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Simple Summary: Inflammation of the colon (colitis) can increase the risk of developing colorectal cancers. Gut microbiota may play a role in the development of this cancer. One potential way to prevent this cancer is to manipulate the community of bacteria in the colon. In a study using mice, we found that transferring the bacteria from the bedding of healthy mice to those with colitis increased the presence of Akkermansia, which may help reduce inflammation in the colon. Meanwhile, untreated mice had an increase in other types of bacteria, such as Anaeroplasma and Alistipes, which may contribute to inflammation. These findings suggest that certain types of bacteria may help reduce inflammation in the colon and prevent cancer. Chronic inflammation of the colon (colitis) is a known risk factor for inflammatory-driven colorectal cancers (id-CRCs), and intestinal microbiota has been implicated in the etiology of id-CRCs. Manipulation of the microbiome is a clinically viable therapeutic approach to limiting id-CRCs. To understand the microbiome changes that occur over time in id-CRCs, we used a mouse model of id-CRCs with the treatment of azoxymethane (AOM) and dextran sodium sulfate (DSS) and measured the microbiome over time. We included cohorts where the microbiome was restored using cage bedding swapping and where the microbiome was depleted using antibiotics to compare to untreated animals. We identified consistent increases in Akkermansia in mice receiving horizontal microbiome transfer (HMT) via cage bedding swapping, while the control cohort had consistent longitudinal increases in Anaeroplasma and Alistipes. Additionally, fecal lipocalin-2 (Lcn-2), a marker of intestinal inflammation, was elevated in unrestored animals compared to restored and antibiotic-treated counterparts following HMT. These observations suggest a potential role for Akkermansia, Anaeroplasma, and Alistipes in regulating colonic inflammation in id-CRCs. [ABSTRACT FROM AUTHOR]

Background and aims: Glioblastoma (GBM) is a common and aggressive primary brain tumor, and the prognosis for GBM patients remains poor. This study aimed to identify the key genes associated with the development of GBM and provide new diagnostic and therapies for GBM. Methods: Three microarray datasets (GSE111260, GSE103227, and GSE104267) were selected from Gene Expression Omnibus (GEO) database for integrated analysis. The differential expressed genes (DEGs) between GBM and normal tissues were identified. Then, prognosis-related DEGs were screened by survival analysis, followed by functional enrichment analysis. The protein–protein interaction (PPI) network was constructed to explore the hub genes associated with GBM. The mRNA and protein expression levels of hub genes were respectively validated in silico using The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) databases. Subsequently, the small molecule drugs of GBM were predicted by using Connectivity Map (CMAP) database. Results: A total of 78 prognosis-related DEGs were identified, of which10 hub genes with higher degree were obtained by PPI analysis. The mRNA expression and protein expression levels of CETN2, MKI67, ARL13B, and SETDB1 were overexpressed in GBM tissues, while the expression levels of CALN1, ELAVL3, ADCY3, SYN2, SLC12A5, and SOD1 were down-regulated in GBM tissues. Additionally, these genes were significantly associated with the prognosis of GBM. We eventually predicted the 10 most vital small molecule drugs, which potentially imitate or reverse GBM carcinogenic status. Cycloserine and 11-deoxy-16,16-dimethylprostaglandin E2 might be considered as potential therapeutic drugs of GBM. Conclusions: Our study provided 10 key genes for diagnosis, prognosis, and therapy for GBM. These findings might contribute to a better comprehension of molecular mechanisms of GBM development, and provide new perspective for further GBM research. However, specific regulatory mechanism of these genes needed further elaboration. [ABSTRACT FROM AUTHOR]