Background: Tumor mutational burden (TMB) is a potential biomarker for immune checkpoint therapy and prognosis. The impact of TMB on clinical outcomes and the correlation coefficient between exome sequencing and targeted sequencing in glioma have not yet been explored. Methods: Somatic mutations in the coding regions of 897 primary gliomas and the clinical and RNA-seq data of 654 patients in The Cancer Genome Atlas (TCGA) database were analyzed as a training set, while another 286 patients in the Chinese Glioma Genome Atlas (CGGA) database were used for validation. Descriptive and correlational analyses were conducted with TMB. Enrichment map analysis and gene set enrichment analysis (GSEA) were also performed. Results: TMB was higher for the group of mutant genes that are frequently mutated in glioblastomas (GBMs) and lower for the group of mutant genes that are frequently mutated in lower-grade gliomas (LGGs). Patients with a higher TMB exhibited shorter overall survival. TMB was associated with grade, age, subtype and mutations affecting genomic structure. Moreover, univariate and multivariate analyses showed that TMB was an independent prognostic factor for glioma. The signaling pathways of the cell cycle were enriched in the TMB High group. TMB was higher in the mismatch repair (MMR) gene mutant group than in the wild-type group, but the MMR pathway was enriched in the TMB High group of gliomas without mutations in classical MMR genes. The correlation between TMBs calculated through exome sequencing and targeted sequencing was moderate, and panel-based TMB was not correlated with prognosis. Conclusions: TMB is associated with poor outcomes in diffuse glioma. The high proliferative activity in the TMB High group could account for the shorter survival of these patients. This association was not reflected by a pan-cancer targeted sequencing panel.

The traveling-salesman problem can be regarded as an NP-hard problem. To better solve the best solution, many heuristic algorithms, such as simulated annealing, ant-colony optimization, tabu search, and genetic algorithm, were used. However, these algorithms either are easy to fall into local optimization or have low or poor convergence performance. This paper proposes a new algorithm based on simulated annealing and gene-expression programming to better solve the problem. In the algorithm, we use simulated annealing to increase the diversity of the Gene Expression Programming (GEP) population and improve the ability of global search. The comparative experiments results, using six benchmark instances, show that the proposed algorithm outperforms other well-known heuristic algorithms in terms of the best solution, the worst solution, the running time of the algorithm, the rate of difference between the best solution and the known optimal solution, and the convergent speed of algorithms.

No abstract

No abstract