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Background: Ample evidence suggests an important role of the gut microbiome in liver cancer, but the causal relationship between gut microbiome and liver cancer is unclear. This study employed Mendelian randomization (MR) analysis to examine the causal relationship between the gut microbiome and liver cancer in European and East Asian populations. Methods: We sourced genetic variants linked to gut microbiota from the MiBioGen consortium meta-analysis, and procured liver cancer genome-wide association study (GWAS) summary data from the FinnGen consortium and Biobank Japan. We employed the inverse variance weighted method for primary statistical analysis, fortified by several sensitivity analyses such as MR-PRESSO, MR-Egger regression, weighted median, weighted mode, and maximum likelihood methods for rigorous results. We also evaluated heterogeneity and horizontal pleiotropy. Results: The study examined an extensive set of gut microbiota, including 131 genera, 35 families, 20 orders, 16 classes, and 9 phyla. In Europeans, ten gut microbiota types displayed a suggestive association with liver cancer (p < 0.05). Notably, Oscillospira and Mollicutes RF9 exhibited a statistically significant positive association with liver cancer risk, with odds ratios (OR) of 2.59 (95% CI 1.36-4.95) and 2.03 (95% CI 1.21-3.40), respectively, after adjusting for multiple testing. In East Asians, while six microbial types demonstrated suggestive associations with liver cancer, only Oscillibacter displayed a statistically significant positive association (OR = 1.56, 95% CI 1.11-2.19) with an FDR < 0.05. Sensitivity analyses reinforced these findings despite variations in p-values. Conclusion: This study provides evidence for a causal relationship between specific gut microbiota and liver cancer, enhancing the understanding of the role of the gut microbiome in liver cancer and may offer new avenues for preventive and therapeutic strategies.
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Background: Gliomas are brain tumors that arise from glial cells, and they are the most common primary intracranial tumors with a poor prognosis. Cellular senescence plays a critical role in cancer, especially in glioma. In this study, we constructed a senescence-related lncRNA (SRlncRNA) signature to assess the prognosis of glioma. Methods: The Cancer Genome Atlas was used to collect SRlncRNA transcriptome profiles and clinical data about glioma. Patients were randomized to training, testing, and whole cohorts. LASSO and Cox regression analyses were employed to construct the SRlncRNA signature, and Kaplan-Meier (K-M) analysis was performed to determine each cohort's survival. Receiver operating characteristic (ROC) curves were applied to verify the accuracy of this signature. Gene set enrichment analysis was used to visualize functional enrichment (GSEA). The CIBERSORT algorithm, ESTIMATE and TIMER databases were utilized to evaluate the differences in the infiltration of 22 types of immune cells and their association with the signature. RT-qPCR and IHC were used to identify the consistency of the signature in tumor tissue. Results: An SRlncRNA signature consisting of six long non-coding RNAs (lncRNAs) was constructed, and patients were divided into high-risk and low-risk groups by the median of their riskscore. The KM analysis showed that the high-risk group had worse overall survival, and the ROC curve confirmed that the riskscore had more accurate predictive power. A multivariate Cox analysis and its scatter plot with clinical characteristics confirmed the riskscore as an independent risk factor for overall survival. GSEA showed that the GO and KEGG pathways were mainly enriched in the immune response to tumor cells, p53 signaling pathway, mTOR signaling pathway, and Wnt signaling pathway. Further validation also yielded significant differences in the risk signature in terms of immune cell infiltration, which may be closely related to prognostic differences, and qRT-PCR and IHC confirmed the consistency of the expression differences in the major lncRNAs with those in the prediction model. Conclusion Our findings indicated that the SRlncRNA signature might be used as a predictive biomarker and that there is a link between it and immune infiltration. This discovery is consistent with the present categorization system and may open new avenues for research and personalized therapy.
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[This corrects the article DOI: 10.3389/fgene.2022.899125.].
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Background: Glioma is one of the most aggressive cancer types affecting the central nerve system, with poor overall survival (OS) rates. The present study aimed to construct a novel immune-related signature to predict prognosis and the efficiency of immunotherapy in patients with glioma. Methods: The mRNA expression data and other clinical information of patients with glioblastoma multiforme (GBM) and low grade glioma (LGG) were obtained from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. The immune-related genes were obtained from the Immunology Database and Analysis Portal database. Subsequently, an immune-related signature was created following the results obtained from the Least Absolute Shrinkage and Selection Operator regression model. To validate the predictability of the signature, Kaplan-Meier survival curves and time-dependent receiver operating characteristic curves were created. Moreover, both univariate and multivariate analyses were carried out using the OS between this signature and other clinicopathologic factors, and a nomogram was constructed. In addition, the association between signature, immune cell infiltration, tumor mutation burden and immunophenoscore were determined. Results: Results of the present study using 118 GBM and LGG samples uncovered 15 immune-related genes that were also differently expressed in glioma samples. These were subsequently used to construct the immune-related signature. This signature exhibits the ability to predict prognosis, the infiltration of immune cells in the tumor microenvironment and the response of patients with glioma to immunotherapy. Conclusion: Results of the present study demonstrated that the aforementioned novel immune-related signature may accurately predict prognosis and the response of patients with glioma to immunotherapy.
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Premature ovarian insufficiency (POI) is a heterogeneous and multifactorial disorder. In recent years, there has been an increasing interest in research on the pathogenesis and treatment of POI, owing to the implementation of the second-child policy in China. Cytoplasmic polyadenylation element-binding protein 3 (CPEB3) is an RNA-binding protein that can bind to specific RNA sequences. CPEB3 can bind to and affect the expression, cellular location, and stability of target RNAs. Cpeb3 is highly expressed in the ovary; however, its functions remain unknown. In this study, Cpeb3-mutant mice were used to characterize the physiological functions of CPEB3. Cpeb3-mutant female mice manifested signs of gradual loss of ovarian follicles, ovarian follicle development arrest, increased follicle atresia, and subfertility with a phenotype analogous to POI in women. Further analysis showed that granulosa cell proliferation was inhibited and apoptosis was markedly increased in Cpeb3-mutant ovaries. In addition, the expression of Gdf9, a potential target of CPEB3, was decreased in Cpeb3-mutant ovaries and oocytes. Altogether, these results reveal that CPEB3 is essential for ovarian follicle development and female fertility as it regulates the expression of Gdf9 in oocytes, disruption of which leads to impaired ovarian follicle development and POI.
