Accumulating evidence implies that N6-methyladenosine (m6A) methylation participated in the tumorigenesis of gastric cancer (GC). Here we synthetically analyzing the prognostic value and expression profile of seven m6A methylation-relevant genes through silico analysis of sequencing data downloaded from The Cancer Genome Atlas, Kaplan-Meier plotter, and Gene Expression Omnibus database. We explored the methyltransferase-like 3 (METTL3) expression in GC cell line and tumor tissues by reverse transcription quantitative polymerase chain reaction and western blot analysis.The m6A methylation status of total RNA was measured by m6A RNA methylation quantification kit. Small interfering RNA was used to establish METTL3 knockdown cell lines. We also measure the proliferation and migration capability GC cell. Furthermore, we detect the epithelial cell mesenchymal transition marker and m6A methylation level after METTL3 knock down. Our result revealed that METTL3 was significantly increased in GC tissues compared with control in big crowd data sets. Survival analysis showed that METTL3 serve as a poor prognostic factor for GC patients. The expression level of METTL3 gradually increased with the progress of tumor stage and grade. GFI1 is an important transcription factor associated with METTL3. We verified the up-trend of METTL3 in messenger RNA and protein expression and observed a significant increase in the m6A methylation status of total RNA in the GC cells and tissues. METTL3 knockdown inhibited total RNA m6A methylation level, as well as cell proliferation and migration capacity. Moreover, METTL3 knockdown decreased α-smooth muscle actin.Taken together, our finding revealed that m6A methylation writer METTL3 serve as an oncogene in tumorigenesis of GC.

The digestive system cancers are aggressive cancers with the highest mortality worldwide. In this study, we undertook a systematic investigation of the tumor immune microenvironment to identify diagnostic and prognostic biomarkers. The fraction of 22 immune cell types of patients were estimated using CIBERSORT. The least absolute shrinkage and selection operator (LASSO) analysis was carried out to identify important immune predictors. By comparing immune cell compositions in 801 tumor samples and 46 normal samples, we constructed the diagnostic immune score (DIS), showing high specificity and sensitivity in the training (area under the receiver operating characteristic curve [AUC] = 0.929), validation (AUC = 0.935), and different cancer type cohorts (AUC > 0.70 for all).We also established the prognostic immune score (PIS), which was an effective prognostic factor for relapse-free survival in training, validation, and entire cohorts (P < .05). In addition, PIS provided a higher net benefit than TNM stage. A composite nomogram was built based on PIS and patients' clinical information with well-fitted calibration curves (c-index = 0.84). We further used other cohorts from Gene Expression Omnibus databases and obtained similar results, confirming the reliability and validity of the DIS and PIS. In addition, the unsupervised clustering analysis using immune cell proportions revealed 6 immune subtypes, suggesting that the immune types defined as having relatively high levels of M0 or/and M1 macrophages were the high-risk subtypes of relapse. In conclusion, this study comprehensively analyzed the tumor immune microenvironment and identified DIS and PIS for digestive system cancers. K E Y W O R D Sdiagnosis, digestive system cancer, immune cell, prognosis, TCGA

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Abstract:We are now living in the big data era, where firms can improve their decision makings by adopting big data technology to utilize mass information. To explore the effects of the big data technology, we build an analytical model to study the sustainable investment in a supply chain, consisting of one manufacturer and one retailer, by using Bayesian information updating approach. We derive the optimal sustainable investment level for the manufacturer and the optimal order quantity for the retailer. Comparing the results with and without the big data technology, we find that whether the manufacturer should make more sustainable investment when the retailer adopts the big data technology depends on the service level at the retailer side. Interestingly, it is not always optimal for the retailer to adopt the big data technology. We identify the conditions under which the manufacturer and retailer are better off with the big data technology. In addition, we investigate the impact of the number of observations regarding the market information and find that the optimal decisions and profits increase in the number of the observations, if and only if the service level is low.

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