Gastric cancer is one of the most aggressive cancers and is the second leading cause of cancer death worldwide. Approximately 40% of global gastric cancer cases occur in China, with peritoneal metastasis being the prevalent form of recurrence and metastasis in advanced disease. Currently, there are limited clinical approaches for predicting and treatment of peritoneal metastasis, resulting in a 6-month average survival time. By comprehensive genome analysis will uncover the pathogenesis of peritoneal metastasis. Here we describe a comprehensive whole-genome and transcriptome sequencing analysis of one advanced gastric cancer case, including non-cancerous mucosa, primary cancer and matched peritoneal metastatic cancer. The peripheral blood is used as normal control. We identified 27 mutated genes, of which 19 genes are reported in COSMIC database (ZNF208, CRNN, ATXN3, DCTN1, RP1L1, PRB4, PRB1, MUC4, HS6ST3, MUC17, JAM2, ITGAD, IREB2, IQUB, CORO1B, CCDC121, AKAP2, ACAN and ACADL), and eight genes have not previously been described in gastric cancer (CCDC178, ARMC4, TUBB6, PLIN4, PKLR, PDZD2, DMBT1and DAB1).Additionally,GPX4 and MPND in 19q13.3-13.4 region, is characterized as a novel fusion-gene. This study disclosed novel biological markers and tumorigenic pathways that would predict gastric cancer occurring peritoneal metastasis.

Materials and Methods Cell linesHuman lung cancer cell lines (A549, H1299, SK-MES-1) and normal lung epithelial cells (BEAS-2B) were purchased from Procell Life Science & Technology Co., Ltd. (Wuhan, China) and cultured in F-12K, RPMI-1640 and MEM medium (Procell, Wuhan, China) respectively, with 10% fetal bovine serum (Procell, Wuhan, China) and 100 U/ml penicillin and 100 µg/ml streptomycin (Procell, Wuhan, China) at 37°C with 5% CO2. Cell transfection and infectionH1299 cells were transfected with miR-654-3p mimics (RiboBio Co., Ltd., Guangzhou, China),

The AJCC (the American Joint Committee on Cancer) ypTNM (post-neoadjuvant pathologic stage group) staging was established based on patients with lymphadenectomy scope less than D2 and did not include ypT0N0 patients with pathologically complete response (PCR). The purpose of this study was to construct a survival predictive model for gastric cancer patients after neoadjuvant chemotherapy and gastrectomy combined with D2 lymphadenectomy. Patients and Methods: The multicenter data of 838 gastric cancer patients who received neoadjuvant chemotherapy and gastrectomy combined with D2 lymphadenectomy were analyzed retrospectively. These dual center patients were divided into training (n = 671, the Affiliated Hospital of Qingdao University) and validation (n = 167, Qingdao West Coast New Area Central Hospital) cohorts. Based on training cohort, univariate and multivariable COX regression analyses were performed to select the clinicopathological characteristics significantly correlating with overall survival and construct a nomogram. Based on training and validation cohorts, the distinguishing and calibrating capabilities of nomogram was evaluated by the receiver operating characteristic (ROC) curve, Harrell's concordance index (C-index), decision curve analysis (DCA) curve and calibration curve. Results: Platelet-to-lymphocyte ratio (PLR), pathologic stage after neoadjuvant treatment: ypT and ypN stage, tumor regression grade (TRG) became independent variables intimately related to the prognosis and was used to construct nomograms of 3/5-year prognosis. The nomograms showed an accuracy in predicting OS (overall survival) rate, with area under the ROC curve (AUC) of 0.818 (95% CI = 0.753~0.883) and C-index of 0.801 (95% CI = 0.744~0.858) in validation cohort. Calibration curves showed satisfactory agreement between nomogram prediction and actual result, and DCA curves indicated the large positive net benefit and excellent clinical usefulness of nomogram. Conclusion:This study successfully developed a nomogram to predict overall survival of gastric cancer patients after neoadjuvant chemotherapy and gastrectomy combined with D2 lymphadenectomy, which might have excellent predictive performance and clinical application value.