Gut Microbiome Was Highly Related to the Regulation of Metabolism in Lung Adenocarcinoma Patients

Oral microbiome-derived biomarkers for non-invasive diagnosis of head and neck squamous cell carcinoma

Mounting evidence suggests that sustained microbial dysbiosis is associated with the development of multiple cancers, while the species-level bacterial taxa and metabolic dysfunction of oral microbiome in patients with head and neck squamous cell carcinoma (HNSCC) remains unclear. In this cross-sectional study, comprehensive metagenomic and 16S rRNA amplicon sequencing analyses of oral swab samples from 172 patients were performed. Unsupervised clustering algorithms of relative microbial abundance profiles revealed three distinctive microbiome clusters. Based on the metagenomic and 16S rRNA amplicon sequencing data, machine learning-based methods were used to construct the HNSCC diagnostic classifier, which exhibited high area under the curve values of 0.78-0.89. Our study provided the first exhaustive metagenomic and 16S rRNA amplicon sequencing analyses to date, revealing that microbial-metabolic dysbiosis is a potential risk factor for HNSCC progression and therefore providing a robust theoretical basis for potential diagnostic and therapeutic strategies for HNSCC patients.

Causal relationships between gut microbiota, immune cell, and Non-small cell lung cancer: a two-step, two-sample Mendelian randomization study

Background: Regulating the immune system is a crucial measure of gut microbiota (GM) that influences the development of diseases. The causal role of GM on Non-small cell lung cancer (NSCLC) and whether it can be mediated by immune cells is still unknown. Methods: We performed a two-step, two-sample Mendelian randomization study with an Inverse variance weighted (IVW) approach to investigate the causal role of GM on NSCLC and the mediation effect of immune cells between the association of GM and NSCLC. Results: MR analyses determined the protective effects of 6 genera on NSCLC (Bacteroides, Roseburia, Alistipes, Methanobrevibacter, Ruminococcus gauvreauii group, and Peptococcus). In addition, 38 immune cell traits were suggestively associated with NSCLC. Of note, the mediation MR illustrated the causal role of Genus-Peptococcus on NSCLC (Total effect IVW: OR = 0.790, 95% CI [0.657, 0.950], P = 0.012) was to a large proportion mediated by CD45 on HLA DR+ CD4+ in TBNK panel (-034 (95% CI [-0.070, -0.005]; P = 0.037), accounting for 14.4% of Total effect). Conclusion: The study suggested a causal relationship between GM and NSCLC, which may be mediated by immune cells.

The microbiome in the pathogenesis of lung cancer: The role of microbiome in lung cancer pathogenesis

As one of the malignant tumors with high incidence rate and high mortality, lung cancer seriously threatens the life safety of patients. Research shows that microorganisms are closely related to lung cancer. The microbiome is symbiotic with the host and plays a vital role in the functions of the human body. Microbiota dysbiosis is correlated with development of lung cancer. However, the underlying mechanisms are poorly understood. This paper summarizes the composition characteristics of the gut-lung axis microbiome and intratumoral microbiome in patients with lung cancer. We then expound five potential carcinogenic mechanisms based on microorganisms, such as genotoxicity, metabolism, inflammation, immune response, and angiogenesis. Next, we list three high-throughput sequencing methods, and finally looks forward to the prospect of microorganisms as novel targets for early diagnosis and treatment of lung cancer.

Identification of microbial markers associated with lung cancer based on multi-cohort 16 s rRNA analyses: A systematic review and meta-analysis

BACKGROUND

The relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large-scale meta-analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine-learning classifier.

METHODS

In total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony-related functions.

RESULTS

The α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top-ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota-associated functions between cancer-affected and healthy individuals that were primarily associated with metabolic disturbances.

CONCLUSIONS

GM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC-specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC.

Causal relationships between gut microbiota and programmed cell death protein 1/programmed cell death-ligand 1: A bidirectional Mendelian randomization study

Background

Multiple clinical studies have indicated that the gut microbiota influences the effects of immune checkpoint blockade (ICB) therapy comprising PD-1/PD-L1 inhibitors, but the causal relationship is unclear. Because of numerous confounders, many microbes related to PD-1/PD-L1 have not been identified. This study aimed to determine the causal relationship between the microbiota and PD-1/PD-L1 and identify possible biomarkers for ICB therapy.

Method

We used bidirectional two-sample Mendelian randomization with two different thresholds to explore the potential causal relationship between the microbiota and PD-1/PD-L1 and species-level microbiota GWAS to verify the result.

Result

In the primary forward analysis, genus_Holdemanella showed a negative correlation with PD-1 [βIVW = -0.25; 95% CI (-0.43 to -0.07); PFDR = 0.028] and genus_Prevotella9 showed a positive correlation with PD-1 [βIVW = 0.2; 95% CI (0.1 to 0.4); PFDR = 0.027]; order_Rhodospirillales [βIVW = 0.2; 95% CI (0.1 to 0.4); PFDR = 0.044], family_Rhodospirillaceae [βIVW = 0.2; 95% CI (0 to 0.4); PFDR = 0.032], genus_Ruminococcaceae_UCG005 [βIVW = 0.29; 95% CI (0.08 to 0.5); PFDR = 0.028], genus_Ruminococcus_gnavus_group [βIVW = 0.22; 95% CI (0.05 to 0.4); PFDR = 0.029], and genus_Coprococcus_2 [βIVW = 0.4; 95% CI (0.1 to 0.6); PFDR = 0.018] were positively correlated with PD-L1; and phylum_Firmicutes [βIVW = -0.3; 95% CI (-0.4 to -0.1); PFDR = 0.031], family_ClostridialesvadinBB60group [βIVW = -0.31; 95% CI (-0.5 to -0.11), PFDR = 0.008], family_Ruminococcaceae [βIVW = -0.33; 95% CI (-0.58 to -0.07); PFDR = 0.049], and genus_Ruminococcaceae_UCG014 [βIVW = -0.35; 95% CI (-0.57 to -0.13); PFDR = 0.006] were negatively correlated with PD-L1. The one significant species in further analysis was species_Parabacteroides_unclassified [βIVW = 0.2; 95% CI (0-0.4); PFDR = 0.029]. Heterogeneity (P > 0.05) and pleiotropy (P > 0.05) analyses confirmed the robustness of the MR results.