Alterations of thyroid microbiota across different thyroid microhabitats in patients with thyroid carcinoma.

BACKGROUND: In recent years, the incidence rate of Thyroid carcinoma (TC) has been increasing worldwide. Thus, research on factors of TC carcinogenesis may promote TC prevention and decrease the incidence rate. There are several studies targeting the correlation between gut microbiota and thyroid disease. Carcinogenesis of several malignancies is influenced by microbiota. However, thyroid microbiome of TC has not been revealed. This study investigated thyroid microbiota in different TC microhabitats. METHODS: We performed 16s rRNA gene sequencing using tumor tissues and matched peritumor tissues from 30 patients with TC to characterize thyroid microbiota. RESULTS: The richness and diversity of thyroid microbiota were lower in TC tumor samples than in matched peritumor tissues. At the genus level, the core microbiota of thyroid included Sphingomonas, Comamonas, Acinetobacter, Pseudomonas, Microvirgula, and Soonwooa. The abundance of Sphingomonas and Aeromonas was significantly increased in tumor tissues, while the abundance of Comamonas, Acinetobacter, and Peptostreptococcus was significantly enhanced in peritumor tissues. The combination of Comamonas and Sphingomonas could discriminate tumor samples from peritumor samples with an area under the curve (AUC) of 0.981 (95% confidence interval [CI] 0.949-1.000). The abundance of Sphingomonas was significantly higher in N1 stage than in N0 stage. Sphingomonas could distinguish between N0 and N1 stage with an AUC of 0.964 (95% CI 0.907-1.000). CONCLUSIONS: The microbial diversity and composition were significantly different between peritumor and tumor microhabitats from patients with TC, which may eventually affect TC carcinogenesis and progression. The combination of Comamonas and Sphingomonas could serve as a powerful biomarker for discrimination between tumor and peritumor tissues. Furthermore, the higher abundance of Sphingomonas was correlated with lymph node metastasis, indicating that the abundance of Sphingomonas may indicate a poor prognosis for TC patients, and Sphingomonas may play a role in promoting TC progression.

GPX4 inhibits apoptosis of thyroid cancer cells through regulating the FKBP8/Bcl-2 axis.

GPX4 has attracted much attention as a key molecule of cell ferroptosis, but its role in cell apoptosis is rarely reported, and its role in apoptosis of thyroid cancer (TC) cell has not been reported. The analysis of TCGA database showed that both GPX4 and FKBP8 were highly expressed in TC tumor tissues; The expression of GPX4 and FKBP8 were positively correlated. The immunohistochemical analysis further confirmed that GPX4 and FKBP8 were highly expressed in TC tumor tissues. In addition, the high expression of GPX4 and FKBP8 were both significantly correlated with the poor prognosis of TC. Silencing GPX4 significantly inhibited the proliferation, induced apoptosis of TC cells, and reduced tumor growth in mice. The co-immunoprecipitation assay revealed a physical interaction between GPX4 and FKBP8 observed in the TC cells. Knockdown of FKBP8 significantly inhibited the proliferation and induced apoptosis of TC cells. Rescue experiments suggested that knockdown of FKBP8 could reverse the strengthens of cell proliferation and apoptosis and the higher expression of FKBP8 and Bcl-2 caused by overexpression of GPX4. Our results suggest that the GPX4/FKBP8/Bcl-2 axis promotes TC development by inhibiting TC cell apoptosis, which provides potential molecular targets for TC therapeutic strategies.

Interactions between gastric microbiota and metabolites in gastric cancer.

The development and progression of gastric cancer (GC) is greatly influenced by gastric microbiota and their metabolites. Here, we characterized the gastric microbiome and metabolome profiles of 37 GC tumor tissues and matched non-tumor tissues using 16s rRNA gene sequencing and ultrahigh performance liquid chromatography tandem mass spectrometry, respectively. Microbial diversity and richness were higher in GC tumor tissues than in non-tumor tissues. The abundance of Helicobacter was increased in non-tumor tissues, while the abundance of Lactobacillus, Streptococcus, Bacteroides, Prevotella, and 6 additional genera was increased in the tumor tissues. The untargeted metabolome analysis revealed 150 discriminative metabolites, among which the relative abundance of the amino acids, carbohydrates and carbohydrate conjugates, glycerophospholipids, and nucleosides was higher in tumor tissues compared to non-tumor tissues. The targeted metabolome analysis further demonstrated that the combination of 1-methylnicotinamide and N-acetyl-D-glucosamine-6-phosphate could serve as a robust biomarker for distinction between GC tumors and non-tumor tissues. Correlation analysis revealed that Helicobacter and Lactobacillus were negatively and positively correlated with the majority of differential metabolites in the classes of amino acids, carbohydrates, nucleosides, nucleotides, and glycerophospholipids, respectively, suggesting that Helicobacter and Lactobacillus might play a role in degradation and synthesis of the majority of differential metabolites in these classes, respectively. Acinetobacter, Comamonas, Faecalibacterium, Sphingomonas, and Streptococcus were also significantly correlated with many differential amino acids, carbohydrates, nucleosides, nucleotides, and glycerophospholipids. In conclusion, the differences in metabolome profiles between GC tumor and matched non-tumor tissues may be partly due to the collective activities of Helicobacter, Lactobacillus, and other bacteria, which eventually affects GC carcinogenesis and progression.