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Gastric cancer (GC) is the fifth most common type of cancer and the fourth leading cause of cancer death. In Brazil, GC has a high incidence and mortality rates, and it is highly variable by region. The Amazon region has significant rising rates among all Brazil regions. Only very few studies have evaluated the association between genetic variants and the risk of gastric cancer in the Brazilian Amazon population. Therefore, this study aimed to investigate associations between single nucleotide polymorphisms of miRNA processing genes and the risk for GC in this population. Potentially functional single nucleotide polymorphisms from miRNA processing genes were genotyped in 159 cases and 193 healthy controls by QuantStudio Real Time PCR. According to our findings, the genotype GG of the variant rs10739971 presents a lower risk to the development of GC in comparison to the remaining genotypes ( p = 0.000016; OR = 0.055; 95% CI = 0.015-0.206). This is the first study to report the association of pri-let-7a-1 rs10739971 with GC in the Brazilian Amazon population, which is a highly mixed population with a unique genetic constitution that is different from other populations that are studied in the vast majority of scientific research., Competing Interests: The authors declare no conflict of interest.

Gastric cancer (GC) is a multifactorial, complex, and aggressive disease with a prevalence of one million new cases and high global mortality. Factors such as genetic, epigenetic, and environmental changes contribute to the onset and progression of the disease. Identification of INDELs in miRNA and its target sites in current studies showed an important role in the development of cancer. In GC, miRNAs act as oncogenes or tumor suppressors, favoring important cancer pathways, such as cell proliferation and migration. This work aims to investigate INDELs in the coding region of miRNAs (hsa-miR-302c, hsa-miR-548AJ-2, hsa-miR-4274, hsa-miR-630, hsa-miR-516B-2, hsa-miR-4463, hsa-miR-3945, hsa-miR-548H_4, hsa-miR-920, has-mir-3171, and hsa-miR-3652) that may be associated with susceptibility and clinical variants of gastric cancer. For this study, 301 patients with GC and 145 individuals from the control group were selected from an admixed population in the Brazilian Amazon. The results showed the hsa-miR-4463, hsa-miR-3945, hsa-miR-548H_4, hsa-miR-920 and hsa-miR-3652 variants were associated with gastric cancer susceptibility. The hsa-miR-4463 was significantly associated with clinical features of GC such as diffuse gastric tumor histological type, "non-cardia" localization region, and early onset. Our findings indicated that INDELs could be potentially functional genetic variants for gastric cancer risk.

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.