Novel gut microbiota and microbiota-metabolites signatures in gliomas and its predictive/prognosis functions

Abstract

Gliomas are the most common malignant tumors in the central nervous system. Evidence shows that alterations of the gut microbiome play an important role in multiple diseases including central nervous system disorders. However, the influence of gut microbiome to the epigenesis of gliomas remains largely unknown. Here we profiled the gut microbiome and metabolome in fecal samples from healthy volunteers and the patients with gliomas through the 16S rRNA gene sequencing and LC-MS analyses. The fecal samples from primary glioma patients (n=51), recurrent glioma patients (n=11), patients who underwent TMZ radio-chemotherapy (n=16) and healthy volunteers (n=37) were collected. 56 discriminatory OTUs and 144 metabolites were observed in gliomas compared to those in healthy volunteers, and some species were correlated with clinical parameters. Moreover, the gliomas group showed increased activity in pathways associated with ectoine biosynthesis, fatty acid elongation, and protocatechuate degradation. Random forest modeling indicated that a model involving 8 genera and 10 metabolite biomarkers achieved a high accuracy in gliomas prediction (AUC=94.4%). We investigated interassociations between the microbial genera in glioblastoma multiforme (GBM) and progression-free survival (PFS) and overall survival (OS) by Spearman's correlation analysis. Patients with high proportions of fecalFaecalibacteriumhad significantly better median PFS or OS than those with low proportions. Moreover, animal experiments have verified the causal relationship between the structural changes of gut microbiome and glioma growth. Our current study comprehensively characterizes the perturbed interface of gut microbiome and metabolites in glioma patients, which may be used as diagnostic and prognostic biomarkers of gliomas.

Characteristics of the Gut Microbiota and Metabolism in Patients with Unclassified Diabetes in Adults: A Case‒Control Study

“…Signi cant differences in the relative abundances of taxa were identi ed using linear discriminant analysis (LDA) effect size (LEfSe) analysis, and P values were corrected using the Benjamini and Hochberg FDR. Taxa with LDA values > 2.0 and P < 0.05 were considered to be differentially abundant, and taxa with P fdr < 0.1 were considered to be signi cantly different [15]. These analyses were conducted using the R package vegan.…”