Accepting the crucial role of the immune microenvironment (TME) in tumor progression enables us to identify immunotherapeutic targets and develop new therapies. Glycoprotein A repetitions predominant (GARP) plays a vital part in maintaining regulatory T cell (Treg)-mediated immune tolerance. The impact of GARP in TME of gastric cancer is still worth exploring. We investigated public genomic datasets from The Cancer Genome Atlas and Gene Expression Omnibus to analyze the possible role of GARP and its relationship with TME of gastric cancer. Fluorescence-based multiplex immunohistochemistry and immunohistochemistry for T-cell immune signatures in a series of tissue microarrays were used to validate the value of GARP in the TME. We initially found that GARP expression was upregulated in gastric carcinoma cells, and diverse levels o3f immune cell infiltration and immune checkpoint expression were detected. Gene expression profiling revealed that GARP expression was related to the TME of gastric cancer. GARP upregulation was usually accompanied by increased FOXP3+ Treg and CD4+ T cell infiltration. In addition, GARP expression had positive relationships with CTLA-4 and PD-L1 expression in gastric cancer. Cox regression analysis and a nomogram highlighted that the probability of poor overall survival was predicted well by GARP or GARP+CD4+ T cell. Taken together, this research underlines the potential effect of GARP in regulating survival and tumor-infiltrating T-cells. In addition, the function of CD4+ T cell immune signatures in the prognosis can be clinically meaningful, thereby providing a new idea for the immunotherapeutic approach.

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor because of its significant heterogeneity and complicated molecular pathogenesis. Novel prognostic biomarkers are urgently needed because no effective and reliable prognostic biomarkers currently exist for HCC patients. Increasing evidence has revealed that pyroptosis plays a role in the occurrence and progression of malignant tumors. However, the relationship between pyroptosis-related genes (PRGs) and HCC patient prognosis remains unclear. In this study, 57 PRGs were obtained from previous studies and GeneCards. The gene expression profiles and clinical data of HCC patients were acquired from public data portals. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a risk model using TCGA data. Additionally, the risk model was further validated in an independent ICGC dataset. Our results showed that 39 PRGs were significantly differentially expressed between tumor and normal liver tissues in the TCGA cohort. Functional analysis confirmed that these PRGs were enriched in pyroptosis-related pathways. According to univariate Cox regression analysis, 14 differentially expressed PRGs were correlated with the prognosis of HCC patients in the TCGA cohort. A risk model integrating two PRGs was constructed to classify the patients into different risk groups. Poor overall survival was observed in the high-risk group of both TCGA (p < 0.001) and ICGC (p < 0.001) patients. Receiver operating characteristic curves demonstrated the accuracy of the model. Furthermore, the risk score was confirmed as an independent prognostic indicator via multivariate Cox regression analysis (TCGA cohort: HR = 3.346, p < 0.001; ICGC cohort: HR = 3.699, p < 0.001). Moreover, the single-sample gene set enrichment analysis revealed different immune statuses between high- and low-risk groups. In conclusion, our new pyroptosis-related risk model has potential application in predicting the prognosis of HCC patients.

BackgroundThe tumor microenvironment is mainly composed of tumor-infiltrating immune cells (TIICs), fibroblast, extracellular matrix, and secreted factors. TIICs are often associated with sensitivity to immunotherapy and the prognosis of multiple cancers, yet the predictive role of individual cells on tumor prognosis is limited.MethodsBased on single-sample gene set enrichment analysis, we combined three Gene Expression Omnibus (GEO) cohorts to build a TIIC model for risk stratification and prognosis prediction. The performance of the TIIC model was validated using our clinical cohort and the TCGA cohort. To assess the predictive power of the TIIC model for immunotherapy, we plotted the receiver operating characteristic curve with the IMvigor210 and GSE135222 cohorts.ResultsChemokines, tumor-infiltrating immune cells, and immunomodulators differed between the two TIIC groups. The TIIC model was vital for predicting the outcome of immunotherapy. In our clinical samples, we verified that the expression levels of PD-1 and PD-L1 were higher in the low TIIC score group than in the high TIIC score group, both in the tumor and stroma.ConclusionsCollectively, the TIIC model could provide a novel idea for immune cell targeting strategies in gastric cancer and predict the survival outcome of patients.