Search

Background Immune checkpoint inhibitors (ICIs) have been used as a novel treatment for diffuse gliomas, but the efficacy varies with patients, which may be associated with the tumor mutational burden (TMB) and immune infiltration. We aimed to explore the relationship between the two and their impacts on the prognosis. Methods The data of the training set were downloaded from The Cancer Genome Atlas (TCGA). “DESeq2” R package was used for differential analysis and identification of differentially expressed genes (DEGs). A gene risk score model was constructed based on DEGs, and a nomogram was developed combined with clinical features. With the CIBERSORT algorithm, the relationship between TMB and immune infiltration was analyzed, and an immune risk score model was constructed. Two models were verification in the validation set downloaded from the Chinese Glioma Genome Atlas (CGGA). Results Higher TMB was related to worse prognosis, older age, higher grade, and higher immune checkpoint expression. The gene risk score model was constructed based on BIRC5, SAA1, and TNFRSF11B, and their expressions were all negatively correlated with prognosis. The nomogram was developed combined with age and grade. The immune risk score model was constructed based on M0 macrophages, neutrophils, naive CD4+ T cells, and activated mast cells. The proportions of the first two were higher in the high-TMB group and correlated with worse prognosis, while the latter two were precisely opposite. Conclusions In diffuse gliomas, TMB was negatively correlated with prognosis. The association of immune infiltration with TMB and prognosis varied with the type of immune cells. The nomogram and risk score models can accurately predict prognosis. The results can help identify patients suitable for ICIs and potential therapeutic targets, thus improve the treatment of diffuse gliomas.

Background: The aim of this study was to develop and validate a nomogram for predicting the overall survival of patients with gastric cancer (GC). Methods: DNA methylation (DNAm)-driven genes were identified by integrating DNAm and gene expression profiling analyses from The Cancer Genome Atlas (TCGA) GC cohort. Then a risk score model was built based on Kaplan-Meier analysis, the least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analysis. After analyzing with clinical parameters, a nomogram was conducted and assessed with respect to its calibration, discrimination, and clinical usefulness. Another cohort (GSE62254), retrieved from the Gene Expression Omnibus (GEO), was used for external validation at last. Findings: We built a six-gene signature (PODN, NPY, MICU3, TUBB6 and RHOJ were hypermethylated, and MYO1A was hypomethylated) associated with overall survival (OS) status (P < 0·05). Cox regression analysis indicated that risk score, age, and number of positive lymph nodes were significantly and independently associated with survival time in GC patients. A nomogram including these variables was constructed, which performed well in predicting the 1-, 3- and 5-year survival of GC patients. Pathway enrichment analysis suggested that these DNAm-driven genes might impact tumor progression by affecting signaling pathways such as the "ECM RECEPTOR INTERACTION" and "DNA REPLICATION" ones. Interpretation: The altered status of the DNAm-driven gene signature (PODN, MYO1A, NPY, MICU3, TUBB6 and RHOJ) was significantly associated with the OS of GC patients. A nomogram, incorporating the risk score, age and number of positive lymph nodes, can be conveniently used to facilitate the individualized prediction of OS in patients with GC after surgery. Funding Statement: This work was supported by the International Science and Technology Cooperation Projects (2016YFE0107100), the Capital Special Research Project for Health Development (2014-2-4012), the Beijing Natural Science Foundation (L172055 and 7192158), the National Ten-thousand Talent Program, the Fundamental Research Funds for the Central Universities (3332018032), the CAMS Innovation Fund for Medical Science (CIFMS) (2017-I2M-4-003 and 2018-I2M-3-001), the Support Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan (IDHT20190510), the Ministry of Science and Technology of People's Republic of China (2014CB910100), and the National Natural Science Foundation of China (81171899 and 81372230). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: Not required.