Methyltransferase-like 3 (METTL3), a major component of the N6-methyladenosine (m6A) methyltransferase complex, has been suggested to function as an oncogene in several cancers. However, its biological mechanism and the involved pathways in gastric cancer (GC) remain unknown. Here, we reported that frequent upregulation of METTL3 was responsible for the aberrant m6A levels in gastric carcinoma. On the other hand, a high level of METTL3 was significantly associated with several clinicopathological features and poor survival in patients with GC. The knockdown of METTL3 effectively inhibited cell proliferation and migration and invasion capacity. Moreover, overexpression of METTL3 considerably augmented its oncogenic function. Integrated RNA-seq and m6A-seq analysis first indicated that several component molecules (e.g., MCM5, MCM6, etc.) of MYC target genes were mediated by METTL3 via altered m6A modification. Our work uncovers the oncogenic roles of METTL3 in GC and suggests a critical mechanism of GC progression.

Temporal and spatial protein phosphorylation dynamically orchestrates a broad spectrum of biological processes and plays various physiological and pathological roles in diseases and cancers. Recent advancements in high-throughput proteomics techniques greatly promoted the profiling and quantification of phosphoproteome. However, although several comprehensive databases have reserved the phosphorylated proteins and sites, a resource for phosphorylation quantification still remains to be constructed. In this study, we developed the qPhos (http://qphos.cancerbio.info) database to integrate and host the data on phosphorylation dynamics. A total of 3 537 533 quantification events for 199 071 non-redundant phosphorylation sites on 18 402 proteins under 484 conditions were collected through exhaustive curation of published literature. The experimental details, including sample materials, conditions and methods, were recorded. Various annotations, such as protein sequence and structure properties, potential upstream kinases and their inhibitors, were systematically integrated and carefully organized to present details about the quantified phosphorylation sites. Various browse and search functions were implemented for the user-defined filtering of samples, conditions and proteins. Furthermore, the qKinAct service was developed to dissect the kinase activity profile from user-submitted quantitative phosphoproteome data through annotating the kinase activity-related phosphorylation sites. Taken together, the qPhos database provides a comprehensive resource for protein phosphorylation dynamics to facilitate related investigations.

Post-translational modifications (PTMs) are critical molecular mechanisms that regulate protein functions temporally and spatially in various organisms. Since most PTMs are dynamically regulated, quantifying PTM events under different states is crucial for understanding biological processes and diseases. With the rapid development of high-throughput proteomics technologies, massive quantitative PTM proteome datasets have been generated. Thus, a comprehensive one-stop data resource for surfing big data will benefit the community. Here, we updated our previous phosphorylation dynamics database qPhos to the qPTM (http://qptm.omicsbio.info). In qPTM, 11 482 553 quantification events among six types of PTMs, including phosphorylation, acetylation, glycosylation, methylation, SUMOylation and ubiquitylation in four different organisms were collected and integrated, and the matched proteome datasets were included if available. The raw mass spectrometry based false discovery rate control and the recurrences of identifications among datasets were integrated into a scoring system to assess the reliability of the PTM sites. Browse and search functions were improved to facilitate users in swiftly and accurately acquiring specific information. The results page was revised with more abundant annotations, and time-course dynamics data were visualized in trend lines. We expected the qPTM database to be a much more powerful and comprehensive data repository for the PTM research community.

As a widespread post-translational modification of proteins, calpain-mediated cleavage regulates a broad range of cellular processes, including proliferation, differentiation, cytoskeletal reorganization, and apoptosis. The identification of proteins that undergo calpain cleavage in a site-specific manner is the necessary foundation for understanding the exact molecular mechanisms and regulatory roles of calpain-mediated cleavage. In contrast with time-consuming and labor-intensive experimental methods, computational approaches for detecting calpain cleavage sites have attracted wide attention due to their efficiency and convenience. In this study, we established a novel computational tool named DeepCalpain (http://deepcalpain.cancerbio.info/) for predicting the potential calpain cleavage sites by adopting deep neural network and the particle swarm optimization algorithm. Through critical evaluation and comparison, DeepCalpain exhibited superior performance against other existing tools. Meanwhile, we found that protein interactions could enrich the calpain-substrate regulatory relationship. Since calpain-mediated cleavage was critical for cancer development and progression, we comprehensively analyzed the calpain cleavage associated mutations across 11 cancers with the help of DeepCalpain, which demonstrated that the calpain-mediated cleavage events were affected by mutations and heavily implicated in the regulation of cancer cells. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of calpain cleavage in biological pathways and different cancer types, which might open new avenues for the diagnosis and treatment of cancers.