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Human gastrointestinal tumours have been shown to contain massive numbers of tumour infiltrating regulatory T cells (Tregs), the presence of which are closely related to tumour immunity. This study was designed to develop new Treg‐related prognostic biomarkers to monitor the prognosis of patients with gastric cancer (GC). Treg‐related prognostic genes were screened from Treg‐related differentially expressed genes in GC patients by using Cox regression analysis, based on which a prognostic model was constructed. Then, combined with RiskScore, survival curve, survival status assessment and ROC analysis, these genes were used to verify the accuracy of the model, whose independent prognostic ability was also evaluated. Six Treg‐related prognostic genes (CHRDL1, APOC3, NPTX1, TREML4, MCEMP1, GH2) in GC were identified, and a 6‐gene Treg‐related prognostic model was constructed. Survival analysis revealed that patients had a higher survival rate in the low‐risk group. Combining clinicopathological features, we performed univariate and multivariate regression analyses, with results establishing that the RiskScore was an independent prognostic factor. Predicted 1‐, 3‐ and 5‐year survival rates of GC patients had a good fit with the actual survival rates according to nomogram results. In addition patients in the low‐risk group had higher tumour mutational burden (TMB) values. Gene Set Enrichment Analysis (GSEA) demonstrated that genes in the high‐risk group were significantly enriched in pathways related to immune inflammation, tumour proliferation and migration. In general, we constructed a 6‐gene Treg‐associated GC prognostic model with good prediction accuracy, where RiskScore could act as an independent prognostic factor. This model is expected to provide a reference for clinicians to estimate the prognosis of GC patients. [ABSTRACT FROM AUTHOR]

Background: This study evaluated the predictive value of gene signatures for biochemical recurrence (BCR) in primary prostate cancer (PCa) patients. Methods: Clinical features and gene expression profiles of PCa patients were attained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, which were further classified into a training set (n = 419), a validation set (n = 403). The least absolute shrinkage and selection operator Cox (LASSO‐Cox) method was used to select discriminative gene signatures in training set for biochemical recurrence‐free survival (BCRFS). Selected gene signatures established a risk score system. Univariate and multivariate analyses of prognostic factors about BCRFS were performed using the Cox proportional hazards regression models. A nomogram based on multivariate analysis was plotted to facilitate clinical application. Kyoto Encyclopedia of Gene and Genomes (KEGG) and Gene Ontology (GO) analyses were then executed for differentially expressed genes (DEGs). Results: Notably, the risk score could significantly identify BCRFS by time‐dependent receiver operating characteristic (t‐ROC) curves in the training set (3‐year area under the curve (AUC) = 0.820, 5‐year AUC = 0.809) and the validation set (3‐year AUC = 0.723, 5‐year AUC = 0.733). Conclusions: Clinically, the nomogram model, which incorporates Gleason score and the risk score, could effectively predict BCRFS and potentially be utilized as a useful tool for the screening of BCRFS in PCa. [ABSTRACT FROM AUTHOR]