Identifying a microbiome pattern in gastric cancer (GC) is hugely debatable due to the variation resulting from the diversity of the studied populations, clinical scenarios, and metagenomic approach. <i>H. pylori</i> remains the main microorganism impacting gastric carcinogenesis and seems necessary for the initial steps of the process. Nevertheless, an additional non-<i>H. pylori</i> microbiome pattern is also described, mainly at the final steps of the carcinogenesis. Unfortunately, most of the presented results are not reproducible, and there are no consensual candidates to share the <i>H. pylori</i> protagonists. Limitations to reach a consistent interpretation of metagenomic data include contamination along every step of the process, which might cause relevant misinterpretations. In addition, the functional consequences of an altered microbiome might be addressed. Aiming to minimize methodological bias and limitations due to small sample size and the lack of standardization of bioinformatics assessment and interpretation, we carried out a comprehensive analysis of the publicly available metagenomic data from various conditions relevant to gastric carcinogenesis. Mainly, instead of just analyzing the results of each available publication, a new approach was launched, allowing the comprehensive analysis of the total sample amount, aiming to produce a reliable interpretation due to using a significant number of samples, from different origins, in a standard protocol. Among the main results, <i>Helicobacter</i> and <i>Prevotella</i> figured in the “top 6” genera of every group. <i>Helicobacter</i> was the first one in chronic gastritis (CG), gastric cancer (GC), and adjacent (ADJ) groups, while <i>Prevotella</i> was the leader among healthy control (HC) samples. Groups of bacteria are differently abundant in each clinical situation, and bacterial metabolic pathways also diverge along the carcinogenesis cascade. This information may support future microbiome interventions aiming to face the carcinogenesis process and/or reduce GC risk.

Shifts in subsistence strategy among Native American people of the Amazon may be the cause of typically western diseases previously linked to modifications of gut microbial communities. Here, we used 16S ribosomal RNA sequencing to characterise the gut microbiome of 114 rural individuals, namely Xikrin, Suruí and Tupaiú, and urban individuals from Belém city, in the Brazilian Amazon. Our findings show the degree of potential urbanisation occurring in the gut microbiome of rural Amazonian communities characterised by the gradual loss and substitution of taxa associated with rural lifestyles, such as Treponema. Comparisons to worldwide populations indicated that Native American groups are similar to South American agricultural societies and urban groups are comparable to African urban and semi-urban populations. The transitioning profile observed among traditional populations is concerning in light of increasingly urban lifestyles. Lastly, we propose the term “tropical urban” to classify the microbiome of urban populations living in tropical zones.

Introduction: Cancer research has significantly improved in recent years, primarily due to next-generation sequencing (NGS) technology. Consequently, an enormous amount of genomic and transcriptomic data has been generated. In most cases, the data needed for research goals are used, and unwanted reads are discarded. However, these eliminated data contain relevant information. Aiming to test this hypothesis, genomic and transcriptomic data were acquired from public datasets. Materials and Methods: Metagenomic tools were used to explore genomic cancer data; additional annotations were used to explore differentially expressed ncRNAs from miRNA experiments, and variants in adjacent to tumor samples from RNA-seq experiments were also investigated. Results: In all analyses, new data were obtained: from DNA-seq data, microbiome taxonomies were characterized with a similar performance of dedicated metagenomic research; from miRNA-seq data, additional differentially expressed sncRNAs were found; and in tumor and adjacent to tumor tissue data, somatic variants were found. Conclusions: These findings indicate that unexplored data from NGS experiments could help elucidate carcinogenesis and discover putative biomarkers with clinical applications. Further investigations should be considered for experimental design, providing opportunities to optimize data, saving time and resources while granting access to multiple genomic perspectives from the same sample and experimental run.