Background. Microbes have been shown to contribute to gastric cancer (GC), gastric bacteria and viruses are associated with gastric carcinogenesis. However, the relationship between gastric fungi and GC is still unclear. Our aim was to evaluate the gastric fungal microbiota in the GC microenvironment. Methods. Gastric fungal microbiome profiling was performed with internal transcribed spacer (ITS) rDNA sequencing in primary tumor and corresponding paired normal mucosal tissues from 61 GC patients. Differences in microbial composition, taxa diversity, and predicted function were further analyzed. Results. Dysbiosis of gastric mucosal fungal microbiome was observed between the tumor and normal groups in GC. The tumor group had a higher abundance of certain taxa than the normal group. In the taxa classification, the abundances of Pezizomycetes, Sordariales, Chaetomiaceae, and Rozellomycota were lower in the tumor group than in the normal group. At the genus level, Solicoccozyma ( ) was found in higher abundance and was differentially enriched in the tumor group with Lefse analysis. Additionally, Solicoccozyma accounted for 0.3% of gastric fungi in the GC microenvironment. Twenty-seven of the 61 GC patients showed positive Solicoccozyma expression in tumors. Solicoccozyma-positive expression in tumors was associated with the Bormann classification and nerve invasion. Solicoccozyma was considered a gastric fungal marker to classify stage I and stage II-IV GC patients with an area under the receiver-operating curve (AUC) of 0.7061, as well as to classify the nerve invasive and nonnerve invasive tumors from GC patients with an AUC of 0.6978. Functional prediction indicated that the positive expression of Solicoccozyma in tumors was associated with the amino acid- and carbohydrate-related metabolic pathways in GC. Conclusions. This study revealed a novel perspective on the role of Solicoccozyma in tumors and a theoretical basis for therapeutic targets against GC.

Background: Peritoneal recurrence (PR) is the predominant pattern of relapse after curative-intent surgery in gastric cancer (GC) and indicates a dismal prognosis. Accurate prediction of PR is crucial for patient management and treatment. We aimed to develop a noninvasive imaging biomarker from computed tomography (CT) for PR evaluation, and investigate its associations with prognosis and chemotherapy benefit. Methods: In this multicenter study including five independent cohorts of 2005 GC patients, we extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced CT images. The artificial intelligence algorithms were used to select significant PR-related features, and then integrated into a radiomic imaging signature. And improvements of diagnostic accuracy for PR by clinicians with the signature assistance were quantified. Using Shapley values, we determined the most relevant features and provided explanations to prediction. We further evaluated its predictive performance in prognosis and chemotherapy response. Results: The developed radiomics signature had a consistently high accuracy in predicting PR in the training cohort (area under the curve: 0.732), internal and SYSUCC validation cohorts (0.721 and 0.728). The radiomics signature was the most important feature in Shapley interpretation. The diagnostic accuracy of PR with the radiomics signature assistance was improved by 10.13% to 18.86% for clinicians (P<0.001). Furthermore, it was also applicable in the survival prediction. In multivariable analysis, the radiomics signature remained an independent predictor for PR and prognosis (P<0.001 for all). Importantly, patients with predicting high risk of PR from radiomics signature, could gain survival benefit from adjuvant chemotherapy. By contrast, chemotherapy had no impact on survival for patients with a predicted low risk of PR. Conclusion: The noninvasive and explainable model developed from preoperative CT images could accurately predict PR and chemotherapy benefit in patients with GC, which will allow the optimization of individual decision-making. Export

Background. Microbiotas affected the prognosis of cancer patients by regulating programmed death ligand-1 (PD-L1) expression. However, the relationship between gastric fungi and PD-L1 expression is still unclear in gastric cancer (GC). We aimed at exploring the association of gastric fungi with PD-L1 expression and overall survival in GC. Methods. A total of 61 GC patients were divided into the two groups based on the PD-L1 combined positive scores (CPS). Fungal profiling was performed by internal transcribed spacer rDNA sequencing, and the survival analyses were performed by Kaplan-Meier curves. Results. We observed a taxonomic difference of fungi between the PD-L1-High ( ) and PD-L1-Low group ( ) by principal coordinates analysis (PCoA) ( for Bray-Curtis and for Jaccard). Malassezia had a higher abundance in the PD-L1-High group compared to the PD-L1-Low group ( ). Malassezia globosa elevated significantly in the PD-L1-High group. GC patients with PD-L1 low expression and low abundance of Malassezia globosa had a longer overall survival (OS) than others ( ). Malassezia globosa was associated with PD-L1 expression ( , 95% Confidence Interval: 1.056-11.656, ). Malassezia globosa was associated with the tumor size ( ) and PD-L1 status ( ). GC patients with a high abundance of Malassezia globosa had shorter OS than others ( ). Malassezia globosa was an independent factor (