Treasures from trash in cancer research
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Fabiano Cordeiro Moreira1,*, Dionison Pereira Sarquis1,*, Jorge Estefano Santana de Souza2, Daniel de Souza Avelar1, Taíssa Maria Thomaz Araújo1, André Salim Khayat1, Sidney Emanuel Batista dos Santos1,3 and Paulo Pimentel de Assumpção1
1 Núcleo de Pesquisas em Oncologia/Universidade Federal do Pará, Belém, Pará, Brazil
2 Instituto Metrópole Digital/Universidade Federal do Rio Grande do Norte, Natal, Brazil
3 Instituto de Ciências Biológicas/Universidade Federal do Pará, Belém, Pará, Brazil
* Co-first authors
|Paulo Pimentel de Assumpção,||email:||[email protected]|
Keywords: cancer metagenomics; cancer sncRNA expression; RNA-Seq variant calling
Received: February 23, 2022 Accepted: October 26, 2022 Published: November 17, 2022
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.
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