Treasures From Trash in Cancer Research | Oncotarget
Channel: Oncotarget
Date: 2022-11-22

Oncotarget published this trending research perspective in Volume 13, entitled, "Treasures from trash in cancer research" by researchers from Núcleo de Pesquisas em Oncologia/Universidade Federal do Pará, Belém, Pará, Brazil; Instituto Metrópole Digital/Universidade Federal do Rio Grande do Norte, Natal, Brazil; Instituto de Ciências Biológicas/Universidade Federal do Pará, Belém, Pará, Brazil.
#cancerresearch  #openaccess  #research  #cancer  #oncologyresearch  #peerreview  #openscience #rna 

DOI - https://doi.org/10.18632/oncotarget.28308

Correspondence to - Paulo Pimentel de Assumpção - assumpcaopp@gmail.com

Abstract

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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Keywords - cancer metagenomics, cancer sncRNA expression, RNA-Seq variant calling

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