Dissecting the association between gut microbiota and liver cancer in European and East Asian populations using Mendelian randomization analysis

BackgroundAmple 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.MethodsWe 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.ResultsThe 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.ConclusionThis 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

The Role of Gut-Derived Lipopolysaccharides and the Intestinal Barrier in Fatty Liver Diseases

BACKGROUND Hepatosteatosis is the earliest stage in the pathogenesis of nonalcoholic fatty (NAFLD) and alcoholic liver disease (ALD). As NAFLD is affecting 10-24% of the general population and approximately 70% of obese patients, it carries a large economic burden and is becoming a major reason for liver transplantation worldwide. ALD is a major cause of morbidity and mortality causing 50% of liver cirrhosis and 10% of liver cancer related death. Increasing evidence has accumulated that gut-derived factors play a crucial role in the development and progression of chronic liver diseases. METHODS A selective literature search was conducted in Medline and PubMed, using the terms \textquotedblnonalcoholic fatty liver disease,\textquotedbl \textquotedblalcoholic liver disease,\textquotedbl \textquotedbllipopolysaccharide,\textquotedbl \textquotedblgut barrier,\textquotedbl and \textquotedblmicrobiome.\textquotedbl RESULTS Gut dysbiosis and gut barrier dysfunction both contribute to chronic liver disease by abnormal regulation of the gut-liver axis. Thereby, gut-derived lipopolysaccharides (LPS) are a key factor in inducing the inflammatory response of liver tissue. The review further underlines that endotoxemia is observed in both NAFLD and ALD patients. LPS plays an important role in conducting liver damage through the LPS-TLR4 signaling pathway. Treatments targeting the gut microbiome and the gut barrier such as fecal microbiota transplantation (FMT), probiotics, prebiotics, synbiotics, and intestinal alkaline phosphatase (IAP) represent potential treatment modalities for NAFLD and ALD. CONCLUSIONS The gut-liver axis plays an important role in the development of liver disease. Treatments targeting the gut microbiome and the gut barrier have shown beneficial effects in attenuating liver inflammation and need to be further investigated

The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).ConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG

Molecular Cluster Mining of Adrenocortical Carcinoma via Multi-Omics Data Analysis Aids Precise Clinical Therapy

Adrenocortical carcinoma (ACC) is a malignancy of the endocrine system. We collected clinical and pathological features, genomic mutations, DNA methylation profiles, and mRNA, lncRNA, microRNA, and somatic mutations in ACC patients from the TCGA, GSE19750, GSE33371, and GSE49278 cohorts. Based on the MOVICS algorithm, the patients were divided into ACC1-3 subtypes by comprehensive multi-omics data analysis. We found that immune-related pathways were more activated, and drug metabolism pathways were enriched in ACC1 subtype patients. Furthermore, ACC1 patients were sensitive to PD-1 immunotherapy and had the lowest sensitivity to chemotherapeutic drugs. Patients with the ACC2 subtype had the worst survival prognosis and the highest tumor-mutation rate. Meanwhile, cell-cycle-related pathways, amino-acid-synthesis pathways, and immunosuppressive cells were enriched in ACC2 patients. Steroid and cholesterol biosynthetic pathways were enriched in patients with the ACC3 subtype. DNA-repair-related pathways were enriched in subtypes ACC2 and ACC3. The sensitivity of the ACC2 subtype to cisplatin, doxorubicin, gemcitabine, and etoposide was better than that of the other two subtypes. For 5-fluorouracil, there was no significant difference in sensitivity to paclitaxel between the three groups. A comprehensive analysis of multi-omics data will provide new clues for the prognosis and treatment of patients with ACC

Metabolic Role of Autophagy in the Pathogenesis and Development of NAFLD

Non-alcoholic fatty liver disease (NAFLD) is a spectrum of liver disease, ranging from simple steatosis to hepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Liver fibrosis, which portends a poor prognosis in NAFLD, is characterized by the excessive accumulation of extracellular matrix (ECM) proteins resulting from abnormal wound repair response and metabolic disorders. Various metabolic factors play crucial roles in the progression of NAFLD, including abnormal lipid, bile acid, and endotoxin metabolism, leading to chronic inflammation and hepatic stellate cell (HSC) activation. Autophagy is a conserved process within cells that removes unnecessary or dysfunctional components through a lysosome-dependent regulated mechanism. Accumulating evidence has shown the importance of autophagy in NAFLD and its close relation to NAFLD progression. Thus, regulation of autophagy appears to be beneficial in treating NAFLD and could become an important therapeutic target

Image_2_The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.tif

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (PConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.</p

Image_1_The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.tif

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (PConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.</p

Table_1_The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.xlsx

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (PConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.</p

Image_3_The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.tif

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (PConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.</p

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity