Xiao Zhu

Glioblastoma multiforme (GBM) is the most malignant intracranial tumor in adults, characterized by extensive infiltrative growth, high vascularization, and resistance to multiple therapeutic approaches. Among the many factors affecting the therapeutic effect, the immunosuppressive GBM microenvironment that is created by cells and associated molecules via complex mechanisms plays a particularly important role in facilitating evasion of the tumor from the immune response. Accumulating evidence is also revealing a close association of the gut microbiota with the challenges in the treatment of GBM. The gut microbiota establishes a connection with the central nervous system through bidirectional signals of the gut–brain axis, thus affecting the occurrence and development of GBM. In this review, we discuss the key immunosuppressive components in the tumor microenvironment, along with the regulatory mechanism of the gut microbiota involved in immunity and metabolism in the GBM microenvironment. Lastly, we concentrate on the immunotherapeutic strategies currently under investigation, which hold promise to overcome the hurdles of the immunosuppressive tumor microenvironment and improve the therapeutic outcome for patients with GBM.

The sequence assembly algorithms have rapidly evolved with the vigorous growth of genome sequencing technology over the past two decades. Assembly mainly uses the iterative expansion of overlap relationships between sequences to construct the target genome. The assembly algorithms can be typically classified into several categories, such as the Greedy strategy, Overlap-Layout-Consensus (OLC) strategy, and de Bruijn graph (DBG) strategy. In particular, due to the rapid development of third-generation sequencing (TGS) technology, some prevalent assembly algorithms have been proposed to generate high-quality chromosome-level assemblies. However, due to the genome complexity, the length of short reads, and the high error rate of long reads, contigs produced by assembly may contain misassemblies adversely affecting downstream data analysis. Therefore, several read-based and reference-based methods for misassembly identification have been developed to improve assembly quality. This work primarily reviewed the development of DNA sequencing technologies and summarized sequencing data simulation methods, sequencing error correction methods, various mainstream sequence assembly algorithms, and misassembly identification methods. A large amount of computation makes the sequence assembly problem more challenging, and therefore, it is necessary to develop more efficient and accurate assembly algorithms and alternative algorithms.