Objectives To investigate sex difference in global gastric cancer incidence by year, age and socioeconomical status. Design An international comparative study. Setting We obtained the global and national sex-specific incidence of gastric caner by year and age from the Global Burden of Disease Study 2017. The human development index (HDI) in 2017 as an indicator of national socioeconomical status was extracted from the Human Development Report. Main outcome measures Sex-specific incidence of gastric cancer was compared by year and age at the global level. Linear regression analyses were performed to explore socioeconomic-associated sex difference in gastric cancer incidence. Results Despite declining incidence of global gastric cancer in both sexes between 1990 and 2017, relative sex difference showed an increasing trend, with male to female ratios of age-standardised incidence rates (ASRs) rising from 1.86 to 2.20. Sex difference was almost negligible under 45 years of age and relative difference maximised in the age range of 65–69 years with male to female ratios of ASRs being 2.74. Both absolute sex difference (standardised β=0.256, p<0.001) and relative difference (standardised β=0.387, p<0.001) in ASRs were positively associated with HDI. Conclusions This study revealed that decreasing incidence of global gastric cancer was accompanied by widening sex difference in the past few decades. Men always had higher incidence than women. Greater sex difference was found in older age and in more developed countries. These findings highlight the importance of making sex-sensitive health policy to cope with the global gastric cancer burden.

Gastric cancer (GC) is a major cancer type characterized by high heterogeneity in both tumor cells and the tumor immune microenvironment (TIME). One intractable GC subtype is gastric signet-ring cell carcinoma (GSRCC), which is associated with poor prognosis. However, it remains unclear what the GSRCC TIME characteristics are and how these characteristics may contribute to clinical outcomes. We enrolled 32 patients with advanced GC of diverse subtypes and profiled their TIME using an immune-targeted single-cell profiling strategy, including (1) immune-targeted single-cell RNA sequencing (n = 20 patients) and (2) protein expression profiling by a targeted antibody panel for mass cytometry (n = 12 patients). We also generated matched V(D)J (variable, diversity, and joining gene segments) sequencing of T and B cells along CD45⁺ immunocytes. We found that compared to non-GSRCC, the GSRCC TIME appears to be quiescent, where both CD4⁺ and CD8⁺ T cells are difficult to be mobilized, which further impairs the proper functions of B cells. CXCL13, mainly produced by follicular helper T cells, T helper type 17, and exhausted CD8⁺ T cells, is a central coordinator of this transformation. We show that CXCL13 expression can predict the response to immune checkpoint blockade in GC patients, which may be related to its effects on tertiary lymphoid structures. Our study provides a comprehensive molecular portrait of immune cell compositions and cell states in advanced GC patients, highlighting adaptive immune irresponsiveness in GSRCC and a mediator role of CXCL13 in TIME. Our targeted single-cell transcriptomic and proteomic profiling represents a powerful approach for TIME-oriented translational research.

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with an estimated 1.8 million new cases worldwide and associated with high mortality rates of 881 000 CRC‐related deaths in 2018. Screening programs and new therapies have only marginally improved the survival of CRC patients. Immune‐related genes (IRGs) have attracted attention in recent years as therapeutic targets. The aim of this study was to identify an immune‐related prognostic signature for CRC. To this end, we combined gene expression and clinical data from the CRC data sets of The Cancer Genome Atlas (TCGA) into an integrated immune landscape profile. We identified a total of 476 IRGs that were differentially expressed in CRC vs normal tissues, of which 18 were survival related according to univariate Cox analysis. Stepwise multivariate Cox proportional hazards analysis established an immune‐related prognostic signature consisting of SLC10A2, FGF2, CCL28, NDRG1, ESM1, UCN, UTS2 and TRDC. The predictive ability of this signature for 3‐ and 5‐year overall survival was determined using receiver operating characteristics (ROC), and the respective areas under the curve (AUC) were 79.2% and 76.6%. The signature showed moderate predictive accuracy in the validation and GSE38832 data sets as well. Furthermore, the 8‐IRG signature correlated significantly with tumour stage, invasion, lymph node metastasis and distant metastasis by univariate Cox analysis, and was established an independent prognostic factor by multivariate Cox regression analysis for CRC. Gene set enrichment analysis (GSEA) revealed a relationship between the IRG prognostic signature and various biological pathways. Focal adhesions and ECM‐receptor interactions were positively correlated with the risk scores, while cytosolic DNA sensing and metabolism‐related pathways were negatively correlated. Finally, the bioinformatics results were validated by real‐time RT‑qPCR. In conclusion, we identified and validated a novel, immune‐related prognostic signature for patients with CRC, and this signature reflects the dysregulated tumour immune microenvironment and has a potential for better CRC patient management.

Whether the presence of postoperative complications was associated with poor prognosis of gastric carcinoma (GC) patients remain controversial. This meta-analysis was designed and reported to compare the survival difference between patients with complications and non-complications. Cochrane Library, PubMed and Embase databases were comprehensively searched for published literatures to review current evidence on this topic. The survival data were extracted, and a random-effect or fixed-effect model was used to analyze the correlation between postoperative complications and oncologic outcome of GC patients. Of all studies identified, 32 were eligible for this pooled analysis, with a total of 32,067 GC patients. The incidence of postoperative complications was approximately 12.5% to 51.0%. Among them, infectious complications varied from 3.0% to 28.6%, anastomotic leakage varied from 1.1% to 8.7% and postoperative pneumonia varied from 1.6% to 12.8%. The presence of postoperative complications resulted in a significant poorer overall survival (OS) of gastric carcinoma patients (hazard ratio [HR]:1.49, 95% confidence interval [CI]: 1.33-1.67, P < 0.001). Additionally, the pooled results showed a significant correlation between infectious complications and decreased OS (HR: 1.61, 95%CI: 1.38-1.88, P < 0.001). Concerning specific postoperative complications, we found that both anastomotic leakage (HR: 2.36, 95%CI: 1.62-3.42, P < 0.001) and postoperative pneumonia (HR: 1.74, 95%CI: 1.22-2.49, P = 0.002) impaired the OS of gastric carcinoma patients. Postoperative complications were significantly correlated to recurrence and poor survival in gastric carcinoma patients. To gain a better surgical outcome and long-term oncological outcome, postoperative complications should be minimized as much as possible.

Objective: In order to enhance the detection rate of multiple myeloma and execute an early and more precise disease management, an artificial intelligence assistant diagnosis system is developed. Methods: 4,187 routine blood and biochemical examination records were collected from Shengjing Hospital affiliated to China Medical University from January 2010 to January 2020, which include 1,741 records of multiple myeloma (MM) and 2,446 records of non-myeloma (infectious diseases, rheumatic immune system diseases, hepatic diseases and renal diseases). The data set was split into training and test subsets with the ratio of 4:1 while connecting hemoglobin, serum creatinine, serum calcium, immunoglobulin (A, G and M), albumin, total protein, and the ratio of albumin to globulin data. An early assistant diagnostic model of MM was established by Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Deep Neural Networks (DNN), and Random Forest (RF). Out team calculated the precision and recall of the system. The performance of the diagnostic model was evaluated by using the receiver operating characteristic (ROC) curve. Results: By designing the features properly, the typical machine learning algorithms SVM, DNN, RF and GBDT all performed well. GBDT had the highest precision (92.9%), recall (90.0%) and F1 score (0.915) for the myeloma group. The maximized area under the ROC (AUROC) was calculated, and the results of GBDT (AUC: 0.975; 95% confidence interval (CI): 0.963–0.986) outperformed that of SVM, DNN and RF. Conclusion: The model established by artificial intelligence derived from routine laboratory results can accurately diagnose MM, which can boost the rate of early diagnosis.

T cells require rapid proliferation to initiate adaptive immunity to prevent pathogen attacks. The nucleolus, a distinct subnuclear membrane-less compartment for ribosomal biogenesis, is indispensable for cell proliferation. However, specific nucleolar proteins involved in rapid T cell proliferation and their underlying molecular regulatory mechanism remain elusive. Here, we identified an essential nucleolar protein, DCAF13, in T cells and revealed its significant regulation of rapid T cell proliferation. Its depletion drastically impairs T cell proliferation due to severe 18S rRNA maturation failure, consequent abnormal ribosome assembly in nucleoli, and insufficient production of nascent proteins. Mechanistically, we propose that DCAF13 promotes NPM1 phase separation to accelerate pre-RNA enrichment and its endonuclease UTP23 for 18S rRNA maturation during T cell proliferation. Our findings reveal the modulatory effect of nucleolar NPM1/DCAF13 phase separation on ribosomal maturation to ensure rapid T cell proliferation and further pathogen clearance for the first time.