Tumor microenvironment (TME) cells constitute a vital element of tumor tissue. Increasing evidence has elucidated their clinicopathologic significance in predicting outcomes and therapeutic efficacy. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the TME. In this study, we comprehensively estimated the TME infiltration patterns of 1,524 gastric cancer patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathologic features of gastric cancer using two proposed computational algorithms. Three TME phenotypes were defined, and the TMEscore was constructed using principal component analysis algorithms. The high TMEscore subtype was characterized by immune activation and response to virus and IFNg. Activation of transforming growth factor b, epithelial-mesenchymal transition, and angiogenesis pathways were observed in the low TMEscore subtype, which are considered T-cell suppressive and may be responsible for significantly worse prognosis in gastric cancer [hazard ratio (HR), 0.42; 95% confidence interval (CI), 0.33-0.54; P < 0.001]. Multivariate analysis revealed that the TMEscore was an independent prognostic biomarker, and its value in predicting immunotherapeutic outcomes was also confirmed (IMvigor210 cohort: HR, 0.63; 95% CI, 0.46-0.89; P ¼ 0.008; GSE78220 cohort: HR, 0.25; 95% CI, 0.07-0.89; P ¼ 0.021). Depicting a comprehensive landscape of the TME characteristics of gastric cancer may, therefore, help to interpret the responses of gastric tumors to immunotherapies and provide new strategies for the treatment of cancers.

Background METTL3 is known to be involved in all stages in the life cycle of RNA. It affects the tumor formation by the regulation the m6A modification in the mRNAs of critical oncogenes or tumor suppressors. In bladder cancer, METTL3 could promote the bladder cancer progression via AFF4/NF-κB/MYC signaling network by an m6A dependent manner. Recently, METTL3 was also found to affect the m6A modification in non-coding RNAs including miRNAs, lincRNAs and circRNAs. However, whether this mechanism is related to the proliferation of tumors induced by METTL3 is not reported yet. Methods Quantitative real-time PCR, western blot and immunohistochemistry were used to detect the expression of METTL3 in bladder cancer. The survival analysis was adopted to explore the association between METTL3 expression and the prognosis of bladder cancer. Bladder cancer cells were stably transfected with lentivirus and cell proliferation and cell cycle, as well as tumorigenesis in nude mice were performed to assess the effect of METTL3 in bladder cancer. RNA immunoprecipitation (RIP), co-immunoprecipitations and RNA m6A dot blot assays were conducted to confirm that METTL3 interacted with the microprocessor protein DGCR8 and modulated the pri-miR221/222 process in an m6A-dependent manner. Luciferase reporter assay was employed to identify the direct binding sites of miR221/222 with PTEN. Colony formation assay and CCK8 assays were conducted to confirm the function of miR-221/222 in METTL3-induced cell growth in bladder cancer. Results We confirmed the oncogenic role of METTL3 in bladder cancer by accelerating the maturation of pri-miR221/222, resulting in the reduction of PTEN, which ultimately leads to the proliferation of bladder cancer. Moreover, we found that METTL3 was significantly increased in bladder cancer and correlated with poor prognosis of bladder cancer patients. Conclusions Our findings suggested that METTL3 may have an oncogenic role in bladder cancer through interacting with the microprocessor protein DGCR8 and positively modulating the pri-miR221/222 process in an m6A-dependent manner. To our knowledge, this is the first comprehensive study that METTL3 affected the tumor formation by the regulation the m6A modification in non-coding RNAs, which might provide fresh insights into bladder cancer therapy. Electronic supplementary material The online version of this article (10.1186/s12943-019-1036-9) contains supplementary material, which is available to authorized users.

Recent advances in next-generation sequencing (NGS) technologies have triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to explore robust signatures for clinical translation is increasingly emphasized, and this is attributed to the clinical success of immune checkpoint blockades in diverse malignancies. However, effective tools for comprehensively interpreting multi-omics data are still warranted to provide increased granularity into the intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. Therefore, we developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing a comprehensive investigation of the estimation of reported or user-built signatures, TME deconvolution, and signature construction based on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, long non-coding RNA (lncRNA) profiling, genomic characteristics, and signatures generated from single-cell RNA sequencing (scRNA-seq) data in different cancer settings. Additionally, IOBR integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool for leveraging multi-omics data to facilitate immuno-oncology exploration and to unveil tumor-immune interactions and accelerating precision immunotherapy.