The Sociobiology of Brain Tumors

Immune prognostic model for glioblastoma based on the ssGSEA enrichment score

PURPOSE

Few effective immune prognostic models based on the tumor immune microenvironment (TIME) for glioblastoma have been reported. Therefore, this study aimed to construct an immune prognostic model for glioblastoma by analyzing enriched biological processes and pathways in tumors.

METHODS

A comprehensive single-sample gene set enrichment analysis (ssGSEA) of gene sets from the Molecular Signatures Database was performed using TCGA RNA sequencing data (141 glioblastoma cases). After evaluating gene sets associated with prognosis using univariable Cox regression, gene sets related to biological processes and tumor immunity in gliomas were extracted. Finally, the least absolute shrinkage and selection operator Cox regression refined the gene sets and a nomogram was constructed. The model was validated using CGGA (183 cases) and Aichi Cancer Center (42 cases) datasets.

RESULTS

The immune prognostic model consisted of three gene sets related to biological processes (sphingolipids, steroid hormones, and intermediate filaments) and one related to tumor immunity (immunosuppressive chemokine pathways involving tumor-associated microglia and macrophages). Kaplan-Meier curves for the training (TCGA) and validation (CGGA) cohorts showed significantly worse overall survival in the high-risk group compared to the low-risk group (p < 0.001 and p = 0.04, respectively). Furthermore, in silico cytometry revealed a significant increase in macrophages with immunosuppressive properties and T cells with effector functions in the high-risk group (p < 0.01) across all cohorts.

CONCLUSION

Construction of an immune prognostic model based on the TIME assessment using ssGSEA could potentially provide valuable insights into the prognosis and immune profiles of patients with glioblastoma and guide treatment strategies.

Applying causal discovery to single-cell analyses using CausalCell

Correlation between objects is prone to occur coincidentally, and exploring correlation or association in most situations does not answer scientific questions rich in causality. Causal discovery (also called causal inference) infers causal interactions between objects from observational data. Reported causal discovery methods and single-cell datasets make applying causal discovery to single cells a promising direction. However, evaluating and choosing causal discovery methods and developing and performing proper workflow remain challenges. We report the workflow and platform CausalCell (http://www.gaemons.net/causalcell/causalDiscovery/) for performing single-cell causal discovery. The workflow/platform is developed upon benchmarking four kinds of causal discovery methods and is examined by analyzing multiple single-cell RNA-sequencing (scRNA-seq) datasets. Our results suggest that different situations need different methods and the constraint-based PC algorithm with kernel-based conditional independence tests work best in most situations. Related issues are discussed and tips for best practices are given. Inferred causal interactions in single cells provide valuable clues for investigating molecular interactions and gene regulations, identifying critical diagnostic and therapeutic targets, and designing experimental and clinical interventions.

Infrared spectroscopic imaging study of BV-2 microglia altering tumor cell biological activity and cellular fraction

Tumor brain metastasis is a severe threat to patients' neurological function, in which microglia may be involved in the process of tumor cell metastasis among nerve cells. Our study focused on the interaction between microglia and breast and lung cancer cells. Changes in the proliferation and migration ability of cocultured tumor cells were examined; synchrotron radiation-based fourier transform infrared microspectroscopy (SR-FTIR) was used to detect changes in the structures and contents of biomolecules within the tumor cells. The experimental results showed that the proliferation and migration ability of tumor cells increased after coculture, and the structures and contents of biological macromolecules in tumor cells changed. The absorption peak positions of the amide Ⅱ and amide Ⅰ bands observed for the four kinds of tumor cells changed, and the absorption intensities were significantly enhanced, indicating changes in the secondary structures and contents of proteins in tumor cells, which may be the root cause of the change in tumor cell characteristics. Therefore, the metabolites of microglia may be involved in the progression of tumor cells in the nervous system. In this study, we focused on the interaction between microglia and tumor cells by using SR-FTIR and provided a new understanding of the mechanism of brain metastasis.

Long non-coding RNA TP73-AS1 is a potential immune related prognostic biomarker for glioma

Glioma is one of the most common primary brain tumors, and is divided into low-grade and high-grade gliomas. Long non-coding RNAs have been increasingly implicated in the pathogenesis and prognosis of glioma. Here, we demonstrated that the long non-coding RNA TP73-AS1 is differentially expressed among gliomas with different clinicopathological features in The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and GEO glioma datasets; high expression of TP73-AS1 was associated with poor clinical features, including age, stage, IDH mutation status, 1p/19q co-deletion status and overall survival. Measuring TP73-AS1 expression using real-time PCR showed the same result for 76 glioma tissue samples from our hospital. The infiltration levels of various immune cells in the tumor microenvironment were found to be significantly higher in patients with high expression of TP73-AS1. Taken together, our results suggest that TP73-AS1 has potential as a prognostic glioma biomarker. Moreover, the knowledge that TP73-AS1 affects the glioma immune microenvironment may provide new information for the immunological research and treatment of glioma.