Applying bioinformatics to resolve biological problems. This is the objective of the research group of the University of Malaga "BI4NEXT", which, in one of its latest studies, developed in the Supercomputing and Bioinnovation Center (SCBI) based on biobank samples, has identified new biomarkers for the diagnosis, prognosis and even treatment of lung cancer .
A discovery published in the scientific journal PeerJ , since it proves that both tumour cell and healthy cell in the repetitive DNA regions, mainly formed by transposon fosils, are consistently, differentially expressed in a controlled way.
"Since 2010, we have worked in the belief that repetitive elements in healthy cells were dormant, and that when the cell becomes cancerous, these regions deregulate, express wildly and cause resistance to treatment", explains Professor Gonzalo Claros of the UMA Department of Molecular Biology and Biochemistry, who asserts that his research supports that this would not be the case, that repetitive sequences both in normal and tumour tissue are specifically expressed and regulated. "What we did evidence is that such control changes when a normal cell becomes cancerous", he says.
Consequently, this research group of the UMA has identified some repetitive regions that behave similarly in all patients and all types of lung cancer studied. This is the case of AluYg6 and LTR18B elements, which are repressed in all lung cancer cells, and HERVK11D-Int and UCON88 elements, which activate specifically in adenocarcinoma and small-cell lung carcinoma, respectively.
A bioinformatic study that is pending on validation at the laboratory, but nevertheless it represents one step closer to the confirmatory diagnosis of lung cancer, as well as a new source of information for specialists, being extendable to all diseases with a genetic component. New source of biomarkers
The researchers of "BI4NEXT'" propose to study the repetitive elements -more than 50 per cent of the genome- as a new source of biomarkers, still barely exploited. Thus, they highlight the accuracy and cost and time saving that would derive from the analysis of these expression markers, in addition to genome mutations, based on high throughput sequencing. Related Stories
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