Abstract Background Dysbiosis of gut microbiota has been linked to numerous diseases, including cancer. The unique role of gut microbiota in urological tumors is gaining prominence. However, it is still controversial whether the dysbiosis of gut microbiota should be one of the etiological factors of bladder cancer (BCa), prostate cancer (PCa) or kidney cancer (KCa). Materials and methods The microbiome genome-wide association study (GWAS) from the MiBioGen consortium (18,340 samples of 24 population-based cohorts) was utilized as the exposure data. Additionally, outcomes data (951 BCa cases and 307,092 controls; 1,631 KCa cases and 238,678 controls; 79,148 PCa cases and 61,106 controls) were extracted from the GWAS of the FinnGen and PRACTICAL consortia. To detect the potential causative bacterial traits for BCa, PCa, and KCa, a two-sample Mendelian randomization (MR) analysis was performed, employing the inverse-variance weighted or Wald ratio method. Sensitivity analyses were subsequently conducted to explore the robustness of the primary results. Finally, the reverse MR analysis was undertaken to mitigate the reverse causation. Results This study suggested that Bifidobacterium ( p = 0.030), Actinobacteria ( p = 0.037 for phylum, 0.041 for class), and Ruminococcustorques group ( p = 0.018), exhibited an association with an increased risk of BCa using either the inverse-variance weighted or Wald ratio method. By utilizing the Wald ratio method, Allisonella ( p = 0.004, p = 0.038) was associated with a decreased risk of BCa and PCa, respectively. Furthermore, Ruminococcustorques group ( p = 0.028) and Erysipelatoclostridium ( p = 0.048) were causally linked to an elevated risk of KCa. Conclusions This MR study supports that genetically predicted gut microbiota is causally related to BCa, PCa and KCa. Additionally, distinct bacterial traits are identified in relation to each tumor type. 

Causal associations between gut microbiota and urological tumors: a two-sample mendelian randomization study

The human gut microbiota (GM) is a complex microbial ecosystem that colonises the gastrointestinal tract (GIT) and is comprised of bacteria, viruses, fungi, and protozoa. The GM has a symbiotic relationship with its host that is fundamental for body homeostasis. The GM is not limited to the scope of the GIT, but there are bidirectional interactions between the GM and other organs, highlighting the concept of the “gut–organ axis”. Any deviation from the normal composition of the GM, termed ”microbial dysbiosis”, is implicated in the pathogenesis of various diseases. Only a few studies have demonstrated a relationship between GM modifications and disease phenotypes, and it is still unknown whether an altered GM contributes to a disease or simply reflects its status. Restoration of the GM with probiotics and prebiotics has been postulated, but evidence for the effects of prebiotics is limited. Prebiotics are substrates that are “selectively utilized by host microorganisms, conferring a health benefit”. This study highlights the bidirectional relationship between the gut and vital human organs and demonstrates the relationship between GM dysbiosis and the emergence of certain representative diseases. Finally, this article focuses on the potential of prebiotics as a target therapy to manipulate the GM and presents the gaps in the literature and research.

The Gut–Organ Axis within the Human Body: Gut Dysbiosis and the Role of Prebiotics

Background Accumulating evidence suggests that alterations in gut microbiota composition and diversity are associated with Atopic dermatitis (AD). But until now, the causal association between them has been unclear. Methods We employed a two-sample Mendelian Randomization (MR) study to estimate the potential causality of gut microbiota on AD risk. The summary statistics related to the gut microbiota were obtained from a large-scale genome-wide genotype and 16S fecal microbiome dataset from 18,340 individuals (24 cohorts) analyzed by the MiBioGen Consortium, comprising 211 gut microbiota. AD data were also derived from strictly defined AD data collected by FinnGen biobank analysis, which included 218,467 European ancestors (5,321 AD patients and 213,146 controls). The inverse variance weighted method (IVW), weighted median (WME), and MR-Egger were used to determine the changes of AD pathogenic bacterial taxa, followed by sensitivity analysis including horizontal pleiotropy analysis, Cochran’s Q test, and the leave-one-out method to assess the reliability of the results. In addition, MR Steiger’s test was used to test the suppositional relationship between exposure and outcome. Results A total of 2,289 SNPs (p < 1 × 10−5) were included, including 5 taxa and 17 bacterial characteristics (1 phylum, 3 classes, 1 order, 4 families, and 8 genera), after excluding the IVs with linkage disequilibrium (LD). Combining the analysis of the results of the IVW models, there were 6 biological taxa (2 families, and 4 genera) of the intestinal flora positively associated with the risk of AD and 7 biological taxa (1 phylum, 2 classes, 1 order, 1 family, and 2 genera) of the intestinal flora negatively associated. The IVW analysis results showed that Tenericutes, Mollicutes, Clostridia, Bifidobacteriaceae, Bifidobacteriales, Bifidobacterium, and Christensenellaceae R 7 group were negatively correlated with the risk of AD, while Clostridiaceae 1, Bacteroidaceae, Bacteroides, Anaerotruncus, the unknown genus, and Lachnospiraceae UCG001 showed the opposite trend. And the results of the sensitivity analysis were robust. MR Steiger’s test showed a potential causal relationship between the above intestinal flora and AD, but not vice versa. Conclusion The present MR analysis genetically suggests a causal relationship between changes in the abundance of the gut microbiota and AD risk, thus not only providing support for gut microecological therapy of AD but also laying the groundwork for further exploration of the mechanisms by which the gut microbiota contributes to the pathogenesis of AD.

/pdf/gut-microbiota-and-atopic-dermatitis-a-two-sample-mendelian-3vmcgybo.pdf

Gut microbiota and atopic dermatitis: a two-sample Mendelian randomization study

The profound influence of microbiota in cancer initiation and progression has been under the spotlight for years, leading to numerous researches on cancer microbiome entering clinical evaluation. As promising biomarkers and therapeutic targets, the critical involvement of microbiota in cancer clinical practice has been increasingly appreciated. Here, recent progress in this field is reviewed. We describe the potential of tumor-associated microbiota as effective diagnostic and prognostic biomarkers respectively. In addition, we highlight the relationship between microbiota and the therapeutic efficacy, toxicity, or side effects of commonly utilized treatments for cancer, including chemotherapy, radiotherapy, and immunotherapy. Given that microbial factors influence the cancer treatment outcome, we further summarize some dominating microbial interventions and discuss the hidden risks of these strategies. This review aims to provide an overview of the application and advancements of microbes in cancer clinical relevance.

Emerging clinical relevance of microbiome in cancer: promising biomarkers and therapeutic targets.

The microbiota and renal cell carcinoma.

Different gut microbiota is implicated in different diseases, including cancer. However, gut microbiota differences between individuals with clear cell renal cell carcinoma (ccRCC) and healthy individuals are unclear. Here, we analyzed gut microbiota composition in 51 ccRCC patients and 40 healthy controls using 16S rRNA sequencing analysis. We observed that Blautia, Streptococcus, [Ruminococcus]_torques_group, Romboutsia, and [Eubacterium]_hallii_group were dominant and positively associated with ccRCC. We isolated and cultured Streptococcus lutetiensis to characterize specific gut microbiota that promotes ccRCC and found that it promoted in vitro ccRCC proliferation, migration, and invasion via the TGF-signaling pathway. Interactions identified between the gut microbiota and ccRCC suggest the gut microbiota could serve as a potential non-invasive tool for predicting ccRCC risk and also function as a cancer therapy target.

/pdf/characteristics-of-gut-microbiota-in-patients-with-clear-rvd5axw0.pdf

Characteristics of Gut Microbiota in Patients With Clear Cell Renal Cell Carcinoma

In reality, there are many hypernetworks with small-world characteristics, such as social hypernetworks and traffic hypernetworks. At present, most researches about hypernetworks focus on scale-free hypernetworks and random hypernetworks, and there is little research on small-world hypernetworks. In order to better analyze the characteristics of small-world hypernetworks, this paper constructs four types of small-world hypernetworks evolution model algorithms based on hypergraphs, and explores the influence of the hyperedges uniformity, random probability and construction method on the small-world characteristics of the small-world hypernetworks through simulation experiments and data analysis. It can be seen that, under the same conditions, small-world hypernetworks with uniform hyperedges have the smallest average hyperpath length and the largest hyperedge aggregation coefficient, that is, the best small-world characteristics. Comparing with other hypernetworks of the same scale, the four types of small-world hypernetworks constructed in this paper all show better small-world characteristics, that is, they all have shorter average hyperpath lengths and higher hyperedge aggregation coefficients.

The Influence of Hyperedge Uniformity on The Characteristics of Small-world Hypernetworks

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Characteristics of Gut Microbiota in Patients With Clear Cell Renal Cell Carcinoma

The Influence of Hyperedge Uniformity on The Characteristics of Small-world Hypernetworks