysis was used to determine the prognostic accuracy.The 1-, 3-, and 5-year OS for patients with UTGC were 81%, 47.6%, and 38.6% respectively. Univariate analysis showed that tumor size (P = .019), tumor invasion depth (P < .001), and lymph node metastasis (P < .001) were the risk factors for 5-year OS. Multivariate analysis identified tumor invasion depth (P < .001) and lymph node metastasis (P < .001) as independent prognostic factors for the 5-year OS in patients with UTGC. In addition, ROC curve analysis showed that tumor invasion depth (P = .017) or lymph node metastasis (P = .001) alone showed significantly effective prognosis for the 5-year OS in patients with UTGC. For UTGC patients with lymph node metastasis, tumor size (P = .023), lym embolism (P = .003), tumor invasion depth (P = .002), and invasion of tunica serosa (P = .004) were the risk factors for the 5-year OS. Multivariate analysis identified tumor size (P = .048), lym embolism (P = .032), tumor invasion depth (P = .004), and invasion of tunica serosa (P = .031) as independent prognostic factors for the 5-year OS. For UTGC patients with distant metastasis or tumor recurrence, univariate and multivariate analyses demonstrated that tumor invasion depth and lymph node metastasis were independent prognostic factors for the 5-year OS.The results suggested that for UPGC patients undergoing the radical surgical treatment, tumor invasion depth and/or lymph node metastasis are the independent prognostic factors for the 5-year OS, lymph node metastasis, distant metastasis and tumor recurrence.

Hepatic alveolar echinococcosis (HAE) and liver cancer had similarities in imaging results, clinical characteristics, and so on. And it is difficult for clinicians to distinguish them before operation. The aim of our study was to build a differential diagnosis nomogram based on platelet (PLT) score model and use internal validation to check the model. The predicting model was constructed by the retrospective database that included in 153 patients with HAE (66 cases) or liver cancer (87 cases), and all cases was confirmed by clinicopathology and collected from November 2011 to December 2018. Lasso regression analysis model was used to construct data dimensionality reduction, elements selection, and building prediction model based on the 9 PLT-based scores. A multi-factor regression analysis was performed to construct a simplified prediction model, and we added the selected PLT-based scores and relevant clinicopathologic features into the nomogram. Identification capability, calibration, and clinical serviceability of the simplified model were evaluated by the Harrell’s concordance index (C-index), calibration plot, receiver operating characteristic curve (ROC), and decision curve. An internal validation was also evaluated by the bootstrap resampling. The simplified model, including in 4 selected factors, was significantly associated with differential diagnosis of HAE and liver cancer. Predictors of the simplified diagnosis nomogram consisted of the API index, the FIB-4 index, fibro-quotent (FibroQ), and fibrosis index constructed by King’s College Hospital (King’s score). The model presented a perfect identification capability, with a high C-index of 0.929 (0.919 through internal validation), and good calibration. The area under the curve (AUC) values of this simplified prediction nomogram was 0.929, and the result of ROC indicated that this nomogram had a good predictive value. Decision curve analysis showed that our differential diagnosis nomogram had clinically identification capability. In conclusion, the differential diagnosis nomogram could be feasibly performed to verify the preoperative individualized diagnosis of HAE and liver cancer.

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BACKGROUND Pyroptosis is an inflammatory form of programmed cell death, which has been shown to be related to the prognosis of many tumors. However, its role in gastric cancer (GC) is not fully understood. AIM To evaluate the expression of pyroptosis-related genes in GC and its correlation with prognosis. METHODS We constructed prognostic multigene markers of differentially expressed genes associated with pyroptosis by least absolute contraction and selection operator Cox regression. The risk model was analyzed by Kaplan-Meier curve, two-sided log-rank test and functional enrichment analysis. RESULTS Sixty-three pyroptosis-related genes were differentially expressed in tumor tissues and adjacent nontumor tissues. Based on these differentially expressed genes, 5 gene signature were constructed and all GC patients were classified into two risk groups. Kaplan-Meier survival curve showed that the overall survival (OS) of patients in the high-risk group was significantly lower than that of the low-risk group. Multivariate Cox regression analyses showed that the risk score was an independent risk factor for OS. Receiver operating characteristic curve analysis confirmed the predictive ability of the model. External validation indicated increased OS in the low-risk group. The immune function and immune cell scores of the high-risk group were generally higher than those of the low-risk group. CONCLUSION Pyroptosis-related genes play a significant role in tumor immune microenvironment. This novel model, which contains 5 pyroptosis-related genes, is an independent predicting factor for OS in GC patients, and may help to evaluate the prognosis of GC.