The diffuse-type gastric cancer (DGC) is a subtype of gastric cancer with the worst prognosis and few treatment options. Here we present a dataset from 84 DGC patients, composed of a proteome of 11,340 gene products and mutation information of 274 cancer driver genes covering paired tumor and nearby tissue. DGC can be classified into three subtypes (PX1–3) based on the altered proteome alone. PX1 and PX2 exhibit dysregulation in the cell cycle and PX2 features an additional EMT process; PX3 is enriched in immune response proteins, has the worst survival, and is insensitive to chemotherapy. Data analysis revealed four major vulnerabilities in DGC that may be targeted for treatment, and allowed the nomination of potential immunotherapy targets for DGC patients, particularly for those in PX3. This dataset provides a rich resource for information and knowledge mining toward altered signaling pathways in DGC and demonstrates the benefit of proteomic analysis in cancer molecular subtyping.

COVID‐19 is characterized by dysregulated immune responses, metabolic dysfunction and adverse effects on the function of multiple organs. To understand host responses to COVID‐19 pathophysiology, we combined transcriptomics, proteomics, and metabolomics to identify molecular markers in peripheral blood and plasma samples of 66 COVID‐19‐infected patients experiencing a range of disease severities and 17 healthy controls. A large number of expressed genes, proteins, metabolites, and extracellular RNAs (exRNAs) exhibit strong associations with various clinical parameters. Multiple sets of tissue‐specific proteins and exRNAs varied significantly in both mild and severe patients suggesting a potential impact on tissue function. Chronic activation of neutrophils, IFN‐I signaling, and a high level of inflammatory cytokines were observed in patients with severe disease progression. In contrast, COVID‐19‐infected patients experiencing milder disease symptoms showed robust T‐cell responses. Finally, we identified genes, proteins, and exRNAs as potential biomarkers that might assist in predicting the prognosis of SARS‐CoV‐2 infection. These data refine our understanding of the pathophysiology and clinical progress of COVID‐19.

Transcription factors (TFs) drive various biological processes ranging from embryonic development to carcinogenesis. Here, we employ a recently developed concatenated tandem array of consensus TF response elements (catTFRE) approach to profile the activated TFs in 24 adult and 8 fetal mouse tissues on proteome scale. A total of 941 TFs are quantitatively identified, representing over 60% of the TFs in the mouse genome. Using an integrated omics approach, we present a TF network in the major organs of the mouse, allowing data mining and generating knowledge to elucidate the roles of TFs in various biological processes, including tissue type maintenance and determining the general features of a physiological system. This study provides a landscape of TFs in mouse tissues that can be used to elucidate transcriptional regulatory specificity and programming and as a baseline that may facilitate understanding diseases that are regulated by TFs.